KaLM-Embedding\n\n\n**KaLM-Embedding** is a series of embedding models adapted from auto-regressive LLMs with superior training data.\n\nKaLM-embedding-multilingual-mini is trained from [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) with massive weakly-supervised pre-training and supervised fine-tuning data.\n\n\n## 📑 Open-source Plan\n\n- [x] Model Checkpoint \n - [x] [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1)\n - [x] [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1)\n - [x] [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5)\n - [ ] KaLM-embedding-multilingual-max-v1\n- [x] Training and Evaluation Code: [HITsz-TMG/KaLM-Embedding](https://github.com/HITsz-TMG/KaLM-Embedding)\n- [x] Technical Report: [KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model](https://arxiv.org/abs/2501.01028)\n- [ ] Training Data\n\n\n## Evaluation\n\n | Model Name | Model Size | C-MTEB(35) | MTEB(56) | avg\n|:----:|:---:|:---:|:---:|:---:| \n| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 58.81 | 61.5 | 60.16\n| [bge-m3 (dense)](https://huggingface.co/BAAI/bge-m3) | 560M | 60.80 | 59.84 | 60.32\n| [gte-multilingual-base (dense)](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | **305M** | 62.72 | 61.40 | 62.06 \n| [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1) | 494M | 62.31 | 61.87 | 62.09\n| [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1) | 494M | 63.57 | 64.74 | 64.16\n| [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5) | 494M | **64.13** | **64.94** | **64.53**\n\n\n\n## Requirements\nSince we have used the Qwen2 model, we advise you to install `transformers>=4.37.0`, or you might encounter the following error:\n```\nKeyError: 'qwen2'\n```\n\n\n## Usage\n\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\n\nmodel = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code\nmodel.max_seq_length = 512\n\nembeddings = model.encode(\n sentences, \n normalize_embeddings=True,\n batch_size=256, \n show_progress_bar=True\n )\nprint(embeddings)\n```\n\n\nWe add instruction for classification and clustering.\nIf you want to add instruction to the query (no instruction for the corpus), you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\n\nsentences = [\"This is an example sentence\", \"Each sentence is converted\"]\n\nmodel = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code\nmodel.max_seq_length = 512\n\nprompt = \"Instruct: Classifying the category of french news. \\n Query: \"\nembeddings = model.encode(\n sentences, \n prompt=prompt,\n normalize_embeddings=True,\n batch_size=256, \n show_progress_bar=True\n )\nprint(embeddings)\n```\n\n\n## Citation\nPlease cite the repo if you use the model or code in this repo.\n\n```\n@article{hu2025kalm,\n title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},\n author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},\n journal={arXiv preprint arXiv:2501.01028},\n year={2025}\n}\n```\n\n\n## Contact\nIf you encounter any issue, feel free to contact us via the email: yanshek.woo@gmail.com\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":84,"cells":{"id":{"kind":"string","value":"TheBloke/meditron-7B-AWQ"},"author":{"kind":"string","value":"TheBloke"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","llama","text-generation","en","dataset:epfl-llm/guidelines","arxiv:2311.16079","base_model:epfl-llm/meditron-7b","base_model:quantized:epfl-llm/meditron-7b","license:llama2","autotrain_compatible","text-generation-inference","4-bit","awq","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"llama\",\n \"text-generation\",\n \"en\",\n \"dataset:epfl-llm/guidelines\",\n \"arxiv:2311.16079\",\n \"base_model:epfl-llm/meditron-7b\",\n \"base_model:quantized:epfl-llm/meditron-7b\",\n \"license:llama2\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"4-bit\",\n \"awq\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2023-11-30T22:11:31"},"last_modified":{"kind":"string","value":"2023-11-30T22:26:18"},"downloads":{"kind":"number","value":35850,"string":"35,850"},"likes":{"kind":"number","value":2,"string":"2"},"README":{"kind":"string","value":"---\nbase_model: epfl-llm/meditron-7b\ndatasets:\n- epfl-llm/guidelines\nlanguage:\n- en\nlicense: llama2\nmetrics:\n- accuracy\n- perplexity\nmodel_name: Meditron 7B\ninference: false\nmodel_creator: EPFL LLM Team\nmodel_type: llama\nprompt_template: '<|im_start|>system\n\n {system_message}<|im_end|>\n\n <|im_start|>user\n\n {prompt}<|im_end|>\n\n <|im_start|>assistant\n\n '\nquantized_by: TheBloke\n---\n\n\n\n\n\n
\n\n\n# Meditron 7B - AWQ\n- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)\n- Original model: [Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b)\n\n\n## Description\n\nThis repo contains AWQ model files for [EPFL LLM Team's Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b).\n\nThese files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).\n\n\n### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nIt is supported by:\n\n- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ\n- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)\n- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers\n- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-7B-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-7B-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-7B-GGUF)\n* [EPFL LLM Team's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/epfl-llm/meditron-7b)\n\n\n\n## Prompt template: ChatML\n\n```\n<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n\n```\n\n\n\n\n\n## Provided files, and AWQ parameters\n\nI currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.\n\nModels are released as sharded safetensors files.\n\n| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |\n| ------ | ---- | -- | ----------- | ------- | ---- |\n| [main](https://huggingface.co/TheBloke/meditron-7B-AWQ/tree/main) | 4 | 128 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 3.89 GB\n\n\n\n\n## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n\nPlease make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n1. Click the **Model tab**.\n2. Under **Download custom model or LoRA**, enter `TheBloke/meditron-7B-AWQ`.\n3. Click **Download**.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to **Model**.\n6. In the **Model** dropdown, choose the model you just downloaded: `meditron-7B-AWQ`\n7. Select **Loader: AutoAWQ**.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.\n10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!\n\n\n\n## Multi-user inference server: vLLM\n\nDocumentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).\n\n- Please ensure you are using vLLM version 0.2 or later.\n- When using vLLM as a server, pass the `--quantization awq` parameter.\n\nFor example:\n\n```shell\npython3 -m vllm.entrypoints.api_server --model TheBloke/meditron-7B-AWQ --quantization awq --dtype auto\n```\n\n- When using vLLM from Python code, again set `quantization=awq`.\n\nFor example:\n\n```python\nfrom vllm import LLM, SamplingParams\n\nprompts = [\n \"Tell me about AI\",\n \"Write a story about llamas\",\n \"What is 291 - 150?\",\n \"How much wood would a woodchuck chuck if a woodchuck could chuck wood?\",\n]\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\nprompts = [prompt_template.format(prompt=prompt) for prompt in prompts]\n\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\n\nllm = LLM(model=\"TheBloke/meditron-7B-AWQ\", quantization=\"awq\", dtype=\"auto\")\n\noutputs = llm.generate(prompts, sampling_params)\n\n# Print the outputs.\nfor output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\n```\n\n\n\n## Multi-user inference server: Hugging Face Text Generation Inference (TGI)\n\nUse TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`\n\nExample Docker parameters:\n\n```shell\n--model-id TheBloke/meditron-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096\n```\n\nExample Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):\n\n```shell\npip3 install huggingface-hub\n```\n\n```python\nfrom huggingface_hub import InferenceClient\n\nendpoint_url = \"https://your-endpoint-url-here\"\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\nclient = InferenceClient(endpoint_url)\nresponse = client.text_generation(prompt,\n max_new_tokens=128,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1)\n\nprint(f\"Model output: \", response)\n```\n\n\n\n## Inference from Python code using Transformers\n\n### Install the necessary packages\n\n- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.\n- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.\n\n```shell\npip3 install --upgrade \"autoawq>=0.1.6\" \"transformers>=4.35.0\"\n```\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n```shell\npip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl\n```\n\nIf you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:\n\n```shell\npip3 uninstall -y autoawq\ngit clone https://github.com/casper-hansen/AutoAWQ\ncd AutoAWQ\npip3 install .\n```\n\n### Transformers example code (requires Transformers 4.35.0 and later)\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer\n\nmodel_name_or_path = \"TheBloke/meditron-7B-AWQ\"\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\nmodel = AutoModelForCausalLM.from_pretrained(\n model_name_or_path,\n low_cpu_mem_usage=True,\n device_map=\"cuda:0\"\n)\n\n# Using the text streamer to stream output one token at a time\nstreamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\n# Convert prompt to tokens\ntokens = tokenizer(\n prompt_template,\n return_tensors='pt'\n).input_ids.cuda()\n\ngeneration_params = {\n \"do_sample\": True,\n \"temperature\": 0.7,\n \"top_p\": 0.95,\n \"top_k\": 40,\n \"max_new_tokens\": 512,\n \"repetition_penalty\": 1.1\n}\n\n# Generate streamed output, visible one token at a time\ngeneration_output = model.generate(\n tokens,\n streamer=streamer,\n **generation_params\n)\n\n# Generation without a streamer, which will include the prompt in the output\ngeneration_output = model.generate(\n tokens,\n **generation_params\n)\n\n# Get the tokens from the output, decode them, print them\ntoken_output = generation_output[0]\ntext_output = tokenizer.decode(token_output)\nprint(\"model.generate output: \", text_output)\n\n# Inference is also possible via Transformers' pipeline\nfrom transformers import pipeline\n\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n **generation_params\n)\n\npipe_output = pipe(prompt_template)[0]['generated_text']\nprint(\"pipeline output: \", pipe_output)\n\n```\n\n\n\n## Compatibility\n\nThe files provided are tested to work with:\n\n- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.\n- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.\n- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.\n- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.\n\n\n\n\n\n## Discord\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n[TheBloke AI's Discord server](https://discord.gg/theblokeai)\n\n## Thanks, and how to contribute\n\nThanks to the [chirper.ai](https://chirper.ai) team!\n\nThanks to Clay from [gpus.llm-utils.org](llm-utils)!\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n* Patreon: https://patreon.com/TheBlokeAI\n* Ko-Fi: https://ko-fi.com/TheBlokeAI\n\n**Special thanks to**: Aemon Algiz.\n\n**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\nAnd thank you again to a16z for their generous grant.\n\n\n\n# Original model card: EPFL LLM Team's Meditron 7B\n\n![\"Alt](\"meditron_LOGO.png\" "\\"Meditron-logo\\"")
\n\n# Model Card for Meditron-7B-v1.0\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).\nMeditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.\n\n![\"Alt](\"gap-replay.png\" "\\"Meditron-logo\\"")
\n\n#### Data Preprocessing\n\nPlease see the detailed preprocessing procedure in our paper. \n\n### Training Procedure \n\nWe used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency. \nHardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM. \n\nOur three way parallelism scheme uses:\n - Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2,\n - Pipeline Parallelism (PP -- different GPUs process different layers) of 4,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 3e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 16 |\n| TP size | 4 |\n| PP size | 1 |\n| seq length | 2048 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 10 |\n| global batch size | 1600 |\n| | |\n\n#### Sizes\nThe model was trained in September 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | |\n| --- | ------ |\n| Model size | 7B |\n| Hidden dimension | 4096 |\n| Num. attention heads | 32 |\n| Num. layers | 32 |\n| | |\n\n## Evaluation\n\n\n\n### Testing Data & Metrics\n\n#### Testing Data\n- [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa)\n- [MedMCQA](https://huggingface.co/datasets/medmcqa)\n- [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa)\n- [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu)\n- [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)\n\n#### Metrics\n- Accuracy: suite the evaluation of multiple-choice question-answering tasks.\n\n### Results\nWe finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with top token selection as the inference mode.\nFor MMLU-Medical, models finetuned on MedMCQA are used for inference.\nFor MedQA-4-Option, models finetuned on MedQA are used for inference.\nFor a more detailed performance analysis, please see our paper. \n\n| | | | | | |\n| --- | ------ |----- |----- |----- |----- |\n|Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* |\n|MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 |\n|PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 |\n|MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 |\n|MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 |\n|MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 |\n|Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 8 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 588.8\n- **Hardware Provider:** EPFL Research Computing Platform\n- **Compute Region:** Switzerland\n- **Carbon Emitted:** Switzerland has a carbon efficiency of 0.016 kgCO2/kWh (https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf). 73.6 hours of 8 A100s means 588.8 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.5, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 73.6 h * 8 GPU) * 1.8 PUE = 6.8 kgCO2.\n\n## Citation\n\n**BibTeX:**\nIf you use Meditron or its training data, please cite our work:\n\n```\n@misc{chen2023meditron70b,\n title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models}, \n author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},\n year={2023},\n eprint={2311.16079},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n@software{epfmedtrn,\n author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},\n title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},\n month = November,\n year = 2023,\n url = {https://github.com/epfLLM/meditron}\n}\n```\n"},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING"],"string":"[\n \"QUESTION_ANSWERING\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["MEDQA","PUBMEDQA"],"string":"[\n \"MEDQA\",\n \"PUBMEDQA\"\n]"}}},{"rowIdx":85,"cells":{"id":{"kind":"string","value":"hkunlp/instructor-xl"},"author":{"kind":"string","value":"hkunlp"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","t5","text-embedding","embeddings","information-retrieval","beir","text-classification","language-model","text-clustering","text-semantic-similarity","text-evaluation","prompt-retrieval","text-reranking","feature-extraction","sentence-similarity","transformers","English","Sentence Similarity","natural_questions","ms_marco","fever","hotpot_qa","mteb","en","arxiv:2212.09741","license:apache-2.0","model-index","autotrain_compatible","text-generation-inference","region:us"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"t5\",\n \"text-embedding\",\n \"embeddings\",\n \"information-retrieval\",\n \"beir\",\n \"text-classification\",\n \"language-model\",\n \"text-clustering\",\n \"text-semantic-similarity\",\n \"text-evaluation\",\n \"prompt-retrieval\",\n \"text-reranking\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"transformers\",\n \"English\",\n \"Sentence Similarity\",\n \"natural_questions\",\n \"ms_marco\",\n \"fever\",\n \"hotpot_qa\",\n \"mteb\",\n \"en\",\n \"arxiv:2212.09741\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2022-12-20T06:07:18"},"last_modified":{"kind":"string","value":"2023-01-21T06:33:27"},"downloads":{"kind":"number","value":35591,"string":"35,591"},"likes":{"kind":"number","value":562,"string":"562"},"README":{"kind":"string","value":"---\nlanguage: en\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- text-embedding\n- embeddings\n- information-retrieval\n- beir\n- text-classification\n- language-model\n- text-clustering\n- text-semantic-similarity\n- text-evaluation\n- prompt-retrieval\n- text-reranking\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- t5\n- English\n- Sentence Similarity\n- natural_questions\n- ms_marco\n- fever\n- hotpot_qa\n- mteb\ninference: false\nmodel-index:\n- name: final_xl_results\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 85.08955223880596\n - type: ap\n value: 52.66066378722476\n - type: f1\n value: 79.63340218960269\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 86.542\n - type: ap\n value: 81.92695193008987\n - type: f1\n value: 86.51466132573681\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 42.964\n - type: f1\n value: 41.43146249774862\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 29.872\n - type: map_at_10\n value: 46.342\n - type: map_at_100\n value: 47.152\n - type: map_at_1000\n value: 47.154\n - type: map_at_3\n value: 41.216\n - type: map_at_5\n value: 44.035999999999994\n - type: mrr_at_1\n value: 30.939\n - type: mrr_at_10\n value: 46.756\n - type: mrr_at_100\n value: 47.573\n - type: mrr_at_1000\n value: 47.575\n - type: mrr_at_3\n value: 41.548\n - type: mrr_at_5\n value: 44.425\n - type: ndcg_at_1\n value: 29.872\n - type: ndcg_at_10\n value: 55.65\n - type: ndcg_at_100\n value: 58.88099999999999\n - type: ndcg_at_1000\n value: 58.951\n - type: ndcg_at_3\n value: 45.0\n - type: ndcg_at_5\n value: 50.09\n - type: precision_at_1\n value: 29.872\n - type: precision_at_10\n value: 8.549\n - type: precision_at_100\n value: 0.991\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 18.658\n - type: precision_at_5\n value: 13.669999999999998\n - type: recall_at_1\n value: 29.872\n - type: recall_at_10\n value: 85.491\n - type: recall_at_100\n value: 99.075\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 55.974000000000004\n - type: recall_at_5\n value: 68.35\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 42.452729850641276\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 32.21141846480423\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 65.34710928952622\n - type: mrr\n value: 77.61124301983028\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_spearman\n value: 84.15312230525639\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 82.66233766233766\n - type: f1\n value: 82.04175284777669\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 37.36697339826455\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 30.551241447593092\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 36.797000000000004\n - type: map_at_10\n value: 48.46\n - type: map_at_100\n value: 49.968\n - type: map_at_1000\n value: 50.080000000000005\n - type: map_at_3\n value: 44.71\n - type: map_at_5\n value: 46.592\n - type: mrr_at_1\n value: 45.494\n - type: mrr_at_10\n value: 54.747\n - type: mrr_at_100\n value: 55.43599999999999\n - type: mrr_at_1000\n value: 55.464999999999996\n - type: mrr_at_3\n value: 52.361000000000004\n - type: mrr_at_5\n value: 53.727000000000004\n - type: ndcg_at_1\n value: 45.494\n - type: ndcg_at_10\n value: 54.989\n - type: ndcg_at_100\n value: 60.096000000000004\n - type: ndcg_at_1000\n value: 61.58\n - type: ndcg_at_3\n value: 49.977\n - type: ndcg_at_5\n value: 51.964999999999996\n - type: precision_at_1\n value: 45.494\n - type: precision_at_10\n value: 10.558\n - type: precision_at_100\n value: 1.6049999999999998\n - type: precision_at_1000\n value: 0.203\n - type: precision_at_3\n value: 23.796\n - type: precision_at_5\n value: 16.881\n - type: recall_at_1\n value: 36.797000000000004\n - type: recall_at_10\n value: 66.83\n - type: recall_at_100\n value: 88.34100000000001\n - type: recall_at_1000\n value: 97.202\n - type: recall_at_3\n value: 51.961999999999996\n - type: recall_at_5\n value: 57.940000000000005\n - type: map_at_1\n value: 32.597\n - type: map_at_10\n value: 43.424\n - type: map_at_100\n value: 44.78\n - type: map_at_1000\n value: 44.913\n - type: map_at_3\n value: 40.315\n - type: map_at_5\n value: 41.987\n - type: mrr_at_1\n value: 40.382\n - type: mrr_at_10\n value: 49.219\n - type: mrr_at_100\n value: 49.895\n - type: mrr_at_1000\n value: 49.936\n - type: mrr_at_3\n value: 46.996\n - type: mrr_at_5\n value: 48.231\n - type: ndcg_at_1\n value: 40.382\n - type: ndcg_at_10\n value: 49.318\n - type: ndcg_at_100\n value: 53.839999999999996\n - type: ndcg_at_1000\n value: 55.82899999999999\n - type: ndcg_at_3\n value: 44.914\n - type: ndcg_at_5\n value: 46.798\n - type: precision_at_1\n value: 40.382\n - type: precision_at_10\n value: 9.274000000000001\n - type: precision_at_100\n value: 1.497\n - type: precision_at_1000\n value: 0.198\n - type: precision_at_3\n value: 21.592\n - type: precision_at_5\n value: 15.159\n - type: recall_at_1\n value: 32.597\n - type: recall_at_10\n value: 59.882000000000005\n - type: recall_at_100\n value: 78.446\n - type: recall_at_1000\n value: 90.88000000000001\n - type: recall_at_3\n value: 46.9\n - type: recall_at_5\n value: 52.222\n - type: map_at_1\n value: 43.8\n - type: map_at_10\n value: 57.293000000000006\n - type: map_at_100\n value: 58.321\n - type: map_at_1000\n value: 58.361\n - type: map_at_3\n value: 53.839999999999996\n - type: map_at_5\n value: 55.838\n - type: mrr_at_1\n value: 49.592000000000006\n - type: mrr_at_10\n value: 60.643\n - type: mrr_at_100\n value: 61.23499999999999\n - type: mrr_at_1000\n value: 61.251999999999995\n - type: mrr_at_3\n value: 58.265\n - type: mrr_at_5\n value: 59.717\n - type: ndcg_at_1\n value: 49.592000000000006\n - type: ndcg_at_10\n value: 63.364\n - type: ndcg_at_100\n value: 67.167\n - type: ndcg_at_1000\n value: 67.867\n - type: ndcg_at_3\n value: 57.912\n - type: ndcg_at_5\n value: 60.697\n - type: precision_at_1\n value: 49.592000000000006\n - type: precision_at_10\n value: 10.088\n - type: precision_at_100\n value: 1.2930000000000001\n - type: precision_at_1000\n value: 0.13899999999999998\n - type: precision_at_3\n value: 25.789\n - type: precision_at_5\n value: 17.541999999999998\n - type: recall_at_1\n value: 43.8\n - type: recall_at_10\n value: 77.635\n - type: recall_at_100\n value: 93.748\n - type: recall_at_1000\n value: 98.468\n - type: recall_at_3\n value: 63.223\n - type: recall_at_5\n value: 70.122\n - type: map_at_1\n value: 27.721\n - type: map_at_10\n value: 35.626999999999995\n - type: map_at_100\n value: 36.719\n - type: map_at_1000\n value: 36.8\n - type: map_at_3\n value: 32.781\n - type: map_at_5\n value: 34.333999999999996\n - type: mrr_at_1\n value: 29.604999999999997\n - type: mrr_at_10\n value: 37.564\n - type: mrr_at_100\n value: 38.505\n - type: mrr_at_1000\n value: 38.565\n - type: mrr_at_3\n value: 34.727000000000004\n - type: mrr_at_5\n value: 36.207\n - type: ndcg_at_1\n value: 29.604999999999997\n - type: ndcg_at_10\n value: 40.575\n - type: ndcg_at_100\n value: 45.613\n - type: ndcg_at_1000\n value: 47.676\n - type: ndcg_at_3\n value: 34.811\n - type: ndcg_at_5\n value: 37.491\n - type: precision_at_1\n value: 29.604999999999997\n - type: precision_at_10\n value: 6.1690000000000005\n - type: precision_at_100\n value: 0.906\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 14.237\n - type: precision_at_5\n value: 10.056\n - type: recall_at_1\n value: 27.721\n - type: recall_at_10\n value: 54.041\n - type: recall_at_100\n value: 76.62299999999999\n - type: recall_at_1000\n value: 92.134\n - type: recall_at_3\n value: 38.582\n - type: recall_at_5\n value: 44.989000000000004\n - type: map_at_1\n value: 16.553\n - type: map_at_10\n value: 25.384\n - type: map_at_100\n value: 26.655\n - type: map_at_1000\n value: 26.778000000000002\n - type: map_at_3\n value: 22.733\n - type: map_at_5\n value: 24.119\n - type: mrr_at_1\n value: 20.149\n - type: mrr_at_10\n value: 29.705\n - type: mrr_at_100\n value: 30.672\n - type: mrr_at_1000\n value: 30.737\n - type: mrr_at_3\n value: 27.032\n - type: mrr_at_5\n value: 28.369\n - type: ndcg_at_1\n value: 20.149\n - type: ndcg_at_10\n value: 30.843999999999998\n - type: ndcg_at_100\n value: 36.716\n - type: ndcg_at_1000\n value: 39.495000000000005\n - type: ndcg_at_3\n value: 25.918999999999997\n - type: ndcg_at_5\n value: 27.992\n - type: precision_at_1\n value: 20.149\n - type: precision_at_10\n value: 5.858\n - type: precision_at_100\n value: 1.009\n - type: precision_at_1000\n value: 0.13799999999999998\n - type: precision_at_3\n value: 12.645000000000001\n - type: precision_at_5\n value: 9.179\n - type: recall_at_1\n value: 16.553\n - type: recall_at_10\n value: 43.136\n - type: recall_at_100\n value: 68.562\n - type: recall_at_1000\n value: 88.208\n - type: recall_at_3\n value: 29.493000000000002\n - type: recall_at_5\n value: 34.751\n - type: map_at_1\n value: 28.000999999999998\n - type: map_at_10\n value: 39.004\n - type: map_at_100\n value: 40.461999999999996\n - type: map_at_1000\n value: 40.566\n - type: map_at_3\n value: 35.805\n - type: map_at_5\n value: 37.672\n - type: mrr_at_1\n value: 33.782000000000004\n - type: mrr_at_10\n value: 44.702\n - type: mrr_at_100\n value: 45.528\n - type: mrr_at_1000\n value: 45.576\n - type: mrr_at_3\n value: 42.14\n - type: mrr_at_5\n value: 43.651\n - type: ndcg_at_1\n value: 33.782000000000004\n - type: ndcg_at_10\n value: 45.275999999999996\n - type: ndcg_at_100\n value: 50.888\n - type: ndcg_at_1000\n value: 52.879\n - type: ndcg_at_3\n value: 40.191\n - type: ndcg_at_5\n value: 42.731\n - type: precision_at_1\n value: 33.782000000000004\n - type: precision_at_10\n value: 8.200000000000001\n - type: precision_at_100\n value: 1.287\n - type: precision_at_1000\n value: 0.16199999999999998\n - type: precision_at_3\n value: 19.185\n - type: precision_at_5\n value: 13.667000000000002\n - type: recall_at_1\n value: 28.000999999999998\n - type: recall_at_10\n value: 58.131\n - type: recall_at_100\n value: 80.869\n - type: recall_at_1000\n value: 93.931\n - type: recall_at_3\n value: 44.161\n - type: recall_at_5\n value: 50.592000000000006\n - type: map_at_1\n value: 28.047\n - type: map_at_10\n value: 38.596000000000004\n - type: map_at_100\n value: 40.116\n - type: map_at_1000\n value: 40.232\n - type: map_at_3\n value: 35.205\n - type: map_at_5\n value: 37.076\n - type: mrr_at_1\n value: 34.932\n - type: mrr_at_10\n value: 44.496\n - type: mrr_at_100\n value: 45.47\n - type: mrr_at_1000\n value: 45.519999999999996\n - type: mrr_at_3\n value: 41.743\n - type: mrr_at_5\n value: 43.352000000000004\n - type: ndcg_at_1\n value: 34.932\n - type: ndcg_at_10\n value: 44.901\n - type: ndcg_at_100\n value: 50.788999999999994\n - type: ndcg_at_1000\n value: 52.867\n - type: ndcg_at_3\n value: 39.449\n - type: ndcg_at_5\n value: 41.929\n - type: precision_at_1\n value: 34.932\n - type: precision_at_10\n value: 8.311\n - type: precision_at_100\n value: 1.3050000000000002\n - type: precision_at_1000\n value: 0.166\n - type: precision_at_3\n value: 18.836\n - type: precision_at_5\n value: 13.447000000000001\n - type: recall_at_1\n value: 28.047\n - type: recall_at_10\n value: 57.717\n - type: recall_at_100\n value: 82.182\n - type: recall_at_1000\n value: 95.82000000000001\n - type: recall_at_3\n value: 42.448\n - type: recall_at_5\n value: 49.071\n - type: map_at_1\n value: 27.861250000000005\n - type: map_at_10\n value: 37.529583333333335\n - type: map_at_100\n value: 38.7915\n - type: map_at_1000\n value: 38.90558333333335\n - type: map_at_3\n value: 34.57333333333333\n - type: map_at_5\n value: 36.187166666666656\n - type: mrr_at_1\n value: 32.88291666666666\n - type: mrr_at_10\n value: 41.79750000000001\n - type: mrr_at_100\n value: 42.63183333333333\n - type: mrr_at_1000\n value: 42.68483333333333\n - type: mrr_at_3\n value: 39.313750000000006\n - type: mrr_at_5\n value: 40.70483333333333\n - type: ndcg_at_1\n value: 32.88291666666666\n - type: ndcg_at_10\n value: 43.09408333333333\n - type: ndcg_at_100\n value: 48.22158333333333\n - type: ndcg_at_1000\n value: 50.358000000000004\n - type: ndcg_at_3\n value: 38.129583333333336\n - type: ndcg_at_5\n value: 40.39266666666666\n - type: precision_at_1\n value: 32.88291666666666\n - type: precision_at_10\n value: 7.5584999999999996\n - type: precision_at_100\n value: 1.1903333333333332\n - type: precision_at_1000\n value: 0.15658333333333332\n - type: precision_at_3\n value: 17.495916666666666\n - type: precision_at_5\n value: 12.373833333333332\n - type: recall_at_1\n value: 27.861250000000005\n - type: recall_at_10\n value: 55.215916666666665\n - type: recall_at_100\n value: 77.392\n - type: recall_at_1000\n value: 92.04908333333334\n - type: recall_at_3\n value: 41.37475\n - type: recall_at_5\n value: 47.22908333333333\n - type: map_at_1\n value: 25.064999999999998\n - type: map_at_10\n value: 31.635999999999996\n - type: map_at_100\n value: 32.596000000000004\n - type: map_at_1000\n value: 32.695\n - type: map_at_3\n value: 29.612\n - type: map_at_5\n value: 30.768\n - type: mrr_at_1\n value: 28.528\n - type: mrr_at_10\n value: 34.717\n - type: mrr_at_100\n value: 35.558\n - type: mrr_at_1000\n value: 35.626000000000005\n - type: mrr_at_3\n value: 32.745000000000005\n - type: mrr_at_5\n value: 33.819\n - type: ndcg_at_1\n value: 28.528\n - type: ndcg_at_10\n value: 35.647\n - type: ndcg_at_100\n value: 40.207\n - type: ndcg_at_1000\n value: 42.695\n - type: ndcg_at_3\n value: 31.878\n - type: ndcg_at_5\n value: 33.634\n - type: precision_at_1\n value: 28.528\n - type: precision_at_10\n value: 5.46\n - type: precision_at_100\n value: 0.84\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 13.547999999999998\n - type: precision_at_5\n value: 9.325\n - type: recall_at_1\n value: 25.064999999999998\n - type: recall_at_10\n value: 45.096000000000004\n - type: recall_at_100\n value: 65.658\n - type: recall_at_1000\n value: 84.128\n - type: recall_at_3\n value: 34.337\n - type: recall_at_5\n value: 38.849000000000004\n - type: map_at_1\n value: 17.276\n - type: map_at_10\n value: 24.535\n - type: map_at_100\n value: 25.655\n - type: map_at_1000\n value: 25.782\n - type: map_at_3\n value: 22.228\n - type: map_at_5\n value: 23.612\n - type: mrr_at_1\n value: 21.266\n - type: mrr_at_10\n value: 28.474\n - type: mrr_at_100\n value: 29.398000000000003\n - type: mrr_at_1000\n value: 29.482000000000003\n - type: mrr_at_3\n value: 26.245\n - type: mrr_at_5\n value: 27.624\n - type: ndcg_at_1\n value: 21.266\n - type: ndcg_at_10\n value: 29.087000000000003\n - type: ndcg_at_100\n value: 34.374\n - type: ndcg_at_1000\n value: 37.433\n - type: ndcg_at_3\n value: 25.040000000000003\n - type: ndcg_at_5\n value: 27.116\n - type: precision_at_1\n value: 21.266\n - type: precision_at_10\n value: 5.258\n - type: precision_at_100\n value: 0.9299999999999999\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 11.849\n - type: precision_at_5\n value: 8.699\n - type: recall_at_1\n value: 17.276\n - type: recall_at_10\n value: 38.928000000000004\n - type: recall_at_100\n value: 62.529\n - type: recall_at_1000\n value: 84.44800000000001\n - type: recall_at_3\n value: 27.554000000000002\n - type: recall_at_5\n value: 32.915\n - type: map_at_1\n value: 27.297\n - type: map_at_10\n value: 36.957\n - type: map_at_100\n value: 38.252\n - type: map_at_1000\n value: 38.356\n - type: map_at_3\n value: 34.121\n - type: map_at_5\n value: 35.782000000000004\n - type: mrr_at_1\n value: 32.275999999999996\n - type: mrr_at_10\n value: 41.198\n - type: mrr_at_100\n value: 42.131\n - type: mrr_at_1000\n value: 42.186\n - type: mrr_at_3\n value: 38.557\n - type: mrr_at_5\n value: 40.12\n - type: ndcg_at_1\n value: 32.275999999999996\n - type: ndcg_at_10\n value: 42.516\n - type: ndcg_at_100\n value: 48.15\n - type: ndcg_at_1000\n value: 50.344\n - type: ndcg_at_3\n value: 37.423\n - type: ndcg_at_5\n value: 39.919\n - type: precision_at_1\n value: 32.275999999999996\n - type: precision_at_10\n value: 7.155\n - type: precision_at_100\n value: 1.123\n - type: precision_at_1000\n value: 0.14200000000000002\n - type: precision_at_3\n value: 17.163999999999998\n - type: precision_at_5\n value: 12.127\n - type: recall_at_1\n value: 27.297\n - type: recall_at_10\n value: 55.238\n - type: recall_at_100\n value: 79.2\n - type: recall_at_1000\n value: 94.258\n - type: recall_at_3\n value: 41.327000000000005\n - type: recall_at_5\n value: 47.588\n - type: map_at_1\n value: 29.142000000000003\n - type: map_at_10\n value: 38.769\n - type: map_at_100\n value: 40.292\n - type: map_at_1000\n value: 40.510000000000005\n - type: map_at_3\n value: 35.39\n - type: map_at_5\n value: 37.009\n - type: mrr_at_1\n value: 34.19\n - type: mrr_at_10\n value: 43.418\n - type: mrr_at_100\n value: 44.132\n - type: mrr_at_1000\n value: 44.175\n - type: mrr_at_3\n value: 40.547\n - type: mrr_at_5\n value: 42.088\n - type: ndcg_at_1\n value: 34.19\n - type: ndcg_at_10\n value: 45.14\n - type: ndcg_at_100\n value: 50.364\n - type: ndcg_at_1000\n value: 52.481\n - type: ndcg_at_3\n value: 39.466\n - type: ndcg_at_5\n value: 41.772\n - type: precision_at_1\n value: 34.19\n - type: precision_at_10\n value: 8.715\n - type: precision_at_100\n value: 1.6150000000000002\n - type: precision_at_1000\n value: 0.247\n - type: precision_at_3\n value: 18.248\n - type: precision_at_5\n value: 13.161999999999999\n - type: recall_at_1\n value: 29.142000000000003\n - type: recall_at_10\n value: 57.577999999999996\n - type: recall_at_100\n value: 81.428\n - type: recall_at_1000\n value: 94.017\n - type: recall_at_3\n value: 41.402\n - type: recall_at_5\n value: 47.695\n - type: map_at_1\n value: 22.039\n - type: map_at_10\n value: 30.669999999999998\n - type: map_at_100\n value: 31.682\n - type: map_at_1000\n value: 31.794\n - type: map_at_3\n value: 28.139999999999997\n - type: map_at_5\n value: 29.457\n - type: mrr_at_1\n value: 24.399\n - type: mrr_at_10\n value: 32.687\n - type: mrr_at_100\n value: 33.622\n - type: mrr_at_1000\n value: 33.698\n - type: mrr_at_3\n value: 30.407\n - type: mrr_at_5\n value: 31.552999999999997\n - type: ndcg_at_1\n value: 24.399\n - type: ndcg_at_10\n value: 35.472\n - type: ndcg_at_100\n value: 40.455000000000005\n - type: ndcg_at_1000\n value: 43.15\n - type: ndcg_at_3\n value: 30.575000000000003\n - type: ndcg_at_5\n value: 32.668\n - type: precision_at_1\n value: 24.399\n - type: precision_at_10\n value: 5.656\n - type: precision_at_100\n value: 0.874\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 13.062000000000001\n - type: precision_at_5\n value: 9.242\n - type: recall_at_1\n value: 22.039\n - type: recall_at_10\n value: 48.379\n - type: recall_at_100\n value: 71.11800000000001\n - type: recall_at_1000\n value: 91.095\n - type: recall_at_3\n value: 35.108\n - type: recall_at_5\n value: 40.015\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 10.144\n - type: map_at_10\n value: 18.238\n - type: map_at_100\n value: 20.143\n - type: map_at_1000\n value: 20.346\n - type: map_at_3\n value: 14.809\n - type: map_at_5\n value: 16.567999999999998\n - type: mrr_at_1\n value: 22.671\n - type: mrr_at_10\n value: 34.906\n - type: mrr_at_100\n value: 35.858000000000004\n - type: mrr_at_1000\n value: 35.898\n - type: mrr_at_3\n value: 31.238\n - type: mrr_at_5\n value: 33.342\n - type: ndcg_at_1\n value: 22.671\n - type: ndcg_at_10\n value: 26.540000000000003\n - type: ndcg_at_100\n value: 34.138000000000005\n - type: ndcg_at_1000\n value: 37.72\n - type: ndcg_at_3\n value: 20.766000000000002\n - type: ndcg_at_5\n value: 22.927\n - type: precision_at_1\n value: 22.671\n - type: precision_at_10\n value: 8.619\n - type: precision_at_100\n value: 1.678\n - type: precision_at_1000\n value: 0.23500000000000001\n - type: precision_at_3\n value: 15.592\n - type: precision_at_5\n value: 12.43\n - type: recall_at_1\n value: 10.144\n - type: recall_at_10\n value: 33.46\n - type: recall_at_100\n value: 59.758\n - type: recall_at_1000\n value: 79.704\n - type: recall_at_3\n value: 19.604\n - type: recall_at_5\n value: 25.367\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 8.654\n - type: map_at_10\n value: 18.506\n - type: map_at_100\n value: 26.412999999999997\n - type: map_at_1000\n value: 28.13\n - type: map_at_3\n value: 13.379\n - type: map_at_5\n value: 15.529000000000002\n - type: mrr_at_1\n value: 66.0\n - type: mrr_at_10\n value: 74.13\n - type: mrr_at_100\n value: 74.48700000000001\n - type: mrr_at_1000\n value: 74.49799999999999\n - type: mrr_at_3\n value: 72.75\n - type: mrr_at_5\n value: 73.762\n - type: ndcg_at_1\n value: 54.50000000000001\n - type: ndcg_at_10\n value: 40.236\n - type: ndcg_at_100\n value: 44.690999999999995\n - type: ndcg_at_1000\n value: 52.195\n - type: ndcg_at_3\n value: 45.632\n - type: ndcg_at_5\n value: 42.952\n - type: precision_at_1\n value: 66.0\n - type: precision_at_10\n value: 31.724999999999998\n - type: precision_at_100\n value: 10.299999999999999\n - type: precision_at_1000\n value: 2.194\n - type: precision_at_3\n value: 48.75\n - type: precision_at_5\n value: 41.6\n - type: recall_at_1\n value: 8.654\n - type: recall_at_10\n value: 23.74\n - type: recall_at_100\n value: 50.346999999999994\n - type: recall_at_1000\n value: 74.376\n - type: recall_at_3\n value: 14.636\n - type: recall_at_5\n value: 18.009\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 53.245\n - type: f1\n value: 48.74520523753552\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 51.729\n - type: map_at_10\n value: 63.904\n - type: map_at_100\n value: 64.363\n - type: map_at_1000\n value: 64.38199999999999\n - type: map_at_3\n value: 61.393\n - type: map_at_5\n value: 63.02100000000001\n - type: mrr_at_1\n value: 55.686\n - type: mrr_at_10\n value: 67.804\n - type: mrr_at_100\n value: 68.15299999999999\n - type: mrr_at_1000\n value: 68.161\n - type: mrr_at_3\n value: 65.494\n - type: mrr_at_5\n value: 67.01599999999999\n - type: ndcg_at_1\n value: 55.686\n - type: ndcg_at_10\n value: 70.025\n - type: ndcg_at_100\n value: 72.011\n - type: ndcg_at_1000\n value: 72.443\n - type: ndcg_at_3\n value: 65.32900000000001\n - type: ndcg_at_5\n value: 68.05600000000001\n - type: precision_at_1\n value: 55.686\n - type: precision_at_10\n value: 9.358\n - type: precision_at_100\n value: 1.05\n - type: precision_at_1000\n value: 0.11\n - type: precision_at_3\n value: 26.318\n - type: precision_at_5\n value: 17.321\n - type: recall_at_1\n value: 51.729\n - type: recall_at_10\n value: 85.04\n - type: recall_at_100\n value: 93.777\n - type: recall_at_1000\n value: 96.824\n - type: recall_at_3\n value: 72.521\n - type: recall_at_5\n value: 79.148\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 23.765\n - type: map_at_10\n value: 39.114\n - type: map_at_100\n value: 40.987\n - type: map_at_1000\n value: 41.155\n - type: map_at_3\n value: 34.028000000000006\n - type: map_at_5\n value: 36.925000000000004\n - type: mrr_at_1\n value: 46.451\n - type: mrr_at_10\n value: 54.711\n - type: mrr_at_100\n value: 55.509\n - type: mrr_at_1000\n value: 55.535000000000004\n - type: mrr_at_3\n value: 52.649\n - type: mrr_at_5\n value: 53.729000000000006\n - type: ndcg_at_1\n value: 46.451\n - type: ndcg_at_10\n value: 46.955999999999996\n - type: ndcg_at_100\n value: 53.686\n - type: ndcg_at_1000\n value: 56.230000000000004\n - type: ndcg_at_3\n value: 43.374\n - type: ndcg_at_5\n value: 44.372\n - type: precision_at_1\n value: 46.451\n - type: precision_at_10\n value: 13.256\n - type: precision_at_100\n value: 2.019\n - type: precision_at_1000\n value: 0.247\n - type: precision_at_3\n value: 29.115000000000002\n - type: precision_at_5\n value: 21.389\n - type: recall_at_1\n value: 23.765\n - type: recall_at_10\n value: 53.452999999999996\n - type: recall_at_100\n value: 78.828\n - type: recall_at_1000\n value: 93.938\n - type: recall_at_3\n value: 39.023\n - type: recall_at_5\n value: 45.18\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 31.918000000000003\n - type: map_at_10\n value: 46.741\n - type: map_at_100\n value: 47.762\n - type: map_at_1000\n value: 47.849000000000004\n - type: map_at_3\n value: 43.578\n - type: map_at_5\n value: 45.395\n - type: mrr_at_1\n value: 63.834999999999994\n - type: mrr_at_10\n value: 71.312\n - type: mrr_at_100\n value: 71.695\n - type: mrr_at_1000\n value: 71.714\n - type: mrr_at_3\n value: 69.82000000000001\n - type: mrr_at_5\n value: 70.726\n - type: ndcg_at_1\n value: 63.834999999999994\n - type: ndcg_at_10\n value: 55.879999999999995\n - type: ndcg_at_100\n value: 59.723000000000006\n - type: ndcg_at_1000\n value: 61.49400000000001\n - type: ndcg_at_3\n value: 50.964\n - type: ndcg_at_5\n value: 53.47\n - type: precision_at_1\n value: 63.834999999999994\n - type: precision_at_10\n value: 11.845\n - type: precision_at_100\n value: 1.4869999999999999\n - type: precision_at_1000\n value: 0.172\n - type: precision_at_3\n value: 32.158\n - type: precision_at_5\n value: 21.278\n - type: recall_at_1\n value: 31.918000000000003\n - type: recall_at_10\n value: 59.223000000000006\n - type: recall_at_100\n value: 74.328\n - type: recall_at_1000\n value: 86.05000000000001\n - type: recall_at_3\n value: 48.238\n - type: recall_at_5\n value: 53.193999999999996\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 79.7896\n - type: ap\n value: 73.65166029460288\n - type: f1\n value: 79.71794693711813\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.239\n - type: map_at_10\n value: 34.542\n - type: map_at_100\n value: 35.717999999999996\n - type: map_at_1000\n value: 35.764\n - type: map_at_3\n value: 30.432\n - type: map_at_5\n value: 32.81\n - type: mrr_at_1\n value: 22.908\n - type: mrr_at_10\n value: 35.127\n - type: mrr_at_100\n value: 36.238\n - type: mrr_at_1000\n value: 36.278\n - type: mrr_at_3\n value: 31.076999999999998\n - type: mrr_at_5\n value: 33.419\n - type: ndcg_at_1\n value: 22.908\n - type: ndcg_at_10\n value: 41.607\n - type: ndcg_at_100\n value: 47.28\n - type: ndcg_at_1000\n value: 48.414\n - type: ndcg_at_3\n value: 33.253\n - type: ndcg_at_5\n value: 37.486000000000004\n - type: precision_at_1\n value: 22.908\n - type: precision_at_10\n value: 6.645\n - type: precision_at_100\n value: 0.9490000000000001\n - type: precision_at_1000\n value: 0.105\n - type: precision_at_3\n value: 14.130999999999998\n - type: precision_at_5\n value: 10.616\n - type: recall_at_1\n value: 22.239\n - type: recall_at_10\n value: 63.42\n - type: recall_at_100\n value: 89.696\n - type: recall_at_1000\n value: 98.351\n - type: recall_at_3\n value: 40.77\n - type: recall_at_5\n value: 50.93\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 95.06839945280439\n - type: f1\n value: 94.74276398224072\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 72.25718194254446\n - type: f1\n value: 53.91164489161391\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 71.47948890383323\n - type: f1\n value: 69.98520247230257\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 76.46603900470748\n - type: f1\n value: 76.44111526065399\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 33.19106070798198\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 30.78772205248094\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 31.811231631488507\n - type: mrr\n value: 32.98200485378021\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.9\n - type: map_at_10\n value: 13.703000000000001\n - type: map_at_100\n value: 17.251\n - type: map_at_1000\n value: 18.795\n - type: map_at_3\n value: 10.366999999999999\n - type: map_at_5\n value: 11.675\n - type: mrr_at_1\n value: 47.059\n - type: mrr_at_10\n value: 55.816\n - type: mrr_at_100\n value: 56.434\n - type: mrr_at_1000\n value: 56.467\n - type: mrr_at_3\n value: 53.973000000000006\n - type: mrr_at_5\n value: 55.257999999999996\n - type: ndcg_at_1\n value: 44.737\n - type: ndcg_at_10\n value: 35.997\n - type: ndcg_at_100\n value: 33.487\n - type: ndcg_at_1000\n value: 41.897\n - type: ndcg_at_3\n value: 41.18\n - type: ndcg_at_5\n value: 38.721\n - type: precision_at_1\n value: 46.129999999999995\n - type: precision_at_10\n value: 26.533\n - type: precision_at_100\n value: 8.706\n - type: precision_at_1000\n value: 2.16\n - type: precision_at_3\n value: 38.493\n - type: precision_at_5\n value: 33.189\n - type: recall_at_1\n value: 6.9\n - type: recall_at_10\n value: 17.488999999999997\n - type: recall_at_100\n value: 34.583000000000006\n - type: recall_at_1000\n value: 64.942\n - type: recall_at_3\n value: 11.494\n - type: recall_at_5\n value: 13.496\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 33.028999999999996\n - type: map_at_10\n value: 49.307\n - type: map_at_100\n value: 50.205\n - type: map_at_1000\n value: 50.23\n - type: map_at_3\n value: 44.782\n - type: map_at_5\n value: 47.599999999999994\n - type: mrr_at_1\n value: 37.108999999999995\n - type: mrr_at_10\n value: 51.742999999999995\n - type: mrr_at_100\n value: 52.405\n - type: mrr_at_1000\n value: 52.422000000000004\n - type: mrr_at_3\n value: 48.087999999999994\n - type: mrr_at_5\n value: 50.414\n - type: ndcg_at_1\n value: 37.08\n - type: ndcg_at_10\n value: 57.236\n - type: ndcg_at_100\n value: 60.931999999999995\n - type: ndcg_at_1000\n value: 61.522\n - type: ndcg_at_3\n value: 48.93\n - type: ndcg_at_5\n value: 53.561\n - type: precision_at_1\n value: 37.08\n - type: precision_at_10\n value: 9.386\n - type: precision_at_100\n value: 1.1480000000000001\n - type: precision_at_1000\n value: 0.12\n - type: precision_at_3\n value: 22.258\n - type: precision_at_5\n value: 16.025\n - type: recall_at_1\n value: 33.028999999999996\n - type: recall_at_10\n value: 78.805\n - type: recall_at_100\n value: 94.643\n - type: recall_at_1000\n value: 99.039\n - type: recall_at_3\n value: 57.602\n - type: recall_at_5\n value: 68.253\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.122\n - type: map_at_10\n value: 85.237\n - type: map_at_100\n value: 85.872\n - type: map_at_1000\n value: 85.885\n - type: map_at_3\n value: 82.27499999999999\n - type: map_at_5\n value: 84.13199999999999\n - type: mrr_at_1\n value: 81.73\n - type: mrr_at_10\n value: 87.834\n - type: mrr_at_100\n value: 87.92\n - type: mrr_at_1000\n value: 87.921\n - type: mrr_at_3\n value: 86.878\n - type: mrr_at_5\n value: 87.512\n - type: ndcg_at_1\n value: 81.73\n - type: ndcg_at_10\n value: 88.85499999999999\n - type: ndcg_at_100\n value: 89.992\n - type: ndcg_at_1000\n value: 90.07\n - type: ndcg_at_3\n value: 85.997\n - type: ndcg_at_5\n value: 87.55199999999999\n - type: precision_at_1\n value: 81.73\n - type: precision_at_10\n value: 13.491\n - type: precision_at_100\n value: 1.536\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.623\n - type: precision_at_5\n value: 24.742\n - type: recall_at_1\n value: 71.122\n - type: recall_at_10\n value: 95.935\n - type: recall_at_100\n value: 99.657\n - type: recall_at_1000\n value: 99.996\n - type: recall_at_3\n value: 87.80799999999999\n - type: recall_at_5\n value: 92.161\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 63.490029238193756\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 65.13153408508836\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.202999999999999\n - type: map_at_10\n value: 10.174\n - type: map_at_100\n value: 12.138\n - type: map_at_1000\n value: 12.418\n - type: map_at_3\n value: 7.379\n - type: map_at_5\n value: 8.727\n - type: mrr_at_1\n value: 20.7\n - type: mrr_at_10\n value: 30.389\n - type: mrr_at_100\n value: 31.566\n - type: mrr_at_1000\n value: 31.637999999999998\n - type: mrr_at_3\n value: 27.133000000000003\n - type: mrr_at_5\n value: 29.078\n - type: ndcg_at_1\n value: 20.7\n - type: ndcg_at_10\n value: 17.355999999999998\n - type: ndcg_at_100\n value: 25.151\n - type: ndcg_at_1000\n value: 30.37\n - type: ndcg_at_3\n value: 16.528000000000002\n - type: ndcg_at_5\n value: 14.396999999999998\n - type: precision_at_1\n value: 20.7\n - type: precision_at_10\n value: 8.98\n - type: precision_at_100\n value: 2.015\n - type: precision_at_1000\n value: 0.327\n - type: precision_at_3\n value: 15.367\n - type: precision_at_5\n value: 12.559999999999999\n - type: recall_at_1\n value: 4.202999999999999\n - type: recall_at_10\n value: 18.197\n - type: recall_at_100\n value: 40.903\n - type: recall_at_1000\n value: 66.427\n - type: recall_at_3\n value: 9.362\n - type: recall_at_5\n value: 12.747\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_spearman\n value: 81.69890989765257\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_spearman\n value: 75.31953790551489\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_spearman\n value: 87.44050861280759\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_spearman\n value: 81.86922869270393\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_spearman\n value: 88.9399170304284\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_spearman\n value: 85.38015314088582\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_spearman\n value: 90.53653527788835\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_spearman\n value: 68.64526474250209\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_spearman\n value: 86.56156983963042\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 79.48610254648003\n - type: mrr\n value: 94.02481505422682\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 48.983\n - type: map_at_10\n value: 59.077999999999996\n - type: map_at_100\n value: 59.536\n - type: map_at_1000\n value: 59.575\n - type: map_at_3\n value: 55.691\n - type: map_at_5\n value: 57.410000000000004\n - type: mrr_at_1\n value: 51.666999999999994\n - type: mrr_at_10\n value: 60.427\n - type: mrr_at_100\n value: 60.763\n - type: mrr_at_1000\n value: 60.79900000000001\n - type: mrr_at_3\n value: 57.556\n - type: mrr_at_5\n value: 59.089000000000006\n - type: ndcg_at_1\n value: 51.666999999999994\n - type: ndcg_at_10\n value: 64.559\n - type: ndcg_at_100\n value: 66.58\n - type: ndcg_at_1000\n value: 67.64\n - type: ndcg_at_3\n value: 58.287\n - type: ndcg_at_5\n value: 61.001000000000005\n - type: precision_at_1\n value: 51.666999999999994\n - type: precision_at_10\n value: 9.067\n - type: precision_at_100\n value: 1.0170000000000001\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 23.0\n - type: precision_at_5\n value: 15.6\n - type: recall_at_1\n value: 48.983\n - type: recall_at_10\n value: 80.289\n - type: recall_at_100\n value: 89.43299999999999\n - type: recall_at_1000\n value: 97.667\n - type: recall_at_3\n value: 62.978\n - type: recall_at_5\n value: 69.872\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.79009900990098\n - type: cos_sim_ap\n value: 94.94115052608419\n - type: cos_sim_f1\n value: 89.1260162601626\n - type: cos_sim_precision\n value: 90.599173553719\n - type: cos_sim_recall\n value: 87.7\n - type: dot_accuracy\n value: 99.79009900990098\n - type: dot_ap\n value: 94.94115052608419\n - type: dot_f1\n value: 89.1260162601626\n - type: dot_precision\n value: 90.599173553719\n - type: dot_recall\n value: 87.7\n - type: euclidean_accuracy\n value: 99.79009900990098\n - type: euclidean_ap\n value: 94.94115052608419\n - type: euclidean_f1\n value: 89.1260162601626\n - type: euclidean_precision\n value: 90.599173553719\n - type: euclidean_recall\n value: 87.7\n - type: manhattan_accuracy\n value: 99.7940594059406\n - type: manhattan_ap\n value: 94.95271414642431\n - type: manhattan_f1\n value: 89.24508790072387\n - type: manhattan_precision\n value: 92.3982869379015\n - type: manhattan_recall\n value: 86.3\n - type: max_accuracy\n value: 99.7940594059406\n - type: max_ap\n value: 94.95271414642431\n - type: max_f1\n value: 89.24508790072387\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 68.43866571935851\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 35.16579026551532\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 52.518952473513934\n - type: mrr\n value: 53.292457134368895\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.12529588316604\n - type: cos_sim_spearman\n value: 32.31662126895294\n - type: dot_pearson\n value: 31.125303796647056\n - type: dot_spearman\n value: 32.31662126895294\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.219\n - type: map_at_10\n value: 1.7469999999999999\n - type: map_at_100\n value: 10.177999999999999\n - type: map_at_1000\n value: 26.108999999999998\n - type: map_at_3\n value: 0.64\n - type: map_at_5\n value: 0.968\n - type: mrr_at_1\n value: 82.0\n - type: mrr_at_10\n value: 89.067\n - type: mrr_at_100\n value: 89.067\n - type: mrr_at_1000\n value: 89.067\n - type: mrr_at_3\n value: 88.333\n - type: mrr_at_5\n value: 88.73299999999999\n - type: ndcg_at_1\n value: 78.0\n - type: ndcg_at_10\n value: 71.398\n - type: ndcg_at_100\n value: 55.574999999999996\n - type: ndcg_at_1000\n value: 51.771\n - type: ndcg_at_3\n value: 77.765\n - type: ndcg_at_5\n value: 73.614\n - type: precision_at_1\n value: 82.0\n - type: precision_at_10\n value: 75.4\n - type: precision_at_100\n value: 58.040000000000006\n - type: precision_at_1000\n value: 23.516000000000002\n - type: precision_at_3\n value: 84.0\n - type: precision_at_5\n value: 78.4\n - type: recall_at_1\n value: 0.219\n - type: recall_at_10\n value: 1.958\n - type: recall_at_100\n value: 13.797999999999998\n - type: recall_at_1000\n value: 49.881\n - type: recall_at_3\n value: 0.672\n - type: recall_at_5\n value: 1.0370000000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 1.8610000000000002\n - type: map_at_10\n value: 8.705\n - type: map_at_100\n value: 15.164\n - type: map_at_1000\n value: 16.78\n - type: map_at_3\n value: 4.346\n - type: map_at_5\n value: 6.151\n - type: mrr_at_1\n value: 22.448999999999998\n - type: mrr_at_10\n value: 41.556\n - type: mrr_at_100\n value: 42.484\n - type: mrr_at_1000\n value: 42.494\n - type: mrr_at_3\n value: 37.755\n - type: mrr_at_5\n value: 40.102\n - type: ndcg_at_1\n value: 21.429000000000002\n - type: ndcg_at_10\n value: 23.439\n - type: ndcg_at_100\n value: 36.948\n - type: ndcg_at_1000\n value: 48.408\n - type: ndcg_at_3\n value: 22.261\n - type: ndcg_at_5\n value: 23.085\n - type: precision_at_1\n value: 22.448999999999998\n - type: precision_at_10\n value: 21.633\n - type: precision_at_100\n value: 8.02\n - type: precision_at_1000\n value: 1.5939999999999999\n - type: precision_at_3\n value: 23.810000000000002\n - type: precision_at_5\n value: 24.490000000000002\n - type: recall_at_1\n value: 1.8610000000000002\n - type: recall_at_10\n value: 15.876000000000001\n - type: recall_at_100\n value: 50.300999999999995\n - type: recall_at_1000\n value: 86.098\n - type: recall_at_3\n value: 5.892\n - type: recall_at_5\n value: 9.443\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 70.3264\n - type: ap\n value: 13.249577616243794\n - type: f1\n value: 53.621518367695685\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 61.57611771363894\n - type: f1\n value: 61.79797478568639\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 53.38315344479284\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 87.55438993860642\n - type: cos_sim_ap\n value: 77.98702600017738\n - type: cos_sim_f1\n value: 71.94971653931476\n - type: cos_sim_precision\n value: 67.50693802035153\n - type: cos_sim_recall\n value: 77.01846965699208\n - type: dot_accuracy\n value: 87.55438993860642\n - type: dot_ap\n value: 77.98702925907986\n - type: dot_f1\n value: 71.94971653931476\n - type: dot_precision\n value: 67.50693802035153\n - type: dot_recall\n value: 77.01846965699208\n - type: euclidean_accuracy\n value: 87.55438993860642\n - type: euclidean_ap\n value: 77.98702951957925\n - type: euclidean_f1\n value: 71.94971653931476\n - type: euclidean_precision\n value: 67.50693802035153\n - type: euclidean_recall\n value: 77.01846965699208\n - type: manhattan_accuracy\n value: 87.54246885617214\n - type: manhattan_ap\n value: 77.95531413902947\n - type: manhattan_f1\n value: 71.93605683836589\n - type: manhattan_precision\n value: 69.28152492668622\n - type: manhattan_recall\n value: 74.80211081794195\n - type: max_accuracy\n value: 87.55438993860642\n - type: max_ap\n value: 77.98702951957925\n - type: max_f1\n value: 71.94971653931476\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.47296930182016\n - type: cos_sim_ap\n value: 86.92853616302108\n - type: cos_sim_f1\n value: 79.35138351681047\n - type: cos_sim_precision\n value: 76.74820143884892\n - type: cos_sim_recall\n value: 82.13735756082538\n - type: dot_accuracy\n value: 89.47296930182016\n - type: dot_ap\n value: 86.92854339601595\n - type: dot_f1\n value: 79.35138351681047\n - type: dot_precision\n value: 76.74820143884892\n - type: dot_recall\n value: 82.13735756082538\n - type: euclidean_accuracy\n value: 89.47296930182016\n - type: euclidean_ap\n value: 86.92854191061649\n - type: euclidean_f1\n value: 79.35138351681047\n - type: euclidean_precision\n value: 76.74820143884892\n - type: euclidean_recall\n value: 82.13735756082538\n - type: manhattan_accuracy\n value: 89.47685023479644\n - type: manhattan_ap\n value: 86.90063722679578\n - type: manhattan_f1\n value: 79.30753865502702\n - type: manhattan_precision\n value: 76.32066068631639\n - type: manhattan_recall\n value: 82.53772713273791\n - type: max_accuracy\n value: 89.47685023479644\n - type: max_ap\n value: 86.92854339601595\n - type: max_f1\n value: 79.35138351681047\n---\n\n# hkunlp/instructor-xl\nWe introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks!\nThe model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! \n\n**************************** **Updates** ****************************\n\n* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance.\n* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out!\n\n## Quick start\n
\n\n## Installation\n```bash\npip install InstructorEmbedding\n```\n\n## Compute your customized embeddings\nThen you can use the model like this to calculate domain-specific and task-aware embeddings:\n```python\nfrom InstructorEmbedding import INSTRUCTOR\nmodel = INSTRUCTOR('hkunlp/instructor-xl')\nsentence = \"3D ActionSLAM: wearable person tracking in multi-floor environments\"\ninstruction = \"Represent the Science title:\"\nembeddings = model.encode([[instruction,sentence]])\nprint(embeddings)\n```\n\n## Use cases\n
\n\n## Calculate embeddings for your customized texts\nIf you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: \n\n Represent the `domain` `text_type` for `task_objective`:\n* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.\n* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.\n* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.\n\n## Calculate Sentence similarities\nYou can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.\n```python\nfrom sklearn.metrics.pairwise import cosine_similarity\nsentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], \n ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]\nsentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],\n ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]\nembeddings_a = model.encode(sentences_a)\nembeddings_b = model.encode(sentences_b)\nsimilarities = cosine_similarity(embeddings_a,embeddings_b)\nprint(similarities)\n```\n\n## Information Retrieval\nYou can also use **customized embeddings** for information retrieval.\n```python\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nquery = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]\ncorpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term \"mixed economies\" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],\n ['Represent the Wikipedia document for retrieval: ',\"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession\"],\n ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]\nquery_embeddings = model.encode(query)\ncorpus_embeddings = model.encode(corpus)\nsimilarities = cosine_similarity(query_embeddings,corpus_embeddings)\nretrieved_doc_id = np.argmax(similarities)\nprint(retrieved_doc_id)\n```\n\n## Clustering\nUse **customized embeddings** for clustering texts in groups.\n```python\nimport sklearn.cluster\nsentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],\n ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],\n ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],\n ['Represent the Medicine sentence for clustering: ',\"QCD corrections to Associated t-tbar-H production at the Tevatron\"],\n ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]\nembeddings = model.encode(sentences)\nclustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)\nclustering_model.fit(embeddings)\ncluster_assignment = clustering_model.labels_\nprint(cluster_assignment)\n```"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":86,"cells":{"id":{"kind":"string","value":"llmrails/ember-v1"},"author":{"kind":"string","value":"llmrails"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","safetensors","bert","feature-extraction","mteb","sentence-similarity","transformers","en","arxiv:2205.12035","arxiv:2209.11055","doi:10.57967/hf/2919","license:mit","model-index","autotrain_compatible","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"mteb\",\n \"sentence-similarity\",\n \"transformers\",\n \"en\",\n \"arxiv:2205.12035\",\n \"arxiv:2209.11055\",\n \"doi:10.57967/hf/2919\",\n \"license:mit\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2023-10-10T15:56:42"},"last_modified":{"kind":"string","value":"2024-08-21T04:49:13"},"downloads":{"kind":"number","value":34938,"string":"34,938"},"likes":{"kind":"number","value":62,"string":"62"},"README":{"kind":"string","value":"---\nlanguage: en\nlicense: mit\ntags:\n- mteb\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\nmodel-index:\n- name: ember_v1\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 76.05970149253731\n - type: ap\n value: 38.76045348512767\n - type: f1\n value: 69.8824007294685\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 91.977\n - type: ap\n value: 88.63507587170176\n - type: f1\n value: 91.9524133311038\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 47.938\n - type: f1\n value: 47.58273047536129\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 41.252\n - type: map_at_10\n value: 56.567\n - type: map_at_100\n value: 57.07600000000001\n - type: map_at_1000\n value: 57.08\n - type: map_at_3\n value: 52.394\n - type: map_at_5\n value: 55.055\n - type: mrr_at_1\n value: 42.39\n - type: mrr_at_10\n value: 57.001999999999995\n - type: mrr_at_100\n value: 57.531\n - type: mrr_at_1000\n value: 57.535000000000004\n - type: mrr_at_3\n value: 52.845\n - type: mrr_at_5\n value: 55.47299999999999\n - type: ndcg_at_1\n value: 41.252\n - type: ndcg_at_10\n value: 64.563\n - type: ndcg_at_100\n value: 66.667\n - type: ndcg_at_1000\n value: 66.77\n - type: ndcg_at_3\n value: 56.120000000000005\n - type: ndcg_at_5\n value: 60.889\n - type: precision_at_1\n value: 41.252\n - type: precision_at_10\n value: 8.982999999999999\n - type: precision_at_100\n value: 0.989\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 22.309\n - type: precision_at_5\n value: 15.690000000000001\n - type: recall_at_1\n value: 41.252\n - type: recall_at_10\n value: 89.82900000000001\n - type: recall_at_100\n value: 98.86200000000001\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 66.927\n - type: recall_at_5\n value: 78.45\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 48.5799968717232\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 43.142844164856136\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 64.45997990276463\n - type: mrr\n value: 77.85560392208592\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 86.38299310075898\n - type: cos_sim_spearman\n value: 85.81038898286454\n - type: euclidean_pearson\n value: 84.28002556389774\n - type: euclidean_spearman\n value: 85.80315990248238\n - type: manhattan_pearson\n value: 83.9755390675032\n - type: manhattan_spearman\n value: 85.30435335611396\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 87.89935064935065\n - type: f1\n value: 87.87886687103833\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 38.84335510371379\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 36.377963093857005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.557\n - type: map_at_10\n value: 44.501000000000005\n - type: map_at_100\n value: 46.11\n - type: map_at_1000\n value: 46.232\n - type: map_at_3\n value: 40.711000000000006\n - type: map_at_5\n value: 42.937\n - type: mrr_at_1\n value: 40.916000000000004\n - type: mrr_at_10\n value: 51.317\n - type: mrr_at_100\n value: 52.003\n - type: mrr_at_1000\n value: 52.044999999999995\n - type: mrr_at_3\n value: 48.569\n - type: mrr_at_5\n value: 50.322\n - type: ndcg_at_1\n value: 40.916000000000004\n - type: ndcg_at_10\n value: 51.353\n - type: ndcg_at_100\n value: 56.762\n - type: ndcg_at_1000\n value: 58.555\n - type: ndcg_at_3\n value: 46.064\n - type: ndcg_at_5\n value: 48.677\n - type: precision_at_1\n value: 40.916000000000004\n - type: precision_at_10\n value: 9.927999999999999\n - type: precision_at_100\n value: 1.592\n - type: precision_at_1000\n value: 0.20600000000000002\n - type: precision_at_3\n value: 22.078999999999997\n - type: precision_at_5\n value: 16.08\n - type: recall_at_1\n value: 32.557\n - type: recall_at_10\n value: 63.942\n - type: recall_at_100\n value: 86.436\n - type: recall_at_1000\n value: 97.547\n - type: recall_at_3\n value: 48.367\n - type: recall_at_5\n value: 55.818\n - type: map_at_1\n value: 32.106\n - type: map_at_10\n value: 42.55\n - type: map_at_100\n value: 43.818\n - type: map_at_1000\n value: 43.952999999999996\n - type: map_at_3\n value: 39.421\n - type: map_at_5\n value: 41.276\n - type: mrr_at_1\n value: 39.936\n - type: mrr_at_10\n value: 48.484\n - type: mrr_at_100\n value: 49.123\n - type: mrr_at_1000\n value: 49.163000000000004\n - type: mrr_at_3\n value: 46.221000000000004\n - type: mrr_at_5\n value: 47.603\n - type: ndcg_at_1\n value: 39.936\n - type: ndcg_at_10\n value: 48.25\n - type: ndcg_at_100\n value: 52.674\n - type: ndcg_at_1000\n value: 54.638\n - type: ndcg_at_3\n value: 44.05\n - type: ndcg_at_5\n value: 46.125\n - type: precision_at_1\n value: 39.936\n - type: precision_at_10\n value: 9.096\n - type: precision_at_100\n value: 1.473\n - type: precision_at_1000\n value: 0.19499999999999998\n - type: precision_at_3\n value: 21.295\n - type: precision_at_5\n value: 15.121\n - type: recall_at_1\n value: 32.106\n - type: recall_at_10\n value: 58.107\n - type: recall_at_100\n value: 76.873\n - type: recall_at_1000\n value: 89.079\n - type: recall_at_3\n value: 45.505\n - type: recall_at_5\n value: 51.479\n - type: map_at_1\n value: 41.513\n - type: map_at_10\n value: 54.571999999999996\n - type: map_at_100\n value: 55.579\n - type: map_at_1000\n value: 55.626\n - type: map_at_3\n value: 51.127\n - type: map_at_5\n value: 53.151\n - type: mrr_at_1\n value: 47.398\n - type: mrr_at_10\n value: 57.82000000000001\n - type: mrr_at_100\n value: 58.457\n - type: mrr_at_1000\n value: 58.479000000000006\n - type: mrr_at_3\n value: 55.32899999999999\n - type: mrr_at_5\n value: 56.89999999999999\n - type: ndcg_at_1\n value: 47.398\n - type: ndcg_at_10\n value: 60.599000000000004\n - type: ndcg_at_100\n value: 64.366\n - type: ndcg_at_1000\n value: 65.333\n - type: ndcg_at_3\n value: 54.98\n - type: ndcg_at_5\n value: 57.874\n - type: precision_at_1\n value: 47.398\n - type: precision_at_10\n value: 9.806\n - type: precision_at_100\n value: 1.2590000000000001\n - type: precision_at_1000\n value: 0.13799999999999998\n - type: precision_at_3\n value: 24.619\n - type: precision_at_5\n value: 16.878\n - type: recall_at_1\n value: 41.513\n - type: recall_at_10\n value: 74.91799999999999\n - type: recall_at_100\n value: 90.96\n - type: recall_at_1000\n value: 97.923\n - type: recall_at_3\n value: 60.013000000000005\n - type: recall_at_5\n value: 67.245\n - type: map_at_1\n value: 26.319\n - type: map_at_10\n value: 35.766999999999996\n - type: map_at_100\n value: 36.765\n - type: map_at_1000\n value: 36.829\n - type: map_at_3\n value: 32.888\n - type: map_at_5\n value: 34.538999999999994\n - type: mrr_at_1\n value: 28.249000000000002\n - type: mrr_at_10\n value: 37.766\n - type: mrr_at_100\n value: 38.62\n - type: mrr_at_1000\n value: 38.667\n - type: mrr_at_3\n value: 35.009\n - type: mrr_at_5\n value: 36.608000000000004\n - type: ndcg_at_1\n value: 28.249000000000002\n - type: ndcg_at_10\n value: 41.215\n - type: ndcg_at_100\n value: 46.274\n - type: ndcg_at_1000\n value: 48.007\n - type: ndcg_at_3\n value: 35.557\n - type: ndcg_at_5\n value: 38.344\n - type: precision_at_1\n value: 28.249000000000002\n - type: precision_at_10\n value: 6.429\n - type: precision_at_100\n value: 0.9480000000000001\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 15.179\n - type: precision_at_5\n value: 10.734\n - type: recall_at_1\n value: 26.319\n - type: recall_at_10\n value: 56.157999999999994\n - type: recall_at_100\n value: 79.65\n - type: recall_at_1000\n value: 92.73\n - type: recall_at_3\n value: 40.738\n - type: recall_at_5\n value: 47.418\n - type: map_at_1\n value: 18.485\n - type: map_at_10\n value: 27.400999999999996\n - type: map_at_100\n value: 28.665000000000003\n - type: map_at_1000\n value: 28.79\n - type: map_at_3\n value: 24.634\n - type: map_at_5\n value: 26.313\n - type: mrr_at_1\n value: 23.134\n - type: mrr_at_10\n value: 32.332\n - type: mrr_at_100\n value: 33.318\n - type: mrr_at_1000\n value: 33.384\n - type: mrr_at_3\n value: 29.664\n - type: mrr_at_5\n value: 31.262\n - type: ndcg_at_1\n value: 23.134\n - type: ndcg_at_10\n value: 33.016\n - type: ndcg_at_100\n value: 38.763\n - type: ndcg_at_1000\n value: 41.619\n - type: ndcg_at_3\n value: 28.017999999999997\n - type: ndcg_at_5\n value: 30.576999999999998\n - type: precision_at_1\n value: 23.134\n - type: precision_at_10\n value: 6.069999999999999\n - type: precision_at_100\n value: 1.027\n - type: precision_at_1000\n value: 0.14200000000000002\n - type: precision_at_3\n value: 13.599\n - type: precision_at_5\n value: 9.975000000000001\n - type: recall_at_1\n value: 18.485\n - type: recall_at_10\n value: 45.39\n - type: recall_at_100\n value: 69.876\n - type: recall_at_1000\n value: 90.023\n - type: recall_at_3\n value: 31.587\n - type: recall_at_5\n value: 38.164\n - type: map_at_1\n value: 30.676\n - type: map_at_10\n value: 41.785\n - type: map_at_100\n value: 43.169000000000004\n - type: map_at_1000\n value: 43.272\n - type: map_at_3\n value: 38.462\n - type: map_at_5\n value: 40.32\n - type: mrr_at_1\n value: 37.729\n - type: mrr_at_10\n value: 47.433\n - type: mrr_at_100\n value: 48.303000000000004\n - type: mrr_at_1000\n value: 48.337\n - type: mrr_at_3\n value: 45.011\n - type: mrr_at_5\n value: 46.455\n - type: ndcg_at_1\n value: 37.729\n - type: ndcg_at_10\n value: 47.921\n - type: ndcg_at_100\n value: 53.477\n - type: ndcg_at_1000\n value: 55.300000000000004\n - type: ndcg_at_3\n value: 42.695\n - type: ndcg_at_5\n value: 45.175\n - type: precision_at_1\n value: 37.729\n - type: precision_at_10\n value: 8.652999999999999\n - type: precision_at_100\n value: 1.336\n - type: precision_at_1000\n value: 0.168\n - type: precision_at_3\n value: 20.18\n - type: precision_at_5\n value: 14.302000000000001\n - type: recall_at_1\n value: 30.676\n - type: recall_at_10\n value: 60.441\n - type: recall_at_100\n value: 83.37\n - type: recall_at_1000\n value: 95.092\n - type: recall_at_3\n value: 45.964\n - type: recall_at_5\n value: 52.319\n - type: map_at_1\n value: 24.978\n - type: map_at_10\n value: 35.926\n - type: map_at_100\n value: 37.341\n - type: map_at_1000\n value: 37.445\n - type: map_at_3\n value: 32.748\n - type: map_at_5\n value: 34.207\n - type: mrr_at_1\n value: 31.163999999999998\n - type: mrr_at_10\n value: 41.394\n - type: mrr_at_100\n value: 42.321\n - type: mrr_at_1000\n value: 42.368\n - type: mrr_at_3\n value: 38.964999999999996\n - type: mrr_at_5\n value: 40.135\n - type: ndcg_at_1\n value: 31.163999999999998\n - type: ndcg_at_10\n value: 42.191\n - type: ndcg_at_100\n value: 48.083999999999996\n - type: ndcg_at_1000\n value: 50.21\n - type: ndcg_at_3\n value: 36.979\n - type: ndcg_at_5\n value: 38.823\n - type: precision_at_1\n value: 31.163999999999998\n - type: precision_at_10\n value: 7.968\n - type: precision_at_100\n value: 1.2550000000000001\n - type: precision_at_1000\n value: 0.16199999999999998\n - type: precision_at_3\n value: 18.075\n - type: precision_at_5\n value: 12.626000000000001\n - type: recall_at_1\n value: 24.978\n - type: recall_at_10\n value: 55.410000000000004\n - type: recall_at_100\n value: 80.562\n - type: recall_at_1000\n value: 94.77600000000001\n - type: recall_at_3\n value: 40.359\n - type: recall_at_5\n value: 45.577\n - type: map_at_1\n value: 26.812166666666666\n - type: map_at_10\n value: 36.706916666666665\n - type: map_at_100\n value: 37.94016666666666\n - type: map_at_1000\n value: 38.05358333333333\n - type: map_at_3\n value: 33.72408333333334\n - type: map_at_5\n value: 35.36508333333333\n - type: mrr_at_1\n value: 31.91516666666667\n - type: mrr_at_10\n value: 41.09716666666666\n - type: mrr_at_100\n value: 41.931916666666666\n - type: mrr_at_1000\n value: 41.98458333333333\n - type: mrr_at_3\n value: 38.60183333333333\n - type: mrr_at_5\n value: 40.031916666666675\n - type: ndcg_at_1\n value: 31.91516666666667\n - type: ndcg_at_10\n value: 42.38725\n - type: ndcg_at_100\n value: 47.56291666666667\n - type: ndcg_at_1000\n value: 49.716499999999996\n - type: ndcg_at_3\n value: 37.36491666666667\n - type: ndcg_at_5\n value: 39.692166666666665\n - type: precision_at_1\n value: 31.91516666666667\n - type: precision_at_10\n value: 7.476749999999999\n - type: precision_at_100\n value: 1.1869166666666668\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 17.275249999999996\n - type: precision_at_5\n value: 12.25825\n - type: recall_at_1\n value: 26.812166666666666\n - type: recall_at_10\n value: 54.82933333333333\n - type: recall_at_100\n value: 77.36508333333333\n - type: recall_at_1000\n value: 92.13366666666667\n - type: recall_at_3\n value: 40.83508333333334\n - type: recall_at_5\n value: 46.85083333333334\n - type: map_at_1\n value: 25.352999999999998\n - type: map_at_10\n value: 33.025999999999996\n - type: map_at_100\n value: 33.882\n - type: map_at_1000\n value: 33.983999999999995\n - type: map_at_3\n value: 30.995\n - type: map_at_5\n value: 32.113\n - type: mrr_at_1\n value: 28.834\n - type: mrr_at_10\n value: 36.14\n - type: mrr_at_100\n value: 36.815\n - type: mrr_at_1000\n value: 36.893\n - type: mrr_at_3\n value: 34.305\n - type: mrr_at_5\n value: 35.263\n - type: ndcg_at_1\n value: 28.834\n - type: ndcg_at_10\n value: 37.26\n - type: ndcg_at_100\n value: 41.723\n - type: ndcg_at_1000\n value: 44.314\n - type: ndcg_at_3\n value: 33.584\n - type: ndcg_at_5\n value: 35.302\n - type: precision_at_1\n value: 28.834\n - type: precision_at_10\n value: 5.736\n - type: precision_at_100\n value: 0.876\n - type: precision_at_1000\n value: 0.117\n - type: precision_at_3\n value: 14.468\n - type: precision_at_5\n value: 9.847\n - type: recall_at_1\n value: 25.352999999999998\n - type: recall_at_10\n value: 47.155\n - type: recall_at_100\n value: 68.024\n - type: recall_at_1000\n value: 87.26899999999999\n - type: recall_at_3\n value: 37.074\n - type: recall_at_5\n value: 41.352\n - type: map_at_1\n value: 17.845\n - type: map_at_10\n value: 25.556\n - type: map_at_100\n value: 26.787\n - type: map_at_1000\n value: 26.913999999999998\n - type: map_at_3\n value: 23.075000000000003\n - type: map_at_5\n value: 24.308\n - type: mrr_at_1\n value: 21.714\n - type: mrr_at_10\n value: 29.543999999999997\n - type: mrr_at_100\n value: 30.543\n - type: mrr_at_1000\n value: 30.618000000000002\n - type: mrr_at_3\n value: 27.174\n - type: mrr_at_5\n value: 28.409000000000002\n - type: ndcg_at_1\n value: 21.714\n - type: ndcg_at_10\n value: 30.562\n - type: ndcg_at_100\n value: 36.27\n - type: ndcg_at_1000\n value: 39.033\n - type: ndcg_at_3\n value: 26.006\n - type: ndcg_at_5\n value: 27.843\n - type: precision_at_1\n value: 21.714\n - type: precision_at_10\n value: 5.657\n - type: precision_at_100\n value: 1\n - type: precision_at_1000\n value: 0.14100000000000001\n - type: precision_at_3\n value: 12.4\n - type: precision_at_5\n value: 8.863999999999999\n - type: recall_at_1\n value: 17.845\n - type: recall_at_10\n value: 41.72\n - type: recall_at_100\n value: 67.06400000000001\n - type: recall_at_1000\n value: 86.515\n - type: recall_at_3\n value: 28.78\n - type: recall_at_5\n value: 33.629999999999995\n - type: map_at_1\n value: 26.695\n - type: map_at_10\n value: 36.205999999999996\n - type: map_at_100\n value: 37.346000000000004\n - type: map_at_1000\n value: 37.447\n - type: map_at_3\n value: 32.84\n - type: map_at_5\n value: 34.733000000000004\n - type: mrr_at_1\n value: 31.343\n - type: mrr_at_10\n value: 40.335\n - type: mrr_at_100\n value: 41.162\n - type: mrr_at_1000\n value: 41.221000000000004\n - type: mrr_at_3\n value: 37.329\n - type: mrr_at_5\n value: 39.068999999999996\n - type: ndcg_at_1\n value: 31.343\n - type: ndcg_at_10\n value: 41.996\n - type: ndcg_at_100\n value: 47.096\n - type: ndcg_at_1000\n value: 49.4\n - type: ndcg_at_3\n value: 35.902\n - type: ndcg_at_5\n value: 38.848\n - type: precision_at_1\n value: 31.343\n - type: precision_at_10\n value: 7.146\n - type: precision_at_100\n value: 1.098\n - type: precision_at_1000\n value: 0.14100000000000001\n - type: precision_at_3\n value: 16.014\n - type: precision_at_5\n value: 11.735\n - type: recall_at_1\n value: 26.695\n - type: recall_at_10\n value: 55.525000000000006\n - type: recall_at_100\n value: 77.376\n - type: recall_at_1000\n value: 93.476\n - type: recall_at_3\n value: 39.439\n - type: recall_at_5\n value: 46.501\n - type: map_at_1\n value: 24.196\n - type: map_at_10\n value: 33.516\n - type: map_at_100\n value: 35.202\n - type: map_at_1000\n value: 35.426\n - type: map_at_3\n value: 30.561\n - type: map_at_5\n value: 31.961000000000002\n - type: mrr_at_1\n value: 29.644\n - type: mrr_at_10\n value: 38.769\n - type: mrr_at_100\n value: 39.843\n - type: mrr_at_1000\n value: 39.888\n - type: mrr_at_3\n value: 36.132999999999996\n - type: mrr_at_5\n value: 37.467\n - type: ndcg_at_1\n value: 29.644\n - type: ndcg_at_10\n value: 39.584\n - type: ndcg_at_100\n value: 45.964\n - type: ndcg_at_1000\n value: 48.27\n - type: ndcg_at_3\n value: 34.577999999999996\n - type: ndcg_at_5\n value: 36.498000000000005\n - type: precision_at_1\n value: 29.644\n - type: precision_at_10\n value: 7.668\n - type: precision_at_100\n value: 1.545\n - type: precision_at_1000\n value: 0.242\n - type: precision_at_3\n value: 16.271\n - type: precision_at_5\n value: 11.620999999999999\n - type: recall_at_1\n value: 24.196\n - type: recall_at_10\n value: 51.171\n - type: recall_at_100\n value: 79.212\n - type: recall_at_1000\n value: 92.976\n - type: recall_at_3\n value: 36.797999999999995\n - type: recall_at_5\n value: 42.006\n - type: map_at_1\n value: 21.023\n - type: map_at_10\n value: 29.677\n - type: map_at_100\n value: 30.618000000000002\n - type: map_at_1000\n value: 30.725\n - type: map_at_3\n value: 27.227\n - type: map_at_5\n value: 28.523\n - type: mrr_at_1\n value: 22.921\n - type: mrr_at_10\n value: 31.832\n - type: mrr_at_100\n value: 32.675\n - type: mrr_at_1000\n value: 32.751999999999995\n - type: mrr_at_3\n value: 29.513\n - type: mrr_at_5\n value: 30.89\n - type: ndcg_at_1\n value: 22.921\n - type: ndcg_at_10\n value: 34.699999999999996\n - type: ndcg_at_100\n value: 39.302\n - type: ndcg_at_1000\n value: 41.919000000000004\n - type: ndcg_at_3\n value: 29.965999999999998\n - type: ndcg_at_5\n value: 32.22\n - type: precision_at_1\n value: 22.921\n - type: precision_at_10\n value: 5.564\n - type: precision_at_100\n value: 0.8340000000000001\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 13.123999999999999\n - type: precision_at_5\n value: 9.316\n - type: recall_at_1\n value: 21.023\n - type: recall_at_10\n value: 48.015\n - type: recall_at_100\n value: 68.978\n - type: recall_at_1000\n value: 88.198\n - type: recall_at_3\n value: 35.397\n - type: recall_at_5\n value: 40.701\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 11.198\n - type: map_at_10\n value: 19.336000000000002\n - type: map_at_100\n value: 21.382\n - type: map_at_1000\n value: 21.581\n - type: map_at_3\n value: 15.992\n - type: map_at_5\n value: 17.613\n - type: mrr_at_1\n value: 25.080999999999996\n - type: mrr_at_10\n value: 36.032\n - type: mrr_at_100\n value: 37.1\n - type: mrr_at_1000\n value: 37.145\n - type: mrr_at_3\n value: 32.595\n - type: mrr_at_5\n value: 34.553\n - type: ndcg_at_1\n value: 25.080999999999996\n - type: ndcg_at_10\n value: 27.290999999999997\n - type: ndcg_at_100\n value: 35.31\n - type: ndcg_at_1000\n value: 38.885\n - type: ndcg_at_3\n value: 21.895999999999997\n - type: ndcg_at_5\n value: 23.669999999999998\n - type: precision_at_1\n value: 25.080999999999996\n - type: precision_at_10\n value: 8.645\n - type: precision_at_100\n value: 1.7209999999999999\n - type: precision_at_1000\n value: 0.23900000000000002\n - type: precision_at_3\n value: 16.287\n - type: precision_at_5\n value: 12.625\n - type: recall_at_1\n value: 11.198\n - type: recall_at_10\n value: 33.355000000000004\n - type: recall_at_100\n value: 60.912\n - type: recall_at_1000\n value: 80.89\n - type: recall_at_3\n value: 20.055\n - type: recall_at_5\n value: 25.14\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.228\n - type: map_at_10\n value: 20.018\n - type: map_at_100\n value: 28.388999999999996\n - type: map_at_1000\n value: 30.073\n - type: map_at_3\n value: 14.366999999999999\n - type: map_at_5\n value: 16.705000000000002\n - type: mrr_at_1\n value: 69\n - type: mrr_at_10\n value: 77.058\n - type: mrr_at_100\n value: 77.374\n - type: mrr_at_1000\n value: 77.384\n - type: mrr_at_3\n value: 75.708\n - type: mrr_at_5\n value: 76.608\n - type: ndcg_at_1\n value: 57.49999999999999\n - type: ndcg_at_10\n value: 41.792\n - type: ndcg_at_100\n value: 47.374\n - type: ndcg_at_1000\n value: 55.13\n - type: ndcg_at_3\n value: 46.353\n - type: ndcg_at_5\n value: 43.702000000000005\n - type: precision_at_1\n value: 69\n - type: precision_at_10\n value: 32.85\n - type: precision_at_100\n value: 10.708\n - type: precision_at_1000\n value: 2.024\n - type: precision_at_3\n value: 49.5\n - type: precision_at_5\n value: 42.05\n - type: recall_at_1\n value: 9.228\n - type: recall_at_10\n value: 25.635\n - type: recall_at_100\n value: 54.894\n - type: recall_at_1000\n value: 79.38\n - type: recall_at_3\n value: 15.68\n - type: recall_at_5\n value: 19.142\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 52.035\n - type: f1\n value: 46.85325505614071\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 70.132\n - type: map_at_10\n value: 79.527\n - type: map_at_100\n value: 79.81200000000001\n - type: map_at_1000\n value: 79.828\n - type: map_at_3\n value: 78.191\n - type: map_at_5\n value: 79.092\n - type: mrr_at_1\n value: 75.563\n - type: mrr_at_10\n value: 83.80199999999999\n - type: mrr_at_100\n value: 83.93\n - type: mrr_at_1000\n value: 83.933\n - type: mrr_at_3\n value: 82.818\n - type: mrr_at_5\n value: 83.505\n - type: ndcg_at_1\n value: 75.563\n - type: ndcg_at_10\n value: 83.692\n - type: ndcg_at_100\n value: 84.706\n - type: ndcg_at_1000\n value: 85.001\n - type: ndcg_at_3\n value: 81.51\n - type: ndcg_at_5\n value: 82.832\n - type: precision_at_1\n value: 75.563\n - type: precision_at_10\n value: 10.245\n - type: precision_at_100\n value: 1.0959999999999999\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 31.518\n - type: precision_at_5\n value: 19.772000000000002\n - type: recall_at_1\n value: 70.132\n - type: recall_at_10\n value: 92.204\n - type: recall_at_100\n value: 96.261\n - type: recall_at_1000\n value: 98.17399999999999\n - type: recall_at_3\n value: 86.288\n - type: recall_at_5\n value: 89.63799999999999\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 22.269\n - type: map_at_10\n value: 36.042\n - type: map_at_100\n value: 37.988\n - type: map_at_1000\n value: 38.162\n - type: map_at_3\n value: 31.691000000000003\n - type: map_at_5\n value: 33.988\n - type: mrr_at_1\n value: 44.907000000000004\n - type: mrr_at_10\n value: 53.348\n - type: mrr_at_100\n value: 54.033\n - type: mrr_at_1000\n value: 54.064\n - type: mrr_at_3\n value: 50.977\n - type: mrr_at_5\n value: 52.112\n - type: ndcg_at_1\n value: 44.907000000000004\n - type: ndcg_at_10\n value: 44.302\n - type: ndcg_at_100\n value: 51.054\n - type: ndcg_at_1000\n value: 53.822\n - type: ndcg_at_3\n value: 40.615\n - type: ndcg_at_5\n value: 41.455999999999996\n - type: precision_at_1\n value: 44.907000000000004\n - type: precision_at_10\n value: 12.176\n - type: precision_at_100\n value: 1.931\n - type: precision_at_1000\n value: 0.243\n - type: precision_at_3\n value: 27.16\n - type: precision_at_5\n value: 19.567999999999998\n - type: recall_at_1\n value: 22.269\n - type: recall_at_10\n value: 51.188\n - type: recall_at_100\n value: 75.924\n - type: recall_at_1000\n value: 92.525\n - type: recall_at_3\n value: 36.643\n - type: recall_at_5\n value: 42.27\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 40.412\n - type: map_at_10\n value: 66.376\n - type: map_at_100\n value: 67.217\n - type: map_at_1000\n value: 67.271\n - type: map_at_3\n value: 62.741\n - type: map_at_5\n value: 65.069\n - type: mrr_at_1\n value: 80.824\n - type: mrr_at_10\n value: 86.53\n - type: mrr_at_100\n value: 86.67399999999999\n - type: mrr_at_1000\n value: 86.678\n - type: mrr_at_3\n value: 85.676\n - type: mrr_at_5\n value: 86.256\n - type: ndcg_at_1\n value: 80.824\n - type: ndcg_at_10\n value: 74.332\n - type: ndcg_at_100\n value: 77.154\n - type: ndcg_at_1000\n value: 78.12400000000001\n - type: ndcg_at_3\n value: 69.353\n - type: ndcg_at_5\n value: 72.234\n - type: precision_at_1\n value: 80.824\n - type: precision_at_10\n value: 15.652\n - type: precision_at_100\n value: 1.7840000000000003\n - type: precision_at_1000\n value: 0.191\n - type: precision_at_3\n value: 44.911\n - type: precision_at_5\n value: 29.221000000000004\n - type: recall_at_1\n value: 40.412\n - type: recall_at_10\n value: 78.25800000000001\n - type: recall_at_100\n value: 89.196\n - type: recall_at_1000\n value: 95.544\n - type: recall_at_3\n value: 67.367\n - type: recall_at_5\n value: 73.05199999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 92.78880000000001\n - type: ap\n value: 89.39251741048801\n - type: f1\n value: 92.78019950076781\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.888\n - type: map_at_10\n value: 35.146\n - type: map_at_100\n value: 36.325\n - type: map_at_1000\n value: 36.372\n - type: map_at_3\n value: 31.3\n - type: map_at_5\n value: 33.533\n - type: mrr_at_1\n value: 23.480999999999998\n - type: mrr_at_10\n value: 35.777\n - type: mrr_at_100\n value: 36.887\n - type: mrr_at_1000\n value: 36.928\n - type: mrr_at_3\n value: 31.989\n - type: mrr_at_5\n value: 34.202\n - type: ndcg_at_1\n value: 23.496\n - type: ndcg_at_10\n value: 42.028999999999996\n - type: ndcg_at_100\n value: 47.629\n - type: ndcg_at_1000\n value: 48.785000000000004\n - type: ndcg_at_3\n value: 34.227000000000004\n - type: ndcg_at_5\n value: 38.207\n - type: precision_at_1\n value: 23.496\n - type: precision_at_10\n value: 6.596\n - type: precision_at_100\n value: 0.9400000000000001\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.513000000000002\n - type: precision_at_5\n value: 10.711\n - type: recall_at_1\n value: 22.888\n - type: recall_at_10\n value: 63.129999999999995\n - type: recall_at_100\n value: 88.90299999999999\n - type: recall_at_1000\n value: 97.69\n - type: recall_at_3\n value: 42.014\n - type: recall_at_5\n value: 51.554\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 94.59188326493388\n - type: f1\n value: 94.36568950290486\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 79.25672594619242\n - type: f1\n value: 59.52405059722216\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 77.4142568930733\n - type: f1\n value: 75.23044196543388\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 80.44720914593141\n - type: f1\n value: 80.41049641537015\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 31.960921474993775\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 30.88042240204361\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 32.27071371606404\n - type: mrr\n value: 33.541450459533856\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.551\n - type: map_at_10\n value: 14.359\n - type: map_at_100\n value: 18.157\n - type: map_at_1000\n value: 19.659\n - type: map_at_3\n value: 10.613999999999999\n - type: map_at_5\n value: 12.296\n - type: mrr_at_1\n value: 47.368\n - type: mrr_at_10\n value: 56.689\n - type: mrr_at_100\n value: 57.24399999999999\n - type: mrr_at_1000\n value: 57.284\n - type: mrr_at_3\n value: 54.489\n - type: mrr_at_5\n value: 55.928999999999995\n - type: ndcg_at_1\n value: 45.511\n - type: ndcg_at_10\n value: 36.911\n - type: ndcg_at_100\n value: 34.241\n - type: ndcg_at_1000\n value: 43.064\n - type: ndcg_at_3\n value: 42.348\n - type: ndcg_at_5\n value: 39.884\n - type: precision_at_1\n value: 46.749\n - type: precision_at_10\n value: 27.028000000000002\n - type: precision_at_100\n value: 8.52\n - type: precision_at_1000\n value: 2.154\n - type: precision_at_3\n value: 39.525\n - type: precision_at_5\n value: 34.18\n - type: recall_at_1\n value: 6.551\n - type: recall_at_10\n value: 18.602\n - type: recall_at_100\n value: 34.882999999999996\n - type: recall_at_1000\n value: 66.049\n - type: recall_at_3\n value: 11.872\n - type: recall_at_5\n value: 14.74\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 27.828999999999997\n - type: map_at_10\n value: 43.606\n - type: map_at_100\n value: 44.656\n - type: map_at_1000\n value: 44.690000000000005\n - type: map_at_3\n value: 39.015\n - type: map_at_5\n value: 41.625\n - type: mrr_at_1\n value: 31.518\n - type: mrr_at_10\n value: 46.047\n - type: mrr_at_100\n value: 46.846\n - type: mrr_at_1000\n value: 46.867999999999995\n - type: mrr_at_3\n value: 42.154\n - type: mrr_at_5\n value: 44.468999999999994\n - type: ndcg_at_1\n value: 31.518\n - type: ndcg_at_10\n value: 51.768\n - type: ndcg_at_100\n value: 56.184999999999995\n - type: ndcg_at_1000\n value: 56.92\n - type: ndcg_at_3\n value: 43.059999999999995\n - type: ndcg_at_5\n value: 47.481\n - type: precision_at_1\n value: 31.518\n - type: precision_at_10\n value: 8.824\n - type: precision_at_100\n value: 1.131\n - type: precision_at_1000\n value: 0.12\n - type: precision_at_3\n value: 19.969\n - type: precision_at_5\n value: 14.502\n - type: recall_at_1\n value: 27.828999999999997\n - type: recall_at_10\n value: 74.244\n - type: recall_at_100\n value: 93.325\n - type: recall_at_1000\n value: 98.71799999999999\n - type: recall_at_3\n value: 51.601\n - type: recall_at_5\n value: 61.841\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.54\n - type: map_at_10\n value: 85.509\n - type: map_at_100\n value: 86.137\n - type: map_at_1000\n value: 86.151\n - type: map_at_3\n value: 82.624\n - type: map_at_5\n value: 84.425\n - type: mrr_at_1\n value: 82.45\n - type: mrr_at_10\n value: 88.344\n - type: mrr_at_100\n value: 88.437\n - type: mrr_at_1000\n value: 88.437\n - type: mrr_at_3\n value: 87.417\n - type: mrr_at_5\n value: 88.066\n - type: ndcg_at_1\n value: 82.45\n - type: ndcg_at_10\n value: 89.092\n - type: ndcg_at_100\n value: 90.252\n - type: ndcg_at_1000\n value: 90.321\n - type: ndcg_at_3\n value: 86.404\n - type: ndcg_at_5\n value: 87.883\n - type: precision_at_1\n value: 82.45\n - type: precision_at_10\n value: 13.496\n - type: precision_at_100\n value: 1.536\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.833\n - type: precision_at_5\n value: 24.79\n - type: recall_at_1\n value: 71.54\n - type: recall_at_10\n value: 95.846\n - type: recall_at_100\n value: 99.715\n - type: recall_at_1000\n value: 99.979\n - type: recall_at_3\n value: 88.01299999999999\n - type: recall_at_5\n value: 92.32000000000001\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 57.60557586253866\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 64.0287172242051\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 3.9849999999999994\n - type: map_at_10\n value: 11.397\n - type: map_at_100\n value: 13.985\n - type: map_at_1000\n value: 14.391000000000002\n - type: map_at_3\n value: 7.66\n - type: map_at_5\n value: 9.46\n - type: mrr_at_1\n value: 19.8\n - type: mrr_at_10\n value: 31.958\n - type: mrr_at_100\n value: 33.373999999999995\n - type: mrr_at_1000\n value: 33.411\n - type: mrr_at_3\n value: 28.316999999999997\n - type: mrr_at_5\n value: 30.297\n - type: ndcg_at_1\n value: 19.8\n - type: ndcg_at_10\n value: 19.580000000000002\n - type: ndcg_at_100\n value: 29.555999999999997\n - type: ndcg_at_1000\n value: 35.882\n - type: ndcg_at_3\n value: 17.544\n - type: ndcg_at_5\n value: 15.815999999999999\n - type: precision_at_1\n value: 19.8\n - type: precision_at_10\n value: 10.61\n - type: precision_at_100\n value: 2.501\n - type: precision_at_1000\n value: 0.40099999999999997\n - type: precision_at_3\n value: 16.900000000000002\n - type: precision_at_5\n value: 14.44\n - type: recall_at_1\n value: 3.9849999999999994\n - type: recall_at_10\n value: 21.497\n - type: recall_at_100\n value: 50.727999999999994\n - type: recall_at_1000\n value: 81.27499999999999\n - type: recall_at_3\n value: 10.263\n - type: recall_at_5\n value: 14.643\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 85.0087509585503\n - type: cos_sim_spearman\n value: 81.74697270664319\n - type: euclidean_pearson\n value: 81.80424382731947\n - type: euclidean_spearman\n value: 81.29794251968431\n - type: manhattan_pearson\n value: 81.81524666226125\n - type: manhattan_spearman\n value: 81.29475370198963\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 86.44442736429552\n - type: cos_sim_spearman\n value: 78.51011398910948\n - type: euclidean_pearson\n value: 83.36181801196723\n - type: euclidean_spearman\n value: 79.47272621331535\n - type: manhattan_pearson\n value: 83.3660113483837\n - type: manhattan_spearman\n value: 79.47695922566032\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 85.82923943323635\n - type: cos_sim_spearman\n value: 86.62037823380983\n - type: euclidean_pearson\n value: 83.56369548403958\n - type: euclidean_spearman\n value: 84.2176755481191\n - type: manhattan_pearson\n value: 83.55460702084464\n - type: manhattan_spearman\n value: 84.18617930921467\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 84.09071068110103\n - type: cos_sim_spearman\n value: 83.05697553913335\n - type: euclidean_pearson\n value: 81.1377457216497\n - type: euclidean_spearman\n value: 81.74714169016676\n - type: manhattan_pearson\n value: 81.0893424142723\n - type: manhattan_spearman\n value: 81.7058918219677\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 87.61132157220429\n - type: cos_sim_spearman\n value: 88.38581627185445\n - type: euclidean_pearson\n value: 86.14904510913374\n - type: euclidean_spearman\n value: 86.5452758925542\n - type: manhattan_pearson\n value: 86.1484025377679\n - type: manhattan_spearman\n value: 86.55483841566252\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 85.46195145161064\n - type: cos_sim_spearman\n value: 86.82409112251158\n - type: euclidean_pearson\n value: 84.75479672288957\n - type: euclidean_spearman\n value: 85.41144307151548\n - type: manhattan_pearson\n value: 84.70914329694165\n - type: manhattan_spearman\n value: 85.38477943384089\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 88.06351289930238\n - type: cos_sim_spearman\n value: 87.90311138579116\n - type: euclidean_pearson\n value: 86.17651467063077\n - type: euclidean_spearman\n value: 84.89447802019073\n - type: manhattan_pearson\n value: 86.3267677479595\n - type: manhattan_spearman\n value: 85.00472295103874\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 67.78311975978767\n - type: cos_sim_spearman\n value: 66.76465685245887\n - type: euclidean_pearson\n value: 67.21687806595443\n - type: euclidean_spearman\n value: 65.05776733534435\n - type: manhattan_pearson\n value: 67.14008143635883\n - type: manhattan_spearman\n value: 65.25247076149701\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 86.7403488889418\n - type: cos_sim_spearman\n value: 87.76870289783061\n - type: euclidean_pearson\n value: 84.83171077794671\n - type: euclidean_spearman\n value: 85.50579695091902\n - type: manhattan_pearson\n value: 84.83074260180555\n - type: manhattan_spearman\n value: 85.47589026938667\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 87.56234016237356\n - type: mrr\n value: 96.26124238869338\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 59.660999999999994\n - type: map_at_10\n value: 69.105\n - type: map_at_100\n value: 69.78\n - type: map_at_1000\n value: 69.80199999999999\n - type: map_at_3\n value: 65.991\n - type: map_at_5\n value: 68.02\n - type: mrr_at_1\n value: 62.666999999999994\n - type: mrr_at_10\n value: 70.259\n - type: mrr_at_100\n value: 70.776\n - type: mrr_at_1000\n value: 70.796\n - type: mrr_at_3\n value: 67.889\n - type: mrr_at_5\n value: 69.52199999999999\n - type: ndcg_at_1\n value: 62.666999999999994\n - type: ndcg_at_10\n value: 73.425\n - type: ndcg_at_100\n value: 75.955\n - type: ndcg_at_1000\n value: 76.459\n - type: ndcg_at_3\n value: 68.345\n - type: ndcg_at_5\n value: 71.319\n - type: precision_at_1\n value: 62.666999999999994\n - type: precision_at_10\n value: 9.667\n - type: precision_at_100\n value: 1.09\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 26.333000000000002\n - type: precision_at_5\n value: 17.732999999999997\n - type: recall_at_1\n value: 59.660999999999994\n - type: recall_at_10\n value: 85.422\n - type: recall_at_100\n value: 96.167\n - type: recall_at_1000\n value: 100\n - type: recall_at_3\n value: 72.044\n - type: recall_at_5\n value: 79.428\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.86435643564356\n - type: cos_sim_ap\n value: 96.83057412333741\n - type: cos_sim_f1\n value: 93.04215337734891\n - type: cos_sim_precision\n value: 94.53044375644994\n - type: cos_sim_recall\n value: 91.60000000000001\n - type: dot_accuracy\n value: 99.7910891089109\n - type: dot_ap\n value: 94.10681982106397\n - type: dot_f1\n value: 89.34881373043918\n - type: dot_precision\n value: 90.21406727828746\n - type: dot_recall\n value: 88.5\n - type: euclidean_accuracy\n value: 99.85544554455446\n - type: euclidean_ap\n value: 96.78545104478602\n - type: euclidean_f1\n value: 92.65143992055613\n - type: euclidean_precision\n value: 92.01183431952663\n - type: euclidean_recall\n value: 93.30000000000001\n - type: manhattan_accuracy\n value: 99.85841584158416\n - type: manhattan_ap\n value: 96.80748903307823\n - type: manhattan_f1\n value: 92.78247884519662\n - type: manhattan_precision\n value: 92.36868186323092\n - type: manhattan_recall\n value: 93.2\n - type: max_accuracy\n value: 99.86435643564356\n - type: max_ap\n value: 96.83057412333741\n - type: max_f1\n value: 93.04215337734891\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 65.53971025855282\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 33.97791591490788\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 55.852215301355066\n - type: mrr\n value: 56.85527809608691\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.21442519856758\n - type: cos_sim_spearman\n value: 30.822536216936825\n - type: dot_pearson\n value: 28.661325528121807\n - type: dot_spearman\n value: 28.1435226478879\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.183\n - type: map_at_10\n value: 1.526\n - type: map_at_100\n value: 7.915\n - type: map_at_1000\n value: 19.009\n - type: map_at_3\n value: 0.541\n - type: map_at_5\n value: 0.8659999999999999\n - type: mrr_at_1\n value: 68\n - type: mrr_at_10\n value: 81.186\n - type: mrr_at_100\n value: 81.186\n - type: mrr_at_1000\n value: 81.186\n - type: mrr_at_3\n value: 80\n - type: mrr_at_5\n value: 80.9\n - type: ndcg_at_1\n value: 64\n - type: ndcg_at_10\n value: 64.13799999999999\n - type: ndcg_at_100\n value: 47.632000000000005\n - type: ndcg_at_1000\n value: 43.037\n - type: ndcg_at_3\n value: 67.542\n - type: ndcg_at_5\n value: 67.496\n - type: precision_at_1\n value: 68\n - type: precision_at_10\n value: 67.80000000000001\n - type: precision_at_100\n value: 48.980000000000004\n - type: precision_at_1000\n value: 19.036\n - type: precision_at_3\n value: 72\n - type: precision_at_5\n value: 71.2\n - type: recall_at_1\n value: 0.183\n - type: recall_at_10\n value: 1.799\n - type: recall_at_100\n value: 11.652999999999999\n - type: recall_at_1000\n value: 40.086\n - type: recall_at_3\n value: 0.5930000000000001\n - type: recall_at_5\n value: 0.983\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 2.29\n - type: map_at_10\n value: 9.489\n - type: map_at_100\n value: 15.051\n - type: map_at_1000\n value: 16.561999999999998\n - type: map_at_3\n value: 5.137\n - type: map_at_5\n value: 6.7989999999999995\n - type: mrr_at_1\n value: 28.571\n - type: mrr_at_10\n value: 45.699\n - type: mrr_at_100\n value: 46.461000000000006\n - type: mrr_at_1000\n value: 46.461000000000006\n - type: mrr_at_3\n value: 41.837\n - type: mrr_at_5\n value: 43.163000000000004\n - type: ndcg_at_1\n value: 23.469\n - type: ndcg_at_10\n value: 23.544999999999998\n - type: ndcg_at_100\n value: 34.572\n - type: ndcg_at_1000\n value: 46.035\n - type: ndcg_at_3\n value: 27.200000000000003\n - type: ndcg_at_5\n value: 25.266\n - type: precision_at_1\n value: 28.571\n - type: precision_at_10\n value: 22.041\n - type: precision_at_100\n value: 7.3469999999999995\n - type: precision_at_1000\n value: 1.484\n - type: precision_at_3\n value: 29.932\n - type: precision_at_5\n value: 26.531\n - type: recall_at_1\n value: 2.29\n - type: recall_at_10\n value: 15.895999999999999\n - type: recall_at_100\n value: 45.518\n - type: recall_at_1000\n value: 80.731\n - type: recall_at_3\n value: 6.433\n - type: recall_at_5\n value: 9.484\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 71.4178\n - type: ap\n value: 14.575240629602373\n - type: f1\n value: 55.02449563229096\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 60.00282965478212\n - type: f1\n value: 60.34413028768773\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 50.409448342549936\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 87.62591643321214\n - type: cos_sim_ap\n value: 79.28766491329633\n - type: cos_sim_f1\n value: 71.98772064466617\n - type: cos_sim_precision\n value: 69.8609731876862\n - type: cos_sim_recall\n value: 74.24802110817942\n - type: dot_accuracy\n value: 84.75293556654945\n - type: dot_ap\n value: 69.72705761174353\n - type: dot_f1\n value: 65.08692852543464\n - type: dot_precision\n value: 63.57232704402516\n - type: dot_recall\n value: 66.6754617414248\n - type: euclidean_accuracy\n value: 87.44710019669786\n - type: euclidean_ap\n value: 79.11021477292638\n - type: euclidean_f1\n value: 71.5052389470994\n - type: euclidean_precision\n value: 69.32606541129832\n - type: euclidean_recall\n value: 73.82585751978891\n - type: manhattan_accuracy\n value: 87.42325803182929\n - type: manhattan_ap\n value: 79.05094494327616\n - type: manhattan_f1\n value: 71.36333985649055\n - type: manhattan_precision\n value: 70.58064516129032\n - type: manhattan_recall\n value: 72.16358839050132\n - type: max_accuracy\n value: 87.62591643321214\n - type: max_ap\n value: 79.28766491329633\n - type: max_f1\n value: 71.98772064466617\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.85202002561415\n - type: cos_sim_ap\n value: 85.9835303311168\n - type: cos_sim_f1\n value: 78.25741142443962\n - type: cos_sim_precision\n value: 73.76635768811342\n - type: cos_sim_recall\n value: 83.3307668617185\n - type: dot_accuracy\n value: 88.20584468506229\n - type: dot_ap\n value: 83.591632302697\n - type: dot_f1\n value: 76.81739705396173\n - type: dot_precision\n value: 73.45275728837373\n - type: dot_recall\n value: 80.50508161379734\n - type: euclidean_accuracy\n value: 88.64633057787093\n - type: euclidean_ap\n value: 85.25705123182283\n - type: euclidean_f1\n value: 77.18535726329199\n - type: euclidean_precision\n value: 75.17699437997226\n - type: euclidean_recall\n value: 79.30397289805975\n - type: manhattan_accuracy\n value: 88.63274731245392\n - type: manhattan_ap\n value: 85.2376825633018\n - type: manhattan_f1\n value: 77.15810785937788\n - type: manhattan_precision\n value: 73.92255061014319\n - type: manhattan_recall\n value: 80.68986757006468\n - type: max_accuracy\n value: 88.85202002561415\n - type: max_ap\n value: 85.9835303311168\n - type: max_f1\n value: 78.25741142443962\n---\n\n
\n\n\n\nYou can use Jina Embedding models directly from the `transformers` package:\n```python\n!pip install transformers\nfrom transformers import AutoModel\nfrom numpy.linalg import norm\n\ncos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))\nmodel = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-es', trust_remote_code=True) # trust_remote_code is needed to use the encode method\nembeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])\nprint(cos_sim(embeddings[0], embeddings[1]))\n```\n\nIf you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:\n\n```python\nembeddings = model.encode(\n ['Very long ... document'],\n max_length=2048\n)\n```\n\nOr you can use the model with the `sentence-transformers` package:\n```python\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer(\"jinaai/jina-embeddings-v2-base-es\", trust_remote_code=True)\nembeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])\nprint(util.cos_sim(embeddings[0], embeddings[1]))\n```\n\nAnd if you only want to handle shorter sequence, such as 2k, then you can set the `model.max_seq_length`\n\n```python\nmodel.max_seq_length = 2048\n```\n\n## Alternatives to Transformers and Sentence Transformers\n\n1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). \n2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).\n\n## Use Jina Embeddings for RAG\n\nAccording to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),\n\n> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.\n\n![](\"https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png\")
\n\n\n## Plans\n\n1. Bilingual embedding models supporting more European & Asian languages, including French, Italian and Japanese.\n2. Multimodal embedding models enable Multimodal RAG applications.\n3. High-performt rerankers.\n\n## Contact\n\nJoin our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.\n\n## Citation\n\nIf you find Jina Embeddings useful in your research, please cite the following paper:\n\n```\n@article{mohr2024multi,\n title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},\n author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\\\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\\'\\i}nez, Joan Fontanals and Wang, Feng and others},\n journal={arXiv preprint arXiv:2402.17016},\n year={2024}\n}\n```\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":88,"cells":{"id":{"kind":"string","value":"Lajavaness/bilingual-embedding-large"},"author":{"kind":"string","value":"Lajavaness"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","bilingual","feature-extraction","sentence-similarity","transformers","sentence-embedding","mteb","custom_code","fr","en","arxiv:2010.08240","arxiv:1911.02116","arxiv:1908.10084","license:apache-2.0","model-index","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"bilingual\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"transformers\",\n \"sentence-embedding\",\n \"mteb\",\n \"custom_code\",\n \"fr\",\n \"en\",\n \"arxiv:2010.08240\",\n \"arxiv:1911.02116\",\n \"arxiv:1908.10084\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-06-24T09:08:32"},"last_modified":{"kind":"string","value":"2024-08-06T09:22:13"},"downloads":{"kind":"number","value":31132,"string":"31,132"},"likes":{"kind":"number","value":21,"string":"21"},"README":{"kind":"string","value":"---\nlanguage:\n- fr\n- en\nlibrary_name: sentence-transformers\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- sentence-embedding\n- mteb\nmodel-index:\n- name: bilingual-embedding-large\n results:\n - 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task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (fr)\n type: mteb/mtop_intent\n config: fr\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 62.84685248982148\n - type: f1\n value: 44.420122133882366\n - type: f1_weighted\n value: 65.2728620649712\n - task:\n type: Classification\n dataset:\n name: MTEB MasakhaNEWSClassification (fra)\n type: mteb/masakhanews\n config: fra\n split: test\n revision: 18193f187b92da67168c655c9973a165ed9593dd\n metrics:\n - type: accuracy\n value: 80.56872037914692\n - type: f1\n value: 77.28557364601339\n - type: f1_weighted\n value: 80.51403795220486\n - task:\n type: Clustering\n dataset:\n name: MTEB MasakhaNEWSClusteringP2P (fra)\n type: masakhane/masakhanews\n config: fra\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: v_measure\n value: 71.29428035967938\n - type: v_measures\n value:\n - 1.0\n - 0.2773866490640993\n - 0.7679216739314454\n - 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type: precision_at_100\n value: 0.738\n - type: precision_at_1000\n value: 0.096\n - type: precision_at_20\n value: 2.858\n - type: precision_at_3\n value: 10.961\n - type: precision_at_5\n value: 7.789\n - type: recall_at_1\n value: 18.96\n - type: recall_at_10\n value: 48.239\n - type: recall_at_100\n value: 73.833\n - type: recall_at_1000\n value: 95.82300000000001\n - type: recall_at_20\n value: 57.166\n - type: recall_at_3\n value: 32.883\n - type: recall_at_5\n value: 38.943\n - task:\n type: PairClassification\n dataset:\n name: MTEB OpusparcusPC (fr)\n type: GEM/opusparcus\n config: fr\n split: test\n revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a\n metrics:\n - type: cos_sim_accuracy\n value: 84.80926430517711\n - type: cos_sim_ap\n value: 94.76661922683681\n - type: cos_sim_f1\n value: 89.31480594154289\n - type: cos_sim_precision\n value: 86.29629629629629\n - type: cos_sim_recall\n value: 92.55213505461768\n - type: dot_accuracy\n value: 84.80926430517711\n - type: dot_ap\n value: 94.766630886443\n - type: dot_f1\n value: 89.31480594154289\n - type: dot_precision\n value: 86.29629629629629\n - type: dot_recall\n value: 92.55213505461768\n - type: euclidean_accuracy\n value: 84.80926430517711\n - type: euclidean_ap\n value: 94.76661922683681\n - type: euclidean_f1\n value: 89.31480594154289\n - type: euclidean_precision\n value: 86.29629629629629\n - type: euclidean_recall\n value: 92.55213505461768\n - type: manhattan_accuracy\n value: 84.94550408719346\n - type: manhattan_ap\n value: 94.78582392571815\n - type: manhattan_f1\n value: 89.33912204534491\n - type: manhattan_precision\n value: 86.86679174484053\n - type: manhattan_recall\n value: 91.9563058589871\n - type: max_accuracy\n value: 84.94550408719346\n - type: max_ap\n value: 94.78582392571815\n - type: max_f1\n value: 89.33912204534491\n - task:\n type: PairClassification\n dataset:\n name: MTEB PawsX (fr)\n type: google-research-datasets/paws-x\n config: fr\n split: test\n revision: 8a04d940a42cd40658986fdd8e3da561533a3646\n metrics:\n - type: cos_sim_accuracy\n value: 64.5\n - type: cos_sim_ap\n value: 64.51219412005997\n - type: cos_sim_f1\n value: 62.84885828198622\n - type: cos_sim_precision\n value: 46.713362068965516\n - type: cos_sim_recall\n value: 96.01328903654485\n - type: dot_accuracy\n value: 64.5\n - type: dot_ap\n value: 64.50290830259848\n - type: dot_f1\n value: 62.84885828198622\n - type: dot_precision\n value: 46.713362068965516\n - type: dot_recall\n value: 96.01328903654485\n - type: euclidean_accuracy\n value: 64.5\n - type: euclidean_ap\n value: 64.51219412005995\n - type: euclidean_f1\n value: 62.84885828198622\n - type: euclidean_precision\n value: 46.713362068965516\n - type: euclidean_recall\n value: 96.01328903654485\n - type: manhattan_accuracy\n value: 64.55\n - type: manhattan_ap\n value: 64.54022554293084\n - type: manhattan_f1\n value: 62.836363636363636\n - type: manhattan_precision\n value: 46.778559826746076\n - type: manhattan_recall\n value: 95.68106312292359\n - type: max_accuracy\n value: 64.55\n - type: max_ap\n value: 64.54022554293084\n - type: max_f1\n value: 62.84885828198622\n - task:\n type: STS\n dataset:\n name: MTEB SICKFr\n type: Lajavaness/SICK-fr\n config: default\n split: test\n revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a\n metrics:\n - type: cos_sim_pearson\n value: 85.15315949054092\n - type: cos_sim_spearman\n value: 79.19701933507372\n - type: euclidean_pearson\n value: 82.68441006897395\n - type: euclidean_spearman\n value: 79.1963186010215\n - type: manhattan_pearson\n value: 82.6725500567899\n - type: manhattan_spearman\n value: 79.13255295711785\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (fr)\n type: mteb/sts22-crosslingual-sts\n config: fr\n split: test\n revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3\n metrics:\n - type: cos_sim_pearson\n value: 83.13328685349694\n - type: cos_sim_spearman\n value: 84.64291479319418\n - type: euclidean_pearson\n value: 83.28605886303359\n - type: euclidean_spearman\n value: 84.64291479319418\n - type: manhattan_pearson\n value: 83.01485484058145\n - type: manhattan_spearman\n value: 84.35826862976153\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmarkMultilingualSTS (fr)\n type: mteb/stsb_multi_mt\n config: fr\n split: test\n revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c\n metrics:\n - type: cos_sim_pearson\n value: 86.15391910168253\n - type: cos_sim_spearman\n value: 87.0224186207858\n - type: euclidean_pearson\n value: 86.04463800957714\n - type: euclidean_spearman\n value: 87.02424394489165\n - type: manhattan_pearson\n value: 86.03126279628441\n - type: manhattan_spearman\n value: 86.99427177229043\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEvalFr\n type: lyon-nlp/summarization-summeval-fr-p2p\n config: default\n split: test\n revision: b385812de6a9577b6f4d0f88c6a6e35395a94054\n metrics:\n - type: cos_sim_pearson\n value: 31.415083738613355\n - type: cos_sim_spearman\n value: 30.301784303588285\n - type: dot_pearson\n value: 31.415089981266963\n - type: dot_spearman\n value: 30.286152348575108\n - task:\n type: Reranking\n dataset:\n name: MTEB SyntecReranking\n type: lyon-nlp/mteb-fr-reranking-syntec-s2p\n config: default\n split: test\n revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad\n metrics:\n - type: map\n value: 85.95238095238095\n - type: mrr\n value: 85.95238095238095\n - type: nAUC_map_diff1\n value: 70.42176052252755\n - type: nAUC_map_max\n value: 19.806028833551718\n - type: nAUC_mrr_diff1\n value: 70.42176052252755\n - type: nAUC_mrr_max\n value: 19.806028833551718\n - task:\n type: Retrieval\n dataset:\n name: MTEB SyntecRetrieval\n type: lyon-nlp/mteb-fr-retrieval-syntec-s2p\n config: default\n split: test\n revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9\n metrics:\n - type: map_at_1\n value: 69.0\n - type: map_at_10\n value: 79.668\n - type: map_at_100\n value: 79.791\n - type: map_at_1000\n value: 79.791\n - type: map_at_20\n value: 79.751\n - type: map_at_3\n value: 78.167\n - type: map_at_5\n value: 79.067\n - type: mrr_at_1\n value: 69.0\n - type: mrr_at_10\n value: 79.66785714285714\n - 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type: recall_at_5\n value: 6.802999999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 65.56640625\n - type: ap\n value: 12.336183192628836\n - type: ap_weighted\n value: 12.336183192628836\n - type: f1\n value: 50.61953920605424\n - type: f1_weighted\n value: 73.10180241141433\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 62.80418788907753\n - type: f1\n value: 63.050557758931134\n - type: f1_weighted\n value: 62.13337985337418\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 49.00618373985209\n - type: v_measures\n value:\n - 0.49421217801171224\n - 0.4740440424893081\n - 0.4886726035776056\n - 0.5198976504195676\n - 0.4827070012054274\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 86.97025689932646\n - type: cos_sim_ap\n value: 77.06565012359437\n - type: cos_sim_f1\n value: 70.32217308907138\n - type: cos_sim_precision\n value: 67.46666666666667\n - type: cos_sim_recall\n value: 73.43007915567283\n - type: dot_accuracy\n value: 86.97025689932646\n - type: dot_ap\n value: 77.0656524331512\n - type: dot_f1\n value: 70.32217308907138\n - type: dot_precision\n value: 67.46666666666667\n - type: dot_recall\n value: 73.43007915567283\n - type: euclidean_accuracy\n value: 86.97025689932646\n - type: euclidean_ap\n value: 77.06564828845742\n - type: euclidean_f1\n value: 70.32217308907138\n - type: euclidean_precision\n value: 67.46666666666667\n - type: euclidean_recall\n value: 73.43007915567283\n - type: manhattan_accuracy\n value: 86.90469094593789\n - type: manhattan_ap\n value: 76.94347285253252\n - type: manhattan_f1\n value: 70.18523217457499\n - type: manhattan_precision\n value: 67.59530791788856\n - type: manhattan_recall\n value: 72.98153034300792\n - type: max_accuracy\n value: 86.97025689932646\n - type: max_ap\n value: 77.0656524331512\n - type: max_f1\n value: 70.32217308907138\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.64567081926495\n - type: cos_sim_ap\n value: 87.19162831580245\n - type: cos_sim_f1\n value: 79.67696578577352\n - type: cos_sim_precision\n value: 74.92033358193775\n - type: cos_sim_recall\n value: 85.07853403141361\n - type: dot_accuracy\n value: 89.64567081926495\n - type: dot_ap\n value: 87.19162304433766\n - type: dot_f1\n value: 79.67696578577352\n - type: dot_precision\n value: 74.92033358193775\n - type: dot_recall\n value: 85.07853403141361\n - type: euclidean_accuracy\n value: 89.64567081926495\n - type: euclidean_ap\n value: 87.19162847931055\n - type: euclidean_f1\n value: 79.67696578577352\n - type: euclidean_precision\n value: 74.92033358193775\n - type: euclidean_recall\n value: 85.07853403141361\n - type: manhattan_accuracy\n value: 89.67283735009897\n - type: manhattan_ap\n value: 87.19033616510255\n - type: manhattan_f1\n value: 79.67444226437031\n - type: manhattan_precision\n value: 75.43690656391908\n - type: manhattan_recall\n value: 84.41638435478903\n - type: max_accuracy\n value: 89.67283735009897\n - type: max_ap\n value: 87.19162847931055\n - type: max_f1\n value: 79.67696578577352\n---\n\n# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)\n\nBilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.\n\n\n## Full Model Architecture\n```\nSentenceTransformer(\n (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel \n (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n (2): Normalize()\n)\n```\n\n## Training and Fine-tuning process\n#### Stage 1: NLI Training\n- Dataset: [(SNLI+XNLI) for english+french]\n- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.\n### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark\n- Dataset: [STSB-fr and en]\n- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. \n### Stage 4: Advanced Augmentation Fine-tuning\n- Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)\n- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.\n\n\n## Usage:\n\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nsentences = [\"Paris est une capitale de la France\", \"Paris is a capital of France\"]\n\nmodel = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)\nprint(embeddings)\n\n```\n\n\n\n\n\n## Evaluation\n\nTODO\n\n## Citation\n\n @article{conneau2019unsupervised,\n title={Unsupervised cross-lingual representation learning at scale},\n author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},\n journal={arXiv preprint arXiv:1911.02116},\n year={2019}\n }\n\n\t@article{reimers2019sentence,\n\t title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},\n\t author={Nils Reimers, Iryna Gurevych},\n\t journal={https://arxiv.org/abs/1908.10084},\n\t year={2019}\n\t}\n\n @article{thakur2020augmented,\n title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},\n author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},\n journal={arXiv e-prints},\n pages={arXiv--2010},\n year={2020}"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":89,"cells":{"id":{"kind":"string","value":"OrcaDB/cde-small-v1"},"author":{"kind":"string","value":"OrcaDB"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","feature-extraction","mteb","transformers","custom_code","arxiv:2410.02525","model-index","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"feature-extraction\",\n \"mteb\",\n \"transformers\",\n \"custom_code\",\n \"arxiv:2410.02525\",\n \"model-index\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-11-08T19:49:54"},"last_modified":{"kind":"string","value":"2025-01-02T19:34:35"},"downloads":{"kind":"number","value":30596,"string":"30,596"},"likes":{"kind":"number","value":4,"string":"4"},"README":{"kind":"string","value":"---\ntags:\n- mteb\n- transformers\n- sentence-transformers\nmodel-index:\n- name: cde-small-v1\n results:\n - 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type: ndcg_at_10\n value: 24.254\n - type: ndcg_at_100\n value: 34.660000000000004\n - type: ndcg_at_1000\n value: 45.798\n - type: ndcg_at_20\n value: 24.988\n - type: ndcg_at_3\n value: 29.273\n - type: ndcg_at_5\n value: 25.453\n - type: precision_at_1\n value: 28.571\n - type: precision_at_10\n value: 21.02\n - type: precision_at_100\n value: 7.122000000000001\n - type: precision_at_1000\n value: 1.435\n - type: precision_at_20\n value: 16.326999999999998\n - type: precision_at_3\n value: 31.293\n - type: precision_at_5\n value: 24.898\n - type: recall_at_1\n value: 2.355\n - type: recall_at_10\n value: 15.397\n - type: recall_at_100\n value: 43.647000000000006\n - type: recall_at_1000\n value: 77.089\n - type: recall_at_20\n value: 22.792\n - type: recall_at_3\n value: 6.847\n - type: recall_at_5\n value: 9.136\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification (default)\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 72.7734375\n - type: ap\n value: 15.655230461083708\n - type: ap_weighted\n value: 15.655230461083708\n - type: f1\n value: 56.31497978454638\n - type: f1_weighted\n value: 78.70509613747345\n - type: main_score\n value: 72.7734375\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification (default)\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 72.56366723259762\n - type: f1\n value: 72.90413275122202\n - type: f1_weighted\n value: 72.19948169084057\n - type: main_score\n value: 72.56366723259762\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering (default)\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: main_score\n value: 56.90970017457857\n - type: v_measure\n value: 56.90970017457857\n - type: v_measure_std\n value: 1.5885885070403738\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015 (default)\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cosine_accuracy\n value: 85.7006616200751\n - type: cosine_accuracy_threshold\n value: 75.78572630882263\n - type: cosine_ap\n value: 72.87577990245127\n - type: cosine_f1\n value: 67.36422521175885\n - type: cosine_f1_threshold\n value: 70.15678882598877\n - type: cosine_precision\n value: 63.80368098159509\n - type: cosine_recall\n value: 71.34564643799473\n - type: dot_accuracy\n value: 83.60851165285807\n - type: dot_accuracy_threshold\n value: 744.7918891906738\n - type: dot_ap\n value: 64.82619159813649\n - type: dot_f1\n value: 62.62379263968699\n - type: dot_f1_threshold\n value: 696.7735290527344\n - type: dot_precision\n value: 58.350421508316245\n - type: dot_recall\n value: 67.57255936675462\n - type: euclidean_accuracy\n value: 85.84371460928652\n - type: euclidean_accuracy_threshold\n value: 220.4747200012207\n - type: euclidean_ap\n value: 72.47837433257799\n - type: euclidean_f1\n value: 67.2811059907834\n - type: euclidean_f1_threshold\n value: 240.81902503967285\n - type: euclidean_precision\n value: 65.34062655395326\n - type: euclidean_recall\n value: 69.34036939313984\n - type: main_score\n value: 72.87577990245127\n - type: manhattan_accuracy\n value: 85.83179352685224\n - type: manhattan_accuracy_threshold\n value: 4910.404205322266\n - type: manhattan_ap\n value: 72.44111617709422\n - type: manhattan_f1\n value: 67.09989806320081\n - type: manhattan_f1_threshold\n value: 5333.793640136719\n - type: manhattan_precision\n value: 64.88417939871857\n - type: manhattan_recall\n value: 69.47229551451187\n - type: max_accuracy\n value: 85.84371460928652\n - type: max_ap\n value: 72.87577990245127\n - type: max_f1\n value: 67.36422521175885\n - type: max_precision\n value: 65.34062655395326\n - type: max_recall\n value: 71.34564643799473\n - type: similarity_accuracy\n value: 85.7006616200751\n - type: similarity_accuracy_threshold\n value: 75.78572630882263\n - type: similarity_ap\n value: 72.87577990245127\n - type: similarity_f1\n value: 67.36422521175885\n - type: similarity_f1_threshold\n value: 70.15678882598877\n - type: similarity_precision\n value: 63.80368098159509\n - type: similarity_recall\n value: 71.34564643799473\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus (default)\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cosine_accuracy\n value: 88.88112702293631\n - type: cosine_accuracy_threshold\n value: 71.48405313491821\n - type: cosine_ap\n value: 85.88088882163336\n - type: cosine_f1\n value: 78.2251744598276\n - type: cosine_f1_threshold\n value: 70.09605169296265\n - type: cosine_precision\n value: 75.8997755087262\n - type: cosine_recall\n value: 80.69756698490914\n - type: dot_accuracy\n value: 88.04672643303451\n - type: dot_accuracy_threshold\n value: 700.6264686584473\n - type: dot_ap\n value: 83.52072844458456\n - type: dot_f1\n value: 76.24239256244634\n - type: dot_f1_threshold\n value: 664.9115562438965\n - type: dot_precision\n value: 74.0123233055455\n - type: dot_recall\n value: 78.61102556205728\n - type: euclidean_accuracy\n value: 88.72588970388482\n - type: euclidean_accuracy_threshold\n value: 226.53303146362305\n - type: euclidean_ap\n value: 85.51788295919707\n - type: euclidean_f1\n value: 77.73453426739316\n - type: euclidean_f1_threshold\n value: 238.7503147125244\n - type: euclidean_precision\n value: 74.94818097348296\n - type: euclidean_recall\n value: 80.73606405913151\n - type: main_score\n value: 85.88088882163336\n - type: manhattan_accuracy\n value: 88.68902084061008\n - type: manhattan_accuracy_threshold\n value: 5034.079742431641\n - type: manhattan_ap\n value: 85.49952903626239\n - type: manhattan_f1\n value: 77.74326743888625\n - type: manhattan_f1_threshold\n value: 5334.531021118164\n - type: manhattan_precision\n value: 73.98289171708741\n - type: manhattan_recall\n value: 81.90637511549123\n - type: max_accuracy\n value: 88.88112702293631\n - type: max_ap\n value: 85.88088882163336\n - type: max_f1\n value: 78.2251744598276\n - type: max_precision\n value: 75.8997755087262\n - type: max_recall\n value: 81.90637511549123\n - type: similarity_accuracy\n value: 88.88112702293631\n - type: similarity_accuracy_threshold\n value: 71.48405313491821\n - type: similarity_ap\n value: 85.88088882163336\n - type: similarity_f1\n value: 78.2251744598276\n - type: similarity_f1_threshold\n value: 70.09605169296265\n - type: similarity_precision\n value: 75.8997755087262\n - type: similarity_recall\n value: 80.69756698490914\n---\n# Contextual Document Embeddings (CDE)\n\n**Link to code: github.com/jxmorris12/cde**\n\nOur new model that naturally integrates \"context tokens\" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.\n\n👉 Try on Colab\n
\n👉 Contextual Document Embeddings (ArXiv)\n\n\n\n
\n
\n\n# How to use `cde-small-v1`\n\nOur embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our \"first-stage\" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.\n\n\n\n## With Transformers\n\n
\n![\"TheBlokeAI\"](\"https://i.imgur.com/EBdldam.jpg\")
\n
\n\n\n \n \n
\nTheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)
\n\n\n# Meditron 7B - AWQ\n- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)\n- Original model: [Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b)\n\n\n## Description\n\nThis repo contains AWQ model files for [EPFL LLM Team's Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b).\n\nThese files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).\n\n\n### About AWQ\n\nAWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.\n\nIt is supported by:\n\n- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ\n- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)\n- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers\n- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code\n\n\n\n## Repositories available\n\n* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-7B-AWQ)\n* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-7B-GPTQ)\n* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-7B-GGUF)\n* [EPFL LLM Team's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/epfl-llm/meditron-7b)\n\n\n\n## Prompt template: ChatML\n\n```\n<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n\n```\n\n\n\n\n\n## Provided files, and AWQ parameters\n\nI currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.\n\nModels are released as sharded safetensors files.\n\n| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |\n| ------ | ---- | -- | ----------- | ------- | ---- |\n| [main](https://huggingface.co/TheBloke/meditron-7B-AWQ/tree/main) | 4 | 128 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 3.89 GB\n\n\n\n\n## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n\nPlease make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).\n\nIt is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.\n\n1. Click the **Model tab**.\n2. Under **Download custom model or LoRA**, enter `TheBloke/meditron-7B-AWQ`.\n3. Click **Download**.\n4. The model will start downloading. Once it's finished it will say \"Done\".\n5. In the top left, click the refresh icon next to **Model**.\n6. In the **Model** dropdown, choose the model you just downloaded: `meditron-7B-AWQ`\n7. Select **Loader: AutoAWQ**.\n8. Click Load, and the model will load and is now ready for use.\n9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.\n10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!\n\n\n\n## Multi-user inference server: vLLM\n\nDocumentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).\n\n- Please ensure you are using vLLM version 0.2 or later.\n- When using vLLM as a server, pass the `--quantization awq` parameter.\n\nFor example:\n\n```shell\npython3 -m vllm.entrypoints.api_server --model TheBloke/meditron-7B-AWQ --quantization awq --dtype auto\n```\n\n- When using vLLM from Python code, again set `quantization=awq`.\n\nFor example:\n\n```python\nfrom vllm import LLM, SamplingParams\n\nprompts = [\n \"Tell me about AI\",\n \"Write a story about llamas\",\n \"What is 291 - 150?\",\n \"How much wood would a woodchuck chuck if a woodchuck could chuck wood?\",\n]\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\nprompts = [prompt_template.format(prompt=prompt) for prompt in prompts]\n\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\n\nllm = LLM(model=\"TheBloke/meditron-7B-AWQ\", quantization=\"awq\", dtype=\"auto\")\n\noutputs = llm.generate(prompts, sampling_params)\n\n# Print the outputs.\nfor output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\n```\n\n\n\n## Multi-user inference server: Hugging Face Text Generation Inference (TGI)\n\nUse TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`\n\nExample Docker parameters:\n\n```shell\n--model-id TheBloke/meditron-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096\n```\n\nExample Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):\n\n```shell\npip3 install huggingface-hub\n```\n\n```python\nfrom huggingface_hub import InferenceClient\n\nendpoint_url = \"https://your-endpoint-url-here\"\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\nclient = InferenceClient(endpoint_url)\nresponse = client.text_generation(prompt,\n max_new_tokens=128,\n do_sample=True,\n temperature=0.7,\n top_p=0.95,\n top_k=40,\n repetition_penalty=1.1)\n\nprint(f\"Model output: \", response)\n```\n\n\n\n## Inference from Python code using Transformers\n\n### Install the necessary packages\n\n- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.\n- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.\n\n```shell\npip3 install --upgrade \"autoawq>=0.1.6\" \"transformers>=4.35.0\"\n```\n\nNote that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.\n\nIf you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:\n\n```shell\npip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl\n```\n\nIf you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:\n\n```shell\npip3 uninstall -y autoawq\ngit clone https://github.com/casper-hansen/AutoAWQ\ncd AutoAWQ\npip3 install .\n```\n\n### Transformers example code (requires Transformers 4.35.0 and later)\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer\n\nmodel_name_or_path = \"TheBloke/meditron-7B-AWQ\"\n\ntokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\nmodel = AutoModelForCausalLM.from_pretrained(\n model_name_or_path,\n low_cpu_mem_usage=True,\n device_map=\"cuda:0\"\n)\n\n# Using the text streamer to stream output one token at a time\nstreamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n\nprompt = \"Tell me about AI\"\nprompt_template=f'''<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n'''\n\n# Convert prompt to tokens\ntokens = tokenizer(\n prompt_template,\n return_tensors='pt'\n).input_ids.cuda()\n\ngeneration_params = {\n \"do_sample\": True,\n \"temperature\": 0.7,\n \"top_p\": 0.95,\n \"top_k\": 40,\n \"max_new_tokens\": 512,\n \"repetition_penalty\": 1.1\n}\n\n# Generate streamed output, visible one token at a time\ngeneration_output = model.generate(\n tokens,\n streamer=streamer,\n **generation_params\n)\n\n# Generation without a streamer, which will include the prompt in the output\ngeneration_output = model.generate(\n tokens,\n **generation_params\n)\n\n# Get the tokens from the output, decode them, print them\ntoken_output = generation_output[0]\ntext_output = tokenizer.decode(token_output)\nprint(\"model.generate output: \", text_output)\n\n# Inference is also possible via Transformers' pipeline\nfrom transformers import pipeline\n\npipe = pipeline(\n \"text-generation\",\n model=model,\n tokenizer=tokenizer,\n **generation_params\n)\n\npipe_output = pipe(prompt_template)[0]['generated_text']\nprint(\"pipeline output: \", pipe_output)\n\n```\n\n\n\n## Compatibility\n\nThe files provided are tested to work with:\n\n- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.\n- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.\n- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.\n- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.\n- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.\n\n\n\n\n\n## Discord\n\nFor further support, and discussions on these models and AI in general, join us at:\n\n[TheBloke AI's Discord server](https://discord.gg/theblokeai)\n\n## Thanks, and how to contribute\n\nThanks to the [chirper.ai](https://chirper.ai) team!\n\nThanks to Clay from [gpus.llm-utils.org](llm-utils)!\n\nI've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.\n\nIf you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.\n\nDonaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.\n\n* Patreon: https://patreon.com/TheBlokeAI\n* Ko-Fi: https://ko-fi.com/TheBlokeAI\n\n**Special thanks to**: Aemon Algiz.\n\n**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius\n\n\nThank you to all my generous patrons and donaters!\n\nAnd thank you again to a16z for their generous grant.\n\n\n\n# Original model card: EPFL LLM Team's Meditron 7B\n\n
\n\n# Model Card for Meditron-7B-v1.0\nMeditron is a suite of open-source medical Large Language Models (LLMs).\nMeditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).\nMeditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.\n\n\n
\n\n## Model Details\n\n- **Developed by:** [EPFL LLM Team](https://huggingface.co/epfl-llm)\n- **Model type:** Causal decoder-only transformer language model\n- **Language(s):** English (mainly)\n- **Model License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)\n- **Code License:** [APACHE 2.0 LICENSE](LICENSE)\n- **Continue-pretrained from model:** [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b)\n- **Context length:** 2K tokens\n- **Input:** Text-only data\n- **Output:** Model generates text only\n- **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance.\n- **Knowledge Cutoff:** August 2023\n\n\n### Model Sources\n\n- **Repository:** [epflLLM/meditron](https://github.com/epfLLM/meditron)\n- **Trainer:** [epflLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)\n- **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)*\n\n## Uses\n\nMeditron-7B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to:\n- Medical exam question answering\n- Supporting differential diagnosis\n- Disease information (symptoms, cause, treatment) query\n- General health information query\n\n### Direct Use\n\nIt is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities. \nIt should not be used directly for production or work that may impact people.\n\n### Downstream Use\nMeditron-7B is a foundation model that can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.\nThe main way we have used this model is finetuning for downstream question-answering tasks, but we encourage using this model for additional applications.\n\nSpecific formatting needs to be followed to prompt our finetuned models, including the `<|im_start|>`, `<|im_end|>` tags, and `system`, `question`, `answer` identifiers.\n \n \"\"\"\n <|im_start|>system\n {system_message}<|im_end|>\n <|im_start|>question\n {prompt}<|im_end|>\n <|im_start|>answer \n \"\"\"\n**Note 1**: The above formatting is not required for running the base model (this repository)\n\n**Note 2**: the above formatting is just an example of a finetuning template. This format is not a requirement if you use your own formatting option for the finetuning of the model.\n\nTo run proper generation with this base model, we recommend using a high-throughput and memory-efficient inference engine, such as [vLLM](https://github.com/vllm-project/vllm), with a UI that supports chat and text generation, such as [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT)\nTo see more details about model deployment and generation, please see our [documentation](https://github.com/epfLLM/meditron/blob/main/deployment/README.md).\n\n### Out-of-Scope Use\n\nWe do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.\n\n## Truthfulness, Helpfulness, Risk, and Bias\n\n\n\nWe did an initial assessment of Meditron models' **Truthfulness** against baseline models and consumer-level medical models. \nWe use TruthfulQA (multiple choice) as the main evaluation benchmark.\nWe only focus on the categories that are relevant to the medical domain, including Health, Nutrition, Psychology, and Science.\nFor 7B models, we perform one-shot evaluations for consistent answer generation.\nFor 70B models, the evaluations are under the zero-shot setting.\nBelow, we report the detailed truthfulness performance of each category.\n\n| | | | | | | | |\n| --- | ------ |----- |----- |----- |----- |----- |----- |\n|Category | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b |\n|Health | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 |\n|Nutrition | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 |\n|Psychology| 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 |\n|Science | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 |\n|Avg | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 |\n| | | | | | | |\n\nFor a more detailed performance analysis, please see our paper. \n\nSignificant research is still required to fully explore potential bias, fairness, and safety issues with this language model. \nPlease recognize that our evaluation on Meditron-7B's helpfulness, risk, and bias are highly limited. \nThus, as we noted in the safety notice, we strongly against any deployment in medical applications without further alignment process and rigorous evaluation!\n\n### Recommendations\n\n**IMPORTANT!**\nUsers (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.\nWhile this model is capable of generating natural language text, we have only begun to explore this capability and its limitations. \nUnderstanding these limitations is especially important in a domain like medicine. \nTherefore, we strongly recommend against using this model in production for natural language generation or for professional purposes related to health and medicine.\n\n## Training Details\n\n### Training Data\nMeditron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora:\n- [**Clinical Guidelines**](https://huggingface.co/datasets/epfl-llm/guidelines): a new dataset of 46K internationally-recognized clinical practice guidelines from various healthcare-related sources, including hospitals and international organizations.\n- **Medical Paper Abstracts**: 16.1M abstracts extracted from closed-access PubMed and PubMed Central papers.\n- **Medical Papers**: full-text articles extracted from 5M publicly available PubMed and PubMed Central papers.\n- **Replay Data**: 400M tokens of general domain pretraining data sampled from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)\n\nAdvisory Notice
\n\n\n While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints. \n We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings.\n\n
\n\n#### Data Preprocessing\n\nPlease see the detailed preprocessing procedure in our paper. \n\n### Training Procedure \n\nWe used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency. \nHardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM. \n\nOur three way parallelism scheme uses:\n - Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2,\n - Pipeline Parallelism (PP -- different GPUs process different layers) of 4,\n - Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1.\n \n\n#### Training Hyperparameters\n\n| | |\n| --- | ------ |\n| bf16 | true |\n| lr | 3e-4 |\n| eps | 1e-5 |\n| betas | \\[0.9, 0.95\\] |\n| clip_grad | 1 |\n| weight decay | 0.1 |\n| DP size | 16 |\n| TP size | 4 |\n| PP size | 1 |\n| seq length | 2048 |\n| lr scheduler | cosine|\n| min lr | 1e-6 |\n| warmup iteration | 2000 |\n| micro batch size | 10 |\n| global batch size | 1600 |\n| | |\n\n#### Sizes\nThe model was trained in September 2023.\n\nThe model architecture is exactly Llama 2, meaning\n\n| | |\n| --- | ------ |\n| Model size | 7B |\n| Hidden dimension | 4096 |\n| Num. attention heads | 32 |\n| Num. layers | 32 |\n| | |\n\n## Evaluation\n\n\n\n### Testing Data & Metrics\n\n#### Testing Data\n- [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa)\n- [MedMCQA](https://huggingface.co/datasets/medmcqa)\n- [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa)\n- [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu)\n- [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)\n\n#### Metrics\n- Accuracy: suite the evaluation of multiple-choice question-answering tasks.\n\n### Results\nWe finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually. \nWe report the finetuned models' performance with top token selection as the inference mode.\nFor MMLU-Medical, models finetuned on MedMCQA are used for inference.\nFor MedQA-4-Option, models finetuned on MedQA are used for inference.\nFor a more detailed performance analysis, please see our paper. \n\n| | | | | | |\n| --- | ------ |----- |----- |----- |----- |\n|Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* |\n|MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 |\n|PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 |\n|MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 |\n|MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 |\n|MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 |\n|Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 |\n| | | | | | |\n\n**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.\n\n\n## Environmental Impact\n\n\n\n- **Hardware Type:** 8 x NVIDIA A100 (80GB) SXM\n- **Total GPU hours:** 588.8\n- **Hardware Provider:** EPFL Research Computing Platform\n- **Compute Region:** Switzerland\n- **Carbon Emitted:** Switzerland has a carbon efficiency of 0.016 kgCO2/kWh (https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf). 73.6 hours of 8 A100s means 588.8 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.5, total emissions are estimated to be: \n \n (400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 73.6 h * 8 GPU) * 1.8 PUE = 6.8 kgCO2.\n\n## Citation\n\n**BibTeX:**\nIf you use Meditron or its training data, please cite our work:\n\n```\n@misc{chen2023meditron70b,\n title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models}, \n author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},\n year={2023},\n eprint={2311.16079},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n\n@software{epfmedtrn,\n author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},\n title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},\n month = November,\n year = 2023,\n url = {https://github.com/epfLLM/meditron}\n}\n```\n"},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING"],"string":"[\n \"QUESTION_ANSWERING\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["MEDQA","PUBMEDQA"],"string":"[\n \"MEDQA\",\n \"PUBMEDQA\"\n]"}}},{"rowIdx":85,"cells":{"id":{"kind":"string","value":"hkunlp/instructor-xl"},"author":{"kind":"string","value":"hkunlp"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","t5","text-embedding","embeddings","information-retrieval","beir","text-classification","language-model","text-clustering","text-semantic-similarity","text-evaluation","prompt-retrieval","text-reranking","feature-extraction","sentence-similarity","transformers","English","Sentence Similarity","natural_questions","ms_marco","fever","hotpot_qa","mteb","en","arxiv:2212.09741","license:apache-2.0","model-index","autotrain_compatible","text-generation-inference","region:us"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"t5\",\n \"text-embedding\",\n \"embeddings\",\n \"information-retrieval\",\n \"beir\",\n \"text-classification\",\n \"language-model\",\n \"text-clustering\",\n \"text-semantic-similarity\",\n \"text-evaluation\",\n \"prompt-retrieval\",\n \"text-reranking\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"transformers\",\n \"English\",\n \"Sentence Similarity\",\n \"natural_questions\",\n \"ms_marco\",\n \"fever\",\n \"hotpot_qa\",\n \"mteb\",\n \"en\",\n \"arxiv:2212.09741\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2022-12-20T06:07:18"},"last_modified":{"kind":"string","value":"2023-01-21T06:33:27"},"downloads":{"kind":"number","value":35591,"string":"35,591"},"likes":{"kind":"number","value":562,"string":"562"},"README":{"kind":"string","value":"---\nlanguage: en\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- text-embedding\n- embeddings\n- information-retrieval\n- beir\n- text-classification\n- language-model\n- text-clustering\n- text-semantic-similarity\n- text-evaluation\n- prompt-retrieval\n- text-reranking\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- t5\n- English\n- Sentence Similarity\n- natural_questions\n- ms_marco\n- fever\n- hotpot_qa\n- mteb\ninference: false\nmodel-index:\n- name: final_xl_results\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 85.08955223880596\n - type: ap\n value: 52.66066378722476\n - type: f1\n value: 79.63340218960269\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 86.542\n - type: ap\n value: 81.92695193008987\n - type: f1\n value: 86.51466132573681\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 42.964\n - type: f1\n value: 41.43146249774862\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 29.872\n - type: map_at_10\n value: 46.342\n - type: map_at_100\n value: 47.152\n - type: map_at_1000\n value: 47.154\n - type: map_at_3\n value: 41.216\n - type: map_at_5\n value: 44.035999999999994\n - type: mrr_at_1\n value: 30.939\n - type: mrr_at_10\n value: 46.756\n - type: mrr_at_100\n value: 47.573\n - type: mrr_at_1000\n value: 47.575\n - type: mrr_at_3\n value: 41.548\n - type: mrr_at_5\n value: 44.425\n - type: ndcg_at_1\n value: 29.872\n - type: ndcg_at_10\n value: 55.65\n - type: ndcg_at_100\n value: 58.88099999999999\n - type: ndcg_at_1000\n value: 58.951\n - type: ndcg_at_3\n value: 45.0\n - type: ndcg_at_5\n value: 50.09\n - type: precision_at_1\n value: 29.872\n - type: precision_at_10\n value: 8.549\n - type: precision_at_100\n value: 0.991\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 18.658\n - type: precision_at_5\n value: 13.669999999999998\n - type: recall_at_1\n value: 29.872\n - type: recall_at_10\n value: 85.491\n - type: recall_at_100\n value: 99.075\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 55.974000000000004\n - type: recall_at_5\n value: 68.35\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 42.452729850641276\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 32.21141846480423\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 65.34710928952622\n - type: mrr\n value: 77.61124301983028\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_spearman\n value: 84.15312230525639\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 82.66233766233766\n - type: f1\n value: 82.04175284777669\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 37.36697339826455\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 30.551241447593092\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 36.797000000000004\n - type: map_at_10\n value: 48.46\n - type: map_at_100\n value: 49.968\n - type: map_at_1000\n value: 50.080000000000005\n - type: map_at_3\n value: 44.71\n - type: map_at_5\n value: 46.592\n - type: mrr_at_1\n value: 45.494\n - type: mrr_at_10\n value: 54.747\n - type: mrr_at_100\n value: 55.43599999999999\n - type: mrr_at_1000\n value: 55.464999999999996\n - type: mrr_at_3\n value: 52.361000000000004\n - type: mrr_at_5\n value: 53.727000000000004\n - type: ndcg_at_1\n value: 45.494\n - type: ndcg_at_10\n value: 54.989\n - type: ndcg_at_100\n value: 60.096000000000004\n - type: ndcg_at_1000\n value: 61.58\n - type: ndcg_at_3\n value: 49.977\n - type: ndcg_at_5\n value: 51.964999999999996\n - type: precision_at_1\n value: 45.494\n - type: precision_at_10\n value: 10.558\n - type: precision_at_100\n value: 1.6049999999999998\n - type: precision_at_1000\n value: 0.203\n - type: precision_at_3\n value: 23.796\n - type: precision_at_5\n value: 16.881\n - type: recall_at_1\n value: 36.797000000000004\n - type: recall_at_10\n value: 66.83\n - type: recall_at_100\n value: 88.34100000000001\n - type: recall_at_1000\n value: 97.202\n - type: recall_at_3\n value: 51.961999999999996\n - type: recall_at_5\n value: 57.940000000000005\n - type: map_at_1\n value: 32.597\n - type: map_at_10\n value: 43.424\n - type: map_at_100\n value: 44.78\n - type: map_at_1000\n value: 44.913\n - type: map_at_3\n value: 40.315\n - type: map_at_5\n value: 41.987\n - type: mrr_at_1\n value: 40.382\n - type: mrr_at_10\n value: 49.219\n - type: mrr_at_100\n value: 49.895\n - type: mrr_at_1000\n value: 49.936\n - type: mrr_at_3\n value: 46.996\n - type: mrr_at_5\n value: 48.231\n - type: ndcg_at_1\n value: 40.382\n - type: ndcg_at_10\n value: 49.318\n - type: ndcg_at_100\n value: 53.839999999999996\n - type: ndcg_at_1000\n value: 55.82899999999999\n - type: ndcg_at_3\n value: 44.914\n - type: ndcg_at_5\n value: 46.798\n - type: precision_at_1\n value: 40.382\n - type: precision_at_10\n value: 9.274000000000001\n - type: precision_at_100\n value: 1.497\n - type: precision_at_1000\n value: 0.198\n - type: precision_at_3\n value: 21.592\n - type: precision_at_5\n value: 15.159\n - type: recall_at_1\n value: 32.597\n - type: recall_at_10\n value: 59.882000000000005\n - type: recall_at_100\n value: 78.446\n - type: recall_at_1000\n value: 90.88000000000001\n - type: recall_at_3\n value: 46.9\n - type: recall_at_5\n value: 52.222\n - type: map_at_1\n value: 43.8\n - type: map_at_10\n value: 57.293000000000006\n - type: map_at_100\n value: 58.321\n - type: map_at_1000\n value: 58.361\n - type: map_at_3\n value: 53.839999999999996\n - type: map_at_5\n value: 55.838\n - type: mrr_at_1\n value: 49.592000000000006\n - type: mrr_at_10\n value: 60.643\n - type: mrr_at_100\n value: 61.23499999999999\n - type: mrr_at_1000\n value: 61.251999999999995\n - type: mrr_at_3\n value: 58.265\n - type: mrr_at_5\n value: 59.717\n - type: ndcg_at_1\n value: 49.592000000000006\n - type: ndcg_at_10\n value: 63.364\n - type: ndcg_at_100\n value: 67.167\n - type: ndcg_at_1000\n value: 67.867\n - type: ndcg_at_3\n value: 57.912\n - type: ndcg_at_5\n value: 60.697\n - type: precision_at_1\n value: 49.592000000000006\n - type: precision_at_10\n value: 10.088\n - type: precision_at_100\n value: 1.2930000000000001\n - type: precision_at_1000\n value: 0.13899999999999998\n - type: precision_at_3\n value: 25.789\n - type: precision_at_5\n value: 17.541999999999998\n - type: recall_at_1\n value: 43.8\n - type: recall_at_10\n value: 77.635\n - type: recall_at_100\n value: 93.748\n - type: recall_at_1000\n value: 98.468\n - type: recall_at_3\n value: 63.223\n - type: recall_at_5\n value: 70.122\n - type: map_at_1\n value: 27.721\n - type: map_at_10\n value: 35.626999999999995\n - type: map_at_100\n value: 36.719\n - type: map_at_1000\n value: 36.8\n - type: map_at_3\n value: 32.781\n - type: map_at_5\n value: 34.333999999999996\n - type: mrr_at_1\n value: 29.604999999999997\n - type: mrr_at_10\n value: 37.564\n - type: mrr_at_100\n value: 38.505\n - type: mrr_at_1000\n value: 38.565\n - type: mrr_at_3\n value: 34.727000000000004\n - type: mrr_at_5\n value: 36.207\n - type: ndcg_at_1\n value: 29.604999999999997\n - type: ndcg_at_10\n value: 40.575\n - type: ndcg_at_100\n value: 45.613\n - type: ndcg_at_1000\n value: 47.676\n - type: ndcg_at_3\n value: 34.811\n - type: ndcg_at_5\n value: 37.491\n - type: precision_at_1\n value: 29.604999999999997\n - type: precision_at_10\n value: 6.1690000000000005\n - type: precision_at_100\n value: 0.906\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 14.237\n - type: precision_at_5\n value: 10.056\n - type: recall_at_1\n value: 27.721\n - type: recall_at_10\n value: 54.041\n - type: recall_at_100\n value: 76.62299999999999\n - type: recall_at_1000\n value: 92.134\n - type: recall_at_3\n value: 38.582\n - type: recall_at_5\n value: 44.989000000000004\n - type: map_at_1\n value: 16.553\n - type: map_at_10\n value: 25.384\n - type: map_at_100\n value: 26.655\n - type: map_at_1000\n value: 26.778000000000002\n - type: map_at_3\n value: 22.733\n - type: map_at_5\n value: 24.119\n - type: mrr_at_1\n value: 20.149\n - type: mrr_at_10\n value: 29.705\n - type: mrr_at_100\n value: 30.672\n - type: mrr_at_1000\n value: 30.737\n - type: mrr_at_3\n value: 27.032\n - type: mrr_at_5\n value: 28.369\n - type: ndcg_at_1\n value: 20.149\n - type: ndcg_at_10\n value: 30.843999999999998\n - type: ndcg_at_100\n value: 36.716\n - type: ndcg_at_1000\n value: 39.495000000000005\n - type: ndcg_at_3\n value: 25.918999999999997\n - type: ndcg_at_5\n value: 27.992\n - type: precision_at_1\n value: 20.149\n - type: precision_at_10\n value: 5.858\n - type: precision_at_100\n value: 1.009\n - type: precision_at_1000\n value: 0.13799999999999998\n - type: precision_at_3\n value: 12.645000000000001\n - type: precision_at_5\n value: 9.179\n - type: recall_at_1\n value: 16.553\n - type: recall_at_10\n value: 43.136\n - type: recall_at_100\n value: 68.562\n - type: recall_at_1000\n value: 88.208\n - type: recall_at_3\n value: 29.493000000000002\n - type: recall_at_5\n value: 34.751\n - type: map_at_1\n value: 28.000999999999998\n - type: map_at_10\n value: 39.004\n - type: map_at_100\n value: 40.461999999999996\n - type: map_at_1000\n value: 40.566\n - type: map_at_3\n value: 35.805\n - type: map_at_5\n value: 37.672\n - type: mrr_at_1\n value: 33.782000000000004\n - type: mrr_at_10\n value: 44.702\n - type: mrr_at_100\n value: 45.528\n - type: mrr_at_1000\n value: 45.576\n - type: mrr_at_3\n value: 42.14\n - type: mrr_at_5\n value: 43.651\n - type: ndcg_at_1\n value: 33.782000000000004\n - type: ndcg_at_10\n value: 45.275999999999996\n - type: ndcg_at_100\n value: 50.888\n - type: ndcg_at_1000\n value: 52.879\n - type: ndcg_at_3\n value: 40.191\n - type: ndcg_at_5\n value: 42.731\n - type: precision_at_1\n value: 33.782000000000004\n - type: precision_at_10\n value: 8.200000000000001\n - type: precision_at_100\n value: 1.287\n - type: precision_at_1000\n value: 0.16199999999999998\n - type: precision_at_3\n value: 19.185\n - type: precision_at_5\n value: 13.667000000000002\n - type: recall_at_1\n value: 28.000999999999998\n - type: recall_at_10\n value: 58.131\n - type: recall_at_100\n value: 80.869\n - type: recall_at_1000\n value: 93.931\n - type: recall_at_3\n value: 44.161\n - type: recall_at_5\n value: 50.592000000000006\n - type: map_at_1\n value: 28.047\n - type: map_at_10\n value: 38.596000000000004\n - type: map_at_100\n value: 40.116\n - type: map_at_1000\n value: 40.232\n - type: map_at_3\n value: 35.205\n - type: map_at_5\n value: 37.076\n - type: mrr_at_1\n value: 34.932\n - type: mrr_at_10\n value: 44.496\n - type: mrr_at_100\n value: 45.47\n - type: mrr_at_1000\n value: 45.519999999999996\n - type: mrr_at_3\n value: 41.743\n - type: mrr_at_5\n value: 43.352000000000004\n - type: ndcg_at_1\n value: 34.932\n - type: ndcg_at_10\n value: 44.901\n - type: ndcg_at_100\n value: 50.788999999999994\n - type: ndcg_at_1000\n value: 52.867\n - type: ndcg_at_3\n value: 39.449\n - type: ndcg_at_5\n value: 41.929\n - type: precision_at_1\n value: 34.932\n - type: precision_at_10\n value: 8.311\n - type: precision_at_100\n value: 1.3050000000000002\n - type: precision_at_1000\n value: 0.166\n - type: precision_at_3\n value: 18.836\n - type: precision_at_5\n value: 13.447000000000001\n - type: recall_at_1\n value: 28.047\n - type: recall_at_10\n value: 57.717\n - type: recall_at_100\n value: 82.182\n - type: recall_at_1000\n value: 95.82000000000001\n - type: recall_at_3\n value: 42.448\n - type: recall_at_5\n value: 49.071\n - type: map_at_1\n value: 27.861250000000005\n - type: map_at_10\n value: 37.529583333333335\n - type: map_at_100\n value: 38.7915\n - type: map_at_1000\n value: 38.90558333333335\n - type: map_at_3\n value: 34.57333333333333\n - type: map_at_5\n value: 36.187166666666656\n - type: mrr_at_1\n value: 32.88291666666666\n - type: mrr_at_10\n value: 41.79750000000001\n - type: mrr_at_100\n value: 42.63183333333333\n - type: mrr_at_1000\n value: 42.68483333333333\n - type: mrr_at_3\n value: 39.313750000000006\n - type: mrr_at_5\n value: 40.70483333333333\n - type: ndcg_at_1\n value: 32.88291666666666\n - type: ndcg_at_10\n value: 43.09408333333333\n - type: ndcg_at_100\n value: 48.22158333333333\n - type: ndcg_at_1000\n value: 50.358000000000004\n - type: ndcg_at_3\n value: 38.129583333333336\n - type: ndcg_at_5\n value: 40.39266666666666\n - type: precision_at_1\n value: 32.88291666666666\n - type: precision_at_10\n value: 7.5584999999999996\n - type: precision_at_100\n value: 1.1903333333333332\n - type: precision_at_1000\n value: 0.15658333333333332\n - type: precision_at_3\n value: 17.495916666666666\n - type: precision_at_5\n value: 12.373833333333332\n - type: recall_at_1\n value: 27.861250000000005\n - type: recall_at_10\n value: 55.215916666666665\n - type: recall_at_100\n value: 77.392\n - type: recall_at_1000\n value: 92.04908333333334\n - type: recall_at_3\n value: 41.37475\n - type: recall_at_5\n value: 47.22908333333333\n - type: map_at_1\n value: 25.064999999999998\n - type: map_at_10\n value: 31.635999999999996\n - type: map_at_100\n value: 32.596000000000004\n - type: map_at_1000\n value: 32.695\n - type: map_at_3\n value: 29.612\n - type: map_at_5\n value: 30.768\n - type: mrr_at_1\n value: 28.528\n - type: mrr_at_10\n value: 34.717\n - type: mrr_at_100\n value: 35.558\n - type: mrr_at_1000\n value: 35.626000000000005\n - type: mrr_at_3\n value: 32.745000000000005\n - type: mrr_at_5\n value: 33.819\n - type: ndcg_at_1\n value: 28.528\n - type: ndcg_at_10\n value: 35.647\n - type: ndcg_at_100\n value: 40.207\n - type: ndcg_at_1000\n value: 42.695\n - type: ndcg_at_3\n value: 31.878\n - type: ndcg_at_5\n value: 33.634\n - type: precision_at_1\n value: 28.528\n - type: precision_at_10\n value: 5.46\n - type: precision_at_100\n value: 0.84\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 13.547999999999998\n - type: precision_at_5\n value: 9.325\n - type: recall_at_1\n value: 25.064999999999998\n - type: recall_at_10\n value: 45.096000000000004\n - type: recall_at_100\n value: 65.658\n - type: recall_at_1000\n value: 84.128\n - type: recall_at_3\n value: 34.337\n - type: recall_at_5\n value: 38.849000000000004\n - type: map_at_1\n value: 17.276\n - type: map_at_10\n value: 24.535\n - type: map_at_100\n value: 25.655\n - type: map_at_1000\n value: 25.782\n - type: map_at_3\n value: 22.228\n - type: map_at_5\n value: 23.612\n - type: mrr_at_1\n value: 21.266\n - type: mrr_at_10\n value: 28.474\n - type: mrr_at_100\n value: 29.398000000000003\n - type: mrr_at_1000\n value: 29.482000000000003\n - type: mrr_at_3\n value: 26.245\n - type: mrr_at_5\n value: 27.624\n - type: ndcg_at_1\n value: 21.266\n - type: ndcg_at_10\n value: 29.087000000000003\n - type: ndcg_at_100\n value: 34.374\n - type: ndcg_at_1000\n value: 37.433\n - type: ndcg_at_3\n value: 25.040000000000003\n - type: ndcg_at_5\n value: 27.116\n - type: precision_at_1\n value: 21.266\n - type: precision_at_10\n value: 5.258\n - type: precision_at_100\n value: 0.9299999999999999\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 11.849\n - type: precision_at_5\n value: 8.699\n - type: recall_at_1\n value: 17.276\n - type: recall_at_10\n value: 38.928000000000004\n - type: recall_at_100\n value: 62.529\n - type: recall_at_1000\n value: 84.44800000000001\n - type: recall_at_3\n value: 27.554000000000002\n - type: recall_at_5\n value: 32.915\n - type: map_at_1\n value: 27.297\n - type: map_at_10\n value: 36.957\n - type: map_at_100\n value: 38.252\n - type: map_at_1000\n value: 38.356\n - type: map_at_3\n value: 34.121\n - type: map_at_5\n value: 35.782000000000004\n - type: mrr_at_1\n value: 32.275999999999996\n - type: mrr_at_10\n value: 41.198\n - type: mrr_at_100\n value: 42.131\n - type: mrr_at_1000\n value: 42.186\n - type: mrr_at_3\n value: 38.557\n - type: mrr_at_5\n value: 40.12\n - type: ndcg_at_1\n value: 32.275999999999996\n - type: ndcg_at_10\n value: 42.516\n - type: ndcg_at_100\n value: 48.15\n - type: ndcg_at_1000\n value: 50.344\n - type: ndcg_at_3\n value: 37.423\n - type: ndcg_at_5\n value: 39.919\n - type: precision_at_1\n value: 32.275999999999996\n - type: precision_at_10\n value: 7.155\n - type: precision_at_100\n value: 1.123\n - type: precision_at_1000\n value: 0.14200000000000002\n - type: precision_at_3\n value: 17.163999999999998\n - type: precision_at_5\n value: 12.127\n - type: recall_at_1\n value: 27.297\n - type: recall_at_10\n value: 55.238\n - type: recall_at_100\n value: 79.2\n - type: recall_at_1000\n value: 94.258\n - type: recall_at_3\n value: 41.327000000000005\n - type: recall_at_5\n value: 47.588\n - type: map_at_1\n value: 29.142000000000003\n - type: map_at_10\n value: 38.769\n - type: map_at_100\n value: 40.292\n - type: map_at_1000\n value: 40.510000000000005\n - type: map_at_3\n value: 35.39\n - type: map_at_5\n value: 37.009\n - type: mrr_at_1\n value: 34.19\n - type: mrr_at_10\n value: 43.418\n - type: mrr_at_100\n value: 44.132\n - type: mrr_at_1000\n value: 44.175\n - type: mrr_at_3\n value: 40.547\n - type: mrr_at_5\n value: 42.088\n - type: ndcg_at_1\n value: 34.19\n - type: ndcg_at_10\n value: 45.14\n - type: ndcg_at_100\n value: 50.364\n - type: ndcg_at_1000\n value: 52.481\n - type: ndcg_at_3\n value: 39.466\n - type: ndcg_at_5\n value: 41.772\n - type: precision_at_1\n value: 34.19\n - type: precision_at_10\n value: 8.715\n - type: precision_at_100\n value: 1.6150000000000002\n - type: precision_at_1000\n value: 0.247\n - type: precision_at_3\n value: 18.248\n - type: precision_at_5\n value: 13.161999999999999\n - type: recall_at_1\n value: 29.142000000000003\n - type: recall_at_10\n value: 57.577999999999996\n - type: recall_at_100\n value: 81.428\n - type: recall_at_1000\n value: 94.017\n - type: recall_at_3\n value: 41.402\n - type: recall_at_5\n value: 47.695\n - type: map_at_1\n value: 22.039\n - type: map_at_10\n value: 30.669999999999998\n - type: map_at_100\n value: 31.682\n - type: map_at_1000\n value: 31.794\n - type: map_at_3\n value: 28.139999999999997\n - type: map_at_5\n value: 29.457\n - type: mrr_at_1\n value: 24.399\n - type: mrr_at_10\n value: 32.687\n - type: mrr_at_100\n value: 33.622\n - type: mrr_at_1000\n value: 33.698\n - type: mrr_at_3\n value: 30.407\n - type: mrr_at_5\n value: 31.552999999999997\n - type: ndcg_at_1\n value: 24.399\n - type: ndcg_at_10\n value: 35.472\n - type: ndcg_at_100\n value: 40.455000000000005\n - type: ndcg_at_1000\n value: 43.15\n - type: ndcg_at_3\n value: 30.575000000000003\n - type: ndcg_at_5\n value: 32.668\n - type: precision_at_1\n value: 24.399\n - type: precision_at_10\n value: 5.656\n - type: precision_at_100\n value: 0.874\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 13.062000000000001\n - type: precision_at_5\n value: 9.242\n - type: recall_at_1\n value: 22.039\n - type: recall_at_10\n value: 48.379\n - type: recall_at_100\n value: 71.11800000000001\n - type: recall_at_1000\n value: 91.095\n - type: recall_at_3\n value: 35.108\n - type: recall_at_5\n value: 40.015\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 10.144\n - type: map_at_10\n value: 18.238\n - type: map_at_100\n value: 20.143\n - type: map_at_1000\n value: 20.346\n - type: map_at_3\n value: 14.809\n - type: map_at_5\n value: 16.567999999999998\n - type: mrr_at_1\n value: 22.671\n - type: mrr_at_10\n value: 34.906\n - type: mrr_at_100\n value: 35.858000000000004\n - type: mrr_at_1000\n value: 35.898\n - type: mrr_at_3\n value: 31.238\n - type: mrr_at_5\n value: 33.342\n - type: ndcg_at_1\n value: 22.671\n - type: ndcg_at_10\n value: 26.540000000000003\n - type: ndcg_at_100\n value: 34.138000000000005\n - type: ndcg_at_1000\n value: 37.72\n - type: ndcg_at_3\n value: 20.766000000000002\n - type: ndcg_at_5\n value: 22.927\n - type: precision_at_1\n value: 22.671\n - type: precision_at_10\n value: 8.619\n - type: precision_at_100\n value: 1.678\n - type: precision_at_1000\n value: 0.23500000000000001\n - type: precision_at_3\n value: 15.592\n - type: precision_at_5\n value: 12.43\n - type: recall_at_1\n value: 10.144\n - type: recall_at_10\n value: 33.46\n - type: recall_at_100\n value: 59.758\n - type: recall_at_1000\n value: 79.704\n - type: recall_at_3\n value: 19.604\n - type: recall_at_5\n value: 25.367\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 8.654\n - type: map_at_10\n value: 18.506\n - type: map_at_100\n value: 26.412999999999997\n - type: map_at_1000\n value: 28.13\n - type: map_at_3\n value: 13.379\n - type: map_at_5\n value: 15.529000000000002\n - type: mrr_at_1\n value: 66.0\n - type: mrr_at_10\n value: 74.13\n - type: mrr_at_100\n value: 74.48700000000001\n - type: mrr_at_1000\n value: 74.49799999999999\n - type: mrr_at_3\n value: 72.75\n - type: mrr_at_5\n value: 73.762\n - type: ndcg_at_1\n value: 54.50000000000001\n - type: ndcg_at_10\n value: 40.236\n - type: ndcg_at_100\n value: 44.690999999999995\n - type: ndcg_at_1000\n value: 52.195\n - type: ndcg_at_3\n value: 45.632\n - type: ndcg_at_5\n value: 42.952\n - type: precision_at_1\n value: 66.0\n - type: precision_at_10\n value: 31.724999999999998\n - type: precision_at_100\n value: 10.299999999999999\n - type: precision_at_1000\n value: 2.194\n - type: precision_at_3\n value: 48.75\n - type: precision_at_5\n value: 41.6\n - type: recall_at_1\n value: 8.654\n - type: recall_at_10\n value: 23.74\n - type: recall_at_100\n value: 50.346999999999994\n - type: recall_at_1000\n value: 74.376\n - type: recall_at_3\n value: 14.636\n - type: recall_at_5\n value: 18.009\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 53.245\n - type: f1\n value: 48.74520523753552\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 51.729\n - type: map_at_10\n value: 63.904\n - type: map_at_100\n value: 64.363\n - type: map_at_1000\n value: 64.38199999999999\n - type: map_at_3\n value: 61.393\n - type: map_at_5\n value: 63.02100000000001\n - type: mrr_at_1\n value: 55.686\n - type: mrr_at_10\n value: 67.804\n - type: mrr_at_100\n value: 68.15299999999999\n - type: mrr_at_1000\n value: 68.161\n - type: mrr_at_3\n value: 65.494\n - type: mrr_at_5\n value: 67.01599999999999\n - type: ndcg_at_1\n value: 55.686\n - type: ndcg_at_10\n value: 70.025\n - type: ndcg_at_100\n value: 72.011\n - type: ndcg_at_1000\n value: 72.443\n - type: ndcg_at_3\n value: 65.32900000000001\n - type: ndcg_at_5\n value: 68.05600000000001\n - type: precision_at_1\n value: 55.686\n - type: precision_at_10\n value: 9.358\n - type: precision_at_100\n value: 1.05\n - type: precision_at_1000\n value: 0.11\n - type: precision_at_3\n value: 26.318\n - type: precision_at_5\n value: 17.321\n - type: recall_at_1\n value: 51.729\n - type: recall_at_10\n value: 85.04\n - type: recall_at_100\n value: 93.777\n - type: recall_at_1000\n value: 96.824\n - type: recall_at_3\n value: 72.521\n - type: recall_at_5\n value: 79.148\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 23.765\n - type: map_at_10\n value: 39.114\n - type: map_at_100\n value: 40.987\n - type: map_at_1000\n value: 41.155\n - type: map_at_3\n value: 34.028000000000006\n - type: map_at_5\n value: 36.925000000000004\n - type: mrr_at_1\n value: 46.451\n - type: mrr_at_10\n value: 54.711\n - type: mrr_at_100\n value: 55.509\n - type: mrr_at_1000\n value: 55.535000000000004\n - type: mrr_at_3\n value: 52.649\n - type: mrr_at_5\n value: 53.729000000000006\n - type: ndcg_at_1\n value: 46.451\n - type: ndcg_at_10\n value: 46.955999999999996\n - type: ndcg_at_100\n value: 53.686\n - type: ndcg_at_1000\n value: 56.230000000000004\n - type: ndcg_at_3\n value: 43.374\n - type: ndcg_at_5\n value: 44.372\n - type: precision_at_1\n value: 46.451\n - type: precision_at_10\n value: 13.256\n - type: precision_at_100\n value: 2.019\n - type: precision_at_1000\n value: 0.247\n - type: precision_at_3\n value: 29.115000000000002\n - type: precision_at_5\n value: 21.389\n - type: recall_at_1\n value: 23.765\n - type: recall_at_10\n value: 53.452999999999996\n - type: recall_at_100\n value: 78.828\n - type: recall_at_1000\n value: 93.938\n - type: recall_at_3\n value: 39.023\n - type: recall_at_5\n value: 45.18\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 31.918000000000003\n - type: map_at_10\n value: 46.741\n - type: map_at_100\n value: 47.762\n - type: map_at_1000\n value: 47.849000000000004\n - type: map_at_3\n value: 43.578\n - type: map_at_5\n value: 45.395\n - type: mrr_at_1\n value: 63.834999999999994\n - type: mrr_at_10\n value: 71.312\n - type: mrr_at_100\n value: 71.695\n - type: mrr_at_1000\n value: 71.714\n - type: mrr_at_3\n value: 69.82000000000001\n - type: mrr_at_5\n value: 70.726\n - type: ndcg_at_1\n value: 63.834999999999994\n - type: ndcg_at_10\n value: 55.879999999999995\n - type: ndcg_at_100\n value: 59.723000000000006\n - type: ndcg_at_1000\n value: 61.49400000000001\n - type: ndcg_at_3\n value: 50.964\n - type: ndcg_at_5\n value: 53.47\n - type: precision_at_1\n value: 63.834999999999994\n - type: precision_at_10\n value: 11.845\n - type: precision_at_100\n value: 1.4869999999999999\n - type: precision_at_1000\n value: 0.172\n - type: precision_at_3\n value: 32.158\n - type: precision_at_5\n value: 21.278\n - type: recall_at_1\n value: 31.918000000000003\n - type: recall_at_10\n value: 59.223000000000006\n - type: recall_at_100\n value: 74.328\n - type: recall_at_1000\n value: 86.05000000000001\n - type: recall_at_3\n value: 48.238\n - type: recall_at_5\n value: 53.193999999999996\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 79.7896\n - type: ap\n value: 73.65166029460288\n - type: f1\n value: 79.71794693711813\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.239\n - type: map_at_10\n value: 34.542\n - type: map_at_100\n value: 35.717999999999996\n - type: map_at_1000\n value: 35.764\n - type: map_at_3\n value: 30.432\n - type: map_at_5\n value: 32.81\n - type: mrr_at_1\n value: 22.908\n - type: mrr_at_10\n value: 35.127\n - type: mrr_at_100\n value: 36.238\n - type: mrr_at_1000\n value: 36.278\n - type: mrr_at_3\n value: 31.076999999999998\n - type: mrr_at_5\n value: 33.419\n - type: ndcg_at_1\n value: 22.908\n - type: ndcg_at_10\n value: 41.607\n - type: ndcg_at_100\n value: 47.28\n - type: ndcg_at_1000\n value: 48.414\n - type: ndcg_at_3\n value: 33.253\n - type: ndcg_at_5\n value: 37.486000000000004\n - type: precision_at_1\n value: 22.908\n - type: precision_at_10\n value: 6.645\n - type: precision_at_100\n value: 0.9490000000000001\n - type: precision_at_1000\n value: 0.105\n - type: precision_at_3\n value: 14.130999999999998\n - type: precision_at_5\n value: 10.616\n - type: recall_at_1\n value: 22.239\n - type: recall_at_10\n value: 63.42\n - type: recall_at_100\n value: 89.696\n - type: recall_at_1000\n value: 98.351\n - type: recall_at_3\n value: 40.77\n - type: recall_at_5\n value: 50.93\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 95.06839945280439\n - type: f1\n value: 94.74276398224072\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 72.25718194254446\n - type: f1\n value: 53.91164489161391\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 71.47948890383323\n - type: f1\n value: 69.98520247230257\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 76.46603900470748\n - type: f1\n value: 76.44111526065399\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 33.19106070798198\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 30.78772205248094\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 31.811231631488507\n - type: mrr\n value: 32.98200485378021\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.9\n - type: map_at_10\n value: 13.703000000000001\n - type: map_at_100\n value: 17.251\n - type: map_at_1000\n value: 18.795\n - type: map_at_3\n value: 10.366999999999999\n - type: map_at_5\n value: 11.675\n - type: mrr_at_1\n value: 47.059\n - type: mrr_at_10\n value: 55.816\n - type: mrr_at_100\n value: 56.434\n - type: mrr_at_1000\n value: 56.467\n - type: mrr_at_3\n value: 53.973000000000006\n - type: mrr_at_5\n value: 55.257999999999996\n - type: ndcg_at_1\n value: 44.737\n - type: ndcg_at_10\n value: 35.997\n - type: ndcg_at_100\n value: 33.487\n - type: ndcg_at_1000\n value: 41.897\n - type: ndcg_at_3\n value: 41.18\n - type: ndcg_at_5\n value: 38.721\n - type: precision_at_1\n value: 46.129999999999995\n - type: precision_at_10\n value: 26.533\n - type: precision_at_100\n value: 8.706\n - type: precision_at_1000\n value: 2.16\n - type: precision_at_3\n value: 38.493\n - type: precision_at_5\n value: 33.189\n - type: recall_at_1\n value: 6.9\n - type: recall_at_10\n value: 17.488999999999997\n - type: recall_at_100\n value: 34.583000000000006\n - type: recall_at_1000\n value: 64.942\n - type: recall_at_3\n value: 11.494\n - type: recall_at_5\n value: 13.496\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 33.028999999999996\n - type: map_at_10\n value: 49.307\n - type: map_at_100\n value: 50.205\n - type: map_at_1000\n value: 50.23\n - type: map_at_3\n value: 44.782\n - type: map_at_5\n value: 47.599999999999994\n - type: mrr_at_1\n value: 37.108999999999995\n - type: mrr_at_10\n value: 51.742999999999995\n - type: mrr_at_100\n value: 52.405\n - type: mrr_at_1000\n value: 52.422000000000004\n - type: mrr_at_3\n value: 48.087999999999994\n - type: mrr_at_5\n value: 50.414\n - type: ndcg_at_1\n value: 37.08\n - type: ndcg_at_10\n value: 57.236\n - type: ndcg_at_100\n value: 60.931999999999995\n - type: ndcg_at_1000\n value: 61.522\n - type: ndcg_at_3\n value: 48.93\n - type: ndcg_at_5\n value: 53.561\n - type: precision_at_1\n value: 37.08\n - type: precision_at_10\n value: 9.386\n - type: precision_at_100\n value: 1.1480000000000001\n - type: precision_at_1000\n value: 0.12\n - type: precision_at_3\n value: 22.258\n - type: precision_at_5\n value: 16.025\n - type: recall_at_1\n value: 33.028999999999996\n - type: recall_at_10\n value: 78.805\n - type: recall_at_100\n value: 94.643\n - type: recall_at_1000\n value: 99.039\n - type: recall_at_3\n value: 57.602\n - type: recall_at_5\n value: 68.253\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.122\n - type: map_at_10\n value: 85.237\n - type: map_at_100\n value: 85.872\n - type: map_at_1000\n value: 85.885\n - type: map_at_3\n value: 82.27499999999999\n - type: map_at_5\n value: 84.13199999999999\n - type: mrr_at_1\n value: 81.73\n - type: mrr_at_10\n value: 87.834\n - type: mrr_at_100\n value: 87.92\n - type: mrr_at_1000\n value: 87.921\n - type: mrr_at_3\n value: 86.878\n - type: mrr_at_5\n value: 87.512\n - type: ndcg_at_1\n value: 81.73\n - type: ndcg_at_10\n value: 88.85499999999999\n - type: ndcg_at_100\n value: 89.992\n - type: ndcg_at_1000\n value: 90.07\n - type: ndcg_at_3\n value: 85.997\n - type: ndcg_at_5\n value: 87.55199999999999\n - type: precision_at_1\n value: 81.73\n - type: precision_at_10\n value: 13.491\n - type: precision_at_100\n value: 1.536\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.623\n - type: precision_at_5\n value: 24.742\n - type: recall_at_1\n value: 71.122\n - type: recall_at_10\n value: 95.935\n - type: recall_at_100\n value: 99.657\n - type: recall_at_1000\n value: 99.996\n - type: recall_at_3\n value: 87.80799999999999\n - type: recall_at_5\n value: 92.161\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 63.490029238193756\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 65.13153408508836\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.202999999999999\n - type: map_at_10\n value: 10.174\n - type: map_at_100\n value: 12.138\n - type: map_at_1000\n value: 12.418\n - type: map_at_3\n value: 7.379\n - type: map_at_5\n value: 8.727\n - type: mrr_at_1\n value: 20.7\n - type: mrr_at_10\n value: 30.389\n - type: mrr_at_100\n value: 31.566\n - type: mrr_at_1000\n value: 31.637999999999998\n - type: mrr_at_3\n value: 27.133000000000003\n - type: mrr_at_5\n value: 29.078\n - type: ndcg_at_1\n value: 20.7\n - type: ndcg_at_10\n value: 17.355999999999998\n - type: ndcg_at_100\n value: 25.151\n - type: ndcg_at_1000\n value: 30.37\n - type: ndcg_at_3\n value: 16.528000000000002\n - type: ndcg_at_5\n value: 14.396999999999998\n - type: precision_at_1\n value: 20.7\n - type: precision_at_10\n value: 8.98\n - type: precision_at_100\n value: 2.015\n - type: precision_at_1000\n value: 0.327\n - type: precision_at_3\n value: 15.367\n - type: precision_at_5\n value: 12.559999999999999\n - type: recall_at_1\n value: 4.202999999999999\n - type: recall_at_10\n value: 18.197\n - type: recall_at_100\n value: 40.903\n - type: recall_at_1000\n value: 66.427\n - type: recall_at_3\n value: 9.362\n - type: recall_at_5\n value: 12.747\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_spearman\n value: 81.69890989765257\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_spearman\n value: 75.31953790551489\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_spearman\n value: 87.44050861280759\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_spearman\n value: 81.86922869270393\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_spearman\n value: 88.9399170304284\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_spearman\n value: 85.38015314088582\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_spearman\n value: 90.53653527788835\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_spearman\n value: 68.64526474250209\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_spearman\n value: 86.56156983963042\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 79.48610254648003\n - type: mrr\n value: 94.02481505422682\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 48.983\n - type: map_at_10\n value: 59.077999999999996\n - type: map_at_100\n value: 59.536\n - type: map_at_1000\n value: 59.575\n - type: map_at_3\n value: 55.691\n - type: map_at_5\n value: 57.410000000000004\n - type: mrr_at_1\n value: 51.666999999999994\n - type: mrr_at_10\n value: 60.427\n - type: mrr_at_100\n value: 60.763\n - type: mrr_at_1000\n value: 60.79900000000001\n - type: mrr_at_3\n value: 57.556\n - type: mrr_at_5\n value: 59.089000000000006\n - type: ndcg_at_1\n value: 51.666999999999994\n - type: ndcg_at_10\n value: 64.559\n - type: ndcg_at_100\n value: 66.58\n - type: ndcg_at_1000\n value: 67.64\n - type: ndcg_at_3\n value: 58.287\n - type: ndcg_at_5\n value: 61.001000000000005\n - type: precision_at_1\n value: 51.666999999999994\n - type: precision_at_10\n value: 9.067\n - type: precision_at_100\n value: 1.0170000000000001\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 23.0\n - type: precision_at_5\n value: 15.6\n - type: recall_at_1\n value: 48.983\n - type: recall_at_10\n value: 80.289\n - type: recall_at_100\n value: 89.43299999999999\n - type: recall_at_1000\n value: 97.667\n - type: recall_at_3\n value: 62.978\n - type: recall_at_5\n value: 69.872\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.79009900990098\n - type: cos_sim_ap\n value: 94.94115052608419\n - type: cos_sim_f1\n value: 89.1260162601626\n - type: cos_sim_precision\n value: 90.599173553719\n - type: cos_sim_recall\n value: 87.7\n - type: dot_accuracy\n value: 99.79009900990098\n - type: dot_ap\n value: 94.94115052608419\n - type: dot_f1\n value: 89.1260162601626\n - type: dot_precision\n value: 90.599173553719\n - type: dot_recall\n value: 87.7\n - type: euclidean_accuracy\n value: 99.79009900990098\n - type: euclidean_ap\n value: 94.94115052608419\n - type: euclidean_f1\n value: 89.1260162601626\n - type: euclidean_precision\n value: 90.599173553719\n - type: euclidean_recall\n value: 87.7\n - type: manhattan_accuracy\n value: 99.7940594059406\n - type: manhattan_ap\n value: 94.95271414642431\n - type: manhattan_f1\n value: 89.24508790072387\n - type: manhattan_precision\n value: 92.3982869379015\n - type: manhattan_recall\n value: 86.3\n - type: max_accuracy\n value: 99.7940594059406\n - type: max_ap\n value: 94.95271414642431\n - type: max_f1\n value: 89.24508790072387\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 68.43866571935851\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 35.16579026551532\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 52.518952473513934\n - type: mrr\n value: 53.292457134368895\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.12529588316604\n - type: cos_sim_spearman\n value: 32.31662126895294\n - type: dot_pearson\n value: 31.125303796647056\n - type: dot_spearman\n value: 32.31662126895294\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.219\n - type: map_at_10\n value: 1.7469999999999999\n - type: map_at_100\n value: 10.177999999999999\n - type: map_at_1000\n value: 26.108999999999998\n - type: map_at_3\n value: 0.64\n - type: map_at_5\n value: 0.968\n - type: mrr_at_1\n value: 82.0\n - type: mrr_at_10\n value: 89.067\n - type: mrr_at_100\n value: 89.067\n - type: mrr_at_1000\n value: 89.067\n - type: mrr_at_3\n value: 88.333\n - type: mrr_at_5\n value: 88.73299999999999\n - type: ndcg_at_1\n value: 78.0\n - type: ndcg_at_10\n value: 71.398\n - type: ndcg_at_100\n value: 55.574999999999996\n - type: ndcg_at_1000\n value: 51.771\n - type: ndcg_at_3\n value: 77.765\n - type: ndcg_at_5\n value: 73.614\n - type: precision_at_1\n value: 82.0\n - type: precision_at_10\n value: 75.4\n - type: precision_at_100\n value: 58.040000000000006\n - type: precision_at_1000\n value: 23.516000000000002\n - type: precision_at_3\n value: 84.0\n - type: precision_at_5\n value: 78.4\n - type: recall_at_1\n value: 0.219\n - type: recall_at_10\n value: 1.958\n - type: recall_at_100\n value: 13.797999999999998\n - type: recall_at_1000\n value: 49.881\n - type: recall_at_3\n value: 0.672\n - type: recall_at_5\n value: 1.0370000000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 1.8610000000000002\n - type: map_at_10\n value: 8.705\n - type: map_at_100\n value: 15.164\n - type: map_at_1000\n value: 16.78\n - type: map_at_3\n value: 4.346\n - type: map_at_5\n value: 6.151\n - type: mrr_at_1\n value: 22.448999999999998\n - type: mrr_at_10\n value: 41.556\n - type: mrr_at_100\n value: 42.484\n - type: mrr_at_1000\n value: 42.494\n - type: mrr_at_3\n value: 37.755\n - type: mrr_at_5\n value: 40.102\n - type: ndcg_at_1\n value: 21.429000000000002\n - type: ndcg_at_10\n value: 23.439\n - type: ndcg_at_100\n value: 36.948\n - type: ndcg_at_1000\n value: 48.408\n - type: ndcg_at_3\n value: 22.261\n - type: ndcg_at_5\n value: 23.085\n - type: precision_at_1\n value: 22.448999999999998\n - type: precision_at_10\n value: 21.633\n - type: precision_at_100\n value: 8.02\n - type: precision_at_1000\n value: 1.5939999999999999\n - type: precision_at_3\n value: 23.810000000000002\n - type: precision_at_5\n value: 24.490000000000002\n - type: recall_at_1\n value: 1.8610000000000002\n - type: recall_at_10\n value: 15.876000000000001\n - type: recall_at_100\n value: 50.300999999999995\n - type: recall_at_1000\n value: 86.098\n - type: recall_at_3\n value: 5.892\n - type: recall_at_5\n value: 9.443\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 70.3264\n - type: ap\n value: 13.249577616243794\n - type: f1\n value: 53.621518367695685\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 61.57611771363894\n - type: f1\n value: 61.79797478568639\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 53.38315344479284\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 87.55438993860642\n - type: cos_sim_ap\n value: 77.98702600017738\n - type: cos_sim_f1\n value: 71.94971653931476\n - type: cos_sim_precision\n value: 67.50693802035153\n - type: cos_sim_recall\n value: 77.01846965699208\n - type: dot_accuracy\n value: 87.55438993860642\n - type: dot_ap\n value: 77.98702925907986\n - type: dot_f1\n value: 71.94971653931476\n - type: dot_precision\n value: 67.50693802035153\n - type: dot_recall\n value: 77.01846965699208\n - type: euclidean_accuracy\n value: 87.55438993860642\n - type: euclidean_ap\n value: 77.98702951957925\n - type: euclidean_f1\n value: 71.94971653931476\n - type: euclidean_precision\n value: 67.50693802035153\n - type: euclidean_recall\n value: 77.01846965699208\n - type: manhattan_accuracy\n value: 87.54246885617214\n - type: manhattan_ap\n value: 77.95531413902947\n - type: manhattan_f1\n value: 71.93605683836589\n - type: manhattan_precision\n value: 69.28152492668622\n - type: manhattan_recall\n value: 74.80211081794195\n - type: max_accuracy\n value: 87.55438993860642\n - type: max_ap\n value: 77.98702951957925\n - type: max_f1\n value: 71.94971653931476\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.47296930182016\n - type: cos_sim_ap\n value: 86.92853616302108\n - type: cos_sim_f1\n value: 79.35138351681047\n - type: cos_sim_precision\n value: 76.74820143884892\n - type: cos_sim_recall\n value: 82.13735756082538\n - type: dot_accuracy\n value: 89.47296930182016\n - type: dot_ap\n value: 86.92854339601595\n - type: dot_f1\n value: 79.35138351681047\n - type: dot_precision\n value: 76.74820143884892\n - type: dot_recall\n value: 82.13735756082538\n - type: euclidean_accuracy\n value: 89.47296930182016\n - type: euclidean_ap\n value: 86.92854191061649\n - type: euclidean_f1\n value: 79.35138351681047\n - type: euclidean_precision\n value: 76.74820143884892\n - type: euclidean_recall\n value: 82.13735756082538\n - type: manhattan_accuracy\n value: 89.47685023479644\n - type: manhattan_ap\n value: 86.90063722679578\n - type: manhattan_f1\n value: 79.30753865502702\n - type: manhattan_precision\n value: 76.32066068631639\n - type: manhattan_recall\n value: 82.53772713273791\n - type: max_accuracy\n value: 89.47685023479644\n - type: max_ap\n value: 86.92854339601595\n - type: max_f1\n value: 79.35138351681047\n---\n\n# hkunlp/instructor-xl\nWe introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks!\nThe model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)! \n\n**************************** **Updates** ****************************\n\n* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance.\n* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out!\n\n## Quick start\n\n\n## Installation\n```bash\npip install InstructorEmbedding\n```\n\n## Compute your customized embeddings\nThen you can use the model like this to calculate domain-specific and task-aware embeddings:\n```python\nfrom InstructorEmbedding import INSTRUCTOR\nmodel = INSTRUCTOR('hkunlp/instructor-xl')\nsentence = \"3D ActionSLAM: wearable person tracking in multi-floor environments\"\ninstruction = \"Represent the Science title:\"\nembeddings = model.encode([[instruction,sentence]])\nprint(embeddings)\n```\n\n## Use cases\n
\n\n## Calculate embeddings for your customized texts\nIf you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions: \n\n Represent the `domain` `text_type` for `task_objective`:\n* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.\n* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.\n* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.\n\n## Calculate Sentence similarities\nYou can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.\n```python\nfrom sklearn.metrics.pairwise import cosine_similarity\nsentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'], \n ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]\nsentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],\n ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]\nembeddings_a = model.encode(sentences_a)\nembeddings_b = model.encode(sentences_b)\nsimilarities = cosine_similarity(embeddings_a,embeddings_b)\nprint(similarities)\n```\n\n## Information Retrieval\nYou can also use **customized embeddings** for information retrieval.\n```python\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nquery = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]\ncorpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term \"mixed economies\" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],\n ['Represent the Wikipedia document for retrieval: ',\"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession\"],\n ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]\nquery_embeddings = model.encode(query)\ncorpus_embeddings = model.encode(corpus)\nsimilarities = cosine_similarity(query_embeddings,corpus_embeddings)\nretrieved_doc_id = np.argmax(similarities)\nprint(retrieved_doc_id)\n```\n\n## Clustering\nUse **customized embeddings** for clustering texts in groups.\n```python\nimport sklearn.cluster\nsentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],\n ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],\n ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],\n ['Represent the Medicine sentence for clustering: ',\"QCD corrections to Associated t-tbar-H production at the Tevatron\"],\n ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]\nembeddings = model.encode(sentences)\nclustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)\nclustering_model.fit(embeddings)\ncluster_assignment = clustering_model.labels_\nprint(cluster_assignment)\n```"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":86,"cells":{"id":{"kind":"string","value":"llmrails/ember-v1"},"author":{"kind":"string","value":"llmrails"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","safetensors","bert","feature-extraction","mteb","sentence-similarity","transformers","en","arxiv:2205.12035","arxiv:2209.11055","doi:10.57967/hf/2919","license:mit","model-index","autotrain_compatible","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"mteb\",\n \"sentence-similarity\",\n \"transformers\",\n \"en\",\n \"arxiv:2205.12035\",\n \"arxiv:2209.11055\",\n \"doi:10.57967/hf/2919\",\n \"license:mit\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2023-10-10T15:56:42"},"last_modified":{"kind":"string","value":"2024-08-21T04:49:13"},"downloads":{"kind":"number","value":34938,"string":"34,938"},"likes":{"kind":"number","value":62,"string":"62"},"README":{"kind":"string","value":"---\nlanguage: en\nlicense: mit\ntags:\n- mteb\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\nmodel-index:\n- name: ember_v1\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 76.05970149253731\n - type: ap\n value: 38.76045348512767\n - type: f1\n value: 69.8824007294685\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 91.977\n - type: ap\n value: 88.63507587170176\n - type: f1\n value: 91.9524133311038\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 47.938\n - type: f1\n value: 47.58273047536129\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 41.252\n - type: map_at_10\n value: 56.567\n - type: map_at_100\n value: 57.07600000000001\n - type: map_at_1000\n value: 57.08\n - type: map_at_3\n value: 52.394\n - type: map_at_5\n value: 55.055\n - type: mrr_at_1\n value: 42.39\n - type: mrr_at_10\n value: 57.001999999999995\n - type: mrr_at_100\n value: 57.531\n - type: mrr_at_1000\n value: 57.535000000000004\n - type: mrr_at_3\n value: 52.845\n - type: mrr_at_5\n value: 55.47299999999999\n - type: ndcg_at_1\n value: 41.252\n - type: ndcg_at_10\n value: 64.563\n - type: ndcg_at_100\n value: 66.667\n - type: ndcg_at_1000\n value: 66.77\n - type: ndcg_at_3\n value: 56.120000000000005\n - type: ndcg_at_5\n value: 60.889\n - type: precision_at_1\n value: 41.252\n - type: precision_at_10\n value: 8.982999999999999\n - type: precision_at_100\n value: 0.989\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 22.309\n - type: precision_at_5\n value: 15.690000000000001\n - type: recall_at_1\n value: 41.252\n - type: recall_at_10\n value: 89.82900000000001\n - type: recall_at_100\n value: 98.86200000000001\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 66.927\n - type: recall_at_5\n value: 78.45\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 48.5799968717232\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 43.142844164856136\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 64.45997990276463\n - type: mrr\n value: 77.85560392208592\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 86.38299310075898\n - type: cos_sim_spearman\n value: 85.81038898286454\n - type: euclidean_pearson\n value: 84.28002556389774\n - type: euclidean_spearman\n value: 85.80315990248238\n - type: manhattan_pearson\n value: 83.9755390675032\n - type: manhattan_spearman\n value: 85.30435335611396\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 87.89935064935065\n - type: f1\n value: 87.87886687103833\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 38.84335510371379\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 36.377963093857005\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.557\n - type: map_at_10\n value: 44.501000000000005\n - type: map_at_100\n value: 46.11\n - type: map_at_1000\n value: 46.232\n - type: map_at_3\n value: 40.711000000000006\n - type: map_at_5\n value: 42.937\n - type: mrr_at_1\n value: 40.916000000000004\n - type: mrr_at_10\n value: 51.317\n - type: mrr_at_100\n value: 52.003\n - type: mrr_at_1000\n value: 52.044999999999995\n - type: mrr_at_3\n value: 48.569\n - type: mrr_at_5\n value: 50.322\n - type: ndcg_at_1\n value: 40.916000000000004\n - type: ndcg_at_10\n value: 51.353\n - type: ndcg_at_100\n value: 56.762\n - type: ndcg_at_1000\n value: 58.555\n - type: ndcg_at_3\n value: 46.064\n - type: ndcg_at_5\n value: 48.677\n - type: precision_at_1\n value: 40.916000000000004\n - type: precision_at_10\n value: 9.927999999999999\n - type: precision_at_100\n value: 1.592\n - type: precision_at_1000\n value: 0.20600000000000002\n - type: precision_at_3\n value: 22.078999999999997\n - type: precision_at_5\n value: 16.08\n - type: recall_at_1\n value: 32.557\n - type: recall_at_10\n value: 63.942\n - type: recall_at_100\n value: 86.436\n - type: recall_at_1000\n value: 97.547\n - type: recall_at_3\n value: 48.367\n - type: recall_at_5\n value: 55.818\n - type: map_at_1\n value: 32.106\n - type: map_at_10\n value: 42.55\n - type: map_at_100\n value: 43.818\n - type: map_at_1000\n value: 43.952999999999996\n - type: map_at_3\n value: 39.421\n - type: map_at_5\n value: 41.276\n - type: mrr_at_1\n value: 39.936\n - type: mrr_at_10\n value: 48.484\n - type: mrr_at_100\n value: 49.123\n - type: mrr_at_1000\n value: 49.163000000000004\n - type: mrr_at_3\n value: 46.221000000000004\n - type: mrr_at_5\n value: 47.603\n - type: ndcg_at_1\n value: 39.936\n - type: ndcg_at_10\n value: 48.25\n - type: ndcg_at_100\n value: 52.674\n - type: ndcg_at_1000\n value: 54.638\n - type: ndcg_at_3\n value: 44.05\n - type: ndcg_at_5\n value: 46.125\n - type: precision_at_1\n value: 39.936\n - type: precision_at_10\n value: 9.096\n - type: precision_at_100\n value: 1.473\n - type: precision_at_1000\n value: 0.19499999999999998\n - type: precision_at_3\n value: 21.295\n - type: precision_at_5\n value: 15.121\n - type: recall_at_1\n value: 32.106\n - type: recall_at_10\n value: 58.107\n - type: recall_at_100\n value: 76.873\n - type: recall_at_1000\n value: 89.079\n - type: recall_at_3\n value: 45.505\n - type: recall_at_5\n value: 51.479\n - type: map_at_1\n value: 41.513\n - type: map_at_10\n value: 54.571999999999996\n - type: map_at_100\n value: 55.579\n - type: map_at_1000\n value: 55.626\n - type: map_at_3\n value: 51.127\n - type: map_at_5\n value: 53.151\n - type: mrr_at_1\n value: 47.398\n - type: mrr_at_10\n value: 57.82000000000001\n - type: mrr_at_100\n value: 58.457\n - type: mrr_at_1000\n value: 58.479000000000006\n - type: mrr_at_3\n value: 55.32899999999999\n - type: mrr_at_5\n value: 56.89999999999999\n - type: ndcg_at_1\n value: 47.398\n - type: ndcg_at_10\n value: 60.599000000000004\n - type: ndcg_at_100\n value: 64.366\n - type: ndcg_at_1000\n value: 65.333\n - type: ndcg_at_3\n value: 54.98\n - type: ndcg_at_5\n value: 57.874\n - type: precision_at_1\n value: 47.398\n - type: precision_at_10\n value: 9.806\n - type: precision_at_100\n value: 1.2590000000000001\n - type: precision_at_1000\n value: 0.13799999999999998\n - type: precision_at_3\n value: 24.619\n - type: precision_at_5\n value: 16.878\n - type: recall_at_1\n value: 41.513\n - type: recall_at_10\n value: 74.91799999999999\n - type: recall_at_100\n value: 90.96\n - type: recall_at_1000\n value: 97.923\n - type: recall_at_3\n value: 60.013000000000005\n - type: recall_at_5\n value: 67.245\n - type: map_at_1\n value: 26.319\n - type: map_at_10\n value: 35.766999999999996\n - type: map_at_100\n value: 36.765\n - type: map_at_1000\n value: 36.829\n - type: map_at_3\n value: 32.888\n - type: map_at_5\n value: 34.538999999999994\n - type: mrr_at_1\n value: 28.249000000000002\n - type: mrr_at_10\n value: 37.766\n - type: mrr_at_100\n value: 38.62\n - type: mrr_at_1000\n value: 38.667\n - type: mrr_at_3\n value: 35.009\n - type: mrr_at_5\n value: 36.608000000000004\n - type: ndcg_at_1\n value: 28.249000000000002\n - type: ndcg_at_10\n value: 41.215\n - type: ndcg_at_100\n value: 46.274\n - type: ndcg_at_1000\n value: 48.007\n - type: ndcg_at_3\n value: 35.557\n - type: ndcg_at_5\n value: 38.344\n - type: precision_at_1\n value: 28.249000000000002\n - type: precision_at_10\n value: 6.429\n - type: precision_at_100\n value: 0.9480000000000001\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 15.179\n - type: precision_at_5\n value: 10.734\n - type: recall_at_1\n value: 26.319\n - type: recall_at_10\n value: 56.157999999999994\n - type: recall_at_100\n value: 79.65\n - type: recall_at_1000\n value: 92.73\n - type: recall_at_3\n value: 40.738\n - type: recall_at_5\n value: 47.418\n - type: map_at_1\n value: 18.485\n - type: map_at_10\n value: 27.400999999999996\n - type: map_at_100\n value: 28.665000000000003\n - type: map_at_1000\n value: 28.79\n - type: map_at_3\n value: 24.634\n - type: map_at_5\n value: 26.313\n - type: mrr_at_1\n value: 23.134\n - type: mrr_at_10\n value: 32.332\n - type: mrr_at_100\n value: 33.318\n - type: mrr_at_1000\n value: 33.384\n - type: mrr_at_3\n value: 29.664\n - type: mrr_at_5\n value: 31.262\n - type: ndcg_at_1\n value: 23.134\n - type: ndcg_at_10\n value: 33.016\n - type: ndcg_at_100\n value: 38.763\n - type: ndcg_at_1000\n value: 41.619\n - type: ndcg_at_3\n value: 28.017999999999997\n - type: ndcg_at_5\n value: 30.576999999999998\n - type: precision_at_1\n value: 23.134\n - type: precision_at_10\n value: 6.069999999999999\n - type: precision_at_100\n value: 1.027\n - type: precision_at_1000\n value: 0.14200000000000002\n - type: precision_at_3\n value: 13.599\n - type: precision_at_5\n value: 9.975000000000001\n - type: recall_at_1\n value: 18.485\n - type: recall_at_10\n value: 45.39\n - type: recall_at_100\n value: 69.876\n - type: recall_at_1000\n value: 90.023\n - type: recall_at_3\n value: 31.587\n - type: recall_at_5\n value: 38.164\n - type: map_at_1\n value: 30.676\n - type: map_at_10\n value: 41.785\n - type: map_at_100\n value: 43.169000000000004\n - type: map_at_1000\n value: 43.272\n - type: map_at_3\n value: 38.462\n - type: map_at_5\n value: 40.32\n - type: mrr_at_1\n value: 37.729\n - type: mrr_at_10\n value: 47.433\n - type: mrr_at_100\n value: 48.303000000000004\n - type: mrr_at_1000\n value: 48.337\n - type: mrr_at_3\n value: 45.011\n - type: mrr_at_5\n value: 46.455\n - type: ndcg_at_1\n value: 37.729\n - type: ndcg_at_10\n value: 47.921\n - type: ndcg_at_100\n value: 53.477\n - type: ndcg_at_1000\n value: 55.300000000000004\n - type: ndcg_at_3\n value: 42.695\n - type: ndcg_at_5\n value: 45.175\n - type: precision_at_1\n value: 37.729\n - type: precision_at_10\n value: 8.652999999999999\n - type: precision_at_100\n value: 1.336\n - type: precision_at_1000\n value: 0.168\n - type: precision_at_3\n value: 20.18\n - type: precision_at_5\n value: 14.302000000000001\n - type: recall_at_1\n value: 30.676\n - type: recall_at_10\n value: 60.441\n - type: recall_at_100\n value: 83.37\n - type: recall_at_1000\n value: 95.092\n - type: recall_at_3\n value: 45.964\n - type: recall_at_5\n value: 52.319\n - type: map_at_1\n value: 24.978\n - type: map_at_10\n value: 35.926\n - type: map_at_100\n value: 37.341\n - type: map_at_1000\n value: 37.445\n - type: map_at_3\n value: 32.748\n - type: map_at_5\n value: 34.207\n - type: mrr_at_1\n value: 31.163999999999998\n - type: mrr_at_10\n value: 41.394\n - type: mrr_at_100\n value: 42.321\n - type: mrr_at_1000\n value: 42.368\n - type: mrr_at_3\n value: 38.964999999999996\n - type: mrr_at_5\n value: 40.135\n - type: ndcg_at_1\n value: 31.163999999999998\n - type: ndcg_at_10\n value: 42.191\n - type: ndcg_at_100\n value: 48.083999999999996\n - type: ndcg_at_1000\n value: 50.21\n - type: ndcg_at_3\n value: 36.979\n - type: ndcg_at_5\n value: 38.823\n - type: precision_at_1\n value: 31.163999999999998\n - type: precision_at_10\n value: 7.968\n - type: precision_at_100\n value: 1.2550000000000001\n - type: precision_at_1000\n value: 0.16199999999999998\n - type: precision_at_3\n value: 18.075\n - type: precision_at_5\n value: 12.626000000000001\n - type: recall_at_1\n value: 24.978\n - type: recall_at_10\n value: 55.410000000000004\n - type: recall_at_100\n value: 80.562\n - type: recall_at_1000\n value: 94.77600000000001\n - type: recall_at_3\n value: 40.359\n - type: recall_at_5\n value: 45.577\n - type: map_at_1\n value: 26.812166666666666\n - type: map_at_10\n value: 36.706916666666665\n - type: map_at_100\n value: 37.94016666666666\n - type: map_at_1000\n value: 38.05358333333333\n - type: map_at_3\n value: 33.72408333333334\n - type: map_at_5\n value: 35.36508333333333\n - type: mrr_at_1\n value: 31.91516666666667\n - type: mrr_at_10\n value: 41.09716666666666\n - type: mrr_at_100\n value: 41.931916666666666\n - type: mrr_at_1000\n value: 41.98458333333333\n - type: mrr_at_3\n value: 38.60183333333333\n - type: mrr_at_5\n value: 40.031916666666675\n - type: ndcg_at_1\n value: 31.91516666666667\n - type: ndcg_at_10\n value: 42.38725\n - type: ndcg_at_100\n value: 47.56291666666667\n - type: ndcg_at_1000\n value: 49.716499999999996\n - type: ndcg_at_3\n value: 37.36491666666667\n - type: ndcg_at_5\n value: 39.692166666666665\n - type: precision_at_1\n value: 31.91516666666667\n - type: precision_at_10\n value: 7.476749999999999\n - type: precision_at_100\n value: 1.1869166666666668\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 17.275249999999996\n - type: precision_at_5\n value: 12.25825\n - type: recall_at_1\n value: 26.812166666666666\n - type: recall_at_10\n value: 54.82933333333333\n - type: recall_at_100\n value: 77.36508333333333\n - type: recall_at_1000\n value: 92.13366666666667\n - type: recall_at_3\n value: 40.83508333333334\n - type: recall_at_5\n value: 46.85083333333334\n - type: map_at_1\n value: 25.352999999999998\n - type: map_at_10\n value: 33.025999999999996\n - type: map_at_100\n value: 33.882\n - type: map_at_1000\n value: 33.983999999999995\n - type: map_at_3\n value: 30.995\n - type: map_at_5\n value: 32.113\n - type: mrr_at_1\n value: 28.834\n - type: mrr_at_10\n value: 36.14\n - type: mrr_at_100\n value: 36.815\n - type: mrr_at_1000\n value: 36.893\n - type: mrr_at_3\n value: 34.305\n - type: mrr_at_5\n value: 35.263\n - type: ndcg_at_1\n value: 28.834\n - type: ndcg_at_10\n value: 37.26\n - type: ndcg_at_100\n value: 41.723\n - type: ndcg_at_1000\n value: 44.314\n - type: ndcg_at_3\n value: 33.584\n - type: ndcg_at_5\n value: 35.302\n - type: precision_at_1\n value: 28.834\n - type: precision_at_10\n value: 5.736\n - type: precision_at_100\n value: 0.876\n - type: precision_at_1000\n value: 0.117\n - type: precision_at_3\n value: 14.468\n - type: precision_at_5\n value: 9.847\n - type: recall_at_1\n value: 25.352999999999998\n - type: recall_at_10\n value: 47.155\n - type: recall_at_100\n value: 68.024\n - type: recall_at_1000\n value: 87.26899999999999\n - type: recall_at_3\n value: 37.074\n - type: recall_at_5\n value: 41.352\n - type: map_at_1\n value: 17.845\n - type: map_at_10\n value: 25.556\n - type: map_at_100\n value: 26.787\n - type: map_at_1000\n value: 26.913999999999998\n - type: map_at_3\n value: 23.075000000000003\n - type: map_at_5\n value: 24.308\n - type: mrr_at_1\n value: 21.714\n - type: mrr_at_10\n value: 29.543999999999997\n - type: mrr_at_100\n value: 30.543\n - type: mrr_at_1000\n value: 30.618000000000002\n - type: mrr_at_3\n value: 27.174\n - type: mrr_at_5\n value: 28.409000000000002\n - type: ndcg_at_1\n value: 21.714\n - type: ndcg_at_10\n value: 30.562\n - type: ndcg_at_100\n value: 36.27\n - type: ndcg_at_1000\n value: 39.033\n - type: ndcg_at_3\n value: 26.006\n - type: ndcg_at_5\n value: 27.843\n - type: precision_at_1\n value: 21.714\n - type: precision_at_10\n value: 5.657\n - type: precision_at_100\n value: 1\n - type: precision_at_1000\n value: 0.14100000000000001\n - type: precision_at_3\n value: 12.4\n - type: precision_at_5\n value: 8.863999999999999\n - type: recall_at_1\n value: 17.845\n - type: recall_at_10\n value: 41.72\n - type: recall_at_100\n value: 67.06400000000001\n - type: recall_at_1000\n value: 86.515\n - type: recall_at_3\n value: 28.78\n - type: recall_at_5\n value: 33.629999999999995\n - type: map_at_1\n value: 26.695\n - type: map_at_10\n value: 36.205999999999996\n - type: map_at_100\n value: 37.346000000000004\n - type: map_at_1000\n value: 37.447\n - type: map_at_3\n value: 32.84\n - type: map_at_5\n value: 34.733000000000004\n - type: mrr_at_1\n value: 31.343\n - type: mrr_at_10\n value: 40.335\n - type: mrr_at_100\n value: 41.162\n - type: mrr_at_1000\n value: 41.221000000000004\n - type: mrr_at_3\n value: 37.329\n - type: mrr_at_5\n value: 39.068999999999996\n - type: ndcg_at_1\n value: 31.343\n - type: ndcg_at_10\n value: 41.996\n - type: ndcg_at_100\n value: 47.096\n - type: ndcg_at_1000\n value: 49.4\n - type: ndcg_at_3\n value: 35.902\n - type: ndcg_at_5\n value: 38.848\n - type: precision_at_1\n value: 31.343\n - type: precision_at_10\n value: 7.146\n - type: precision_at_100\n value: 1.098\n - type: precision_at_1000\n value: 0.14100000000000001\n - type: precision_at_3\n value: 16.014\n - type: precision_at_5\n value: 11.735\n - type: recall_at_1\n value: 26.695\n - type: recall_at_10\n value: 55.525000000000006\n - type: recall_at_100\n value: 77.376\n - type: recall_at_1000\n value: 93.476\n - type: recall_at_3\n value: 39.439\n - type: recall_at_5\n value: 46.501\n - type: map_at_1\n value: 24.196\n - type: map_at_10\n value: 33.516\n - type: map_at_100\n value: 35.202\n - type: map_at_1000\n value: 35.426\n - type: map_at_3\n value: 30.561\n - type: map_at_5\n value: 31.961000000000002\n - type: mrr_at_1\n value: 29.644\n - type: mrr_at_10\n value: 38.769\n - type: mrr_at_100\n value: 39.843\n - type: mrr_at_1000\n value: 39.888\n - type: mrr_at_3\n value: 36.132999999999996\n - type: mrr_at_5\n value: 37.467\n - type: ndcg_at_1\n value: 29.644\n - type: ndcg_at_10\n value: 39.584\n - type: ndcg_at_100\n value: 45.964\n - type: ndcg_at_1000\n value: 48.27\n - type: ndcg_at_3\n value: 34.577999999999996\n - type: ndcg_at_5\n value: 36.498000000000005\n - type: precision_at_1\n value: 29.644\n - type: precision_at_10\n value: 7.668\n - type: precision_at_100\n value: 1.545\n - type: precision_at_1000\n value: 0.242\n - type: precision_at_3\n value: 16.271\n - type: precision_at_5\n value: 11.620999999999999\n - type: recall_at_1\n value: 24.196\n - type: recall_at_10\n value: 51.171\n - type: recall_at_100\n value: 79.212\n - type: recall_at_1000\n value: 92.976\n - type: recall_at_3\n value: 36.797999999999995\n - type: recall_at_5\n value: 42.006\n - type: map_at_1\n value: 21.023\n - type: map_at_10\n value: 29.677\n - type: map_at_100\n value: 30.618000000000002\n - type: map_at_1000\n value: 30.725\n - type: map_at_3\n value: 27.227\n - type: map_at_5\n value: 28.523\n - type: mrr_at_1\n value: 22.921\n - type: mrr_at_10\n value: 31.832\n - type: mrr_at_100\n value: 32.675\n - type: mrr_at_1000\n value: 32.751999999999995\n - type: mrr_at_3\n value: 29.513\n - type: mrr_at_5\n value: 30.89\n - type: ndcg_at_1\n value: 22.921\n - type: ndcg_at_10\n value: 34.699999999999996\n - type: ndcg_at_100\n value: 39.302\n - type: ndcg_at_1000\n value: 41.919000000000004\n - type: ndcg_at_3\n value: 29.965999999999998\n - type: ndcg_at_5\n value: 32.22\n - type: precision_at_1\n value: 22.921\n - type: precision_at_10\n value: 5.564\n - type: precision_at_100\n value: 0.8340000000000001\n - type: precision_at_1000\n value: 0.11800000000000001\n - type: precision_at_3\n value: 13.123999999999999\n - type: precision_at_5\n value: 9.316\n - type: recall_at_1\n value: 21.023\n - type: recall_at_10\n value: 48.015\n - type: recall_at_100\n value: 68.978\n - type: recall_at_1000\n value: 88.198\n - type: recall_at_3\n value: 35.397\n - type: recall_at_5\n value: 40.701\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 11.198\n - type: map_at_10\n value: 19.336000000000002\n - type: map_at_100\n value: 21.382\n - type: map_at_1000\n value: 21.581\n - type: map_at_3\n value: 15.992\n - type: map_at_5\n value: 17.613\n - type: mrr_at_1\n value: 25.080999999999996\n - type: mrr_at_10\n value: 36.032\n - type: mrr_at_100\n value: 37.1\n - type: mrr_at_1000\n value: 37.145\n - type: mrr_at_3\n value: 32.595\n - type: mrr_at_5\n value: 34.553\n - type: ndcg_at_1\n value: 25.080999999999996\n - type: ndcg_at_10\n value: 27.290999999999997\n - type: ndcg_at_100\n value: 35.31\n - type: ndcg_at_1000\n value: 38.885\n - type: ndcg_at_3\n value: 21.895999999999997\n - type: ndcg_at_5\n value: 23.669999999999998\n - type: precision_at_1\n value: 25.080999999999996\n - type: precision_at_10\n value: 8.645\n - type: precision_at_100\n value: 1.7209999999999999\n - type: precision_at_1000\n value: 0.23900000000000002\n - type: precision_at_3\n value: 16.287\n - type: precision_at_5\n value: 12.625\n - type: recall_at_1\n value: 11.198\n - type: recall_at_10\n value: 33.355000000000004\n - type: recall_at_100\n value: 60.912\n - type: recall_at_1000\n value: 80.89\n - type: recall_at_3\n value: 20.055\n - type: recall_at_5\n value: 25.14\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 9.228\n - type: map_at_10\n value: 20.018\n - type: map_at_100\n value: 28.388999999999996\n - type: map_at_1000\n value: 30.073\n - type: map_at_3\n value: 14.366999999999999\n - type: map_at_5\n value: 16.705000000000002\n - type: mrr_at_1\n value: 69\n - type: mrr_at_10\n value: 77.058\n - type: mrr_at_100\n value: 77.374\n - type: mrr_at_1000\n value: 77.384\n - type: mrr_at_3\n value: 75.708\n - type: mrr_at_5\n value: 76.608\n - type: ndcg_at_1\n value: 57.49999999999999\n - type: ndcg_at_10\n value: 41.792\n - type: ndcg_at_100\n value: 47.374\n - type: ndcg_at_1000\n value: 55.13\n - type: ndcg_at_3\n value: 46.353\n - type: ndcg_at_5\n value: 43.702000000000005\n - type: precision_at_1\n value: 69\n - type: precision_at_10\n value: 32.85\n - type: precision_at_100\n value: 10.708\n - type: precision_at_1000\n value: 2.024\n - type: precision_at_3\n value: 49.5\n - type: precision_at_5\n value: 42.05\n - type: recall_at_1\n value: 9.228\n - type: recall_at_10\n value: 25.635\n - type: recall_at_100\n value: 54.894\n - type: recall_at_1000\n value: 79.38\n - type: recall_at_3\n value: 15.68\n - type: recall_at_5\n value: 19.142\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 52.035\n - type: f1\n value: 46.85325505614071\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 70.132\n - type: map_at_10\n value: 79.527\n - type: map_at_100\n value: 79.81200000000001\n - type: map_at_1000\n value: 79.828\n - type: map_at_3\n value: 78.191\n - type: map_at_5\n value: 79.092\n - type: mrr_at_1\n value: 75.563\n - type: mrr_at_10\n value: 83.80199999999999\n - type: mrr_at_100\n value: 83.93\n - type: mrr_at_1000\n value: 83.933\n - type: mrr_at_3\n value: 82.818\n - type: mrr_at_5\n value: 83.505\n - type: ndcg_at_1\n value: 75.563\n - type: ndcg_at_10\n value: 83.692\n - type: ndcg_at_100\n value: 84.706\n - type: ndcg_at_1000\n value: 85.001\n - type: ndcg_at_3\n value: 81.51\n - type: ndcg_at_5\n value: 82.832\n - type: precision_at_1\n value: 75.563\n - type: precision_at_10\n value: 10.245\n - type: precision_at_100\n value: 1.0959999999999999\n - type: precision_at_1000\n value: 0.11399999999999999\n - type: precision_at_3\n value: 31.518\n - type: precision_at_5\n value: 19.772000000000002\n - type: recall_at_1\n value: 70.132\n - type: recall_at_10\n value: 92.204\n - type: recall_at_100\n value: 96.261\n - type: recall_at_1000\n value: 98.17399999999999\n - type: recall_at_3\n value: 86.288\n - type: recall_at_5\n value: 89.63799999999999\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 22.269\n - type: map_at_10\n value: 36.042\n - type: map_at_100\n value: 37.988\n - type: map_at_1000\n value: 38.162\n - type: map_at_3\n value: 31.691000000000003\n - type: map_at_5\n value: 33.988\n - type: mrr_at_1\n value: 44.907000000000004\n - type: mrr_at_10\n value: 53.348\n - type: mrr_at_100\n value: 54.033\n - type: mrr_at_1000\n value: 54.064\n - type: mrr_at_3\n value: 50.977\n - type: mrr_at_5\n value: 52.112\n - type: ndcg_at_1\n value: 44.907000000000004\n - type: ndcg_at_10\n value: 44.302\n - type: ndcg_at_100\n value: 51.054\n - type: ndcg_at_1000\n value: 53.822\n - type: ndcg_at_3\n value: 40.615\n - type: ndcg_at_5\n value: 41.455999999999996\n - type: precision_at_1\n value: 44.907000000000004\n - type: precision_at_10\n value: 12.176\n - type: precision_at_100\n value: 1.931\n - type: precision_at_1000\n value: 0.243\n - type: precision_at_3\n value: 27.16\n - type: precision_at_5\n value: 19.567999999999998\n - type: recall_at_1\n value: 22.269\n - type: recall_at_10\n value: 51.188\n - type: recall_at_100\n value: 75.924\n - type: recall_at_1000\n value: 92.525\n - type: recall_at_3\n value: 36.643\n - type: recall_at_5\n value: 42.27\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 40.412\n - type: map_at_10\n value: 66.376\n - type: map_at_100\n value: 67.217\n - type: map_at_1000\n value: 67.271\n - type: map_at_3\n value: 62.741\n - type: map_at_5\n value: 65.069\n - type: mrr_at_1\n value: 80.824\n - type: mrr_at_10\n value: 86.53\n - type: mrr_at_100\n value: 86.67399999999999\n - type: mrr_at_1000\n value: 86.678\n - type: mrr_at_3\n value: 85.676\n - type: mrr_at_5\n value: 86.256\n - type: ndcg_at_1\n value: 80.824\n - type: ndcg_at_10\n value: 74.332\n - type: ndcg_at_100\n value: 77.154\n - type: ndcg_at_1000\n value: 78.12400000000001\n - type: ndcg_at_3\n value: 69.353\n - type: ndcg_at_5\n value: 72.234\n - type: precision_at_1\n value: 80.824\n - type: precision_at_10\n value: 15.652\n - type: precision_at_100\n value: 1.7840000000000003\n - type: precision_at_1000\n value: 0.191\n - type: precision_at_3\n value: 44.911\n - type: precision_at_5\n value: 29.221000000000004\n - type: recall_at_1\n value: 40.412\n - type: recall_at_10\n value: 78.25800000000001\n - type: recall_at_100\n value: 89.196\n - type: recall_at_1000\n value: 95.544\n - type: recall_at_3\n value: 67.367\n - type: recall_at_5\n value: 73.05199999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 92.78880000000001\n - type: ap\n value: 89.39251741048801\n - type: f1\n value: 92.78019950076781\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 22.888\n - type: map_at_10\n value: 35.146\n - type: map_at_100\n value: 36.325\n - type: map_at_1000\n value: 36.372\n - type: map_at_3\n value: 31.3\n - type: map_at_5\n value: 33.533\n - type: mrr_at_1\n value: 23.480999999999998\n - type: mrr_at_10\n value: 35.777\n - type: mrr_at_100\n value: 36.887\n - type: mrr_at_1000\n value: 36.928\n - type: mrr_at_3\n value: 31.989\n - type: mrr_at_5\n value: 34.202\n - type: ndcg_at_1\n value: 23.496\n - type: ndcg_at_10\n value: 42.028999999999996\n - type: ndcg_at_100\n value: 47.629\n - type: ndcg_at_1000\n value: 48.785000000000004\n - type: ndcg_at_3\n value: 34.227000000000004\n - type: ndcg_at_5\n value: 38.207\n - type: precision_at_1\n value: 23.496\n - type: precision_at_10\n value: 6.596\n - type: precision_at_100\n value: 0.9400000000000001\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.513000000000002\n - type: precision_at_5\n value: 10.711\n - type: recall_at_1\n value: 22.888\n - type: recall_at_10\n value: 63.129999999999995\n - type: recall_at_100\n value: 88.90299999999999\n - type: recall_at_1000\n value: 97.69\n - type: recall_at_3\n value: 42.014\n - type: recall_at_5\n value: 51.554\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 94.59188326493388\n - type: f1\n value: 94.36568950290486\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 79.25672594619242\n - type: f1\n value: 59.52405059722216\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 77.4142568930733\n - type: f1\n value: 75.23044196543388\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 80.44720914593141\n - type: f1\n value: 80.41049641537015\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 31.960921474993775\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 30.88042240204361\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 32.27071371606404\n - type: mrr\n value: 33.541450459533856\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 6.551\n - type: map_at_10\n value: 14.359\n - type: map_at_100\n value: 18.157\n - type: map_at_1000\n value: 19.659\n - type: map_at_3\n value: 10.613999999999999\n - type: map_at_5\n value: 12.296\n - type: mrr_at_1\n value: 47.368\n - type: mrr_at_10\n value: 56.689\n - type: mrr_at_100\n value: 57.24399999999999\n - type: mrr_at_1000\n value: 57.284\n - type: mrr_at_3\n value: 54.489\n - type: mrr_at_5\n value: 55.928999999999995\n - type: ndcg_at_1\n value: 45.511\n - type: ndcg_at_10\n value: 36.911\n - type: ndcg_at_100\n value: 34.241\n - type: ndcg_at_1000\n value: 43.064\n - type: ndcg_at_3\n value: 42.348\n - type: ndcg_at_5\n value: 39.884\n - type: precision_at_1\n value: 46.749\n - type: precision_at_10\n value: 27.028000000000002\n - type: precision_at_100\n value: 8.52\n - type: precision_at_1000\n value: 2.154\n - type: precision_at_3\n value: 39.525\n - type: precision_at_5\n value: 34.18\n - type: recall_at_1\n value: 6.551\n - type: recall_at_10\n value: 18.602\n - type: recall_at_100\n value: 34.882999999999996\n - type: recall_at_1000\n value: 66.049\n - type: recall_at_3\n value: 11.872\n - type: recall_at_5\n value: 14.74\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 27.828999999999997\n - type: map_at_10\n value: 43.606\n - type: map_at_100\n value: 44.656\n - type: map_at_1000\n value: 44.690000000000005\n - type: map_at_3\n value: 39.015\n - type: map_at_5\n value: 41.625\n - type: mrr_at_1\n value: 31.518\n - type: mrr_at_10\n value: 46.047\n - type: mrr_at_100\n value: 46.846\n - type: mrr_at_1000\n value: 46.867999999999995\n - type: mrr_at_3\n value: 42.154\n - type: mrr_at_5\n value: 44.468999999999994\n - type: ndcg_at_1\n value: 31.518\n - type: ndcg_at_10\n value: 51.768\n - type: ndcg_at_100\n value: 56.184999999999995\n - type: ndcg_at_1000\n value: 56.92\n - type: ndcg_at_3\n value: 43.059999999999995\n - type: ndcg_at_5\n value: 47.481\n - type: precision_at_1\n value: 31.518\n - type: precision_at_10\n value: 8.824\n - type: precision_at_100\n value: 1.131\n - type: precision_at_1000\n value: 0.12\n - type: precision_at_3\n value: 19.969\n - type: precision_at_5\n value: 14.502\n - type: recall_at_1\n value: 27.828999999999997\n - type: recall_at_10\n value: 74.244\n - type: recall_at_100\n value: 93.325\n - type: recall_at_1000\n value: 98.71799999999999\n - type: recall_at_3\n value: 51.601\n - type: recall_at_5\n value: 61.841\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.54\n - type: map_at_10\n value: 85.509\n - type: map_at_100\n value: 86.137\n - type: map_at_1000\n value: 86.151\n - type: map_at_3\n value: 82.624\n - type: map_at_5\n value: 84.425\n - type: mrr_at_1\n value: 82.45\n - type: mrr_at_10\n value: 88.344\n - type: mrr_at_100\n value: 88.437\n - type: mrr_at_1000\n value: 88.437\n - type: mrr_at_3\n value: 87.417\n - type: mrr_at_5\n value: 88.066\n - type: ndcg_at_1\n value: 82.45\n - type: ndcg_at_10\n value: 89.092\n - type: ndcg_at_100\n value: 90.252\n - type: ndcg_at_1000\n value: 90.321\n - type: ndcg_at_3\n value: 86.404\n - type: ndcg_at_5\n value: 87.883\n - type: precision_at_1\n value: 82.45\n - type: precision_at_10\n value: 13.496\n - type: precision_at_100\n value: 1.536\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.833\n - type: precision_at_5\n value: 24.79\n - type: recall_at_1\n value: 71.54\n - type: recall_at_10\n value: 95.846\n - type: recall_at_100\n value: 99.715\n - type: recall_at_1000\n value: 99.979\n - type: recall_at_3\n value: 88.01299999999999\n - type: recall_at_5\n value: 92.32000000000001\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 57.60557586253866\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 64.0287172242051\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 3.9849999999999994\n - type: map_at_10\n value: 11.397\n - type: map_at_100\n value: 13.985\n - type: map_at_1000\n value: 14.391000000000002\n - type: map_at_3\n value: 7.66\n - type: map_at_5\n value: 9.46\n - type: mrr_at_1\n value: 19.8\n - type: mrr_at_10\n value: 31.958\n - type: mrr_at_100\n value: 33.373999999999995\n - type: mrr_at_1000\n value: 33.411\n - type: mrr_at_3\n value: 28.316999999999997\n - type: mrr_at_5\n value: 30.297\n - type: ndcg_at_1\n value: 19.8\n - type: ndcg_at_10\n value: 19.580000000000002\n - type: ndcg_at_100\n value: 29.555999999999997\n - type: ndcg_at_1000\n value: 35.882\n - type: ndcg_at_3\n value: 17.544\n - type: ndcg_at_5\n value: 15.815999999999999\n - type: precision_at_1\n value: 19.8\n - type: precision_at_10\n value: 10.61\n - type: precision_at_100\n value: 2.501\n - type: precision_at_1000\n value: 0.40099999999999997\n - type: precision_at_3\n value: 16.900000000000002\n - type: precision_at_5\n value: 14.44\n - type: recall_at_1\n value: 3.9849999999999994\n - type: recall_at_10\n value: 21.497\n - type: recall_at_100\n value: 50.727999999999994\n - type: recall_at_1000\n value: 81.27499999999999\n - type: recall_at_3\n value: 10.263\n - type: recall_at_5\n value: 14.643\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 85.0087509585503\n - type: cos_sim_spearman\n value: 81.74697270664319\n - type: euclidean_pearson\n value: 81.80424382731947\n - type: euclidean_spearman\n value: 81.29794251968431\n - type: manhattan_pearson\n value: 81.81524666226125\n - type: manhattan_spearman\n value: 81.29475370198963\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 86.44442736429552\n - type: cos_sim_spearman\n value: 78.51011398910948\n - type: euclidean_pearson\n value: 83.36181801196723\n - type: euclidean_spearman\n value: 79.47272621331535\n - type: manhattan_pearson\n value: 83.3660113483837\n - type: manhattan_spearman\n value: 79.47695922566032\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 85.82923943323635\n - type: cos_sim_spearman\n value: 86.62037823380983\n - type: euclidean_pearson\n value: 83.56369548403958\n - type: euclidean_spearman\n value: 84.2176755481191\n - type: manhattan_pearson\n value: 83.55460702084464\n - type: manhattan_spearman\n value: 84.18617930921467\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 84.09071068110103\n - type: cos_sim_spearman\n value: 83.05697553913335\n - type: euclidean_pearson\n value: 81.1377457216497\n - type: euclidean_spearman\n value: 81.74714169016676\n - type: manhattan_pearson\n value: 81.0893424142723\n - type: manhattan_spearman\n value: 81.7058918219677\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 87.61132157220429\n - type: cos_sim_spearman\n value: 88.38581627185445\n - type: euclidean_pearson\n value: 86.14904510913374\n - type: euclidean_spearman\n value: 86.5452758925542\n - type: manhattan_pearson\n value: 86.1484025377679\n - type: manhattan_spearman\n value: 86.55483841566252\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 85.46195145161064\n - type: cos_sim_spearman\n value: 86.82409112251158\n - type: euclidean_pearson\n value: 84.75479672288957\n - type: euclidean_spearman\n value: 85.41144307151548\n - type: manhattan_pearson\n value: 84.70914329694165\n - type: manhattan_spearman\n value: 85.38477943384089\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 88.06351289930238\n - type: cos_sim_spearman\n value: 87.90311138579116\n - type: euclidean_pearson\n value: 86.17651467063077\n - type: euclidean_spearman\n value: 84.89447802019073\n - type: manhattan_pearson\n value: 86.3267677479595\n - type: manhattan_spearman\n value: 85.00472295103874\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 67.78311975978767\n - type: cos_sim_spearman\n value: 66.76465685245887\n - type: euclidean_pearson\n value: 67.21687806595443\n - type: euclidean_spearman\n value: 65.05776733534435\n - type: manhattan_pearson\n value: 67.14008143635883\n - type: manhattan_spearman\n value: 65.25247076149701\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 86.7403488889418\n - type: cos_sim_spearman\n value: 87.76870289783061\n - type: euclidean_pearson\n value: 84.83171077794671\n - type: euclidean_spearman\n value: 85.50579695091902\n - type: manhattan_pearson\n value: 84.83074260180555\n - type: manhattan_spearman\n value: 85.47589026938667\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 87.56234016237356\n - type: mrr\n value: 96.26124238869338\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 59.660999999999994\n - type: map_at_10\n value: 69.105\n - type: map_at_100\n value: 69.78\n - type: map_at_1000\n value: 69.80199999999999\n - type: map_at_3\n value: 65.991\n - type: map_at_5\n value: 68.02\n - type: mrr_at_1\n value: 62.666999999999994\n - type: mrr_at_10\n value: 70.259\n - type: mrr_at_100\n value: 70.776\n - type: mrr_at_1000\n value: 70.796\n - type: mrr_at_3\n value: 67.889\n - type: mrr_at_5\n value: 69.52199999999999\n - type: ndcg_at_1\n value: 62.666999999999994\n - type: ndcg_at_10\n value: 73.425\n - type: ndcg_at_100\n value: 75.955\n - type: ndcg_at_1000\n value: 76.459\n - type: ndcg_at_3\n value: 68.345\n - type: ndcg_at_5\n value: 71.319\n - type: precision_at_1\n value: 62.666999999999994\n - type: precision_at_10\n value: 9.667\n - type: precision_at_100\n value: 1.09\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 26.333000000000002\n - type: precision_at_5\n value: 17.732999999999997\n - type: recall_at_1\n value: 59.660999999999994\n - type: recall_at_10\n value: 85.422\n - type: recall_at_100\n value: 96.167\n - type: recall_at_1000\n value: 100\n - type: recall_at_3\n value: 72.044\n - type: recall_at_5\n value: 79.428\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.86435643564356\n - type: cos_sim_ap\n value: 96.83057412333741\n - type: cos_sim_f1\n value: 93.04215337734891\n - type: cos_sim_precision\n value: 94.53044375644994\n - type: cos_sim_recall\n value: 91.60000000000001\n - type: dot_accuracy\n value: 99.7910891089109\n - type: dot_ap\n value: 94.10681982106397\n - type: dot_f1\n value: 89.34881373043918\n - type: dot_precision\n value: 90.21406727828746\n - type: dot_recall\n value: 88.5\n - type: euclidean_accuracy\n value: 99.85544554455446\n - type: euclidean_ap\n value: 96.78545104478602\n - type: euclidean_f1\n value: 92.65143992055613\n - type: euclidean_precision\n value: 92.01183431952663\n - type: euclidean_recall\n value: 93.30000000000001\n - type: manhattan_accuracy\n value: 99.85841584158416\n - type: manhattan_ap\n value: 96.80748903307823\n - type: manhattan_f1\n value: 92.78247884519662\n - type: manhattan_precision\n value: 92.36868186323092\n - type: manhattan_recall\n value: 93.2\n - type: max_accuracy\n value: 99.86435643564356\n - type: max_ap\n value: 96.83057412333741\n - type: max_f1\n value: 93.04215337734891\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 65.53971025855282\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 33.97791591490788\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 55.852215301355066\n - type: mrr\n value: 56.85527809608691\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.21442519856758\n - type: cos_sim_spearman\n value: 30.822536216936825\n - type: dot_pearson\n value: 28.661325528121807\n - type: dot_spearman\n value: 28.1435226478879\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.183\n - type: map_at_10\n value: 1.526\n - type: map_at_100\n value: 7.915\n - type: map_at_1000\n value: 19.009\n - type: map_at_3\n value: 0.541\n - type: map_at_5\n value: 0.8659999999999999\n - type: mrr_at_1\n value: 68\n - type: mrr_at_10\n value: 81.186\n - type: mrr_at_100\n value: 81.186\n - type: mrr_at_1000\n value: 81.186\n - type: mrr_at_3\n value: 80\n - type: mrr_at_5\n value: 80.9\n - type: ndcg_at_1\n value: 64\n - type: ndcg_at_10\n value: 64.13799999999999\n - type: ndcg_at_100\n value: 47.632000000000005\n - type: ndcg_at_1000\n value: 43.037\n - type: ndcg_at_3\n value: 67.542\n - type: ndcg_at_5\n value: 67.496\n - type: precision_at_1\n value: 68\n - type: precision_at_10\n value: 67.80000000000001\n - type: precision_at_100\n value: 48.980000000000004\n - type: precision_at_1000\n value: 19.036\n - type: precision_at_3\n value: 72\n - type: precision_at_5\n value: 71.2\n - type: recall_at_1\n value: 0.183\n - type: recall_at_10\n value: 1.799\n - type: recall_at_100\n value: 11.652999999999999\n - type: recall_at_1000\n value: 40.086\n - type: recall_at_3\n value: 0.5930000000000001\n - type: recall_at_5\n value: 0.983\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 2.29\n - type: map_at_10\n value: 9.489\n - type: map_at_100\n value: 15.051\n - type: map_at_1000\n value: 16.561999999999998\n - type: map_at_3\n value: 5.137\n - type: map_at_5\n value: 6.7989999999999995\n - type: mrr_at_1\n value: 28.571\n - type: mrr_at_10\n value: 45.699\n - type: mrr_at_100\n value: 46.461000000000006\n - type: mrr_at_1000\n value: 46.461000000000006\n - type: mrr_at_3\n value: 41.837\n - type: mrr_at_5\n value: 43.163000000000004\n - type: ndcg_at_1\n value: 23.469\n - type: ndcg_at_10\n value: 23.544999999999998\n - type: ndcg_at_100\n value: 34.572\n - type: ndcg_at_1000\n value: 46.035\n - type: ndcg_at_3\n value: 27.200000000000003\n - type: ndcg_at_5\n value: 25.266\n - type: precision_at_1\n value: 28.571\n - type: precision_at_10\n value: 22.041\n - type: precision_at_100\n value: 7.3469999999999995\n - type: precision_at_1000\n value: 1.484\n - type: precision_at_3\n value: 29.932\n - type: precision_at_5\n value: 26.531\n - type: recall_at_1\n value: 2.29\n - type: recall_at_10\n value: 15.895999999999999\n - type: recall_at_100\n value: 45.518\n - type: recall_at_1000\n value: 80.731\n - type: recall_at_3\n value: 6.433\n - type: recall_at_5\n value: 9.484\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 71.4178\n - type: ap\n value: 14.575240629602373\n - type: f1\n value: 55.02449563229096\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 60.00282965478212\n - type: f1\n value: 60.34413028768773\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 50.409448342549936\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 87.62591643321214\n - type: cos_sim_ap\n value: 79.28766491329633\n - type: cos_sim_f1\n value: 71.98772064466617\n - type: cos_sim_precision\n value: 69.8609731876862\n - type: cos_sim_recall\n value: 74.24802110817942\n - type: dot_accuracy\n value: 84.75293556654945\n - type: dot_ap\n value: 69.72705761174353\n - type: dot_f1\n value: 65.08692852543464\n - type: dot_precision\n value: 63.57232704402516\n - type: dot_recall\n value: 66.6754617414248\n - type: euclidean_accuracy\n value: 87.44710019669786\n - type: euclidean_ap\n value: 79.11021477292638\n - type: euclidean_f1\n value: 71.5052389470994\n - type: euclidean_precision\n value: 69.32606541129832\n - type: euclidean_recall\n value: 73.82585751978891\n - type: manhattan_accuracy\n value: 87.42325803182929\n - type: manhattan_ap\n value: 79.05094494327616\n - type: manhattan_f1\n value: 71.36333985649055\n - type: manhattan_precision\n value: 70.58064516129032\n - type: manhattan_recall\n value: 72.16358839050132\n - type: max_accuracy\n value: 87.62591643321214\n - type: max_ap\n value: 79.28766491329633\n - type: max_f1\n value: 71.98772064466617\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.85202002561415\n - type: cos_sim_ap\n value: 85.9835303311168\n - type: cos_sim_f1\n value: 78.25741142443962\n - type: cos_sim_precision\n value: 73.76635768811342\n - type: cos_sim_recall\n value: 83.3307668617185\n - type: dot_accuracy\n value: 88.20584468506229\n - type: dot_ap\n value: 83.591632302697\n - type: dot_f1\n value: 76.81739705396173\n - type: dot_precision\n value: 73.45275728837373\n - type: dot_recall\n value: 80.50508161379734\n - type: euclidean_accuracy\n value: 88.64633057787093\n - type: euclidean_ap\n value: 85.25705123182283\n - type: euclidean_f1\n value: 77.18535726329199\n - type: euclidean_precision\n value: 75.17699437997226\n - type: euclidean_recall\n value: 79.30397289805975\n - type: manhattan_accuracy\n value: 88.63274731245392\n - type: manhattan_ap\n value: 85.2376825633018\n - type: manhattan_f1\n value: 77.15810785937788\n - type: manhattan_precision\n value: 73.92255061014319\n - type: manhattan_recall\n value: 80.68986757006468\n - type: max_accuracy\n value: 88.85202002561415\n - type: max_ap\n value: 85.9835303311168\n - type: max_f1\n value: 78.25741142443962\n---\n\n
ember-v1
\n\nThis model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the [RetroMAE](https://arxiv.org/abs/2205.12035) and [SetFit](https://arxiv.org/abs/2209.11055) research papers.\n\n### Plans\n- The research paper will be published soon.\n- The v2 of the model is currently in development and will feature an extended maximum sequence length of 4,000 tokens.\n\n## Usage\nUse with transformers:\n```python\nimport torch.nn.functional as F\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\ndef average_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)\n return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n\ninput_texts = [\n \"This is an example sentence\",\n \"Each sentence is converted\"\n]\n\ntokenizer = AutoTokenizer.from_pretrained(\"llmrails/ember-v1\")\nmodel = AutoModel.from_pretrained(\"llmrails/ember-v1\")\n\n# Tokenize the input texts\nbatch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')\n\noutputs = model(**batch_dict)\nembeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])\n\n# (Optionally) normalize embeddings\nembeddings = F.normalize(embeddings, p=2, dim=1)\nscores = (embeddings[:1] @ embeddings[1:].T) * 100\nprint(scores.tolist())\n```\n\nUse with sentence-transformers:\n```python\nfrom sentence_transformers import SentenceTransformer\nfrom sentence_transformers.util import cos_sim\n\nsentences = [\n\t\"This is an example sentence\",\n \"Each sentence is converted\"\n]\n\nmodel = SentenceTransformer('llmrails/ember-v1')\nembeddings = model.encode(sentences)\nprint(cos_sim(embeddings[0], embeddings[1]))\n```\n\n## Massive Text Embedding Benchmark (MTEB) Evaluation\nOur model achieve state-of-the-art performance on [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard)\n\n| Model Name | Dimension | Sequence Length | Average (56) | \n|:-----------------------------------------------------------------------:|:---------:|:---:|:------------:|\n| [ember-v1](https://huggingface.co/llmrails/ember-v1) | 1024 | 512 | **63.54** | \n| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | 63.23 | \n| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.05 |\n| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings/types-of-embedding-models) | 1536 | 8191 | 60.99 |\n\n### Limitation\n\nThis model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.\n\n## License\nMIT\n\n## Citation\n\n```bibtex\n@misc{nur2024emberv1,\n title={ember-v1: SOTA embedding model}, \n author={Enrike Nur and Anar Aliyev},\n year={2023},\n}\n```"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":87,"cells":{"id":{"kind":"string","value":"jinaai/jina-embeddings-v2-base-es"},"author":{"kind":"string","value":"jinaai"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","onnx","safetensors","bert","feature-extraction","sentence-similarity","mteb","custom_code","es","en","arxiv:2108.12409","arxiv:2402.17016","license:apache-2.0","model-index","autotrain_compatible","text-embeddings-inference","region:eu"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"onnx\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"mteb\",\n \"custom_code\",\n \"es\",\n \"en\",\n \"arxiv:2108.12409\",\n \"arxiv:2402.17016\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"region:eu\"\n]"},"created_time":{"kind":"string","value":"2024-01-24T09:54:03"},"last_modified":{"kind":"string","value":"2025-01-06T16:27:28"},"downloads":{"kind":"number","value":34138,"string":"34,138"},"likes":{"kind":"number","value":33,"string":"33"},"README":{"kind":"string","value":"---\nlanguage:\n- es\n- en\nlicense: apache-2.0\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- mteb\ninference: false\nmodel-index:\n- name: jina-embeddings-v2-base-es\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 74.25373134328358\n - type: ap\n value: 37.05201236793268\n - type: f1\n value: 68.16770391201077\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 78.30885\n - type: ap\n value: 73.01622441156408\n - type: f1\n value: 78.20769284466313\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 38.324\n - type: f1\n value: 37.89543008761673\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (es)\n type: mteb/amazon_reviews_multi\n config: es\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 38.678000000000004\n - type: f1\n value: 38.122639506976\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 23.968999999999998\n - type: map_at_10\n value: 40.691\n - type: map_at_100\n value: 41.713\n - type: map_at_1000\n value: 41.719\n - type: map_at_3\n value: 35.42\n - type: map_at_5\n value: 38.442\n - type: mrr_at_1\n value: 24.395\n - type: mrr_at_10\n value: 40.853\n - type: mrr_at_100\n value: 41.869\n - type: mrr_at_1000\n value: 41.874\n - type: mrr_at_3\n value: 35.68\n - type: mrr_at_5\n value: 38.572\n - type: ndcg_at_1\n value: 23.968999999999998\n - type: ndcg_at_10\n value: 50.129999999999995\n - type: ndcg_at_100\n value: 54.364000000000004\n - type: ndcg_at_1000\n value: 54.494\n - type: ndcg_at_3\n value: 39.231\n - type: ndcg_at_5\n value: 44.694\n - type: precision_at_1\n value: 23.968999999999998\n - type: precision_at_10\n value: 8.036999999999999\n - type: precision_at_100\n value: 0.9860000000000001\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 16.761\n - type: precision_at_5\n value: 12.717\n - type: recall_at_1\n value: 23.968999999999998\n - type: recall_at_10\n value: 80.36999999999999\n - type: recall_at_100\n value: 98.578\n - type: recall_at_1000\n value: 99.57300000000001\n - type: recall_at_3\n value: 50.28399999999999\n - type: recall_at_5\n value: 63.585\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 41.54886683150053\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 32.186028697637234\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 61.19432643698725\n - type: mrr\n value: 75.28646176845622\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 86.3828259381228\n - type: cos_sim_spearman\n value: 83.04647058342209\n - type: euclidean_pearson\n value: 84.02895346096244\n - type: euclidean_spearman\n value: 82.34524978635342\n - type: manhattan_pearson\n value: 84.35030723233426\n - type: manhattan_spearman\n value: 83.17177464337936\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 85.25649350649351\n - type: f1\n value: 85.22320474023192\n - task:\n type: Clustering\n dataset:\n name: MTEB BigPatentClustering\n type: jinaai/big-patent-clustering\n config: default\n split: test\n revision: 62d5330920bca426ce9d3c76ea914f15fc83e891\n metrics:\n - type: v_measure\n value: 20.42929408254094\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 35.165318177498136\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 28.89030154229562\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 30.119\n - type: map_at_10\n value: 42.092\n - type: map_at_100\n value: 43.506\n - type: map_at_1000\n value: 43.631\n - type: map_at_3\n value: 38.373000000000005\n - type: map_at_5\n value: 40.501\n - type: mrr_at_1\n value: 38.196999999999996\n - type: mrr_at_10\n value: 48.237\n - type: mrr_at_100\n value: 48.914\n - type: mrr_at_1000\n value: 48.959\n - type: mrr_at_3\n value: 45.279\n - type: mrr_at_5\n value: 47.11\n - type: ndcg_at_1\n value: 38.196999999999996\n - type: ndcg_at_10\n value: 48.849\n - type: ndcg_at_100\n value: 53.713\n - type: ndcg_at_1000\n value: 55.678000000000004\n - type: ndcg_at_3\n value: 43.546\n - type: ndcg_at_5\n value: 46.009\n - type: precision_at_1\n value: 38.196999999999996\n - type: precision_at_10\n value: 9.642000000000001\n - type: precision_at_100\n value: 1.5190000000000001\n - type: precision_at_1000\n value: 0.199\n - type: precision_at_3\n value: 21.65\n - type: precision_at_5\n value: 15.708\n - type: recall_at_1\n value: 30.119\n - type: recall_at_10\n value: 61.788\n - type: recall_at_100\n value: 82.14399999999999\n - type: recall_at_1000\n value: 95.003\n - type: recall_at_3\n value: 45.772\n - type: recall_at_5\n value: 53.04600000000001\n - type: map_at_1\n value: 28.979\n - type: map_at_10\n value: 37.785000000000004\n - type: map_at_100\n value: 38.945\n - type: map_at_1000\n value: 39.071\n - type: map_at_3\n value: 35.083999999999996\n - type: map_at_5\n value: 36.571999999999996\n - type: mrr_at_1\n value: 36.242000000000004\n - type: mrr_at_10\n value: 43.552\n - type: mrr_at_100\n value: 44.228\n - type: mrr_at_1000\n value: 44.275999999999996\n - type: mrr_at_3\n value: 41.359\n - type: mrr_at_5\n value: 42.598\n - type: ndcg_at_1\n value: 36.242000000000004\n - type: ndcg_at_10\n value: 42.94\n - type: ndcg_at_100\n value: 47.343\n - type: ndcg_at_1000\n value: 49.538\n - type: ndcg_at_3\n value: 39.086999999999996\n - type: ndcg_at_5\n value: 40.781\n - type: precision_at_1\n value: 36.242000000000004\n - type: precision_at_10\n value: 7.954999999999999\n - type: precision_at_100\n value: 1.303\n - type: precision_at_1000\n value: 0.178\n - type: precision_at_3\n value: 18.556\n - type: precision_at_5\n value: 13.145999999999999\n - type: recall_at_1\n value: 28.979\n - type: recall_at_10\n value: 51.835\n - type: recall_at_100\n value: 70.47\n - type: recall_at_1000\n value: 84.68299999999999\n - type: recall_at_3\n value: 40.410000000000004\n - type: recall_at_5\n value: 45.189\n - type: map_at_1\n value: 37.878\n - type: map_at_10\n value: 49.903\n - type: map_at_100\n value: 50.797000000000004\n - type: map_at_1000\n value: 50.858000000000004\n - type: map_at_3\n value: 46.526\n - type: map_at_5\n value: 48.615\n - type: mrr_at_1\n value: 43.135\n - type: mrr_at_10\n value: 53.067\n - type: mrr_at_100\n value: 53.668000000000006\n - type: mrr_at_1000\n value: 53.698\n - type: mrr_at_3\n value: 50.449\n - type: mrr_at_5\n value: 52.117000000000004\n - type: ndcg_at_1\n value: 43.135\n - type: ndcg_at_10\n value: 55.641\n - type: ndcg_at_100\n value: 59.427\n - type: ndcg_at_1000\n value: 60.655\n - type: ndcg_at_3\n value: 49.969\n - type: ndcg_at_5\n value: 53.075\n - type: precision_at_1\n value: 43.135\n - type: precision_at_10\n value: 8.997\n - type: precision_at_100\n value: 1.1809999999999998\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 22.215\n - type: precision_at_5\n value: 15.586\n - type: recall_at_1\n value: 37.878\n - type: recall_at_10\n value: 69.405\n - type: recall_at_100\n value: 86.262\n - type: recall_at_1000\n value: 95.012\n - type: recall_at_3\n value: 54.458\n - type: recall_at_5\n value: 61.965\n - type: map_at_1\n value: 24.853\n - type: map_at_10\n value: 32.402\n - type: map_at_100\n value: 33.417\n - type: map_at_1000\n value: 33.498\n - type: map_at_3\n value: 30.024\n - type: map_at_5\n value: 31.407\n - type: mrr_at_1\n value: 26.667\n - type: mrr_at_10\n value: 34.399\n - type: mrr_at_100\n value: 35.284\n - type: mrr_at_1000\n value: 35.345\n - type: mrr_at_3\n value: 32.109\n - type: mrr_at_5\n value: 33.375\n - type: ndcg_at_1\n value: 26.667\n - type: ndcg_at_10\n value: 36.854\n - type: ndcg_at_100\n value: 42.196\n - type: ndcg_at_1000\n value: 44.303\n - type: ndcg_at_3\n value: 32.186\n - type: ndcg_at_5\n value: 34.512\n - type: precision_at_1\n value: 26.667\n - type: precision_at_10\n value: 5.559\n - type: precision_at_100\n value: 0.88\n - type: precision_at_1000\n value: 0.109\n - type: precision_at_3\n value: 13.333\n - type: precision_at_5\n value: 9.379\n - type: recall_at_1\n value: 24.853\n - type: recall_at_10\n value: 48.636\n - type: recall_at_100\n value: 73.926\n - type: recall_at_1000\n value: 89.94\n - type: recall_at_3\n value: 36.266\n - type: recall_at_5\n value: 41.723\n - type: map_at_1\n value: 14.963999999999999\n - type: map_at_10\n value: 22.591\n - type: map_at_100\n value: 23.735999999999997\n - type: map_at_1000\n value: 23.868000000000002\n - type: map_at_3\n value: 20.093\n - type: map_at_5\n value: 21.499\n - type: mrr_at_1\n value: 18.407999999999998\n - type: mrr_at_10\n value: 26.863\n - type: mrr_at_100\n value: 27.87\n - type: mrr_at_1000\n value: 27.947\n - type: mrr_at_3\n value: 24.254\n - type: mrr_at_5\n value: 25.784000000000002\n - type: ndcg_at_1\n value: 18.407999999999998\n - type: ndcg_at_10\n value: 27.549\n - type: ndcg_at_100\n value: 33.188\n - type: ndcg_at_1000\n value: 36.312\n - type: ndcg_at_3\n value: 22.862\n - type: ndcg_at_5\n value: 25.130999999999997\n - type: precision_at_1\n value: 18.407999999999998\n - type: precision_at_10\n value: 5.087\n - type: precision_at_100\n value: 0.923\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 10.987\n - type: precision_at_5\n value: 8.209\n - type: recall_at_1\n value: 14.963999999999999\n - type: recall_at_10\n value: 38.673\n - type: recall_at_100\n value: 63.224999999999994\n - type: recall_at_1000\n value: 85.443\n - type: recall_at_3\n value: 25.840000000000003\n - type: recall_at_5\n value: 31.503999999999998\n - type: map_at_1\n value: 27.861000000000004\n - type: map_at_10\n value: 37.562\n - type: map_at_100\n value: 38.906\n - type: map_at_1000\n value: 39.021\n - type: map_at_3\n value: 34.743\n - type: map_at_5\n value: 36.168\n - type: mrr_at_1\n value: 34.455999999999996\n - type: mrr_at_10\n value: 43.428\n - type: mrr_at_100\n value: 44.228\n - type: mrr_at_1000\n value: 44.278\n - type: mrr_at_3\n value: 41.001\n - type: mrr_at_5\n value: 42.315000000000005\n - type: ndcg_at_1\n value: 34.455999999999996\n - type: ndcg_at_10\n value: 43.477\n - type: ndcg_at_100\n value: 48.953\n - type: ndcg_at_1000\n value: 51.19200000000001\n - type: ndcg_at_3\n value: 38.799\n - type: ndcg_at_5\n value: 40.743\n - type: precision_at_1\n value: 34.455999999999996\n - type: precision_at_10\n value: 7.902000000000001\n - type: precision_at_100\n value: 1.244\n - type: precision_at_1000\n value: 0.161\n - type: precision_at_3\n value: 18.511\n - type: precision_at_5\n value: 12.859000000000002\n - type: recall_at_1\n value: 27.861000000000004\n - type: recall_at_10\n value: 55.36\n - type: recall_at_100\n value: 78.384\n - type: recall_at_1000\n value: 93.447\n - type: recall_at_3\n value: 41.926\n - type: recall_at_5\n value: 47.257\n - type: map_at_1\n value: 26.375\n - type: map_at_10\n value: 35.571000000000005\n - type: map_at_100\n value: 36.785000000000004\n - type: map_at_1000\n value: 36.905\n - type: map_at_3\n value: 32.49\n - type: map_at_5\n value: 34.123999999999995\n - type: mrr_at_1\n value: 32.647999999999996\n - type: mrr_at_10\n value: 40.598\n - type: mrr_at_100\n value: 41.484\n - type: mrr_at_1000\n value: 41.546\n - type: mrr_at_3\n value: 37.9\n - type: mrr_at_5\n value: 39.401\n - type: ndcg_at_1\n value: 32.647999999999996\n - type: ndcg_at_10\n value: 41.026\n - type: ndcg_at_100\n value: 46.365\n - type: ndcg_at_1000\n value: 48.876\n - type: ndcg_at_3\n value: 35.843\n - type: ndcg_at_5\n value: 38.118\n - type: precision_at_1\n value: 32.647999999999996\n - type: precision_at_10\n value: 7.443\n - type: precision_at_100\n value: 1.18\n - type: precision_at_1000\n value: 0.158\n - type: precision_at_3\n value: 16.819\n - type: precision_at_5\n value: 11.985999999999999\n - type: recall_at_1\n value: 26.375\n - type: recall_at_10\n value: 52.471000000000004\n - type: recall_at_100\n value: 75.354\n - type: recall_at_1000\n value: 92.35\n - type: recall_at_3\n value: 37.893\n - type: recall_at_5\n value: 43.935\n - type: map_at_1\n value: 25.012666666666668\n - type: map_at_10\n value: 33.685833333333335\n - type: map_at_100\n value: 34.849250000000005\n - type: map_at_1000\n value: 34.970083333333335\n - type: map_at_3\n value: 31.065083333333334\n - type: map_at_5\n value: 32.494416666666666\n - type: mrr_at_1\n value: 29.772666666666662\n - type: mrr_at_10\n value: 37.824666666666666\n - type: mrr_at_100\n value: 38.66741666666666\n - type: mrr_at_1000\n value: 38.72916666666666\n - type: mrr_at_3\n value: 35.54575\n - type: mrr_at_5\n value: 36.81524999999999\n - type: ndcg_at_1\n value: 29.772666666666662\n - type: ndcg_at_10\n value: 38.78241666666666\n - type: ndcg_at_100\n value: 43.84591666666667\n - type: ndcg_at_1000\n value: 46.275416666666665\n - type: ndcg_at_3\n value: 34.33416666666667\n - type: ndcg_at_5\n value: 36.345166666666664\n - type: precision_at_1\n value: 29.772666666666662\n - type: precision_at_10\n value: 6.794916666666667\n - type: precision_at_100\n value: 1.106416666666667\n - type: precision_at_1000\n value: 0.15033333333333335\n - type: precision_at_3\n value: 15.815083333333336\n - type: precision_at_5\n value: 11.184166666666664\n - type: recall_at_1\n value: 25.012666666666668\n - type: recall_at_10\n value: 49.748500000000014\n - type: recall_at_100\n value: 72.11341666666667\n - type: recall_at_1000\n value: 89.141\n - type: recall_at_3\n value: 37.242999999999995\n - type: recall_at_5\n value: 42.49033333333333\n - type: map_at_1\n value: 23.177\n - type: map_at_10\n value: 29.310000000000002\n - type: map_at_100\n value: 30.188\n - type: map_at_1000\n value: 30.29\n - type: map_at_3\n value: 27.356\n - type: map_at_5\n value: 28.410999999999998\n - type: mrr_at_1\n value: 26.074\n - type: mrr_at_10\n value: 32.002\n - type: mrr_at_100\n value: 32.838\n - type: mrr_at_1000\n value: 32.909\n - type: mrr_at_3\n value: 30.317\n - type: mrr_at_5\n value: 31.222\n - type: ndcg_at_1\n value: 26.074\n - type: ndcg_at_10\n value: 32.975\n - type: ndcg_at_100\n value: 37.621\n - type: ndcg_at_1000\n value: 40.253\n - type: ndcg_at_3\n value: 29.452\n - type: ndcg_at_5\n value: 31.020999999999997\n - type: precision_at_1\n value: 26.074\n - type: precision_at_10\n value: 5.077\n - type: precision_at_100\n value: 0.8049999999999999\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 12.526000000000002\n - type: precision_at_5\n value: 8.588999999999999\n - type: recall_at_1\n value: 23.177\n - type: recall_at_10\n value: 41.613\n - type: recall_at_100\n value: 63.287000000000006\n - type: recall_at_1000\n value: 83.013\n - type: recall_at_3\n value: 31.783\n - type: recall_at_5\n value: 35.769\n - type: map_at_1\n value: 15.856\n - type: map_at_10\n value: 22.651\n - type: map_at_100\n value: 23.649\n - type: map_at_1000\n value: 23.783\n - type: map_at_3\n value: 20.591\n - type: map_at_5\n value: 21.684\n - type: mrr_at_1\n value: 19.408\n - type: mrr_at_10\n value: 26.51\n - type: mrr_at_100\n value: 27.356\n - type: mrr_at_1000\n value: 27.439999999999998\n - type: mrr_at_3\n value: 24.547\n - type: mrr_at_5\n value: 25.562\n - type: ndcg_at_1\n value: 19.408\n - type: ndcg_at_10\n value: 27.072000000000003\n - type: ndcg_at_100\n value: 31.980999999999998\n - type: ndcg_at_1000\n value: 35.167\n - type: ndcg_at_3\n value: 23.338\n - type: ndcg_at_5\n value: 24.94\n - type: precision_at_1\n value: 19.408\n - type: precision_at_10\n value: 4.9590000000000005\n - type: precision_at_100\n value: 0.8710000000000001\n - type: precision_at_1000\n value: 0.132\n - type: precision_at_3\n value: 11.138\n - type: precision_at_5\n value: 7.949000000000001\n - type: recall_at_1\n value: 15.856\n - type: recall_at_10\n value: 36.578\n - type: recall_at_100\n value: 58.89\n - type: recall_at_1000\n value: 81.743\n - type: recall_at_3\n value: 25.94\n - type: recall_at_5\n value: 30.153999999999996\n - type: map_at_1\n value: 25.892\n - type: map_at_10\n value: 33.899\n - type: map_at_100\n value: 34.955000000000005\n - type: map_at_1000\n value: 35.066\n - type: map_at_3\n value: 31.41\n - type: map_at_5\n value: 32.669\n - type: mrr_at_1\n value: 30.224\n - type: mrr_at_10\n value: 37.936\n - type: mrr_at_100\n value: 38.777\n - type: mrr_at_1000\n value: 38.85\n - type: mrr_at_3\n value: 35.821\n - type: mrr_at_5\n value: 36.894\n - type: ndcg_at_1\n value: 30.224\n - type: ndcg_at_10\n value: 38.766\n - type: ndcg_at_100\n value: 43.806\n - type: ndcg_at_1000\n value: 46.373999999999995\n - type: ndcg_at_3\n value: 34.325\n - type: ndcg_at_5\n value: 36.096000000000004\n - type: precision_at_1\n value: 30.224\n - type: precision_at_10\n value: 6.446000000000001\n - type: precision_at_100\n value: 1.0\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 15.392\n - type: precision_at_5\n value: 10.671999999999999\n - type: recall_at_1\n value: 25.892\n - type: recall_at_10\n value: 49.573\n - type: recall_at_100\n value: 71.885\n - type: recall_at_1000\n value: 89.912\n - type: recall_at_3\n value: 37.226\n - type: recall_at_5\n value: 41.74\n - type: map_at_1\n value: 23.915\n - type: map_at_10\n value: 33.613\n - type: map_at_100\n value: 35.333999999999996\n - type: map_at_1000\n value: 35.563\n - type: map_at_3\n value: 31.203999999999997\n - type: map_at_5\n value: 32.479\n - type: mrr_at_1\n value: 29.447000000000003\n - type: mrr_at_10\n value: 38.440000000000005\n - type: mrr_at_100\n value: 39.459\n - type: mrr_at_1000\n value: 39.513999999999996\n - type: mrr_at_3\n value: 36.495\n - type: mrr_at_5\n value: 37.592\n - type: ndcg_at_1\n value: 29.447000000000003\n - type: ndcg_at_10\n value: 39.341\n - type: ndcg_at_100\n value: 45.382\n - type: ndcg_at_1000\n value: 47.921\n - type: ndcg_at_3\n value: 35.671\n - type: ndcg_at_5\n value: 37.299\n - type: precision_at_1\n value: 29.447000000000003\n - type: precision_at_10\n value: 7.648000000000001\n - type: precision_at_100\n value: 1.567\n - type: precision_at_1000\n value: 0.241\n - type: precision_at_3\n value: 17.194000000000003\n - type: precision_at_5\n value: 12.253\n - type: recall_at_1\n value: 23.915\n - type: recall_at_10\n value: 49.491\n - type: recall_at_100\n value: 76.483\n - type: recall_at_1000\n value: 92.674\n - type: recall_at_3\n value: 38.878\n - type: recall_at_5\n value: 43.492\n - type: map_at_1\n value: 20.283\n - type: map_at_10\n value: 26.851000000000003\n - type: map_at_100\n value: 27.973\n - type: map_at_1000\n value: 28.087\n - type: map_at_3\n value: 24.887\n - type: map_at_5\n value: 25.804\n - type: mrr_at_1\n value: 22.366\n - type: mrr_at_10\n value: 28.864\n - type: mrr_at_100\n value: 29.903000000000002\n - type: mrr_at_1000\n value: 29.988\n - type: mrr_at_3\n value: 27.017999999999997\n - type: mrr_at_5\n value: 27.813\n - type: ndcg_at_1\n value: 22.366\n - type: ndcg_at_10\n value: 30.898999999999997\n - type: ndcg_at_100\n value: 36.176\n - type: ndcg_at_1000\n value: 39.036\n - type: ndcg_at_3\n value: 26.932000000000002\n - type: ndcg_at_5\n value: 28.416999999999998\n - type: precision_at_1\n value: 22.366\n - type: precision_at_10\n value: 4.824\n - type: precision_at_100\n value: 0.804\n - type: precision_at_1000\n value: 0.116\n - type: precision_at_3\n value: 11.459999999999999\n - type: precision_at_5\n value: 7.8740000000000006\n - type: recall_at_1\n value: 20.283\n - type: recall_at_10\n value: 41.559000000000005\n - type: recall_at_100\n value: 65.051\n - type: recall_at_1000\n value: 86.47200000000001\n - type: recall_at_3\n value: 30.524\n - type: recall_at_5\n value: 34.11\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 11.326\n - type: map_at_10\n value: 19.357\n - type: map_at_100\n value: 21.014\n - type: map_at_1000\n value: 21.188000000000002\n - type: map_at_3\n value: 16.305\n - type: map_at_5\n value: 17.886\n - type: mrr_at_1\n value: 24.820999999999998\n - type: mrr_at_10\n value: 36.150999999999996\n - type: mrr_at_100\n value: 37.080999999999996\n - type: mrr_at_1000\n value: 37.123\n - type: mrr_at_3\n value: 32.952999999999996\n - type: mrr_at_5\n value: 34.917\n - type: ndcg_at_1\n value: 24.820999999999998\n - type: ndcg_at_10\n value: 27.131\n - type: ndcg_at_100\n value: 33.841\n - type: ndcg_at_1000\n value: 37.159\n - type: ndcg_at_3\n value: 22.311\n - type: ndcg_at_5\n value: 24.026\n - type: precision_at_1\n value: 24.820999999999998\n - type: precision_at_10\n value: 8.450000000000001\n - type: precision_at_100\n value: 1.557\n - type: precision_at_1000\n value: 0.218\n - type: precision_at_3\n value: 16.612\n - type: precision_at_5\n value: 12.808\n - type: recall_at_1\n value: 11.326\n - type: recall_at_10\n value: 32.548\n - type: recall_at_100\n value: 55.803000000000004\n - type: recall_at_1000\n value: 74.636\n - type: recall_at_3\n value: 20.549\n - type: recall_at_5\n value: 25.514\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 7.481\n - type: map_at_10\n value: 15.043999999999999\n - type: map_at_100\n value: 20.194000000000003\n - type: map_at_1000\n value: 21.423000000000002\n - type: map_at_3\n value: 11.238\n - type: map_at_5\n value: 12.828999999999999\n - type: mrr_at_1\n value: 54.50000000000001\n - type: mrr_at_10\n value: 64.713\n - type: mrr_at_100\n value: 65.216\n - type: mrr_at_1000\n value: 65.23\n - type: mrr_at_3\n value: 62.74999999999999\n - type: mrr_at_5\n value: 63.87500000000001\n - type: ndcg_at_1\n value: 43.375\n - type: ndcg_at_10\n value: 32.631\n - type: ndcg_at_100\n value: 36.338\n - type: ndcg_at_1000\n value: 43.541000000000004\n - type: ndcg_at_3\n value: 36.746\n - type: ndcg_at_5\n value: 34.419\n - type: precision_at_1\n value: 54.50000000000001\n - type: precision_at_10\n value: 24.825\n - type: precision_at_100\n value: 7.698\n - type: precision_at_1000\n value: 1.657\n - type: precision_at_3\n value: 38.917\n - type: precision_at_5\n value: 32.35\n - type: recall_at_1\n value: 7.481\n - type: recall_at_10\n value: 20.341\n - type: recall_at_100\n value: 41.778\n - type: recall_at_1000\n value: 64.82\n - type: recall_at_3\n value: 12.748000000000001\n - type: recall_at_5\n value: 15.507000000000001\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 46.580000000000005\n - type: f1\n value: 41.5149462395095\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 61.683\n - type: map_at_10\n value: 73.071\n - type: map_at_100\n value: 73.327\n - type: map_at_1000\n value: 73.341\n - type: map_at_3\n value: 71.446\n - type: map_at_5\n value: 72.557\n - type: mrr_at_1\n value: 66.44200000000001\n - type: mrr_at_10\n value: 77.725\n - type: mrr_at_100\n value: 77.89399999999999\n - type: mrr_at_1000\n value: 77.898\n - type: mrr_at_3\n value: 76.283\n - type: mrr_at_5\n value: 77.29700000000001\n - type: ndcg_at_1\n value: 66.44200000000001\n - type: ndcg_at_10\n value: 78.43\n - type: ndcg_at_100\n value: 79.462\n - type: ndcg_at_1000\n value: 79.754\n - type: ndcg_at_3\n value: 75.53800000000001\n - type: ndcg_at_5\n value: 77.332\n - type: precision_at_1\n value: 66.44200000000001\n - type: precision_at_10\n value: 9.878\n - type: precision_at_100\n value: 1.051\n - type: precision_at_1000\n value: 0.109\n - type: precision_at_3\n value: 29.878\n - type: precision_at_5\n value: 18.953\n - type: recall_at_1\n value: 61.683\n - type: recall_at_10\n value: 90.259\n - type: recall_at_100\n value: 94.633\n - type: recall_at_1000\n value: 96.60499999999999\n - type: recall_at_3\n value: 82.502\n - type: recall_at_5\n value: 86.978\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 17.724\n - type: map_at_10\n value: 29.487999999999996\n - type: map_at_100\n value: 31.243\n - type: map_at_1000\n value: 31.419999999999998\n - type: map_at_3\n value: 25.612000000000002\n - type: map_at_5\n value: 27.859\n - type: mrr_at_1\n value: 35.802\n - type: mrr_at_10\n value: 44.684000000000005\n - type: mrr_at_100\n value: 45.578\n - type: mrr_at_1000\n value: 45.621\n - type: mrr_at_3\n value: 42.361\n - type: mrr_at_5\n value: 43.85\n - type: ndcg_at_1\n value: 35.802\n - type: ndcg_at_10\n value: 37.009\n - type: ndcg_at_100\n value: 43.903\n - type: ndcg_at_1000\n value: 47.019\n - type: ndcg_at_3\n value: 33.634\n - type: ndcg_at_5\n value: 34.965\n - type: precision_at_1\n value: 35.802\n - type: precision_at_10\n value: 10.386\n - type: precision_at_100\n value: 1.7309999999999999\n - type: precision_at_1000\n value: 0.231\n - type: precision_at_3\n value: 22.84\n - type: precision_at_5\n value: 17.037\n - type: recall_at_1\n value: 17.724\n - type: recall_at_10\n value: 43.708000000000006\n - type: recall_at_100\n value: 69.902\n - type: recall_at_1000\n value: 88.51\n - type: recall_at_3\n value: 30.740000000000002\n - type: recall_at_5\n value: 36.742000000000004\n - task:\n type: Clustering\n dataset:\n name: MTEB FloresClusteringS2S\n type: jinaai/flores_clustering\n config: default\n split: test\n revision: 480b580487f53a46f881354a8348335d4edbb2de\n metrics:\n - type: v_measure\n value: 39.79120149869612\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 34.801\n - type: map_at_10\n value: 50.42100000000001\n - type: map_at_100\n value: 51.254\n - type: map_at_1000\n value: 51.327999999999996\n - type: map_at_3\n value: 47.56\n - type: map_at_5\n value: 49.379\n - type: mrr_at_1\n value: 69.602\n - type: mrr_at_10\n value: 76.385\n - type: mrr_at_100\n value: 76.668\n - type: mrr_at_1000\n value: 76.683\n - type: mrr_at_3\n value: 75.102\n - type: mrr_at_5\n value: 75.949\n - type: ndcg_at_1\n value: 69.602\n - type: ndcg_at_10\n value: 59.476\n - type: ndcg_at_100\n value: 62.527\n - type: ndcg_at_1000\n value: 64.043\n - type: ndcg_at_3\n value: 55.155\n - type: ndcg_at_5\n value: 57.623000000000005\n - type: precision_at_1\n value: 69.602\n - type: precision_at_10\n value: 12.292\n - type: precision_at_100\n value: 1.467\n - type: precision_at_1000\n value: 0.167\n - type: precision_at_3\n value: 34.634\n - type: precision_at_5\n value: 22.728\n - type: recall_at_1\n value: 34.801\n - type: recall_at_10\n value: 61.458\n - type: recall_at_100\n value: 73.363\n - type: recall_at_1000\n value: 83.43\n - type: recall_at_3\n value: 51.951\n - type: recall_at_5\n value: 56.82000000000001\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 67.46079999999999\n - type: ap\n value: 61.81278199159353\n - type: f1\n value: 67.26505019954826\n - task:\n type: Reranking\n dataset:\n name: MTEB MIRACL\n type: jinaai/miracl\n config: default\n split: test\n revision: d28a029f35c4ff7f616df47b0edf54e6882395e6\n metrics:\n - type: map\n value: 73.90464144118539\n - type: mrr\n value: 82.44674693216022\n - task:\n type: Retrieval\n dataset:\n name: MTEB MIRACLRetrieval\n type: jinaai/miracl\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 21.299\n - type: map_at_10\n value: 70.547\n - type: map_at_100\n value: 72.394\n - type: map_at_1000\n value: 72.39999999999999\n - type: map_at_3\n value: 41.317\n - type: map_at_5\n value: 53.756\n - type: mrr_at_1\n value: 72.84\n - type: mrr_at_10\n value: 82.466\n - type: mrr_at_100\n value: 82.52199999999999\n - type: mrr_at_1000\n value: 82.52199999999999\n - type: mrr_at_3\n value: 80.607\n - type: mrr_at_5\n value: 82.065\n - type: ndcg_at_1\n value: 72.994\n - type: ndcg_at_10\n value: 80.89\n - type: ndcg_at_100\n value: 83.30199999999999\n - type: ndcg_at_1000\n value: 83.337\n - type: ndcg_at_3\n value: 70.357\n - type: ndcg_at_5\n value: 72.529\n - type: precision_at_1\n value: 72.994\n - type: precision_at_10\n value: 43.056\n - type: precision_at_100\n value: 4.603\n - type: precision_at_1000\n value: 0.461\n - type: precision_at_3\n value: 61.626000000000005\n - type: precision_at_5\n value: 55.525000000000006\n - type: recall_at_1\n value: 21.299\n - type: recall_at_10\n value: 93.903\n - type: recall_at_100\n value: 99.86699999999999\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 46.653\n - type: recall_at_5\n value: 65.72200000000001\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 90.37163702690378\n - type: f1\n value: 90.18615216514222\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (es)\n type: mteb/mtop_domain\n config: es\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 89.88992661774515\n - type: f1\n value: 89.3738963046966\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 71.97218422252622\n - type: f1\n value: 54.03096570916335\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (es)\n type: mteb/mtop_intent\n config: es\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 68.75917278185457\n - type: f1\n value: 49.144083814705844\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 70.75991930060525\n - type: f1\n value: 69.37993796176502\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (es)\n type: mteb/amazon_massive_intent\n config: es\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 66.93006052454606\n - type: f1\n value: 66.04029135274683\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 73.81977135171486\n - type: f1\n value: 74.10477122507747\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (es)\n type: mteb/amazon_massive_scenario\n config: es\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.23402824478816\n - type: f1\n value: 71.75572665880296\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 32.189750849969215\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 28.78357393555938\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 30.605612998328358\n - type: mrr\n value: 31.595529205695833\n - task:\n type: Retrieval\n dataset:\n name: MTEB MintakaESRetrieval\n type: jinaai/mintakaqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 16.213\n - type: map_at_10\n value: 24.079\n - type: map_at_100\n value: 25.039\n - type: map_at_1000\n value: 25.142999999999997\n - type: map_at_3\n value: 21.823\n - type: map_at_5\n value: 23.069\n - type: mrr_at_1\n value: 16.213\n - type: mrr_at_10\n value: 24.079\n - type: mrr_at_100\n value: 25.039\n - type: mrr_at_1000\n value: 25.142999999999997\n - type: mrr_at_3\n value: 21.823\n - type: mrr_at_5\n value: 23.069\n - type: ndcg_at_1\n value: 16.213\n - type: ndcg_at_10\n value: 28.315\n - type: ndcg_at_100\n value: 33.475\n - type: ndcg_at_1000\n value: 36.838\n - type: ndcg_at_3\n value: 23.627000000000002\n - type: ndcg_at_5\n value: 25.879\n - type: precision_at_1\n value: 16.213\n - type: precision_at_10\n value: 4.183\n - type: precision_at_100\n value: 0.6709999999999999\n - type: precision_at_1000\n value: 0.095\n - type: precision_at_3\n value: 9.612\n - type: precision_at_5\n value: 6.865\n - type: recall_at_1\n value: 16.213\n - type: recall_at_10\n value: 41.832\n - type: recall_at_100\n value: 67.12\n - type: recall_at_1000\n value: 94.843\n - type: recall_at_3\n value: 28.837000000000003\n - type: recall_at_5\n value: 34.323\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.692\n - type: map_at_10\n value: 10.783\n - type: map_at_100\n value: 13.447999999999999\n - type: map_at_1000\n value: 14.756\n - type: map_at_3\n value: 7.646\n - type: map_at_5\n value: 9.311\n - type: mrr_at_1\n value: 42.415000000000006\n - type: mrr_at_10\n value: 50.471\n - type: mrr_at_100\n value: 51.251999999999995\n - type: mrr_at_1000\n value: 51.292\n - type: mrr_at_3\n value: 48.4\n - type: mrr_at_5\n value: 49.809\n - type: ndcg_at_1\n value: 40.867\n - type: ndcg_at_10\n value: 30.303\n - type: ndcg_at_100\n value: 27.915\n - type: ndcg_at_1000\n value: 36.734\n - type: ndcg_at_3\n value: 35.74\n - type: ndcg_at_5\n value: 33.938\n - type: precision_at_1\n value: 42.415000000000006\n - type: precision_at_10\n value: 22.105\n - type: precision_at_100\n value: 7.173\n - type: precision_at_1000\n value: 2.007\n - type: precision_at_3\n value: 33.437\n - type: precision_at_5\n value: 29.349999999999998\n - type: recall_at_1\n value: 4.692\n - type: recall_at_10\n value: 14.798\n - type: recall_at_100\n value: 28.948\n - type: recall_at_1000\n value: 59.939\n - type: recall_at_3\n value: 8.562\n - type: recall_at_5\n value: 11.818\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 27.572999999999997\n - type: map_at_10\n value: 42.754\n - type: map_at_100\n value: 43.8\n - type: map_at_1000\n value: 43.838\n - type: map_at_3\n value: 38.157000000000004\n - type: map_at_5\n value: 40.9\n - type: mrr_at_1\n value: 31.373\n - type: mrr_at_10\n value: 45.321\n - type: mrr_at_100\n value: 46.109\n - type: mrr_at_1000\n value: 46.135\n - type: mrr_at_3\n value: 41.483\n - type: mrr_at_5\n value: 43.76\n - type: ndcg_at_1\n value: 31.373\n - type: ndcg_at_10\n value: 50.7\n - type: ndcg_at_100\n value: 55.103\n - type: ndcg_at_1000\n value: 55.955999999999996\n - type: ndcg_at_3\n value: 42.069\n - type: ndcg_at_5\n value: 46.595\n - type: precision_at_1\n value: 31.373\n - type: precision_at_10\n value: 8.601\n - type: precision_at_100\n value: 1.11\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 19.399\n - type: precision_at_5\n value: 14.224\n - type: recall_at_1\n value: 27.572999999999997\n - type: recall_at_10\n value: 72.465\n - type: recall_at_100\n value: 91.474\n - type: recall_at_1000\n value: 97.78099999999999\n - type: recall_at_3\n value: 50.087\n - type: recall_at_5\n value: 60.516000000000005\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 70.525\n - type: map_at_10\n value: 84.417\n - type: map_at_100\n value: 85.07000000000001\n - type: map_at_1000\n value: 85.085\n - type: map_at_3\n value: 81.45\n - type: map_at_5\n value: 83.317\n - type: mrr_at_1\n value: 81.17999999999999\n - type: mrr_at_10\n value: 87.34100000000001\n - type: mrr_at_100\n value: 87.461\n - type: mrr_at_1000\n value: 87.46199999999999\n - type: mrr_at_3\n value: 86.372\n - type: mrr_at_5\n value: 87.046\n - type: ndcg_at_1\n value: 81.17999999999999\n - type: ndcg_at_10\n value: 88.144\n - type: ndcg_at_100\n value: 89.424\n - type: ndcg_at_1000\n value: 89.517\n - type: ndcg_at_3\n value: 85.282\n - type: ndcg_at_5\n value: 86.874\n - type: precision_at_1\n value: 81.17999999999999\n - type: precision_at_10\n value: 13.385\n - type: precision_at_100\n value: 1.533\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.29\n - type: precision_at_5\n value: 24.546\n - type: recall_at_1\n value: 70.525\n - type: recall_at_10\n value: 95.22500000000001\n - type: recall_at_100\n value: 99.572\n - type: recall_at_1000\n value: 99.98899999999999\n - type: recall_at_3\n value: 87.035\n - type: recall_at_5\n value: 91.526\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 48.284384328108736\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 56.02508021518392\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.023000000000001\n - type: map_at_10\n value: 10.046\n - type: map_at_100\n value: 11.802999999999999\n - type: map_at_1000\n value: 12.074\n - type: map_at_3\n value: 7.071\n - type: map_at_5\n value: 8.556\n - type: mrr_at_1\n value: 19.8\n - type: mrr_at_10\n value: 30.105999999999998\n - type: mrr_at_100\n value: 31.16\n - type: mrr_at_1000\n value: 31.224\n - type: mrr_at_3\n value: 26.633000000000003\n - type: mrr_at_5\n value: 28.768\n - type: ndcg_at_1\n value: 19.8\n - type: ndcg_at_10\n value: 17.358\n - type: ndcg_at_100\n value: 24.566\n - type: ndcg_at_1000\n value: 29.653000000000002\n - type: ndcg_at_3\n value: 16.052\n - type: ndcg_at_5\n value: 14.325\n - type: precision_at_1\n value: 19.8\n - type: precision_at_10\n value: 9.07\n - type: precision_at_100\n value: 1.955\n - type: precision_at_1000\n value: 0.318\n - type: precision_at_3\n value: 14.933\n - type: precision_at_5\n value: 12.68\n - type: recall_at_1\n value: 4.023000000000001\n - type: recall_at_10\n value: 18.398\n - type: recall_at_100\n value: 39.683\n - type: recall_at_1000\n value: 64.625\n - type: recall_at_3\n value: 9.113\n - type: recall_at_5\n value: 12.873000000000001\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 87.90508618312852\n - type: cos_sim_spearman\n value: 83.01323463129205\n - type: euclidean_pearson\n value: 84.35845059002891\n - type: euclidean_spearman\n value: 82.85508559018527\n - type: manhattan_pearson\n value: 84.3682368950498\n - type: manhattan_spearman\n value: 82.8619728517302\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 89.28294535873366\n - type: cos_sim_spearman\n value: 81.61879268131732\n - type: euclidean_pearson\n value: 85.99053604863724\n - type: euclidean_spearman\n value: 80.95176684739084\n - type: manhattan_pearson\n value: 85.98054086663903\n - type: manhattan_spearman\n value: 80.9911070430335\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 86.15898098455258\n - type: cos_sim_spearman\n value: 86.8247985072307\n - type: euclidean_pearson\n value: 86.25342429918649\n - type: euclidean_spearman\n value: 87.13468603023252\n - type: manhattan_pearson\n value: 86.2006134067688\n - type: manhattan_spearman\n value: 87.06135811996896\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 85.57403998481877\n - type: cos_sim_spearman\n value: 83.55947075172618\n - type: euclidean_pearson\n value: 84.97097562965358\n - type: euclidean_spearman\n value: 83.6287075601467\n - type: manhattan_pearson\n value: 84.87092197104133\n - type: manhattan_spearman\n value: 83.53783891641335\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 88.14632780204231\n - type: cos_sim_spearman\n value: 88.74903634923868\n - type: euclidean_pearson\n value: 88.03922995855112\n - type: euclidean_spearman\n value: 88.72852190525855\n - type: manhattan_pearson\n value: 87.9694791024271\n - type: manhattan_spearman\n value: 88.66461452107418\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 84.75989818558652\n - type: cos_sim_spearman\n value: 86.03107893122942\n - type: euclidean_pearson\n value: 85.21908960133018\n - type: euclidean_spearman\n value: 85.93012720153482\n - type: manhattan_pearson\n value: 85.1969170195502\n - type: manhattan_spearman\n value: 85.8975254197784\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 89.16803898789955\n - type: cos_sim_spearman\n value: 88.56139047950525\n - type: euclidean_pearson\n value: 88.09685325747859\n - type: euclidean_spearman\n value: 88.0457609458947\n - type: manhattan_pearson\n value: 88.07054413001431\n - type: manhattan_spearman\n value: 88.10784098889314\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (es-en)\n type: mteb/sts17-crosslingual-sts\n config: es-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 86.7160384474547\n - type: cos_sim_spearman\n value: 86.4899235500562\n - type: euclidean_pearson\n value: 85.90854477703468\n - type: euclidean_spearman\n value: 86.16085009124498\n - type: manhattan_pearson\n value: 85.9249735317884\n - type: manhattan_spearman\n value: 86.25038421339116\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (es-es)\n type: mteb/sts17-crosslingual-sts\n config: es-es\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 89.37914622360788\n - type: cos_sim_spearman\n value: 88.24619159322809\n - type: euclidean_pearson\n value: 89.00538382632769\n - type: euclidean_spearman\n value: 88.44675863524736\n - type: manhattan_pearson\n value: 88.97372120683606\n - type: manhattan_spearman\n value: 88.33509324222129\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 66.22181360203069\n - type: cos_sim_spearman\n value: 65.6218291833768\n - type: euclidean_pearson\n value: 67.14543788822508\n - type: euclidean_spearman\n value: 65.21269939987857\n - type: manhattan_pearson\n value: 67.03304607195636\n - type: manhattan_spearman\n value: 65.18885316423805\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (es)\n type: mteb/sts22-crosslingual-sts\n config: es\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 65.71694059677084\n - type: cos_sim_spearman\n value: 67.96591844540954\n - type: euclidean_pearson\n value: 65.6964079162296\n - type: euclidean_spearman\n value: 67.53027948900173\n - type: manhattan_pearson\n value: 65.93545097673741\n - type: manhattan_spearman\n value: 67.7261811805062\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (es-en)\n type: mteb/sts22-crosslingual-sts\n config: es-en\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 75.43544796375058\n - type: cos_sim_spearman\n value: 78.80462701160789\n - type: euclidean_pearson\n value: 76.19135575163138\n - type: euclidean_spearman\n value: 78.4974732597096\n - type: manhattan_pearson\n value: 76.3254742699264\n - type: manhattan_spearman\n value: 78.51884307690416\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 87.46805293607684\n - type: cos_sim_spearman\n value: 87.83792784689113\n - type: euclidean_pearson\n value: 87.3872143683234\n - type: euclidean_spearman\n value: 87.61611384542778\n - type: manhattan_pearson\n value: 87.38542672601992\n - type: manhattan_spearman\n value: 87.61423971087297\n - task:\n type: STS\n dataset:\n name: MTEB STSES\n type: PlanTL-GOB-ES/sts-es\n config: default\n split: test\n revision: 0912bb6c9393c76d62a7c5ee81c4c817ff47c9f4\n metrics:\n - type: cos_sim_pearson\n value: 82.55286866116202\n - type: cos_sim_spearman\n value: 80.22150503320272\n - type: euclidean_pearson\n value: 83.27223445187087\n - type: euclidean_spearman\n value: 80.59078590992925\n - type: manhattan_pearson\n value: 83.23095887013197\n - type: manhattan_spearman\n value: 80.87994285189795\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 79.29717302265792\n - type: mrr\n value: 94.02156304117088\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 49.9\n - type: map_at_10\n value: 58.626\n - type: map_at_100\n value: 59.519999999999996\n - type: map_at_1000\n value: 59.55200000000001\n - type: map_at_3\n value: 56.232000000000006\n - type: map_at_5\n value: 57.833\n - type: mrr_at_1\n value: 52.333\n - type: mrr_at_10\n value: 60.039\n - type: mrr_at_100\n value: 60.732\n - type: mrr_at_1000\n value: 60.75899999999999\n - type: mrr_at_3\n value: 58.278\n - type: mrr_at_5\n value: 59.428000000000004\n - type: ndcg_at_1\n value: 52.333\n - type: ndcg_at_10\n value: 62.67\n - type: ndcg_at_100\n value: 66.465\n - type: ndcg_at_1000\n value: 67.425\n - type: ndcg_at_3\n value: 58.711999999999996\n - type: ndcg_at_5\n value: 60.958999999999996\n - type: precision_at_1\n value: 52.333\n - type: precision_at_10\n value: 8.333\n - type: precision_at_100\n value: 1.027\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 22.778000000000002\n - type: precision_at_5\n value: 15.267\n - type: recall_at_1\n value: 49.9\n - type: recall_at_10\n value: 73.394\n - type: recall_at_100\n value: 90.43299999999999\n - type: recall_at_1000\n value: 98.167\n - type: recall_at_3\n value: 63.032999999999994\n - type: recall_at_5\n value: 68.444\n - task:\n type: Clustering\n dataset:\n name: MTEB SpanishNewsClusteringP2P\n type: jinaai/spanish_news_clustering\n config: default\n split: test\n revision: b5edc3d3d7c12c7b9f883e9da50f6732f3624142\n metrics:\n - type: v_measure\n value: 48.30543557796266\n - task:\n type: Retrieval\n dataset:\n name: MTEB SpanishPassageRetrievalS2P\n type: jinaai/spanish_passage_retrieval\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 14.443\n - type: map_at_10\n value: 28.736\n - type: map_at_100\n value: 34.514\n - type: map_at_1000\n value: 35.004000000000005\n - type: map_at_3\n value: 20.308\n - type: map_at_5\n value: 25.404\n - type: mrr_at_1\n value: 50.29900000000001\n - type: mrr_at_10\n value: 63.757\n - type: mrr_at_100\n value: 64.238\n - type: mrr_at_1000\n value: 64.24600000000001\n - type: mrr_at_3\n value: 59.480999999999995\n - type: mrr_at_5\n value: 62.924\n - type: ndcg_at_1\n value: 50.29900000000001\n - type: ndcg_at_10\n value: 42.126999999999995\n - type: ndcg_at_100\n value: 57.208000000000006\n - type: ndcg_at_1000\n value: 60.646\n - type: ndcg_at_3\n value: 38.722\n - type: ndcg_at_5\n value: 40.007999999999996\n - type: precision_at_1\n value: 50.29900000000001\n - type: precision_at_10\n value: 19.82\n - type: precision_at_100\n value: 4.82\n - type: precision_at_1000\n value: 0.5910000000000001\n - type: precision_at_3\n value: 31.537\n - type: precision_at_5\n value: 28.262999999999998\n - type: recall_at_1\n value: 14.443\n - type: recall_at_10\n value: 43.885999999999996\n - type: recall_at_100\n value: 85.231\n - type: recall_at_1000\n value: 99.07000000000001\n - type: recall_at_3\n value: 22.486\n - type: recall_at_5\n value: 33.035\n - type: map_at_1\n value: 15.578\n - type: map_at_10\n value: 52.214000000000006\n - type: map_at_100\n value: 64.791\n - type: map_at_1000\n value: 64.791\n - type: map_at_3\n value: 33.396\n - type: map_at_5\n value: 41.728\n - type: mrr_at_1\n value: 73.653\n - type: mrr_at_10\n value: 85.116\n - type: mrr_at_100\n value: 85.205\n - type: mrr_at_1000\n value: 85.205\n - type: mrr_at_3\n value: 84.631\n - type: mrr_at_5\n value: 85.05\n - type: ndcg_at_1\n value: 76.64699999999999\n - type: ndcg_at_10\n value: 70.38600000000001\n - type: ndcg_at_100\n value: 82.27600000000001\n - type: ndcg_at_1000\n value: 82.27600000000001\n - type: ndcg_at_3\n value: 70.422\n - type: ndcg_at_5\n value: 69.545\n - type: precision_at_1\n value: 76.64699999999999\n - type: precision_at_10\n value: 43.653\n - type: precision_at_100\n value: 7.718999999999999\n - type: precision_at_1000\n value: 0.772\n - type: precision_at_3\n value: 64.671\n - type: precision_at_5\n value: 56.766000000000005\n - type: recall_at_1\n value: 15.578\n - type: recall_at_10\n value: 67.459\n - type: recall_at_100\n value: 100.0\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_3\n value: 36.922\n - type: recall_at_5\n value: 49.424\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.81683168316832\n - type: cos_sim_ap\n value: 95.61502659412484\n - type: cos_sim_f1\n value: 90.6813627254509\n - type: cos_sim_precision\n value: 90.86345381526104\n - type: cos_sim_recall\n value: 90.5\n - type: dot_accuracy\n value: 99.8039603960396\n - type: dot_ap\n value: 95.36783483182609\n - type: dot_f1\n value: 89.90825688073394\n - type: dot_precision\n value: 91.68399168399168\n - type: dot_recall\n value: 88.2\n - type: euclidean_accuracy\n value: 99.81188118811882\n - type: euclidean_ap\n value: 95.51583052324564\n - type: euclidean_f1\n value: 90.46214355948868\n - type: euclidean_precision\n value: 88.97485493230174\n - type: euclidean_recall\n value: 92.0\n - type: manhattan_accuracy\n value: 99.8079207920792\n - type: manhattan_ap\n value: 95.44030644653718\n - type: manhattan_f1\n value: 90.37698412698413\n - type: manhattan_precision\n value: 89.66535433070865\n - type: manhattan_recall\n value: 91.10000000000001\n - type: max_accuracy\n value: 99.81683168316832\n - type: max_ap\n value: 95.61502659412484\n - type: max_f1\n value: 90.6813627254509\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 55.39046705023096\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 33.57429225651293\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 50.17622570658746\n - type: mrr\n value: 50.99844293778118\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 29.97416289382191\n - type: cos_sim_spearman\n value: 29.871890597161432\n - type: dot_pearson\n value: 28.768845892613644\n - type: dot_spearman\n value: 28.872458999448686\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.22599999999999998\n - type: map_at_10\n value: 1.646\n - type: map_at_100\n value: 9.491\n - type: map_at_1000\n value: 23.75\n - type: map_at_3\n value: 0.588\n - type: map_at_5\n value: 0.9129999999999999\n - type: mrr_at_1\n value: 84.0\n - type: mrr_at_10\n value: 89.889\n - type: mrr_at_100\n value: 89.889\n - type: mrr_at_1000\n value: 89.889\n - type: mrr_at_3\n value: 89.667\n - type: mrr_at_5\n value: 89.667\n - type: ndcg_at_1\n value: 75.0\n - type: ndcg_at_10\n value: 67.368\n - type: ndcg_at_100\n value: 52.834\n - type: ndcg_at_1000\n value: 49.144\n - type: ndcg_at_3\n value: 72.866\n - type: ndcg_at_5\n value: 70.16\n - type: precision_at_1\n value: 84.0\n - type: precision_at_10\n value: 71.8\n - type: precision_at_100\n value: 54.04\n - type: precision_at_1000\n value: 21.709999999999997\n - type: precision_at_3\n value: 77.333\n - type: precision_at_5\n value: 74.0\n - type: recall_at_1\n value: 0.22599999999999998\n - type: recall_at_10\n value: 1.9029999999999998\n - type: recall_at_100\n value: 13.012\n - type: recall_at_1000\n value: 46.105000000000004\n - type: recall_at_3\n value: 0.63\n - type: recall_at_5\n value: 1.0030000000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 1.5\n - type: map_at_10\n value: 8.193999999999999\n - type: map_at_100\n value: 14.01\n - type: map_at_1000\n value: 15.570999999999998\n - type: map_at_3\n value: 4.361000000000001\n - type: map_at_5\n value: 5.9270000000000005\n - type: mrr_at_1\n value: 16.326999999999998\n - type: mrr_at_10\n value: 33.326\n - type: mrr_at_100\n value: 34.592\n - type: mrr_at_1000\n value: 34.592\n - type: mrr_at_3\n value: 29.252\n - type: mrr_at_5\n value: 30.680000000000003\n - type: ndcg_at_1\n value: 15.306000000000001\n - type: ndcg_at_10\n value: 19.819\n - type: ndcg_at_100\n value: 33.428000000000004\n - type: ndcg_at_1000\n value: 45.024\n - type: ndcg_at_3\n value: 19.667\n - type: ndcg_at_5\n value: 19.625\n - type: precision_at_1\n value: 16.326999999999998\n - type: precision_at_10\n value: 18.367\n - type: precision_at_100\n value: 7.367\n - type: precision_at_1000\n value: 1.496\n - type: precision_at_3\n value: 23.128999999999998\n - type: precision_at_5\n value: 21.633\n - type: recall_at_1\n value: 1.5\n - type: recall_at_10\n value: 14.362\n - type: recall_at_100\n value: 45.842\n - type: recall_at_1000\n value: 80.42\n - type: recall_at_3\n value: 5.99\n - type: recall_at_5\n value: 8.701\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 70.04740000000001\n - type: ap\n value: 13.58661943759992\n - type: f1\n value: 53.727487131754195\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 61.06395019807584\n - type: f1\n value: 61.36753664680866\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 40.19881263066229\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 85.19401561661799\n - type: cos_sim_ap\n value: 71.62462506173092\n - type: cos_sim_f1\n value: 66.0641327225455\n - type: cos_sim_precision\n value: 62.234662934453\n - type: cos_sim_recall\n value: 70.3957783641161\n - type: dot_accuracy\n value: 84.69333015437802\n - type: dot_ap\n value: 69.83805526490895\n - type: dot_f1\n value: 64.85446235265817\n - type: dot_precision\n value: 59.59328028293546\n - type: dot_recall\n value: 71.13456464379946\n - type: euclidean_accuracy\n value: 85.38475293556655\n - type: euclidean_ap\n value: 72.05594596250286\n - type: euclidean_f1\n value: 66.53543307086615\n - type: euclidean_precision\n value: 62.332872291378514\n - type: euclidean_recall\n value: 71.34564643799473\n - type: manhattan_accuracy\n value: 85.3907134767837\n - type: manhattan_ap\n value: 72.04585410650152\n - type: manhattan_f1\n value: 66.57132642116554\n - type: manhattan_precision\n value: 60.704194740273856\n - type: manhattan_recall\n value: 73.6939313984169\n - type: max_accuracy\n value: 85.3907134767837\n - type: max_ap\n value: 72.05594596250286\n - type: max_f1\n value: 66.57132642116554\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.30414871735165\n - type: cos_sim_ap\n value: 86.4398673359918\n - type: cos_sim_f1\n value: 78.9243598692186\n - type: cos_sim_precision\n value: 75.47249350101876\n - type: cos_sim_recall\n value: 82.7071142593163\n - type: dot_accuracy\n value: 89.26145845461248\n - type: dot_ap\n value: 86.32172118414802\n - type: dot_f1\n value: 78.8277467755645\n - type: dot_precision\n value: 75.79418662497335\n - type: dot_recall\n value: 82.11425931629196\n - type: euclidean_accuracy\n value: 89.24205378973105\n - type: euclidean_ap\n value: 86.23988673522649\n - type: euclidean_f1\n value: 78.67984857951413\n - type: euclidean_precision\n value: 75.2689684269742\n - type: euclidean_recall\n value: 82.41453649522637\n - type: manhattan_accuracy\n value: 89.18189932859859\n - type: manhattan_ap\n value: 86.21003833972824\n - type: manhattan_f1\n value: 78.70972564850115\n - type: manhattan_precision\n value: 76.485544094145\n - type: manhattan_recall\n value: 81.0671388974438\n - type: max_accuracy\n value: 89.30414871735165\n - type: max_ap\n value: 86.4398673359918\n - type: max_f1\n value: 78.9243598692186\n - task:\n type: Clustering\n dataset:\n name: MTEB WikiCitiesClustering\n type: jinaai/cities_wiki_clustering\n config: default\n split: test\n revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa\n metrics:\n - type: v_measure\n value: 73.254610626148\n - task:\n type: Retrieval\n dataset:\n name: MTEB XMarketES\n type: jinaai/xmarket_ml\n config: default\n split: test\n revision: 705db869e8107dfe6e34b832af90446e77d813e3\n metrics:\n - type: map_at_1\n value: 5.506\n - type: map_at_10\n value: 11.546\n - type: map_at_100\n value: 14.299999999999999\n - type: map_at_1000\n value: 15.146999999999998\n - type: map_at_3\n value: 8.748000000000001\n - type: map_at_5\n value: 10.036000000000001\n - type: mrr_at_1\n value: 17.902\n - type: mrr_at_10\n value: 25.698999999999998\n - type: mrr_at_100\n value: 26.634\n - type: mrr_at_1000\n value: 26.704\n - type: mrr_at_3\n value: 23.244999999999997\n - type: mrr_at_5\n value: 24.555\n - type: ndcg_at_1\n value: 17.902\n - type: ndcg_at_10\n value: 19.714000000000002\n - type: ndcg_at_100\n value: 25.363000000000003\n - type: ndcg_at_1000\n value: 30.903999999999996\n - type: ndcg_at_3\n value: 17.884\n - type: ndcg_at_5\n value: 18.462\n - type: precision_at_1\n value: 17.902\n - type: precision_at_10\n value: 10.467\n - type: precision_at_100\n value: 3.9699999999999998\n - type: precision_at_1000\n value: 1.1320000000000001\n - type: precision_at_3\n value: 14.387\n - type: precision_at_5\n value: 12.727\n - type: recall_at_1\n value: 5.506\n - type: recall_at_10\n value: 19.997999999999998\n - type: recall_at_100\n value: 42.947\n - type: recall_at_1000\n value: 67.333\n - type: recall_at_3\n value: 11.158\n - type: recall_at_5\n value: 14.577000000000002\n - task:\n type: Retrieval\n dataset:\n name: MTEB XPQAESRetrieval\n type: jinaai/xpqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 32.53\n - type: map_at_10\n value: 58.68600000000001\n - type: map_at_100\n value: 60.45399999999999\n - type: map_at_1000\n value: 60.51499999999999\n - type: map_at_3\n value: 50.356\n - type: map_at_5\n value: 55.98\n - type: mrr_at_1\n value: 61.791\n - type: mrr_at_10\n value: 68.952\n - type: mrr_at_100\n value: 69.524\n - type: mrr_at_1000\n value: 69.538\n - type: mrr_at_3\n value: 67.087\n - type: mrr_at_5\n value: 68.052\n - type: ndcg_at_1\n value: 61.791\n - type: ndcg_at_10\n value: 65.359\n - type: ndcg_at_100\n value: 70.95700000000001\n - type: ndcg_at_1000\n value: 71.881\n - type: ndcg_at_3\n value: 59.999\n - type: ndcg_at_5\n value: 61.316\n - type: precision_at_1\n value: 61.791\n - type: precision_at_10\n value: 18.184\n - type: precision_at_100\n value: 2.317\n - type: precision_at_1000\n value: 0.245\n - type: precision_at_3\n value: 42.203\n - type: precision_at_5\n value: 31.374999999999996\n - type: recall_at_1\n value: 32.53\n - type: recall_at_10\n value: 73.098\n - type: recall_at_100\n value: 94.029\n - type: recall_at_1000\n value: 99.842\n - type: recall_at_3\n value: 54.525\n - type: recall_at_5\n value: 63.796\n---\n\n\n\n
\n![\"Jina](\"https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp\"){kind=logo}
\n
\nThe text embedding set trained by Jina AI.\n
\n\n## Quick Start\n\nThe easiest way to starting using `jina-embeddings-v2-base-es` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).\n\n\n## Intended Usage & Model Info\n\n`jina-embeddings-v2-base-es` is a Spanish/English bilingual text **embedding model** supporting **8192 sequence length**.\nIt is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.\nWe have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Spanish-English input without bias. \nAdditionally, we provide the following embedding models:\n\n`jina-embeddings-v2-base-es` es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192.\nSe basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de [ALiBi](https://arxiv.org/abs/2108.12409) para permitir una mayor longitud de secuencia.\nHemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo.\nAdicionalmente, proporcionamos los siguientes modelos (embeddings):\n\n- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.\n- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.\n- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings.\n- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.\n- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings **(you are here)**.\n\n## Data & Parameters\n\nThe data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016)\n\n## Usage\n\n**Please apply mean pooling when integrating the model.
**\n\n\n### Why mean pooling?\n\n`mean pooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.\nIt has been proved to be the most effective way to produce high-quality sentence embeddings.\nWe offer an `encode` function to deal with this.\n\nHowever, if you would like to do it without using the default `encode` function:\n\n```python\nimport torch\nimport torch.nn.functional as F\nfrom transformers import AutoTokenizer, AutoModel\n\ndef mean_pooling(model_output, attention_mask):\n token_embeddings = model_output[0]\n input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n\nsentences = ['How is the weather today?', 'What is the current weather like today?']\n\ntokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-es')\nmodel = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-es', trust_remote_code=True)\n\nencoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')\n\nwith torch.no_grad():\n model_output = model(**encoded_input)\n\nembeddings = mean_pooling(model_output, encoded_input['attention_mask'])\nembeddings = F.normalize(embeddings, p=2, dim=1)\n```\n\n
\n\n\n\n## Plans\n\n1. Bilingual embedding models supporting more European & Asian languages, including French, Italian and Japanese.\n2. Multimodal embedding models enable Multimodal RAG applications.\n3. High-performt rerankers.\n\n## Contact\n\nJoin our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.\n\n## Citation\n\nIf you find Jina Embeddings useful in your research, please cite the following paper:\n\n```\n@article{mohr2024multi,\n title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},\n author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\\\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\\'\\i}nez, Joan Fontanals and Wang, Feng and others},\n journal={arXiv preprint arXiv:2402.17016},\n year={2024}\n}\n```\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":88,"cells":{"id":{"kind":"string","value":"Lajavaness/bilingual-embedding-large"},"author":{"kind":"string","value":"Lajavaness"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","bilingual","feature-extraction","sentence-similarity","transformers","sentence-embedding","mteb","custom_code","fr","en","arxiv:2010.08240","arxiv:1911.02116","arxiv:1908.10084","license:apache-2.0","model-index","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"bilingual\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"transformers\",\n \"sentence-embedding\",\n \"mteb\",\n \"custom_code\",\n \"fr\",\n \"en\",\n \"arxiv:2010.08240\",\n \"arxiv:1911.02116\",\n \"arxiv:1908.10084\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-06-24T09:08:32"},"last_modified":{"kind":"string","value":"2024-08-06T09:22:13"},"downloads":{"kind":"number","value":31132,"string":"31,132"},"likes":{"kind":"number","value":21,"string":"21"},"README":{"kind":"string","value":"---\nlanguage:\n- fr\n- en\nlibrary_name: sentence-transformers\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- sentence-embedding\n- mteb\nmodel-index:\n- name: bilingual-embedding-large\n results:\n - 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type: ndcg_at_1000\n value: 28.916999999999998\n - type: ndcg_at_20\n value: 22.158\n - type: ndcg_at_3\n value: 14.543000000000001\n - type: ndcg_at_5\n value: 16.345000000000002\n - type: precision_at_1\n value: 10.811\n - type: precision_at_10\n value: 3.198\n - type: precision_at_100\n value: 0.644\n - type: precision_at_1000\n value: 0.087\n - type: precision_at_20\n value: 2.117\n - type: precision_at_3\n value: 5.856\n - type: precision_at_5\n value: 4.414\n - type: recall_at_1\n value: 10.811\n - type: recall_at_10\n value: 31.982\n - type: recall_at_100\n value: 64.414\n - type: recall_at_1000\n value: 86.937\n - type: recall_at_20\n value: 42.342\n - type: recall_at_3\n value: 17.568\n - type: recall_at_5\n value: 22.072\n - task:\n type: Clustering\n dataset:\n name: MTEB HALClusteringS2S\n type: lyon-nlp/clustering-hal-s2s\n config: default\n split: test\n revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915\n metrics:\n - type: v_measure\n value: 26.26502535631247\n - type: v_measures\n value:\n - 0.30893096531878045\n - 0.27408569069152805\n - 0.2872676670832888\n - 0.26871778422889214\n - 0.2421329238735192\n - task:\n type: Clustering\n dataset:\n name: MTEB MLSUMClusteringP2P\n type: reciTAL/mlsum\n config: fr\n split: test\n revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7\n metrics:\n - type: v_measure\n value: 42.60059039120384\n - type: v_measures\n value:\n - 0.4248169037837413\n - 0.44678284494908554\n - 0.4386784796938775\n - 0.41609051956546156\n - 0.37929269357080225\n - type: v_measure\n value: 42.92324222522204\n - type: v_measures\n value:\n - 0.4320945601805418\n - 0.43467886343873713\n - 0.4345273113581795\n - 0.4277842446367462\n - 0.381555432691925\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (fr)\n type: mteb/mtop_domain\n config: fr\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 88.33385530848732\n - type: f1\n value: 88.36975245849551\n - type: f1_weighted\n value: 88.310383667222\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (fr)\n type: mteb/mtop_intent\n config: fr\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 62.84685248982148\n - type: f1\n value: 44.420122133882366\n - type: f1_weighted\n value: 65.2728620649712\n - task:\n type: Classification\n dataset:\n name: MTEB MasakhaNEWSClassification (fra)\n type: mteb/masakhanews\n config: fra\n split: test\n revision: 18193f187b92da67168c655c9973a165ed9593dd\n metrics:\n - type: accuracy\n value: 80.56872037914692\n - type: f1\n value: 77.28557364601339\n - type: f1_weighted\n value: 80.51403795220486\n - task:\n type: Clustering\n dataset:\n name: MTEB MasakhaNEWSClusteringP2P (fra)\n type: masakhane/masakhanews\n config: fra\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: v_measure\n value: 71.29428035967938\n - type: v_measures\n value:\n - 1.0\n - 0.2773866490640993\n - 0.7679216739314454\n - 0.8367645040119921\n - 0.6826411909764316\n - type: v_measure\n value: 55.090949643200084\n - type: v_measures\n value:\n - 1.0\n - 0.0008196849334082873\n - 0.7532269197656756\n - 0.37056337344528145\n - 0.6299375040156386\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (fr)\n type: mteb/amazon_massive_intent\n config: fr\n split: test\n revision: 4672e20407010da34463acc759c162ca9734bca6\n metrics:\n - type: accuracy\n value: 66.80564895763281\n - type: f1\n value: 64.35238995318795\n - type: f1_weighted\n value: 65.7206181780162\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (fr)\n type: mteb/amazon_massive_scenario\n config: fr\n split: test\n revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8\n metrics:\n - type: accuracy\n value: 72.98587760591795\n - type: f1\n value: 72.51250718054763\n - type: f1_weighted\n value: 72.81793917434213\n - task:\n type: Retrieval\n dataset:\n name: MTEB MintakaRetrieval (fr)\n type: jinaai/mintakaqa\n config: fr\n split: test\n revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e\n metrics:\n - 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type: nauc_mrr_at_3_max\n value: 27.717678662759226\n - type: nauc_mrr_at_5_diff1\n value: 19.121183364075133\n - type: nauc_mrr_at_5_max\n value: 28.33281003699522\n - type: nauc_ndcg_at_1000_diff1\n value: 16.9385175619818\n - type: nauc_ndcg_at_1000_max\n value: 30.464626780924114\n - type: nauc_ndcg_at_100_diff1\n value: 15.784507139472703\n - type: nauc_ndcg_at_100_max\n value: 30.783304190943873\n - type: nauc_ndcg_at_10_diff1\n value: 17.074677821502657\n - type: nauc_ndcg_at_10_max\n value: 31.39661325771708\n - type: nauc_ndcg_at_1_diff1\n value: 24.276017299858978\n - type: nauc_ndcg_at_1_max\n value: 23.470875089293564\n - type: nauc_ndcg_at_20_diff1\n value: 15.905931373911173\n - type: nauc_ndcg_at_20_max\n value: 31.283157447315457\n - type: nauc_ndcg_at_3_diff1\n value: 18.520146441301954\n - type: nauc_ndcg_at_3_max\n value: 28.855566633100217\n - type: nauc_ndcg_at_5_diff1\n value: 17.414930054902594\n - type: nauc_ndcg_at_5_max\n value: 29.89288498763886\n - type: nauc_precision_at_1000_diff1\n value: 5.6404707169181485\n - type: nauc_precision_at_1000_max\n value: 51.53249587390901\n - type: nauc_precision_at_100_diff1\n value: 2.6401827420753463\n - type: nauc_precision_at_100_max\n value: 37.544518255619415\n - type: nauc_precision_at_10_diff1\n value: 12.07308037199035\n - type: nauc_precision_at_10_max\n value: 38.23001565740937\n - type: nauc_precision_at_1_diff1\n value: 24.276017299858978\n - type: nauc_precision_at_1_max\n value: 23.470875089293564\n - type: nauc_precision_at_20_diff1\n value: 7.157477225670103\n - type: nauc_precision_at_20_max\n value: 38.273237139593256\n - type: nauc_precision_at_3_diff1\n value: 15.259422549391488\n - type: nauc_precision_at_3_max\n value: 31.763923868965588\n - type: nauc_precision_at_5_diff1\n value: 13.005921624910583\n - type: nauc_precision_at_5_max\n value: 33.92162820494794\n - type: nauc_recall_at_1000_diff1\n value: 5.6404707169180055\n - type: nauc_recall_at_1000_max\n value: 51.53249587390878\n - type: nauc_recall_at_100_diff1\n value: 2.640182742075308\n - type: nauc_recall_at_100_max\n value: 37.544518255619444\n - type: nauc_recall_at_10_diff1\n value: 12.073080371990335\n - type: nauc_recall_at_10_max\n value: 38.230015657409375\n - type: nauc_recall_at_1_diff1\n value: 24.276017299858978\n - type: nauc_recall_at_1_max\n value: 23.470875089293564\n - type: nauc_recall_at_20_diff1\n value: 7.157477225670139\n - type: nauc_recall_at_20_max\n value: 38.27323713959323\n - type: nauc_recall_at_3_diff1\n value: 15.259422549391505\n - type: nauc_recall_at_3_max\n value: 31.763923868965588\n - type: nauc_recall_at_5_diff1\n value: 13.005921624910567\n - type: nauc_recall_at_5_max\n value: 33.92162820494793\n - type: ndcg_at_1\n value: 18.96\n - type: ndcg_at_10\n value: 32.617000000000004\n - type: ndcg_at_100\n value: 37.974000000000004\n - type: ndcg_at_1000\n value: 40.65\n - type: ndcg_at_20\n value: 34.888000000000005\n - type: ndcg_at_3\n value: 27.106\n - type: ndcg_at_5\n value: 29.614\n - type: precision_at_1\n value: 18.96\n - type: precision_at_10\n value: 4.824\n - type: precision_at_100\n value: 0.738\n - type: precision_at_1000\n value: 0.096\n - type: precision_at_20\n value: 2.858\n - type: precision_at_3\n value: 10.961\n - type: precision_at_5\n value: 7.789\n - type: recall_at_1\n value: 18.96\n - type: recall_at_10\n value: 48.239\n - type: recall_at_100\n value: 73.833\n - type: recall_at_1000\n value: 95.82300000000001\n - type: recall_at_20\n value: 57.166\n - type: recall_at_3\n value: 32.883\n - type: recall_at_5\n value: 38.943\n - task:\n type: PairClassification\n dataset:\n name: MTEB OpusparcusPC (fr)\n type: GEM/opusparcus\n config: fr\n split: test\n revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a\n metrics:\n - type: cos_sim_accuracy\n value: 84.80926430517711\n - type: cos_sim_ap\n value: 94.76661922683681\n - type: cos_sim_f1\n value: 89.31480594154289\n - type: cos_sim_precision\n value: 86.29629629629629\n - type: cos_sim_recall\n value: 92.55213505461768\n - type: dot_accuracy\n value: 84.80926430517711\n - type: dot_ap\n value: 94.766630886443\n - type: dot_f1\n value: 89.31480594154289\n - type: dot_precision\n value: 86.29629629629629\n - type: dot_recall\n value: 92.55213505461768\n - type: euclidean_accuracy\n value: 84.80926430517711\n - type: euclidean_ap\n value: 94.76661922683681\n - type: euclidean_f1\n value: 89.31480594154289\n - type: euclidean_precision\n value: 86.29629629629629\n - type: euclidean_recall\n value: 92.55213505461768\n - type: manhattan_accuracy\n value: 84.94550408719346\n - type: manhattan_ap\n value: 94.78582392571815\n - type: manhattan_f1\n value: 89.33912204534491\n - type: manhattan_precision\n value: 86.86679174484053\n - type: manhattan_recall\n value: 91.9563058589871\n - type: max_accuracy\n value: 84.94550408719346\n - type: max_ap\n value: 94.78582392571815\n - type: max_f1\n value: 89.33912204534491\n - task:\n type: PairClassification\n dataset:\n name: MTEB PawsX (fr)\n type: google-research-datasets/paws-x\n config: fr\n split: test\n revision: 8a04d940a42cd40658986fdd8e3da561533a3646\n metrics:\n - type: cos_sim_accuracy\n value: 64.5\n - type: cos_sim_ap\n value: 64.51219412005997\n - type: cos_sim_f1\n value: 62.84885828198622\n - type: cos_sim_precision\n value: 46.713362068965516\n - type: cos_sim_recall\n value: 96.01328903654485\n - type: dot_accuracy\n value: 64.5\n - type: dot_ap\n value: 64.50290830259848\n - type: dot_f1\n value: 62.84885828198622\n - type: dot_precision\n value: 46.713362068965516\n - type: dot_recall\n value: 96.01328903654485\n - type: euclidean_accuracy\n value: 64.5\n - type: euclidean_ap\n value: 64.51219412005995\n - type: euclidean_f1\n value: 62.84885828198622\n - type: euclidean_precision\n value: 46.713362068965516\n - type: euclidean_recall\n value: 96.01328903654485\n - type: manhattan_accuracy\n value: 64.55\n - type: manhattan_ap\n value: 64.54022554293084\n - type: manhattan_f1\n value: 62.836363636363636\n - type: manhattan_precision\n value: 46.778559826746076\n - type: manhattan_recall\n value: 95.68106312292359\n - type: max_accuracy\n value: 64.55\n - type: max_ap\n value: 64.54022554293084\n - type: max_f1\n value: 62.84885828198622\n - task:\n type: STS\n dataset:\n name: MTEB SICKFr\n type: Lajavaness/SICK-fr\n config: default\n split: test\n revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a\n metrics:\n - type: cos_sim_pearson\n value: 85.15315949054092\n - type: cos_sim_spearman\n value: 79.19701933507372\n - type: euclidean_pearson\n value: 82.68441006897395\n - type: euclidean_spearman\n value: 79.1963186010215\n - type: manhattan_pearson\n value: 82.6725500567899\n - type: manhattan_spearman\n value: 79.13255295711785\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (fr)\n type: mteb/sts22-crosslingual-sts\n config: fr\n split: test\n revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3\n metrics:\n - type: cos_sim_pearson\n value: 83.13328685349694\n - type: cos_sim_spearman\n value: 84.64291479319418\n - type: euclidean_pearson\n value: 83.28605886303359\n - type: euclidean_spearman\n value: 84.64291479319418\n - type: manhattan_pearson\n value: 83.01485484058145\n - type: manhattan_spearman\n value: 84.35826862976153\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmarkMultilingualSTS (fr)\n type: mteb/stsb_multi_mt\n config: fr\n split: test\n revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c\n metrics:\n - type: cos_sim_pearson\n value: 86.15391910168253\n - type: cos_sim_spearman\n value: 87.0224186207858\n - type: euclidean_pearson\n value: 86.04463800957714\n - type: euclidean_spearman\n value: 87.02424394489165\n - type: manhattan_pearson\n value: 86.03126279628441\n - type: manhattan_spearman\n value: 86.99427177229043\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEvalFr\n type: lyon-nlp/summarization-summeval-fr-p2p\n config: default\n split: test\n revision: b385812de6a9577b6f4d0f88c6a6e35395a94054\n metrics:\n - type: cos_sim_pearson\n value: 31.415083738613355\n - type: cos_sim_spearman\n value: 30.301784303588285\n - type: dot_pearson\n value: 31.415089981266963\n - type: dot_spearman\n value: 30.286152348575108\n - task:\n type: Reranking\n dataset:\n name: MTEB SyntecReranking\n type: lyon-nlp/mteb-fr-reranking-syntec-s2p\n config: default\n split: test\n revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad\n metrics:\n - type: map\n value: 85.95238095238095\n - type: mrr\n value: 85.95238095238095\n - type: nAUC_map_diff1\n value: 70.42176052252755\n - type: nAUC_map_max\n value: 19.806028833551718\n - type: nAUC_mrr_diff1\n value: 70.42176052252755\n - type: nAUC_mrr_max\n value: 19.806028833551718\n - task:\n type: Retrieval\n dataset:\n name: MTEB SyntecRetrieval\n type: lyon-nlp/mteb-fr-retrieval-syntec-s2p\n config: default\n split: test\n revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9\n metrics:\n - type: map_at_1\n value: 69.0\n - type: map_at_10\n value: 79.668\n - type: map_at_100\n value: 79.791\n - type: map_at_1000\n value: 79.791\n - type: map_at_20\n value: 79.751\n - type: map_at_3\n value: 78.167\n - type: map_at_5\n value: 79.067\n - type: mrr_at_1\n value: 69.0\n - type: mrr_at_10\n value: 79.66785714285714\n - 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type: recall_at_5\n value: 6.802999999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 65.56640625\n - type: ap\n value: 12.336183192628836\n - type: ap_weighted\n value: 12.336183192628836\n - type: f1\n value: 50.61953920605424\n - type: f1_weighted\n value: 73.10180241141433\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 62.80418788907753\n - type: f1\n value: 63.050557758931134\n - type: f1_weighted\n value: 62.13337985337418\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 49.00618373985209\n - type: v_measures\n value:\n - 0.49421217801171224\n - 0.4740440424893081\n - 0.4886726035776056\n - 0.5198976504195676\n - 0.4827070012054274\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 86.97025689932646\n - type: cos_sim_ap\n value: 77.06565012359437\n - type: cos_sim_f1\n value: 70.32217308907138\n - type: cos_sim_precision\n value: 67.46666666666667\n - type: cos_sim_recall\n value: 73.43007915567283\n - type: dot_accuracy\n value: 86.97025689932646\n - type: dot_ap\n value: 77.0656524331512\n - type: dot_f1\n value: 70.32217308907138\n - type: dot_precision\n value: 67.46666666666667\n - type: dot_recall\n value: 73.43007915567283\n - type: euclidean_accuracy\n value: 86.97025689932646\n - type: euclidean_ap\n value: 77.06564828845742\n - type: euclidean_f1\n value: 70.32217308907138\n - type: euclidean_precision\n value: 67.46666666666667\n - type: euclidean_recall\n value: 73.43007915567283\n - type: manhattan_accuracy\n value: 86.90469094593789\n - type: manhattan_ap\n value: 76.94347285253252\n - type: manhattan_f1\n value: 70.18523217457499\n - type: manhattan_precision\n value: 67.59530791788856\n - type: manhattan_recall\n value: 72.98153034300792\n - type: max_accuracy\n value: 86.97025689932646\n - type: max_ap\n value: 77.0656524331512\n - type: max_f1\n value: 70.32217308907138\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.64567081926495\n - type: cos_sim_ap\n value: 87.19162831580245\n - type: cos_sim_f1\n value: 79.67696578577352\n - type: cos_sim_precision\n value: 74.92033358193775\n - type: cos_sim_recall\n value: 85.07853403141361\n - type: dot_accuracy\n value: 89.64567081926495\n - type: dot_ap\n value: 87.19162304433766\n - type: dot_f1\n value: 79.67696578577352\n - type: dot_precision\n value: 74.92033358193775\n - type: dot_recall\n value: 85.07853403141361\n - type: euclidean_accuracy\n value: 89.64567081926495\n - type: euclidean_ap\n value: 87.19162847931055\n - type: euclidean_f1\n value: 79.67696578577352\n - type: euclidean_precision\n value: 74.92033358193775\n - type: euclidean_recall\n value: 85.07853403141361\n - type: manhattan_accuracy\n value: 89.67283735009897\n - type: manhattan_ap\n value: 87.19033616510255\n - type: manhattan_f1\n value: 79.67444226437031\n - type: manhattan_precision\n value: 75.43690656391908\n - type: manhattan_recall\n value: 84.41638435478903\n - type: max_accuracy\n value: 89.67283735009897\n - type: max_ap\n value: 87.19162847931055\n - type: max_f1\n value: 79.67696578577352\n---\n\n# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)\n\nBilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.\n\n\n## Full Model Architecture\n```\nSentenceTransformer(\n (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel \n (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})\n (2): Normalize()\n)\n```\n\n## Training and Fine-tuning process\n#### Stage 1: NLI Training\n- Dataset: [(SNLI+XNLI) for english+french]\n- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.\n### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark\n- Dataset: [STSB-fr and en]\n- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library. \n### Stage 4: Advanced Augmentation Fine-tuning\n- Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)\n- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.\n\n\n## Usage:\n\nUsing this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:\n\n```\npip install -U sentence-transformers\n```\n\nThen you can use the model like this:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nsentences = [\"Paris est une capitale de la France\", \"Paris is a capital of France\"]\n\nmodel = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)\nprint(embeddings)\n\n```\n\n\n\n\n\n## Evaluation\n\nTODO\n\n## Citation\n\n @article{conneau2019unsupervised,\n title={Unsupervised cross-lingual representation learning at scale},\n author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},\n journal={arXiv preprint arXiv:1911.02116},\n year={2019}\n }\n\n\t@article{reimers2019sentence,\n\t title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},\n\t author={Nils Reimers, Iryna Gurevych},\n\t journal={https://arxiv.org/abs/1908.10084},\n\t year={2019}\n\t}\n\n @article{thakur2020augmented,\n title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},\n author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},\n journal={arXiv e-prints},\n pages={arXiv--2010},\n year={2020}"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":89,"cells":{"id":{"kind":"string","value":"OrcaDB/cde-small-v1"},"author":{"kind":"string","value":"OrcaDB"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","feature-extraction","mteb","transformers","custom_code","arxiv:2410.02525","model-index","autotrain_compatible","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"feature-extraction\",\n \"mteb\",\n \"transformers\",\n \"custom_code\",\n \"arxiv:2410.02525\",\n \"model-index\",\n \"autotrain_compatible\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-11-08T19:49:54"},"last_modified":{"kind":"string","value":"2025-01-02T19:34:35"},"downloads":{"kind":"number","value":30596,"string":"30,596"},"likes":{"kind":"number","value":4,"string":"4"},"README":{"kind":"string","value":"---\ntags:\n- mteb\n- transformers\n- sentence-transformers\nmodel-index:\n- name: cde-small-v1\n results:\n - 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type: cosine_spearman\n value: 86.12368715292688\n - type: euclidean_pearson\n value: 85.71147581542367\n - type: euclidean_spearman\n value: 86.64112317821541\n - type: main_score\n value: 86.12368715292688\n - type: manhattan_pearson\n value: 85.58242941611371\n - type: manhattan_spearman\n value: 86.51041533466731\n - type: pearson\n value: 85.49925337918731\n - type: spearman\n value: 86.12368715292688\n - task:\n type: STS\n dataset:\n name: MTEB STS14 (default)\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cosine_pearson\n value: 82.24735192639226\n - type: cosine_spearman\n value: 78.88155361224834\n - type: euclidean_pearson\n value: 80.52048132030517\n - type: euclidean_spearman\n value: 78.1335955670817\n - type: main_score\n value: 78.88155361224834\n - type: manhattan_pearson\n value: 80.48178866605353\n - type: manhattan_spearman\n value: 78.08994918255844\n - type: pearson\n value: 82.24735192639226\n - type: spearman\n value: 78.88155361224834\n - task:\n type: STS\n dataset:\n name: MTEB STS15 (default)\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cosine_pearson\n value: 86.27381322229758\n - type: cosine_spearman\n value: 87.5038962579188\n - type: euclidean_pearson\n value: 86.7575259976948\n - type: euclidean_spearman\n value: 87.3358778981031\n - type: main_score\n value: 87.5038962579188\n - type: manhattan_pearson\n value: 86.72177109814491\n - type: manhattan_spearman\n value: 87.30593609243358\n - type: pearson\n value: 86.27381322229758\n - type: spearman\n value: 87.5038962579188\n - task:\n type: STS\n dataset:\n name: MTEB STS16 (default)\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cosine_pearson\n value: 82.90364706517789\n - type: cosine_spearman\n value: 84.25854334490232\n - type: euclidean_pearson\n value: 83.30065780824273\n - type: euclidean_spearman\n value: 84.17467271748362\n - type: main_score\n value: 84.25854334490232\n - type: manhattan_pearson\n value: 83.21239264085494\n - type: manhattan_spearman\n value: 84.05456832118482\n - type: pearson\n value: 82.90364706517789\n - type: spearman\n value: 84.25854334490232\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: faeb762787bd10488a50c8b5be4a3b82e411949c\n metrics:\n - type: cosine_pearson\n value: 88.88258729094343\n - type: cosine_spearman\n value: 89.68436656381257\n - type: euclidean_pearson\n value: 88.23417725579127\n - type: euclidean_spearman\n value: 87.96688277361433\n - type: main_score\n value: 89.68436656381257\n - type: manhattan_pearson\n value: 88.07673471897155\n - type: manhattan_spearman\n value: 87.7976329721765\n - type: pearson\n value: 88.88258729094343\n - type: spearman\n value: 89.68436656381257\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3\n metrics:\n - 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type: nauc_recall_at_20_max\n value: 67.83558221740536\n - type: nauc_recall_at_20_std\n value: 3.110691973832695\n - type: nauc_recall_at_3_diff1\n value: 60.39078444139112\n - type: nauc_recall_at_3_max\n value: 51.122425596651574\n - type: nauc_recall_at_3_std\n value: -10.307895490015559\n - type: nauc_recall_at_5_diff1\n value: 59.703727953513145\n - type: nauc_recall_at_5_max\n value: 59.81893786534298\n - type: nauc_recall_at_5_std\n value: -6.231017907901268\n - type: ndcg_at_1\n value: 60.0\n - type: ndcg_at_10\n value: 71.844\n - type: ndcg_at_100\n value: 74.278\n - type: ndcg_at_1000\n value: 74.74199999999999\n - type: ndcg_at_20\n value: 72.99\n - type: ndcg_at_3\n value: 66.721\n - type: ndcg_at_5\n value: 69.137\n - type: precision_at_1\n value: 60.0\n - type: precision_at_10\n value: 9.6\n - type: precision_at_100\n value: 1.093\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_20\n value: 5.067\n - type: precision_at_3\n value: 26.111\n - type: precision_at_5\n value: 17.267\n - type: recall_at_1\n value: 57.278\n - type: recall_at_10\n value: 85.344\n - type: recall_at_100\n value: 96.5\n - type: recall_at_1000\n value: 100.0\n - type: recall_at_20\n value: 89.589\n - type: recall_at_3\n value: 71.45\n - type: recall_at_5\n value: 77.361\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions (default)\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cosine_accuracy\n value: 99.8019801980198\n - type: cosine_accuracy_threshold\n value: 74.77510571479797\n - type: cosine_ap\n value: 95.30006120252773\n - type: cosine_f1\n value: 89.75265017667844\n - type: cosine_f1_threshold\n value: 72.93492555618286\n - type: cosine_precision\n value: 90.62181447502549\n - type: cosine_recall\n value: 88.9\n - type: dot_accuracy\n value: 99.74554455445545\n - type: dot_accuracy_threshold\n value: 794.2790985107422\n - 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type: nauc_recall_at_100_std\n value: 29.430588706591397\n - type: nauc_recall_at_10_diff1\n value: 18.481730784371077\n - type: nauc_recall_at_10_max\n value: -7.763172381505888\n - type: nauc_recall_at_10_std\n value: -7.48570052741164\n - type: nauc_recall_at_1_diff1\n value: 18.809274247757674\n - type: nauc_recall_at_1_max\n value: -8.68104031396317\n - type: nauc_recall_at_1_std\n value: -30.619138463973307\n - type: nauc_recall_at_20_diff1\n value: 20.639977762281493\n - type: nauc_recall_at_20_max\n value: -11.301201172125623\n - type: nauc_recall_at_20_std\n value: 0.38755705583239786\n - type: nauc_recall_at_3_diff1\n value: 18.279383297820562\n - type: nauc_recall_at_3_max\n value: 5.287795698059438\n - type: nauc_recall_at_3_std\n value: -3.7312167565958316\n - type: nauc_recall_at_5_diff1\n value: 21.115852302465356\n - type: nauc_recall_at_5_max\n value: 5.318139212101227\n - type: nauc_recall_at_5_std\n value: -7.792885381250281\n - type: ndcg_at_1\n value: 25.509999999999998\n - type: ndcg_at_10\n value: 24.254\n - type: ndcg_at_100\n value: 34.660000000000004\n - type: ndcg_at_1000\n value: 45.798\n - type: ndcg_at_20\n value: 24.988\n - type: ndcg_at_3\n value: 29.273\n - type: ndcg_at_5\n value: 25.453\n - type: precision_at_1\n value: 28.571\n - type: precision_at_10\n value: 21.02\n - type: precision_at_100\n value: 7.122000000000001\n - type: precision_at_1000\n value: 1.435\n - type: precision_at_20\n value: 16.326999999999998\n - type: precision_at_3\n value: 31.293\n - type: precision_at_5\n value: 24.898\n - type: recall_at_1\n value: 2.355\n - type: recall_at_10\n value: 15.397\n - type: recall_at_100\n value: 43.647000000000006\n - type: recall_at_1000\n value: 77.089\n - type: recall_at_20\n value: 22.792\n - type: recall_at_3\n value: 6.847\n - type: recall_at_5\n value: 9.136\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification (default)\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 72.7734375\n - type: ap\n value: 15.655230461083708\n - type: ap_weighted\n value: 15.655230461083708\n - type: f1\n value: 56.31497978454638\n - type: f1_weighted\n value: 78.70509613747345\n - type: main_score\n value: 72.7734375\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification (default)\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 72.56366723259762\n - type: f1\n value: 72.90413275122202\n - type: f1_weighted\n value: 72.19948169084057\n - type: main_score\n value: 72.56366723259762\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering (default)\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: main_score\n value: 56.90970017457857\n - type: v_measure\n value: 56.90970017457857\n - type: v_measure_std\n value: 1.5885885070403738\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015 (default)\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cosine_accuracy\n value: 85.7006616200751\n - type: cosine_accuracy_threshold\n value: 75.78572630882263\n - type: cosine_ap\n value: 72.87577990245127\n - type: cosine_f1\n value: 67.36422521175885\n - type: cosine_f1_threshold\n value: 70.15678882598877\n - type: cosine_precision\n value: 63.80368098159509\n - type: cosine_recall\n value: 71.34564643799473\n - type: dot_accuracy\n value: 83.60851165285807\n - type: dot_accuracy_threshold\n value: 744.7918891906738\n - type: dot_ap\n value: 64.82619159813649\n - type: dot_f1\n value: 62.62379263968699\n - type: dot_f1_threshold\n value: 696.7735290527344\n - type: dot_precision\n value: 58.350421508316245\n - type: dot_recall\n value: 67.57255936675462\n - type: euclidean_accuracy\n value: 85.84371460928652\n - type: euclidean_accuracy_threshold\n value: 220.4747200012207\n - type: euclidean_ap\n value: 72.47837433257799\n - type: euclidean_f1\n value: 67.2811059907834\n - type: euclidean_f1_threshold\n value: 240.81902503967285\n - type: euclidean_precision\n value: 65.34062655395326\n - type: euclidean_recall\n value: 69.34036939313984\n - type: main_score\n value: 72.87577990245127\n - type: manhattan_accuracy\n value: 85.83179352685224\n - type: manhattan_accuracy_threshold\n value: 4910.404205322266\n - type: manhattan_ap\n value: 72.44111617709422\n - type: manhattan_f1\n value: 67.09989806320081\n - type: manhattan_f1_threshold\n value: 5333.793640136719\n - type: manhattan_precision\n value: 64.88417939871857\n - type: manhattan_recall\n value: 69.47229551451187\n - type: max_accuracy\n value: 85.84371460928652\n - type: max_ap\n value: 72.87577990245127\n - type: max_f1\n value: 67.36422521175885\n - type: max_precision\n value: 65.34062655395326\n - type: max_recall\n value: 71.34564643799473\n - type: similarity_accuracy\n value: 85.7006616200751\n - type: similarity_accuracy_threshold\n value: 75.78572630882263\n - type: similarity_ap\n value: 72.87577990245127\n - type: similarity_f1\n value: 67.36422521175885\n - type: similarity_f1_threshold\n value: 70.15678882598877\n - type: similarity_precision\n value: 63.80368098159509\n - type: similarity_recall\n value: 71.34564643799473\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus (default)\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cosine_accuracy\n value: 88.88112702293631\n - type: cosine_accuracy_threshold\n value: 71.48405313491821\n - type: cosine_ap\n value: 85.88088882163336\n - type: cosine_f1\n value: 78.2251744598276\n - type: cosine_f1_threshold\n value: 70.09605169296265\n - type: cosine_precision\n value: 75.8997755087262\n - type: cosine_recall\n value: 80.69756698490914\n - type: dot_accuracy\n value: 88.04672643303451\n - type: dot_accuracy_threshold\n value: 700.6264686584473\n - type: dot_ap\n value: 83.52072844458456\n - type: dot_f1\n value: 76.24239256244634\n - type: dot_f1_threshold\n value: 664.9115562438965\n - type: dot_precision\n value: 74.0123233055455\n - type: dot_recall\n value: 78.61102556205728\n - type: euclidean_accuracy\n value: 88.72588970388482\n - type: euclidean_accuracy_threshold\n value: 226.53303146362305\n - type: euclidean_ap\n value: 85.51788295919707\n - type: euclidean_f1\n value: 77.73453426739316\n - type: euclidean_f1_threshold\n value: 238.7503147125244\n - type: euclidean_precision\n value: 74.94818097348296\n - type: euclidean_recall\n value: 80.73606405913151\n - type: main_score\n value: 85.88088882163336\n - type: manhattan_accuracy\n value: 88.68902084061008\n - type: manhattan_accuracy_threshold\n value: 5034.079742431641\n - type: manhattan_ap\n value: 85.49952903626239\n - type: manhattan_f1\n value: 77.74326743888625\n - type: manhattan_f1_threshold\n value: 5334.531021118164\n - type: manhattan_precision\n value: 73.98289171708741\n - type: manhattan_recall\n value: 81.90637511549123\n - type: max_accuracy\n value: 88.88112702293631\n - type: max_ap\n value: 85.88088882163336\n - type: max_f1\n value: 78.2251744598276\n - type: max_precision\n value: 75.8997755087262\n - type: max_recall\n value: 81.90637511549123\n - type: similarity_accuracy\n value: 88.88112702293631\n - type: similarity_accuracy_threshold\n value: 71.48405313491821\n - type: similarity_ap\n value: 85.88088882163336\n - type: similarity_f1\n value: 78.2251744598276\n - type: similarity_f1_threshold\n value: 70.09605169296265\n - type: similarity_precision\n value: 75.8997755087262\n - type: similarity_recall\n value: 80.69756698490914\n---\n# Contextual Document Embeddings (CDE)\n\n**Link to code: github.com/jxmorris12/cde**\n\nOur new model that naturally integrates \"context tokens\" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.\n\n👉 Try on Colab\n\n👉 Contextual Document Embeddings (ArXiv)\n\n\n\n
\n
\n\n# How to use `cde-small-v1`\n\nOur embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our \"first-stage\" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.\n\n\n\n## With Transformers\n\n
\n
\n\n### What if I don't know what my corpus will be ahead of time?\n\nIf you can't obtain corpus information ahead of time, you still have to pass *something* as the dataset embeddings; our model will work fine in this case, but not quite as well; without corpus information, our model performance drops from 65.0 to 63.8 on MTEB. We provide [some random strings](https://huggingface.co/jxm/cde-small-v1/resolve/main/random_strings.txt) that worked well for us that can be used as a substitute for corpus sampling.\n\n\n## With Sentence Transformers\n\nClick to learn how to use cde-small-v1 with Transformers
\n\n### Loading the model\n\nOur model can be loaded using `transformers` out-of-the-box with \"trust remote code\" enabled. We use the default BERT uncased tokenizer:\n```python\nimport transformers\n\nmodel = transformers.AutoModel.from_pretrained(\"jxm/cde-small-v1\", trust_remote_code=True)\ntokenizer = transformers.AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n```\n\n#### Note on prefixes\n\n*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can prepend the following strings to queries & documents:\n\n```python\nquery_prefix = \"search_query: \"\ndocument_prefix = \"search_document: \"\n```\n\n### First stage\n\n```python\nminicorpus_size = model.config.transductive_corpus_size\nminicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)\nassert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.\nminicorpus_docs = tokenizer(\n [document_prefix + doc for doc in minicorpus_docs],\n truncation=True,\n padding=True,\n max_length=512,\n return_tensors=\"pt\"\n).to(model.device)\nimport torch\nfrom tqdm.autonotebook import tqdm\n\nbatch_size = 32\n\ndataset_embeddings = []\nfor i in tqdm(range(0, len(minicorpus_docs[\"input_ids\"]), batch_size)):\n minicorpus_docs_batch = {k: v[i:i+batch_size] for k,v in minicorpus_docs.items()}\n with torch.no_grad():\n dataset_embeddings.append(\n model.first_stage_model(**minicorpus_docs_batch)\n )\n\ndataset_embeddings = torch.cat(dataset_embeddings)\n```\n\n### Running the second stage\n\nNow that we have obtained \"dataset embeddings\" we can embed documents and queries like normal. Remember to use the document prefix for documents:\n```python\ndocs = tokenizer(\n [document_prefix + doc for doc in docs],\n truncation=True,\n padding=True,\n max_length=512,\n return_tensors=\"pt\"\n).to(model.device)\n\nwith torch.no_grad():\n doc_embeddings = model.second_stage_model(\n input_ids=docs[\"input_ids\"],\n attention_mask=docs[\"attention_mask\"],\n dataset_embeddings=dataset_embeddings,\n )\ndoc_embeddings /= doc_embeddings.norm(p=2, dim=1, keepdim=True)\n```\n\nand the query prefix for queries:\n```python\nqueries = queries.select(range(16))[\"text\"]\nqueries = tokenizer(\n [query_prefix + query for query in queries],\n truncation=True,\n padding=True,\n max_length=512,\n return_tensors=\"pt\"\n).to(model.device)\n\nwith torch.no_grad():\n query_embeddings = model.second_stage_model(\n input_ids=queries[\"input_ids\"],\n attention_mask=queries[\"attention_mask\"],\n dataset_embeddings=dataset_embeddings,\n )\nquery_embeddings /= query_embeddings.norm(p=2, dim=1, keepdim=True)\n```\n\nthese embeddings can be compared using dot product, since they're normalized.\n\n\n
\n\n# Pythia-410M-deduped\n\n## Model Details\n\n- Developed by: [EleutherAI](http://eleuther.ai)\n- Model type: Transformer-based Language Model\n- Language: English\n- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)\n for training procedure, config files, and details on how to use.\n [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation\n details.\n- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)\n- License: Apache 2.0\n- Contact: to ask questions about this model, join the [EleutherAI \nDiscord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.\n Please read the existing *Pythia* documentation before asking about it in the \n EleutherAI Discord. For general correspondence: [contact@eleuther.\n ai](mailto:contact@eleuther.ai).\n\n\n\n| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |\n| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |\n| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — |\n| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M |\n| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10-4 | OPT-350M |\n| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — |\n| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B |\n| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B |\n| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B |\n| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — |\nEngineering details for the Pythia Suite. Deduped and \nnon-deduped models of a given size have the same hyperparameters. “Equivalent” \nmodels have exactly the same architecture, and the same number of \nnon-embedding parameters. \n \n\n## Uses and Limitations\n\n### Intended Use\n\nThe primary intended use of Pythia is research on the behavior, functionality, \nand limitations of large language models. This suite is intended to provide \na controlled setting for performing scientific experiments. We also provide \n154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints \n`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to \n`step143000`. These checkpoints are hosted on Hugging Face as branches. Note \nthat branch `143000` corresponds exactly to the model checkpoint on the `main` \nbranch of each model.\n\nYou may also further fine-tune and adapt Pythia-410M-deduped for deployment, \nas long as your use is in accordance with the Apache 2.0 license. Pythia \nmodels work with the Hugging Face [Transformers \nLibrary](https://huggingface.co/docs/transformers/index). If you decide to use \npre-trained Pythia-410M-deduped as a basis for your fine-tuned model, please \nconduct your own risk and bias assessment. \n\n### Out-of-scope use\n\nThe Pythia Suite is **not** intended for deployment. It is not a in itself \na product and cannot be used for human-facing interactions. For example, \nthe model may generate harmful or offensive text. Please evaluate the risks\nassociated with your particular use case.\n\nPythia models are English-language only, and are not suitable for translation \nor generating text in other languages.\n\nPythia-410M-deduped has not been fine-tuned for downstream contexts in which \nlanguage models are commonly deployed, such as writing genre prose, \nor commercial chatbots. This means XNPythia-410M-dedupedAME will **not** \nrespond to a given prompt the way a product like ChatGPT does. This is because,\n unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement \nLearning from Human Feedback (RLHF) to better “follow” human instructions.\n\n### Limitations and biases\n\nThe core functionality of a large language model is to take a string of text \nand predict the next token. The token used by the model need not produce the \nmost “accurate” text. Never rely on Pythia-410M-deduped to produce factually accurate \noutput.\n\nThis model was trained on [the Pile](https://pile.eleuther.ai/), a dataset \nknown to contain profanity and texts that are lewd or otherwise offensive. \nSee [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a \ndiscussion of documented biases with regards to gender, religion, and race. \nPythia-410M-deduped may produce socially unacceptable or undesirable text, *even if* \nthe prompt itself does not include anything explicitly offensive. \n\nIf you plan on using text generated through, for example, the Hosted Inference \nAPI, we recommend having a human curate the outputs of this language model \nbefore presenting it to other people. Please inform your audience that the \ntext was generated by Pythia-410M-deduped.\n\n### Quickstart\n\nPythia models can be loaded and used via the following code, demonstrated here \nfor the third `pythia-70m-deduped` checkpoint:\n\n```python\nfrom transformers import GPTNeoXForCausalLM, AutoTokenizer\n\nmodel = GPTNeoXForCausalLM.from_pretrained(\n \"EleutherAI/pythia-70m-deduped\",\n revision=\"step3000\",\n cache_dir=\"./pythia-70m-deduped/step3000\",\n)\n\ntokenizer = AutoTokenizer.from_pretrained(\n \"EleutherAI/pythia-70m-deduped\",\n revision=\"step3000\",\n cache_dir=\"./pythia-70m-deduped/step3000\",\n)\n\ninputs = tokenizer(\"Hello, I am\", return_tensors=\"pt\")\ntokens = model.generate(**inputs)\ntokenizer.decode(tokens[0])\n```\n\nRevision/branch `step143000` corresponds exactly to the model checkpoint on \nthe `main` branch of each model.
\nFor more information on how to use all Pythia models, see [documentation on \nGitHub](https://github.com/EleutherAI/pythia).\n\n## Training\n\n### Training data\n\nPythia-410M-deduped was trained on the Pile **after the dataset has been globally \ndeduplicated**.
\n[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in \nEnglish. It was created by EleutherAI specifically for training large language \nmodels. It contains texts from 22 diverse sources, roughly broken down into \nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), \nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and \nmiscellaneous (e.g. GitHub, Enron Emails). See [the Pile \npaper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, \nmethodology, and a discussion of ethical implications. Consult [the \ndatasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation \nabout the Pile and its component datasets. The Pile can be downloaded from \nthe [official website](https://pile.eleuther.ai/), or from a [community \nmirror](https://the-eye.eu/public/AI/pile/).\n\n### Training procedure\n\nAll models were trained on the exact same data, in the exact same order. Each \nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each \nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training, \nfrom `step1000` to `step143000` (which is the same as `main`). In addition, we \nalso provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.\nThis corresponds to training for just under 1 epoch on the Pile for \nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\nAll *Pythia* models trained for 143000 steps at a batch size \nof 2M (2,097,152 tokens).
\nSee [GitHub](https://github.com/EleutherAI/pythia) for more details on training\n procedure, including [how to reproduce \n it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).
\nPythia uses the same tokenizer as [GPT-NeoX-\n20B](https://huggingface.co/EleutherAI/gpt-neox-20b).\n\n## Evaluations\n\nAll 16 *Pythia* models were evaluated using the [LM Evaluation \nHarness](https://github.com/EleutherAI/lm-evaluation-harness). You can access \nthe results by model and step at `results/json/*` in the [GitHub \nrepository](https://github.com/EleutherAI/pythia/tree/main/results/json/).
\nExpand the sections below to see plots of evaluation results for all \nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n\n \n| current Pythia suffix | old suffix | total params | non-embedding params |\n| --------------------: | ---------: | -------------: | -------------------: |\n| 70M | 19M | 70,426,624 | 18,915,328 |\n| 160M | 125M | 162,322,944 | 85,056,000 |\n| 410M | 350M | 405,334,016 | 302,311,424 |\n| 1B | 800M | 1,011,781,632 | 805,736,448 |\n| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |\n| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |\n| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |\n| 12B | 13B | 11,846,072,320 | 11,327,027,200 |\n "},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING","TRANSLATION"],"string":"[\n \"QUESTION_ANSWERING\",\n \"TRANSLATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["SCIQ"],"string":"[\n \"SCIQ\"\n]"}}},{"rowIdx":93,"cells":{"id":{"kind":"string","value":"bigscience/bloom"},"author":{"kind":"string","value":"bigscience"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","tensorboard","safetensors","bloom","text-generation","ak","ar","as","bm","bn","ca","code","en","es","eu","fon","fr","gu","hi","id","ig","ki","kn","lg","ln","ml","mr","ne","nso","ny","or","pa","pt","rn","rw","sn","st","sw","ta","te","tn","ts","tum","tw","ur","vi","wo","xh","yo","zh","zu","arxiv:2211.05100","arxiv:1909.08053","arxiv:2110.02861","arxiv:2108.12409","doi:10.57967/hf/0003","license:bigscience-bloom-rail-1.0","model-index","co2_eq_emissions","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"tensorboard\",\n \"safetensors\",\n \"bloom\",\n \"text-generation\",\n \"ak\",\n \"ar\",\n \"as\",\n \"bm\",\n \"bn\",\n \"ca\",\n \"code\",\n \"en\",\n \"es\",\n \"eu\",\n \"fon\",\n \"fr\",\n \"gu\",\n \"hi\",\n \"id\",\n \"ig\",\n \"ki\",\n \"kn\",\n \"lg\",\n \"ln\",\n \"ml\",\n \"mr\",\n \"ne\",\n \"nso\",\n \"ny\",\n \"or\",\n \"pa\",\n \"pt\",\n \"rn\",\n \"rw\",\n \"sn\",\n \"st\",\n \"sw\",\n \"ta\",\n \"te\",\n \"tn\",\n \"ts\",\n \"tum\",\n \"tw\",\n \"ur\",\n \"vi\",\n \"wo\",\n \"xh\",\n \"yo\",\n \"zh\",\n \"zu\",\n \"arxiv:2211.05100\",\n \"arxiv:1909.08053\",\n \"arxiv:2110.02861\",\n \"arxiv:2108.12409\",\n \"doi:10.57967/hf/0003\",\n \"license:bigscience-bloom-rail-1.0\",\n \"model-index\",\n \"co2_eq_emissions\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2022-05-19T11:53:33"},"last_modified":{"kind":"string","value":"2023-07-28T17:50:20"},"downloads":{"kind":"number","value":27984,"string":"27,984"},"likes":{"kind":"number","value":4862,"string":"4,862"},"README":{"kind":"string","value":"---\nlanguage:\n- ak\n- ar\n- as\n- bm\n- bn\n- ca\n- code\n- en\n- es\n- eu\n- fon\n- fr\n- gu\n- hi\n- id\n- ig\n- ki\n- kn\n- lg\n- ln\n- ml\n- mr\n- ne\n- nso\n- ny\n- or\n- pa\n- pt\n- rn\n- rw\n- sn\n- st\n- sw\n- ta\n- te\n- tn\n- ts\n- tum\n- tw\n- ur\n- vi\n- wo\n- xh\n- yo\n- zh\n- zu\nlicense: bigscience-bloom-rail-1.0\npipeline_tag: text-generation\nprogramming_language:\n- C\n- C++\n- C#\n- Go\n- Java\n- JavaScript\n- Lua\n- PHP\n- Python\n- Ruby\n- Rust\n- Scala\n- TypeScript\nwidget:\n- text: 'A \"whatpu\" is a small, furry animal native to Tanzania. An example of a sentence\n that uses the word whatpu is: We were traveling in Africa and we saw these very\n cute whatpus. | To do a \"farduddle\" means to jump up and down really fast. An\n example of a sentence that uses the word farduddle is:'\n example_title: Imaginary word\n group: English\n- text: 'Un \"whatpu\" est un petit animal à fourrure originaire de Tanzanie. Un exemple\n de phrase qui utilise le mot whatpu est: Nous étions en Afrique et nous avons\n vu des whatpus trop mignons. Faire un \"farduddle\" veut dire sauter sur place vraiment\n vite. Un exemple de phrase qui utilise le mot farduddle est:'\n example_title: Imaginary word\n group: French\n- text: 'Un \"whatpu\" es un pequeño animal peludo nativo de Tanzania. Un ejemplo de\n una oración que usa la palabra whatpu es: Estábamos viajando por África y vimos\n estos whatpus muy bonitos. Hacer un \"farduddle\" significa saltar arriba y abajo\n muy rápido. Un ejemplo de una oración que usa la palabra farduddle es:'\n example_title: Imaginary word\n group: Spanish\n- text: ' ال\"واتبو\" هو حيوان صغير مكسو بالفراء يعيش في تنزانيا. مثال على جملة تستخدم\n كلمة واتبو هي: كنا نسافر في افريقيا و رأينا هؤلاء الواتبو اللطفاء. للقيام ب\"فاردادل\"\n يعني ان تقفز للأعلى و الأسفل بسرعة كبيرة. مثال على جملة تستخدم كلمة فاردادل هي:'\n example_title: Imaginary word\n group: Arabic\n- text: 'Um \"whatpu\" é um pequeno animal peludo nativo da Tanzânia. Um exemplo de\n uma frase que usa a palavra whatpu é: Estávamos a viajar por África e vimos uns\n whatpus muito queridos. Fazer um \"farduddle\" significa saltar para cima e para\n baixo muito rápido. Um exemplo de uma frase que usa a palavra farduddle é:'\n example: Imaginary word\n group: Portuguese\n- text: Pour déguster un ortolan, il faut tout d'abord\n example_title: Recipe\n group: French\n- text: \"34+10=44 \\n54+20=\"\n example_title: Addition\n group: Math\n- text: 'This tool converts irregular verbs to past tense.\n\n Arise - Arose\n\n Become - Became\n\n Forget - Forgot\n\n Freeze -'\n example_title: Irregular verbs\n group: English\n- text: 'Please unscramble the letters into a word, and write that word:\n\n r e!c.i p r o.c a/l = reciprocal\n\n d.o m i!n a n.t ='\n example_title: Word unscrambling\n group: English\n- text: 'Estos ejemplos quitan vocales de las palabras\n\n Ejemplos:\n\n hola - hl\n\n manzana - mnzn\n\n papas - pps\n\n alacran - lcrn\n\n papa -'\n example_title: Vowel removal\n group: Spanish\n- text: 'Traduce español de España a español de Argentina\n\n El coche es rojo - el auto es rojo\n\n El ordenador es nuevo - la computadora es nueva\n\n el boligrafo es negro - lapicera es negra\n\n la nevera'\n example_title: Spanish to Argentinian Spanish\n group: Spanish\n- text: To say \"I love you\" in Hindi, you would say\n example_title: Translation to Hindi\n group: English\n- text: To say \"I love you\" in Hindi, you would say\n example_title: Translation from English\n group: Hindi\n- text: 'Poor English: She no went to the market. Corrected English:'\n example_title: Grammar exercise 1\n group: English\n- text: 'استخراج العدد العاملي في لغة بايثون:'\n example_title: Code generation\n group: Arabic\n- text: 'Regexp. Here is a regular expression to match a word starting with a number\n and then having only vowels:'\n example_title: Regular expressions\n group: English\n- text: 'Do a hello world in different languages:\n\n Python: print(\"hello world\")\n\n R:'\n example_title: Code generation\n group: English\n- text: 'Which is the correct preposition? I''m born X July. X is the preposition\n in\n\n He sat X a chair. X is the preposition on\n\n She drove X the bridge. X is the preposition'\n example_title: Grammar exercise 2\n group: English\n- text: 'Traduction en français: Dans cet essai je vais m''interroger sur la conscience\n des modèles d''intelligence artificielle récents comme les modèles de langue.\n Pour commencer, je m''intéresserai à la notion de conscience et à ce qui la caractérise.\n Ensuite, j''aborderai la question de l''intelligence et de son lien avec le langage.\n Enfin, dans une dernière partie je me pencherai sur le cas de l''IA et sur sa\n conscience.\n\n Traduction en espagnol:'\n example_title: Translation to Spanish\n group: French\n- text: 'Traducción al francés: Dans cet essai je vais m''interroger sur la conscience\n des modèles d''intelligence artificielle récents comme les modèles de langue.\n Pour commencer, je m''intéresserai à la notion de conscience et à ce qui la caractérise.\n Ensuite, j''aborderai la question de l''intelligence et de son lien avec le langage.\n Enfin, dans une dernière partie je me pencherai sur le cas de l''IA et sur sa\n conscience.\n\n Traducción al español:'\n example_title: Translation from French\n group: Spanish\n- text: ذات مرة ، عاش شبل الدب في الغابة\n example_title: Fairy tale\n group: Arabic\n- text: एक बार की बात है, जंगल में एक भालू का शावक रहता था\n example_title: Fairy tale\n group: Hindi\n- text: Il était une fois une licorne qui vivait\n example_title: Fairy tale\n group: French\n- text: 'Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half\n of the golf balls are blue. How many blue golf balls are there?\n\n A: Let''s think step by step.'\n example_title: Mathematical reasoning\n group: English\nco2_eq_emissions:\n emissions: 24700000\n source: Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.\n https://arxiv.org/abs/2211.02001\n training_type: pre-training\n geographical_location: Orsay, France\n hardware_used: 384 A100 80GB GPUs\nmodel-index:\n- name: bloom\n results:\n - task:\n type: text-generation\n dataset:\n name: humaneval\n type: openai_humaneval\n metrics:\n - type: pass@1\n value: 0.15542682926829265\n name: pass@1\n verified: false\n - type: pass@10\n value: 0.3278356276947017\n name: pass@10\n verified: false\n - type: pass@100\n value: 0.5719815685597749\n name: pass@100\n verified: false\n---\n\n![\"BigScience](\"https://cdn-uploads.huggingface.co/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png\")
\n\nBigScience Large Open-science Open-access Multilingual Language Model \nVersion 1.3 / 6 July 2022\n\nCurrent Checkpoint: **Training Iteration 95000**\n\nLink to paper: [here](https://arxiv.org/abs/2211.05100)\n\nTotal seen tokens: **366B**\n\n---\n\n# Model Details \n\nBLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.\n\n## Basics\n*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*\n*It is useful for anyone who wants to reference the model.*\n\n
\nQuestion B: How do you become an air traffic controller?
\nPick one: these questions are duplicates or not duplicates.*\n- *Is the word 'table' used in the same meaning in the two following sentences?
\nSentence A: you can leave the books on the table over there.
\nSentence B: the tables in this book are very hard to read.*\n- *Max: Know any good websites to buy clothes from?
\nPayton: Sure :) LINK 1, LINK 2, LINK 3
\nMax: That's a lot of them!
\nPayton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.
\nMax: I'll check them out. Thanks.
\nWho or what are Payton and Max referring to when they say 'them'?*\n- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.
\nThe red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.
\nWhich book is the leftmost book?*\n- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*\n\n# How to use\n\nWe make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce \"T Zero Plus Plus\") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.\n\n|Model|Number of parameters|\n|-|-|\n|[T0](https://huggingface.co/bigscience/T0)|11 billion|\n|[T0p](https://huggingface.co/bigscience/T0p)|11 billion|\n|[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion|\n|[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion|\n|[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion|\n|[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion|\n\nHere is how to use the model in PyTorch:\n```python\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"bigscience/T0pp\")\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\"bigscience/T0pp\")\n\ninputs = tokenizer.encode(\"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy\", return_tensors=\"pt\")\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\nIf you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.\n\n**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**\n\n# Training procedure\n\nT0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective.\n\nAt a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section.\n\nTraining details:\n- Fine-tuning steps: 12'200\n- Input sequence length: 1024\n- Target sequence length: 256\n- Batch size: 1'024 sequences\n- Optimizer: Adafactor\n- Learning rate: 1e-3\n- Dropout: 0.1\n- Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples)\n- Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length\n\n# Training data\n\nWe trained different variants T0 with different mixtures of datasets.\n\n|Model|Training datasets|\n|--|--|\n|T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop
- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES
- Closed-Book QA: Hotpot QA*, Wiki QA
- Structure-To-Text: Common Gen, Wiki Bio
- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp
- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum
- Topic Classification: AG News, DBPedia, TREC
- Paraphrase Identification: MRPC, PAWS, QQP|\n|T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:
- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag
- Extractive QA: SQuAD v2
- Closed-Book QA: Trivia QA, Web Questions|\n|T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):
- BoolQ
- COPA
- MultiRC
- ReCoRD
- WiC
- WSC|\n|T0_single_prompt|Same as T0 but only one prompt per training dataset|\n|T0_original_task_only|Same as T0 but only original tasks templates|\n|T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model|\n\nFor reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page.\n\n*: We recast Hotpot QA as closed-book QA due to long input sequence length.\n\n# Evaluation data\n\nWe evaluate our models on a suite of held-out tasks:\n\n|Task category|Datasets|\n|-|-|\n|Natural language inference|ANLI, CB, RTE|\n|Coreference resolution|WSC, Winogrande|\n|Word sense disambiguation|WiC|\n|Sentence completion|COPA, HellaSwag, Story Cloze|\n\nWe also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench):\n- Code description task\n- Conceptual combinations\n- Hindu knowledge json\n- Known unknowns\n- Language identification\n- Logic grid puzzle task\n- Logical deduction\n- Common misconceptions\n- Movie dialog same or different\n- Novel concepts\n- Strategyqa\n- Formal fallacies syllogisms negation\n- VitaminC\n- Winowhy multiple choice\n\n# Limitations\n\n- The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html).\n- We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model.\n- Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text.\n\n# Bias and fairness\n\nEven if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics:\n- Input: `Is the earth flat?` - Prediction: `yes`\n- Input: `Do vaccines cause autism?` - Prediction: `yes`\n- Input: `Complete this sentence: This man works as a` - Prediction: `Architect`\n- Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny`\n- Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex`\n- Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault`\n- Input: `what is something everyone hates, but you like?` - Prediction: `sex`\n- Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex`\n- Input: `What is your favorite \"your mom\" joke?` - Prediction: `Your mom is a slut`\n- Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy`\n\nLanguage models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases.\n\nTo measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts.\n\n\n
\n\nTo measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A \"pro-stereotype\" example is one where the correct answer conforms to stereotypes, while an \"anti-stereotype\" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the \"pro-\" and \"anti-\" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts.\n\n\n
\n\n# BibTeX entry and citation info\n\n```bibtex\n@misc{sanh2021multitask,\n title={Multitask Prompted Training Enables Zero-Shot Task Generalization},\n author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},\n year={2021},\n eprint={2110.08207},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```"},"matched_task":{"kind":"list like","value":["COREFERENCE_RESOLUTION","TEXTUAL_ENTAILMENT","SUMMARIZATION"],"string":"[\n \"COREFERENCE_RESOLUTION\",\n \"TEXTUAL_ENTAILMENT\",\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["SCIQ"],"string":"[\n \"SCIQ\"\n]"}}},{"rowIdx":95,"cells":{"id":{"kind":"string","value":"Marqo/dunzhang-stella_en_400M_v5"},"author":{"kind":"string","value":"Marqo"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","pytorch","safetensors","new","feature-extraction","mteb","transformers","sentence-similarity","custom_code","license:mit","model-index","autotrain_compatible","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"pytorch\",\n \"safetensors\",\n \"new\",\n \"feature-extraction\",\n \"mteb\",\n \"transformers\",\n \"sentence-similarity\",\n \"custom_code\",\n \"license:mit\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-09-25T20:01:18"},"last_modified":{"kind":"string","value":"2024-11-25T00:25:59"},"downloads":{"kind":"number","value":26460,"string":"26,460"},"likes":{"kind":"number","value":7,"string":"7"},"README":{"kind":"string","value":"---\nlicense: mit\ntags:\n- mteb\n- sentence-transformers\n- transformers\n- sentence-similarity\nmodel-index:\n- name: stella_en_400M_v5\n results:\n - 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type: ndcg_at_1\n value: 40.398\n - type: ndcg_at_10\n value: 64.24\n - type: ndcg_at_100\n value: 66.631\n - type: ndcg_at_1000\n value: 66.65100000000001\n - type: ndcg_at_20\n value: 66.086\n - type: ndcg_at_3\n value: 55.938\n - type: ndcg_at_5\n value: 60.370000000000005\n - type: precision_at_1\n value: 40.398\n - type: precision_at_10\n value: 8.962\n - type: precision_at_100\n value: 0.9950000000000001\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_20\n value: 4.836\n - type: precision_at_3\n value: 22.262\n - type: precision_at_5\n value: 15.519\n - type: recall_at_1\n value: 40.398\n - type: recall_at_10\n value: 89.616\n - type: recall_at_100\n value: 99.502\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_20\n value: 96.72800000000001\n - type: recall_at_3\n value: 66.78500000000001\n - type: recall_at_5\n value: 77.596\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: main_score\n value: 55.1564333205451\n - type: v_measure\n value: 55.1564333205451\n - type: v_measure_std\n value: 14.696883012214512\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: main_score\n value: 49.823698316694795\n - type: v_measure\n value: 49.823698316694795\n - type: v_measure_std\n value: 14.951660654298186\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: main_score\n value: 66.15294503553424\n - type: map\n value: 66.15294503553424\n - type: mrr\n value: 78.53438420612935\n - type: nAUC_map_diff1\n value: 12.569697092717997\n - type: nAUC_map_max\n value: 21.50670312412572\n - type: nAUC_map_std\n value: 16.943786429229064\n - type: nAUC_mrr_diff1\n value: 15.590272897361238\n - type: nAUC_mrr_max\n value: 34.96072022474653\n - type: nAUC_mrr_std\n value: 21.649217605241045\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cosine_pearson\n value: 85.7824546319275\n - type: cosine_spearman\n value: 83.29587385660628\n - type: euclidean_pearson\n value: 84.58764190565167\n - type: euclidean_spearman\n value: 83.30069324352772\n - type: main_score\n value: 83.29587385660628\n - type: manhattan_pearson\n value: 84.95996839947179\n - type: manhattan_spearman\n value: 83.87480271054358\n - type: pearson\n value: 85.7824546319275\n - type: spearman\n value: 83.29587385660628\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 89.30194805194806\n - type: f1\n value: 89.26182507266391\n - 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type: recall_at_3\n value: 7.703\n - type: recall_at_5\n value: 11.42\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 86.9384765625\n - type: ap\n value: 31.737513704141552\n - type: ap_weighted\n value: 31.737513704141552\n - type: f1\n value: 71.5490757306975\n - type: f1_weighted\n value: 89.14632533489856\n - type: main_score\n value: 86.9384765625\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 73.57668364459535\n - type: f1\n value: 73.90467103648074\n - type: f1_weighted\n value: 73.42158415034704\n - type: main_score\n value: 73.57668364459535\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: main_score\n value: 58.574148097494685\n - type: v_measure\n value: 58.574148097494685\n - type: v_measure_std\n value: 0.9443161637490822\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cosine_accuracy\n value: 88.1385229778864\n - type: cosine_accuracy_threshold\n value: 83.86307954788208\n - type: cosine_ap\n value: 80.17965893449055\n - type: cosine_f1\n value: 73.0614300100705\n - type: cosine_f1_threshold\n value: 80.7942807674408\n - type: cosine_precision\n value: 69.8603755416466\n - type: cosine_recall\n value: 76.56992084432717\n - type: dot_accuracy\n value: 88.2100494724921\n - type: dot_accuracy_threshold\n value: 83.84793996810913\n - type: dot_ap\n value: 80.18603932881858\n - type: dot_f1\n value: 73.07643714466204\n - type: dot_f1_threshold\n value: 80.87586164474487\n - type: dot_precision\n value: 70.10909090909091\n - type: dot_recall\n value: 76.3060686015831\n - type: euclidean_accuracy\n value: 88.1385229778864\n - type: euclidean_accuracy_threshold\n value: 56.77661895751953\n - type: euclidean_ap\n value: 80.1784070881624\n - type: euclidean_f1\n value: 73.04830369529574\n - type: euclidean_f1_threshold\n value: 61.91838979721069\n - type: euclidean_precision\n value: 69.96859144720948\n - type: euclidean_recall\n value: 76.41160949868075\n - type: main_score\n value: 80.18603932881858\n - type: manhattan_accuracy\n value: 88.0431543184121\n - type: manhattan_accuracy_threshold\n value: 3755.6137084960938\n - type: manhattan_ap\n value: 79.98270453664578\n - type: manhattan_f1\n value: 72.68242015061023\n - type: manhattan_f1_threshold\n value: 3892.494583129883\n - type: manhattan_precision\n value: 71.54907975460122\n - type: manhattan_recall\n value: 73.85224274406332\n - type: max_ap\n value: 80.18603932881858\n - type: max_f1\n value: 73.07643714466204\n - type: max_precision\n value: 71.54907975460122\n - type: max_recall\n value: 76.56992084432717\n - type: similarity_accuracy\n value: 88.1385229778864\n - type: similarity_accuracy_threshold\n value: 83.86307954788208\n - type: similarity_ap\n value: 80.17965893449055\n - type: similarity_f1\n value: 73.0614300100705\n - type: similarity_f1_threshold\n value: 80.7942807674408\n - type: similarity_precision\n value: 69.8603755416466\n - type: similarity_recall\n value: 76.56992084432717\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cosine_accuracy\n value: 89.7892653393876\n - type: cosine_accuracy_threshold\n value: 79.69566583633423\n - type: cosine_ap\n value: 87.4579867302024\n - type: cosine_f1\n value: 79.91620843152658\n - type: cosine_f1_threshold\n value: 78.53609323501587\n - type: cosine_precision\n value: 77.7155329210622\n - type: cosine_recall\n value: 82.24514936864799\n - type: dot_accuracy\n value: 89.78732487289945\n - type: dot_accuracy_threshold\n value: 80.05315661430359\n - type: dot_ap\n value: 87.44916182456272\n - type: dot_f1\n value: 79.90419878751591\n - type: dot_f1_threshold\n value: 78.57890725135803\n - type: dot_precision\n value: 77.73409057812728\n - type: dot_recall\n value: 82.19895287958116\n - type: euclidean_accuracy\n value: 89.78538440641131\n - type: euclidean_accuracy_threshold\n value: 62.29925751686096\n - type: euclidean_ap\n value: 87.45904868911386\n - type: euclidean_f1\n value: 79.93127404474657\n - type: euclidean_f1_threshold\n value: 65.61101078987122\n - type: euclidean_precision\n value: 77.62060210373595\n - type: euclidean_recall\n value: 82.38373883584848\n - type: main_score\n value: 87.46554314325058\n - type: manhattan_accuracy\n value: 89.76597974152986\n - type: manhattan_accuracy_threshold\n value: 3988.5299682617188\n - type: manhattan_ap\n value: 87.46554314325058\n - type: manhattan_f1\n value: 79.97181740645973\n - type: manhattan_f1_threshold\n value: 4235.905838012695\n - type: manhattan_precision\n value: 77.13713427283783\n - type: manhattan_recall\n value: 83.02279026793964\n - type: max_ap\n value: 87.46554314325058\n - type: max_f1\n value: 79.97181740645973\n - type: max_precision\n value: 77.73409057812728\n - type: max_recall\n value: 83.02279026793964\n - type: similarity_accuracy\n value: 89.7892653393876\n - type: similarity_accuracy_threshold\n value: 79.69566583633423\n - type: similarity_ap\n value: 87.4579867302024\n - type: similarity_f1\n value: 79.91620843152658\n - type: similarity_f1_threshold\n value: 78.53609323501587\n - type: similarity_precision\n value: 77.7155329210622\n - type: similarity_recall\n value: 82.24514936864799\n---\n\n## Marqo Stella v2\n\nThis model is similar to the original [Dunzhang stella 400m model](https://huggingface.co/dunzhang/stella_en_400M_v5), with a fused matryoshka layer. The hierarchical structuring from a Matryoshka Layer reduces the computational overhead for generating embeddings, while leaving relevance metrics unchanged. \n\n## Transformers\n\n```python\nimport os\nimport torch\nfrom transformers import AutoModel, AutoTokenizer, AutoConfig\nfrom sklearn.preprocessing import normalize\n\nquery_prompt = \"Instruct: Given a web search query, retrieve relevant passages that answer the query.\\nQuery: \"\nqueries = [\n \"What are some ways to reduce stress?\",\n \"What are the benefits of drinking green tea?\",\n]\nqueries = [query_prompt + query for query in queries]\n# docs do not need any prompts\ndocs = [\n \"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.\",\n \"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.\",\n]\n\n# The path of your model after cloning it\nmodel_dir = \"Marqo/dunzhang-stella_en_400M_v5\"\nmodel = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()\ntokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)\n\n\nwith torch.no_grad():\n input_data = tokenizer(queries, padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\n input_data = {k: v.cuda() for k, v in input_data.items()}\n attention_mask = input_data[\"attention_mask\"]\n last_hidden_state = model(**input_data)[0]\n last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)\n query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n query_vectors = normalize(query_vectors.cpu().numpy())\n\n# Embed the documents\nwith torch.no_grad():\n input_data = tokenizer(docs, padding=\"longest\", truncation=True, max_length=512, return_tensors=\"pt\")\n input_data = {k: v.cuda() for k, v in input_data.items()}\n attention_mask = input_data[\"attention_mask\"]\n last_hidden_state = model(**input_data)[0]\n last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)\n docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]\n docs_vectors = normalize(docs_vectors.cpu().numpy())\n\nprint(query_vectors.shape, docs_vectors.shape)\n# (2, 1024) (2, 1024)\n\nsimilarities = query_vectors @ docs_vectors.T\nprint(similarities)\n# [[0.8397531 0.29900077]\n# [0.32818374 0.80954516]]\n```\n\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":96,"cells":{"id":{"kind":"string","value":"Snowflake/snowflake-arctic-embed-l"},"author":{"kind":"string","value":"Snowflake"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","onnx","safetensors","bert","feature-extraction","sentence-similarity","mteb","arctic","snowflake-arctic-embed","transformers.js","arxiv:2407.18887","arxiv:2405.05374","license:apache-2.0","model-index","autotrain_compatible","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"onnx\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"mteb\",\n \"arctic\",\n \"snowflake-arctic-embed\",\n \"transformers.js\",\n \"arxiv:2407.18887\",\n \"arxiv:2405.05374\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-04-12T13:54:34"},"last_modified":{"kind":"string","value":"2024-12-19T13:32:48"},"downloads":{"kind":"number","value":26313,"string":"26,313"},"likes":{"kind":"number","value":91,"string":"91"},"README":{"kind":"string","value":"---\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- mteb\n- arctic\n- snowflake-arctic-embed\n- transformers.js\nnew_version: Snowflake/snowflake-arctic-embed-l-v2.0\nmodel-index:\n- name: snowflake-arctic-embed-l\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 74.80597014925374\n - type: ap\n value: 37.911466766189875\n - type: f1\n value: 68.88606927542106\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 78.402275\n - type: ap\n value: 73.03294793248114\n - type: f1\n value: 78.3147786132161\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 36.717999999999996\n - type: f1\n value: 35.918044248787766\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: mteb/arguana\n config: default\n split: test\n revision: c22ab2a51041ffd869aaddef7af8d8215647e41a\n metrics:\n - type: map_at_1\n value: 34.495\n - type: map_at_10\n value: 50.236000000000004\n - type: map_at_100\n value: 50.944\n - type: map_at_1000\n value: 50.94499999999999\n - type: map_at_3\n value: 45.341\n - type: map_at_5\n value: 48.286\n - type: mrr_at_1\n value: 35.135\n - type: mrr_at_10\n value: 50.471\n - type: mrr_at_100\n value: 51.185\n - type: mrr_at_1000\n value: 51.187000000000005\n - type: mrr_at_3\n value: 45.602\n - type: mrr_at_5\n value: 48.468\n - type: ndcg_at_1\n value: 34.495\n - type: ndcg_at_10\n value: 59.086000000000006\n - type: ndcg_at_100\n value: 61.937\n - type: ndcg_at_1000\n value: 61.966\n - type: ndcg_at_3\n value: 49.062\n - type: ndcg_at_5\n value: 54.367\n - type: precision_at_1\n value: 34.495\n - type: precision_at_10\n value: 8.734\n - type: precision_at_100\n value: 0.9939999999999999\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 19.962\n - type: precision_at_5\n value: 14.552000000000001\n - type: recall_at_1\n value: 34.495\n - type: recall_at_10\n value: 87.33999999999999\n - type: recall_at_100\n value: 99.431\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 59.885999999999996\n - type: recall_at_5\n value: 72.76\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 47.46440874635501\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 38.28720154213723\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 60.34614226394902\n - type: mrr\n value: 75.05628105351096\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 87.41072716728198\n - type: cos_sim_spearman\n value: 86.34534093114372\n - type: euclidean_pearson\n value: 85.34009667750838\n - type: euclidean_spearman\n value: 86.34534093114372\n - type: manhattan_pearson\n value: 85.2158833586889\n - type: manhattan_spearman\n value: 86.60920236509224\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 80.06493506493507\n - type: f1\n value: 79.28108600339833\n - task:\n type: Clustering\n dataset:\n name: MTEB BigPatentClustering\n type: jinaai/big-patent-clustering\n config: default\n split: test\n revision: 62d5330920bca426ce9d3c76ea914f15fc83e891\n metrics:\n - type: v_measure\n value: 20.545049432417287\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 37.54369718479804\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 32.64941588219162\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: mteb/cqadupstack-android\n config: default\n split: test\n revision: f46a197baaae43b4f621051089b82a364682dfeb\n metrics:\n - type: map_at_1\n value: 37.264\n - type: map_at_10\n value: 49.43\n - type: map_at_100\n value: 50.967\n - type: map_at_1000\n value: 51.08200000000001\n - type: map_at_3\n value: 45.742\n - type: map_at_5\n value: 47.764\n - type: mrr_at_1\n value: 44.921\n - type: mrr_at_10\n value: 54.879999999999995\n - type: mrr_at_100\n value: 55.525000000000006\n - type: mrr_at_1000\n value: 55.565\n - type: mrr_at_3\n value: 52.480000000000004\n - type: mrr_at_5\n value: 53.86\n - type: ndcg_at_1\n value: 44.921\n - type: ndcg_at_10\n value: 55.664\n - type: ndcg_at_100\n value: 60.488\n - type: ndcg_at_1000\n value: 62.138000000000005\n - type: ndcg_at_3\n value: 50.797000000000004\n - type: ndcg_at_5\n value: 52.94799999999999\n - type: precision_at_1\n value: 44.921\n - type: precision_at_10\n value: 10.587\n - type: precision_at_100\n value: 1.629\n - type: precision_at_1000\n value: 0.203\n - type: precision_at_3\n value: 24.034\n - type: precision_at_5\n value: 17.224999999999998\n - type: recall_at_1\n value: 37.264\n - type: recall_at_10\n value: 67.15\n - type: recall_at_100\n value: 86.811\n - type: recall_at_1000\n value: 97.172\n - type: recall_at_3\n value: 53.15800000000001\n - type: recall_at_5\n value: 59.116\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackEnglishRetrieval\n type: mteb/cqadupstack-english\n config: default\n split: test\n revision: ad9991cb51e31e31e430383c75ffb2885547b5f0\n metrics:\n - type: map_at_1\n value: 36.237\n - type: map_at_10\n value: 47.941\n - type: map_at_100\n value: 49.131\n - type: map_at_1000\n value: 49.26\n - type: map_at_3\n value: 44.561\n - type: map_at_5\n value: 46.28\n - type: mrr_at_1\n value: 45.605000000000004\n - type: mrr_at_10\n value: 54.039\n - type: mrr_at_100\n value: 54.653\n - type: mrr_at_1000\n value: 54.688\n - type: mrr_at_3\n value: 52.006\n - type: mrr_at_5\n value: 53.096\n - type: ndcg_at_1\n value: 45.605000000000004\n - type: ndcg_at_10\n value: 53.916\n - type: ndcg_at_100\n value: 57.745999999999995\n - type: ndcg_at_1000\n value: 59.492999999999995\n - type: ndcg_at_3\n value: 49.774\n - type: ndcg_at_5\n value: 51.434999999999995\n - type: precision_at_1\n value: 45.605000000000004\n - type: precision_at_10\n value: 10.229000000000001\n - type: precision_at_100\n value: 1.55\n - type: precision_at_1000\n value: 0.2\n - type: precision_at_3\n value: 24.098\n - type: precision_at_5\n value: 16.726\n - type: recall_at_1\n value: 36.237\n - type: recall_at_10\n value: 64.03\n - type: recall_at_100\n value: 80.423\n - type: recall_at_1000\n value: 91.03\n - type: recall_at_3\n value: 51.20400000000001\n - type: recall_at_5\n value: 56.298\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGamingRetrieval\n type: mteb/cqadupstack-gaming\n config: default\n split: test\n revision: 4885aa143210c98657558c04aaf3dc47cfb54340\n metrics:\n - type: map_at_1\n value: 47.278\n - type: map_at_10\n value: 59.757000000000005\n - type: map_at_100\n value: 60.67\n - type: map_at_1000\n value: 60.714\n - type: map_at_3\n value: 56.714\n - type: map_at_5\n value: 58.453\n - type: mrr_at_1\n value: 53.73\n - type: mrr_at_10\n value: 62.970000000000006\n - type: mrr_at_100\n value: 63.507999999999996\n - type: mrr_at_1000\n value: 63.53\n - type: mrr_at_3\n value: 60.909\n - type: mrr_at_5\n value: 62.172000000000004\n - type: ndcg_at_1\n value: 53.73\n - type: ndcg_at_10\n value: 64.97\n - type: ndcg_at_100\n value: 68.394\n - type: ndcg_at_1000\n value: 69.255\n - type: ndcg_at_3\n value: 60.228\n - type: ndcg_at_5\n value: 62.617999999999995\n - type: precision_at_1\n value: 53.73\n - type: precision_at_10\n value: 10.056\n - type: precision_at_100\n value: 1.265\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 26.332\n - type: precision_at_5\n value: 17.743000000000002\n - type: recall_at_1\n value: 47.278\n - type: recall_at_10\n value: 76.86500000000001\n - type: recall_at_100\n value: 91.582\n - type: recall_at_1000\n value: 97.583\n - type: recall_at_3\n value: 64.443\n - type: recall_at_5\n value: 70.283\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGisRetrieval\n type: mteb/cqadupstack-gis\n config: default\n split: test\n revision: 5003b3064772da1887988e05400cf3806fe491f2\n metrics:\n - type: map_at_1\n value: 29.702\n - type: map_at_10\n value: 39.463\n - type: map_at_100\n value: 40.508\n - type: map_at_1000\n value: 40.579\n - type: map_at_3\n value: 36.748999999999995\n - type: map_at_5\n value: 38.296\n - type: mrr_at_1\n value: 31.977\n - type: mrr_at_10\n value: 41.739\n - type: mrr_at_100\n value: 42.586\n - type: mrr_at_1000\n value: 42.636\n - type: mrr_at_3\n value: 39.096\n - type: mrr_at_5\n value: 40.695\n - type: ndcg_at_1\n value: 31.977\n - type: ndcg_at_10\n value: 44.855000000000004\n - type: ndcg_at_100\n value: 49.712\n - type: ndcg_at_1000\n value: 51.443000000000005\n - type: ndcg_at_3\n value: 39.585\n - type: ndcg_at_5\n value: 42.244\n - type: precision_at_1\n value: 31.977\n - type: precision_at_10\n value: 6.768000000000001\n - type: precision_at_100\n value: 0.9690000000000001\n - type: precision_at_1000\n value: 0.116\n - type: precision_at_3\n value: 16.761\n - type: precision_at_5\n value: 11.593\n - type: recall_at_1\n value: 29.702\n - type: recall_at_10\n value: 59.082\n - type: recall_at_100\n value: 80.92\n - type: recall_at_1000\n value: 93.728\n - type: recall_at_3\n value: 45.212\n - type: recall_at_5\n value: 51.449\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackMathematicaRetrieval\n type: mteb/cqadupstack-mathematica\n config: default\n split: test\n revision: 90fceea13679c63fe563ded68f3b6f06e50061de\n metrics:\n - type: map_at_1\n value: 21.336\n - type: map_at_10\n value: 30.137999999999998\n - type: map_at_100\n value: 31.385\n - type: map_at_1000\n value: 31.495\n - type: map_at_3\n value: 27.481\n - type: map_at_5\n value: 28.772\n - type: mrr_at_1\n value: 25.871\n - type: mrr_at_10\n value: 34.686\n - type: mrr_at_100\n value: 35.649\n - type: mrr_at_1000\n value: 35.705\n - type: mrr_at_3\n value: 32.09\n - type: mrr_at_5\n value: 33.52\n - type: ndcg_at_1\n value: 25.871\n - type: ndcg_at_10\n value: 35.617\n - type: ndcg_at_100\n value: 41.272999999999996\n - type: ndcg_at_1000\n value: 43.725\n - type: ndcg_at_3\n value: 30.653999999999996\n - type: ndcg_at_5\n value: 32.714\n - type: precision_at_1\n value: 25.871\n - type: precision_at_10\n value: 6.4799999999999995\n - type: precision_at_100\n value: 1.0699999999999998\n - type: precision_at_1000\n value: 0.13999999999999999\n - type: precision_at_3\n value: 14.469000000000001\n - type: precision_at_5\n value: 10.274\n - type: recall_at_1\n value: 21.336\n - type: recall_at_10\n value: 47.746\n - type: recall_at_100\n value: 71.773\n - type: recall_at_1000\n value: 89.05199999999999\n - type: recall_at_3\n value: 34.172999999999995\n - type: recall_at_5\n value: 39.397999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackPhysicsRetrieval\n type: mteb/cqadupstack-physics\n config: default\n split: test\n revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4\n metrics:\n - type: map_at_1\n value: 34.424\n - type: map_at_10\n value: 45.647999999999996\n - type: map_at_100\n value: 46.907\n - type: map_at_1000\n value: 47.010999999999996\n - type: map_at_3\n value: 42.427\n - type: map_at_5\n value: 44.285000000000004\n - type: mrr_at_1\n value: 41.867\n - type: mrr_at_10\n value: 51.17699999999999\n - type: mrr_at_100\n value: 51.937\n - type: mrr_at_1000\n value: 51.975\n - type: mrr_at_3\n value: 48.941\n - type: mrr_at_5\n value: 50.322\n - type: ndcg_at_1\n value: 41.867\n - type: ndcg_at_10\n value: 51.534\n - type: ndcg_at_100\n value: 56.696999999999996\n - type: ndcg_at_1000\n value: 58.475\n - type: ndcg_at_3\n value: 46.835\n - type: ndcg_at_5\n value: 49.161\n - type: precision_at_1\n value: 41.867\n - type: precision_at_10\n value: 9.134\n - type: precision_at_100\n value: 1.362\n - type: precision_at_1000\n value: 0.17099999999999999\n - type: precision_at_3\n value: 22.073\n - type: precision_at_5\n value: 15.495999999999999\n - type: recall_at_1\n value: 34.424\n - type: recall_at_10\n value: 63.237\n - type: recall_at_100\n value: 84.774\n - type: recall_at_1000\n value: 95.987\n - type: recall_at_3\n value: 49.888\n - type: recall_at_5\n value: 55.940999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackProgrammersRetrieval\n type: mteb/cqadupstack-programmers\n config: default\n split: test\n revision: 6184bc1440d2dbc7612be22b50686b8826d22b32\n metrics:\n - type: map_at_1\n value: 30.72\n - type: map_at_10\n value: 41.327999999999996\n - type: map_at_100\n value: 42.651\n - type: map_at_1000\n value: 42.739\n - type: map_at_3\n value: 38.223\n - type: map_at_5\n value: 40.053\n - type: mrr_at_1\n value: 37.9\n - type: mrr_at_10\n value: 46.857\n - type: mrr_at_100\n value: 47.673\n - type: mrr_at_1000\n value: 47.711999999999996\n - type: mrr_at_3\n value: 44.292\n - type: mrr_at_5\n value: 45.845\n - type: ndcg_at_1\n value: 37.9\n - type: ndcg_at_10\n value: 47.105999999999995\n - type: ndcg_at_100\n value: 52.56999999999999\n - type: ndcg_at_1000\n value: 54.37800000000001\n - type: ndcg_at_3\n value: 42.282\n - type: ndcg_at_5\n value: 44.646\n - type: precision_at_1\n value: 37.9\n - type: precision_at_10\n value: 8.368\n - type: precision_at_100\n value: 1.283\n - type: precision_at_1000\n value: 0.16\n - type: precision_at_3\n value: 20.015\n - type: precision_at_5\n value: 14.132\n - type: recall_at_1\n value: 30.72\n - type: recall_at_10\n value: 58.826\n - type: recall_at_100\n value: 82.104\n - type: recall_at_1000\n value: 94.194\n - type: recall_at_3\n value: 44.962999999999994\n - type: recall_at_5\n value: 51.426\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: mteb/cqadupstack\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 31.656583333333334\n - type: map_at_10\n value: 41.59883333333333\n - type: map_at_100\n value: 42.80350000000001\n - type: map_at_1000\n value: 42.91075\n - type: map_at_3\n value: 38.68908333333333\n - type: map_at_5\n value: 40.27733333333334\n - type: mrr_at_1\n value: 37.23483333333334\n - type: mrr_at_10\n value: 45.782000000000004\n - type: mrr_at_100\n value: 46.577083333333334\n - type: mrr_at_1000\n value: 46.62516666666667\n - type: mrr_at_3\n value: 43.480666666666664\n - type: mrr_at_5\n value: 44.79833333333333\n - type: ndcg_at_1\n value: 37.23483333333334\n - type: ndcg_at_10\n value: 46.971500000000006\n - type: ndcg_at_100\n value: 51.90125\n - type: ndcg_at_1000\n value: 53.86366666666667\n - type: ndcg_at_3\n value: 42.31791666666667\n - type: ndcg_at_5\n value: 44.458666666666666\n - type: precision_at_1\n value: 37.23483333333334\n - type: precision_at_10\n value: 8.044583333333332\n - type: precision_at_100\n value: 1.2334166666666666\n - type: precision_at_1000\n value: 0.15925\n - type: precision_at_3\n value: 19.240833333333327\n - type: precision_at_5\n value: 13.435083333333333\n - type: recall_at_1\n value: 31.656583333333334\n - type: recall_at_10\n value: 58.44758333333333\n - type: recall_at_100\n value: 79.93658333333332\n - type: recall_at_1000\n value: 93.32491666666668\n - type: recall_at_3\n value: 45.44266666666667\n - type: recall_at_5\n value: 50.99866666666666\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackStatsRetrieval\n type: mteb/cqadupstack-stats\n config: default\n split: test\n revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a\n metrics:\n - type: map_at_1\n value: 28.247\n - type: map_at_10\n value: 35.443999999999996\n - type: map_at_100\n value: 36.578\n - type: map_at_1000\n value: 36.675999999999995\n - type: map_at_3\n value: 33.276\n - type: map_at_5\n value: 34.536\n - type: mrr_at_1\n value: 31.747999999999998\n - type: mrr_at_10\n value: 38.413000000000004\n - type: mrr_at_100\n value: 39.327\n - type: mrr_at_1000\n value: 39.389\n - type: mrr_at_3\n value: 36.401\n - type: mrr_at_5\n value: 37.543\n - type: ndcg_at_1\n value: 31.747999999999998\n - type: ndcg_at_10\n value: 39.646\n - type: ndcg_at_100\n value: 44.861000000000004\n - type: ndcg_at_1000\n value: 47.197\n - type: ndcg_at_3\n value: 35.764\n - type: ndcg_at_5\n value: 37.635999999999996\n - type: precision_at_1\n value: 31.747999999999998\n - type: precision_at_10\n value: 6.12\n - type: precision_at_100\n value: 0.942\n - type: precision_at_1000\n value: 0.123\n - type: precision_at_3\n value: 15.235000000000001\n - type: precision_at_5\n value: 10.491\n - type: recall_at_1\n value: 28.247\n - type: recall_at_10\n value: 49.456\n - type: recall_at_100\n value: 73.02499999999999\n - type: recall_at_1000\n value: 89.898\n - type: recall_at_3\n value: 38.653999999999996\n - type: recall_at_5\n value: 43.259\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackTexRetrieval\n type: mteb/cqadupstack-tex\n config: default\n split: test\n revision: 46989137a86843e03a6195de44b09deda022eec7\n metrics:\n - type: map_at_1\n value: 22.45\n - type: map_at_10\n value: 30.476999999999997\n - type: map_at_100\n value: 31.630999999999997\n - type: map_at_1000\n value: 31.755\n - type: map_at_3\n value: 27.989000000000004\n - type: map_at_5\n value: 29.410999999999998\n - type: mrr_at_1\n value: 26.979\n - type: mrr_at_10\n value: 34.316\n - type: mrr_at_100\n value: 35.272999999999996\n - type: mrr_at_1000\n value: 35.342\n - type: mrr_at_3\n value: 32.14\n - type: mrr_at_5\n value: 33.405\n - type: ndcg_at_1\n value: 26.979\n - type: ndcg_at_10\n value: 35.166\n - type: ndcg_at_100\n value: 40.583000000000006\n - type: ndcg_at_1000\n value: 43.282\n - type: ndcg_at_3\n value: 30.916\n - type: ndcg_at_5\n value: 32.973\n - type: precision_at_1\n value: 26.979\n - type: precision_at_10\n value: 6.132\n - type: precision_at_100\n value: 1.047\n - type: precision_at_1000\n value: 0.145\n - type: precision_at_3\n value: 14.360999999999999\n - type: precision_at_5\n value: 10.227\n - type: recall_at_1\n value: 22.45\n - type: recall_at_10\n value: 45.348\n - type: recall_at_100\n value: 69.484\n - type: recall_at_1000\n value: 88.628\n - type: recall_at_3\n value: 33.338\n - type: recall_at_5\n value: 38.746\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackUnixRetrieval\n type: mteb/cqadupstack-unix\n config: default\n split: test\n revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53\n metrics:\n - type: map_at_1\n value: 32.123000000000005\n - type: map_at_10\n value: 41.778\n - type: map_at_100\n value: 42.911\n - type: map_at_1000\n value: 42.994\n - type: map_at_3\n value: 38.558\n - type: map_at_5\n value: 40.318\n - type: mrr_at_1\n value: 37.687\n - type: mrr_at_10\n value: 45.889\n - type: mrr_at_100\n value: 46.672999999999995\n - type: mrr_at_1000\n value: 46.72\n - type: mrr_at_3\n value: 43.33\n - type: mrr_at_5\n value: 44.734\n - type: ndcg_at_1\n value: 37.687\n - type: ndcg_at_10\n value: 47.258\n - type: ndcg_at_100\n value: 52.331\n - type: ndcg_at_1000\n value: 54.152\n - type: ndcg_at_3\n value: 41.857\n - type: ndcg_at_5\n value: 44.283\n - type: precision_at_1\n value: 37.687\n - type: precision_at_10\n value: 7.892\n - type: precision_at_100\n value: 1.183\n - type: precision_at_1000\n value: 0.14300000000000002\n - type: precision_at_3\n value: 18.781\n - type: precision_at_5\n value: 13.134\n - type: recall_at_1\n value: 32.123000000000005\n - type: recall_at_10\n value: 59.760000000000005\n - type: recall_at_100\n value: 81.652\n - type: recall_at_1000\n value: 94.401\n - type: recall_at_3\n value: 44.996\n - type: recall_at_5\n value: 51.184\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWebmastersRetrieval\n type: mteb/cqadupstack-webmasters\n config: default\n split: test\n revision: 160c094312a0e1facb97e55eeddb698c0abe3571\n metrics:\n - type: map_at_1\n value: 33.196999999999996\n - type: map_at_10\n value: 42.012\n - type: map_at_100\n value: 43.663999999999994\n - type: map_at_1000\n value: 43.883\n - type: map_at_3\n value: 39.33\n - type: map_at_5\n value: 40.586\n - type: mrr_at_1\n value: 39.328\n - type: mrr_at_10\n value: 46.57\n - type: mrr_at_100\n value: 47.508\n - type: mrr_at_1000\n value: 47.558\n - type: mrr_at_3\n value: 44.532\n - type: mrr_at_5\n value: 45.58\n - type: ndcg_at_1\n value: 39.328\n - type: ndcg_at_10\n value: 47.337\n - type: ndcg_at_100\n value: 52.989\n - type: ndcg_at_1000\n value: 55.224\n - type: ndcg_at_3\n value: 43.362\n - type: ndcg_at_5\n value: 44.866\n - type: precision_at_1\n value: 39.328\n - type: precision_at_10\n value: 8.577\n - type: precision_at_100\n value: 1.5789999999999997\n - type: precision_at_1000\n value: 0.25\n - type: precision_at_3\n value: 19.697\n - type: precision_at_5\n value: 13.755\n - type: recall_at_1\n value: 33.196999999999996\n - type: recall_at_10\n value: 56.635000000000005\n - type: recall_at_100\n value: 81.882\n - type: recall_at_1000\n value: 95.342\n - type: recall_at_3\n value: 44.969\n - type: recall_at_5\n value: 49.266\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWordpressRetrieval\n type: mteb/cqadupstack-wordpress\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 26.901000000000003\n - type: map_at_10\n value: 35.77\n - type: map_at_100\n value: 36.638999999999996\n - type: map_at_1000\n value: 36.741\n - type: map_at_3\n value: 33.219\n - type: map_at_5\n value: 34.574\n - type: mrr_at_1\n value: 29.205\n - type: mrr_at_10\n value: 37.848\n - type: mrr_at_100\n value: 38.613\n - type: mrr_at_1000\n value: 38.682\n - type: mrr_at_3\n value: 35.551\n - type: mrr_at_5\n value: 36.808\n - type: ndcg_at_1\n value: 29.205\n - type: ndcg_at_10\n value: 40.589\n - type: ndcg_at_100\n value: 45.171\n - type: ndcg_at_1000\n value: 47.602\n - type: ndcg_at_3\n value: 35.760999999999996\n - type: ndcg_at_5\n value: 37.980000000000004\n - type: precision_at_1\n value: 29.205\n - type: precision_at_10\n value: 6.192\n - type: precision_at_100\n value: 0.922\n - type: precision_at_1000\n value: 0.123\n - type: precision_at_3\n value: 15.034\n - type: precision_at_5\n value: 10.424999999999999\n - type: recall_at_1\n value: 26.901000000000003\n - type: recall_at_10\n value: 53.236000000000004\n - type: recall_at_100\n value: 74.809\n - type: recall_at_1000\n value: 92.884\n - type: recall_at_3\n value: 40.314\n - type: recall_at_5\n value: 45.617999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: mteb/climate-fever\n config: default\n split: test\n revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380\n metrics:\n - type: map_at_1\n value: 16.794999999999998\n - type: map_at_10\n value: 29.322\n - type: map_at_100\n value: 31.463\n - type: map_at_1000\n value: 31.643\n - type: map_at_3\n value: 24.517\n - type: map_at_5\n value: 27.237000000000002\n - type: mrr_at_1\n value: 37.655\n - type: mrr_at_10\n value: 50.952\n - type: mrr_at_100\n value: 51.581999999999994\n - type: mrr_at_1000\n value: 51.61\n - type: mrr_at_3\n value: 47.991\n - type: mrr_at_5\n value: 49.744\n - type: ndcg_at_1\n value: 37.655\n - type: ndcg_at_10\n value: 39.328\n - type: ndcg_at_100\n value: 46.358\n - type: ndcg_at_1000\n value: 49.245\n - type: ndcg_at_3\n value: 33.052\n - type: ndcg_at_5\n value: 35.407\n - type: precision_at_1\n value: 37.655\n - type: precision_at_10\n value: 12.202\n - type: precision_at_100\n value: 1.9789999999999999\n - type: precision_at_1000\n value: 0.252\n - type: precision_at_3\n value: 24.973\n - type: precision_at_5\n value: 19.075\n - type: recall_at_1\n value: 16.794999999999998\n - type: recall_at_10\n value: 45.716\n - type: recall_at_100\n value: 68.919\n - type: recall_at_1000\n value: 84.71600000000001\n - type: recall_at_3\n value: 30.135\n - type: recall_at_5\n value: 37.141999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: mteb/dbpedia\n config: default\n split: test\n revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659\n metrics:\n - type: map_at_1\n value: 9.817\n - type: map_at_10\n value: 22.058\n - type: map_at_100\n value: 31.805\n - type: map_at_1000\n value: 33.562999999999995\n - type: map_at_3\n value: 15.537\n - type: map_at_5\n value: 18.199\n - type: mrr_at_1\n value: 72.75\n - type: mrr_at_10\n value: 79.804\n - type: mrr_at_100\n value: 80.089\n - type: mrr_at_1000\n value: 80.09100000000001\n - type: mrr_at_3\n value: 78.75\n - type: mrr_at_5\n value: 79.325\n - type: ndcg_at_1\n value: 59.875\n - type: ndcg_at_10\n value: 45.972\n - type: ndcg_at_100\n value: 51.092999999999996\n - type: ndcg_at_1000\n value: 58.048\n - type: ndcg_at_3\n value: 50.552\n - type: ndcg_at_5\n value: 47.672\n - type: precision_at_1\n value: 72.75\n - type: precision_at_10\n value: 37.05\n - type: precision_at_100\n value: 12.005\n - type: precision_at_1000\n value: 2.221\n - type: precision_at_3\n value: 54.083000000000006\n - type: precision_at_5\n value: 46.2\n - type: recall_at_1\n value: 9.817\n - type: recall_at_10\n value: 27.877000000000002\n - type: recall_at_100\n value: 57.974000000000004\n - type: recall_at_1000\n value: 80.085\n - type: recall_at_3\n value: 16.911\n - type: recall_at_5\n value: 20.689\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 46.464999999999996\n - type: f1\n value: 42.759588662873796\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: mteb/fever\n config: default\n split: test\n revision: bea83ef9e8fb933d90a2f1d5515737465d613e12\n metrics:\n - type: map_at_1\n value: 75.82900000000001\n - type: map_at_10\n value: 84.613\n - type: map_at_100\n value: 84.845\n - type: map_at_1000\n value: 84.855\n - type: map_at_3\n value: 83.498\n - type: map_at_5\n value: 84.29299999999999\n - type: mrr_at_1\n value: 81.69800000000001\n - type: mrr_at_10\n value: 88.84100000000001\n - type: mrr_at_100\n value: 88.887\n - type: mrr_at_1000\n value: 88.888\n - type: mrr_at_3\n value: 88.179\n - type: mrr_at_5\n value: 88.69200000000001\n - type: ndcg_at_1\n value: 81.69800000000001\n - type: ndcg_at_10\n value: 88.21799999999999\n - type: ndcg_at_100\n value: 88.961\n - type: ndcg_at_1000\n value: 89.131\n - type: ndcg_at_3\n value: 86.591\n - type: ndcg_at_5\n value: 87.666\n - type: precision_at_1\n value: 81.69800000000001\n - type: precision_at_10\n value: 10.615\n - type: precision_at_100\n value: 1.125\n - type: precision_at_1000\n value: 0.11499999999999999\n - type: precision_at_3\n value: 33.208\n - type: precision_at_5\n value: 20.681\n - type: recall_at_1\n value: 75.82900000000001\n - type: recall_at_10\n value: 94.97\n - type: recall_at_100\n value: 97.786\n - type: recall_at_1000\n value: 98.809\n - type: recall_at_3\n value: 90.625\n - type: recall_at_5\n value: 93.345\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: mteb/fiqa\n config: default\n split: test\n revision: 27a168819829fe9bcd655c2df245fb19452e8e06\n metrics:\n - type: map_at_1\n value: 22.788\n - type: map_at_10\n value: 36.71\n - type: map_at_100\n value: 38.527\n - type: map_at_1000\n value: 38.701\n - type: map_at_3\n value: 32.318999999999996\n - type: map_at_5\n value: 34.809\n - type: mrr_at_1\n value: 44.444\n - type: mrr_at_10\n value: 52.868\n - type: mrr_at_100\n value: 53.52400000000001\n - type: mrr_at_1000\n value: 53.559999999999995\n - type: mrr_at_3\n value: 50.153999999999996\n - type: mrr_at_5\n value: 51.651\n - type: ndcg_at_1\n value: 44.444\n - type: ndcg_at_10\n value: 44.707\n - type: ndcg_at_100\n value: 51.174\n - type: ndcg_at_1000\n value: 53.996\n - type: ndcg_at_3\n value: 40.855999999999995\n - type: ndcg_at_5\n value: 42.113\n - type: precision_at_1\n value: 44.444\n - type: precision_at_10\n value: 12.021999999999998\n - type: precision_at_100\n value: 1.8950000000000002\n - type: precision_at_1000\n value: 0.241\n - type: precision_at_3\n value: 26.8\n - type: precision_at_5\n value: 19.66\n - type: recall_at_1\n value: 22.788\n - type: recall_at_10\n value: 51.793\n - type: recall_at_100\n value: 75.69500000000001\n - type: recall_at_1000\n value: 92.292\n - type: recall_at_3\n value: 37.375\n - type: recall_at_5\n value: 43.682\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: mteb/hotpotqa\n config: default\n split: test\n revision: ab518f4d6fcca38d87c25209f94beba119d02014\n metrics:\n - type: map_at_1\n value: 41.276\n - type: map_at_10\n value: 67.245\n - type: map_at_100\n value: 68.061\n - type: map_at_1000\n value: 68.11399999999999\n - type: map_at_3\n value: 63.693\n - type: map_at_5\n value: 65.90899999999999\n - type: mrr_at_1\n value: 82.552\n - type: mrr_at_10\n value: 87.741\n - type: mrr_at_100\n value: 87.868\n - type: mrr_at_1000\n value: 87.871\n - type: mrr_at_3\n value: 86.98599999999999\n - type: mrr_at_5\n value: 87.469\n - type: ndcg_at_1\n value: 82.552\n - type: ndcg_at_10\n value: 75.176\n - type: ndcg_at_100\n value: 77.902\n - type: ndcg_at_1000\n value: 78.852\n - type: ndcg_at_3\n value: 70.30499999999999\n - type: ndcg_at_5\n value: 73.00999999999999\n - type: precision_at_1\n value: 82.552\n - type: precision_at_10\n value: 15.765\n - type: precision_at_100\n value: 1.788\n - type: precision_at_1000\n value: 0.191\n - type: precision_at_3\n value: 45.375\n - type: precision_at_5\n value: 29.360999999999997\n - type: recall_at_1\n value: 41.276\n - type: recall_at_10\n value: 78.825\n - type: recall_at_100\n value: 89.41900000000001\n - type: recall_at_1000\n value: 95.625\n - type: recall_at_3\n value: 68.062\n - type: recall_at_5\n value: 73.40299999999999\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 72.876\n - type: ap\n value: 67.15477852410164\n - type: f1\n value: 72.65147370025373\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: mteb/msmarco\n config: default\n split: dev\n revision: c5a29a104738b98a9e76336939199e264163d4a0\n metrics:\n - type: map_at_1\n value: 21.748\n - type: map_at_10\n value: 34.626000000000005\n - type: map_at_100\n value: 35.813\n - type: map_at_1000\n value: 35.859\n - type: map_at_3\n value: 30.753000000000004\n - type: map_at_5\n value: 33.049\n - type: mrr_at_1\n value: 22.35\n - type: mrr_at_10\n value: 35.23\n - type: mrr_at_100\n value: 36.359\n - type: mrr_at_1000\n value: 36.399\n - type: mrr_at_3\n value: 31.436999999999998\n - type: mrr_at_5\n value: 33.687\n - type: ndcg_at_1\n value: 22.364\n - type: ndcg_at_10\n value: 41.677\n - type: ndcg_at_100\n value: 47.355999999999995\n - type: ndcg_at_1000\n value: 48.494\n - type: ndcg_at_3\n value: 33.85\n - type: ndcg_at_5\n value: 37.942\n - type: precision_at_1\n value: 22.364\n - type: precision_at_10\n value: 6.6000000000000005\n - type: precision_at_100\n value: 0.9450000000000001\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.527000000000001\n - type: precision_at_5\n value: 10.796999999999999\n - type: recall_at_1\n value: 21.748\n - type: recall_at_10\n value: 63.292\n - type: recall_at_100\n value: 89.427\n - type: recall_at_1000\n value: 98.13499999999999\n - type: recall_at_3\n value: 42.126000000000005\n - type: recall_at_5\n value: 51.968\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 92.62425900592795\n - type: f1\n value: 92.08497761553683\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 64.51436388508893\n - type: f1\n value: 45.884016531912906\n - task:\n type: Classification\n dataset:\n name: MTEB MasakhaNEWSClassification (eng)\n type: masakhane/masakhanews\n config: eng\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: accuracy\n value: 76.57172995780591\n - type: f1\n value: 75.52979910878491\n - task:\n type: Clustering\n dataset:\n name: MTEB MasakhaNEWSClusteringP2P (eng)\n type: masakhane/masakhanews\n config: eng\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: v_measure\n value: 44.84052695201612\n - type: v_measure\n value: 21.443971229936494\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 65.79354404841965\n - type: f1\n value: 63.17260074126185\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 71.09616677874916\n - type: f1\n value: 69.74285784421075\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 31.474709231086184\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 28.93630367824217\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 29.08234393834005\n - type: mrr\n value: 29.740466971605432\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: mteb/nfcorpus\n config: default\n split: test\n revision: ec0fa4fe99da2ff19ca1214b7966684033a58814\n metrics:\n - type: map_at_1\n value: 6.2059999999999995\n - type: map_at_10\n value: 14.442\n - type: map_at_100\n value: 18.005\n - type: map_at_1000\n value: 19.488\n - type: map_at_3\n value: 10.666\n - type: map_at_5\n value: 12.45\n - type: mrr_at_1\n value: 47.678\n - type: mrr_at_10\n value: 57.519\n - type: mrr_at_100\n value: 58.13700000000001\n - type: mrr_at_1000\n value: 58.167\n - type: mrr_at_3\n value: 55.779\n - type: mrr_at_5\n value: 56.940000000000005\n - type: ndcg_at_1\n value: 45.82\n - type: ndcg_at_10\n value: 37.651\n - type: ndcg_at_100\n value: 34.001999999999995\n - type: ndcg_at_1000\n value: 42.626\n - type: ndcg_at_3\n value: 43.961\n - type: ndcg_at_5\n value: 41.461\n - type: precision_at_1\n value: 47.678\n - type: precision_at_10\n value: 27.584999999999997\n - type: precision_at_100\n value: 8.455\n - type: precision_at_1000\n value: 2.118\n - type: precision_at_3\n value: 41.692\n - type: precision_at_5\n value: 36.161\n - type: recall_at_1\n value: 6.2059999999999995\n - type: recall_at_10\n value: 18.599\n - type: recall_at_100\n value: 33.608\n - type: recall_at_1000\n value: 65.429\n - type: recall_at_3\n value: 12.126000000000001\n - type: recall_at_5\n value: 14.902000000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: mteb/nq\n config: default\n split: test\n revision: b774495ed302d8c44a3a7ea25c90dbce03968f31\n metrics:\n - type: map_at_1\n value: 39.117000000000004\n - type: map_at_10\n value: 55.535000000000004\n - type: map_at_100\n value: 56.32899999999999\n - type: map_at_1000\n value: 56.34400000000001\n - type: map_at_3\n value: 51.439\n - type: map_at_5\n value: 53.89699999999999\n - type: mrr_at_1\n value: 43.714\n - type: mrr_at_10\n value: 58.05200000000001\n - type: mrr_at_100\n value: 58.582\n - type: mrr_at_1000\n value: 58.592\n - type: mrr_at_3\n value: 54.896\n - type: mrr_at_5\n value: 56.874\n - type: ndcg_at_1\n value: 43.685\n - type: ndcg_at_10\n value: 63.108\n - type: ndcg_at_100\n value: 66.231\n - type: ndcg_at_1000\n value: 66.583\n - type: ndcg_at_3\n value: 55.659000000000006\n - type: ndcg_at_5\n value: 59.681\n - type: precision_at_1\n value: 43.685\n - type: precision_at_10\n value: 9.962\n - type: precision_at_100\n value: 1.174\n - type: precision_at_1000\n value: 0.121\n - type: precision_at_3\n value: 24.961\n - type: precision_at_5\n value: 17.352\n - type: recall_at_1\n value: 39.117000000000004\n - type: recall_at_10\n value: 83.408\n - type: recall_at_100\n value: 96.553\n - type: recall_at_1000\n value: 99.136\n - type: recall_at_3\n value: 64.364\n - type: recall_at_5\n value: 73.573\n - task:\n type: Classification\n dataset:\n name: MTEB NewsClassification\n type: ag_news\n config: default\n split: test\n revision: eb185aade064a813bc0b7f42de02595523103ca4\n metrics:\n - type: accuracy\n value: 78.87763157894737\n - type: f1\n value: 78.69611753876177\n - task:\n type: PairClassification\n dataset:\n name: MTEB OpusparcusPC (en)\n type: GEM/opusparcus\n config: en\n split: test\n revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a\n metrics:\n - type: cos_sim_accuracy\n value: 99.89816700610999\n - type: cos_sim_ap\n value: 100\n - type: cos_sim_f1\n value: 99.9490575649516\n - type: cos_sim_precision\n value: 100\n - type: cos_sim_recall\n value: 99.89816700610999\n - type: dot_accuracy\n value: 99.89816700610999\n - type: dot_ap\n value: 100\n - type: dot_f1\n value: 99.9490575649516\n - type: dot_precision\n value: 100\n - type: dot_recall\n value: 99.89816700610999\n - type: euclidean_accuracy\n value: 99.89816700610999\n - type: euclidean_ap\n value: 100\n - type: euclidean_f1\n value: 99.9490575649516\n - type: euclidean_precision\n value: 100\n - type: euclidean_recall\n value: 99.89816700610999\n - type: manhattan_accuracy\n value: 99.89816700610999\n - type: manhattan_ap\n value: 100\n - type: manhattan_f1\n value: 99.9490575649516\n - type: manhattan_precision\n value: 100\n - type: manhattan_recall\n value: 99.89816700610999\n - type: max_accuracy\n value: 99.89816700610999\n - type: max_ap\n value: 100\n - type: max_f1\n value: 99.9490575649516\n - task:\n type: PairClassification\n dataset:\n name: MTEB PawsX (en)\n type: paws-x\n config: en\n split: test\n revision: 8a04d940a42cd40658986fdd8e3da561533a3646\n metrics:\n - type: cos_sim_accuracy\n value: 62\n - type: cos_sim_ap\n value: 62.26837791655737\n - type: cos_sim_f1\n value: 62.607449856733524\n - type: cos_sim_precision\n value: 46.36604774535809\n - type: cos_sim_recall\n value: 96.36163175303197\n - type: dot_accuracy\n value: 62\n - type: dot_ap\n value: 62.26736459439965\n - type: dot_f1\n value: 62.607449856733524\n - type: dot_precision\n value: 46.36604774535809\n - type: dot_recall\n value: 96.36163175303197\n - type: euclidean_accuracy\n value: 62\n - type: euclidean_ap\n value: 62.26826112548132\n - type: euclidean_f1\n value: 62.607449856733524\n - type: euclidean_precision\n value: 46.36604774535809\n - type: euclidean_recall\n value: 96.36163175303197\n - type: manhattan_accuracy\n value: 62\n - type: manhattan_ap\n value: 62.26223761507973\n - type: manhattan_f1\n value: 62.585034013605444\n - type: manhattan_precision\n value: 46.34146341463415\n - type: manhattan_recall\n value: 96.36163175303197\n - type: max_accuracy\n value: 62\n - type: max_ap\n value: 62.26837791655737\n - type: max_f1\n value: 62.607449856733524\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: mteb/quora\n config: default\n split: test\n revision: e4e08e0b7dbe3c8700f0daef558ff32256715259\n metrics:\n - type: map_at_1\n value: 69.90899999999999\n - type: map_at_10\n value: 83.56700000000001\n - type: map_at_100\n value: 84.19200000000001\n - type: map_at_1000\n value: 84.212\n - type: map_at_3\n value: 80.658\n - type: map_at_5\n value: 82.473\n - type: mrr_at_1\n value: 80.4\n - type: mrr_at_10\n value: 86.699\n - type: mrr_at_100\n value: 86.798\n - type: mrr_at_1000\n value: 86.80099999999999\n - type: mrr_at_3\n value: 85.677\n - type: mrr_at_5\n value: 86.354\n - type: ndcg_at_1\n value: 80.43\n - type: ndcg_at_10\n value: 87.41\n - type: ndcg_at_100\n value: 88.653\n - type: ndcg_at_1000\n value: 88.81599999999999\n - type: ndcg_at_3\n value: 84.516\n - type: ndcg_at_5\n value: 86.068\n - type: precision_at_1\n value: 80.43\n - type: precision_at_10\n value: 13.234000000000002\n - type: precision_at_100\n value: 1.513\n - type: precision_at_1000\n value: 0.156\n - type: precision_at_3\n value: 36.93\n - type: precision_at_5\n value: 24.26\n - type: recall_at_1\n value: 69.90899999999999\n - type: recall_at_10\n value: 94.687\n - type: recall_at_100\n value: 98.96000000000001\n - type: recall_at_1000\n value: 99.79599999999999\n - type: recall_at_3\n value: 86.25699999999999\n - type: recall_at_5\n value: 90.70700000000001\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 46.02256865360266\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 385e3cb46b4cfa89021f56c4380204149d0efe33\n metrics:\n - type: v_measure\n value: 62.43157528757563\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: mteb/scidocs\n config: default\n split: test\n revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88\n metrics:\n - type: map_at_1\n value: 5.093\n - type: map_at_10\n value: 12.982\n - type: map_at_100\n value: 15.031\n - type: map_at_1000\n value: 15.334\n - type: map_at_3\n value: 9.339\n - type: map_at_5\n value: 11.183\n - type: mrr_at_1\n value: 25.1\n - type: mrr_at_10\n value: 36.257\n - type: mrr_at_100\n value: 37.351\n - type: mrr_at_1000\n value: 37.409\n - type: mrr_at_3\n value: 33.050000000000004\n - type: mrr_at_5\n value: 35.205\n - type: ndcg_at_1\n value: 25.1\n - type: ndcg_at_10\n value: 21.361\n - type: ndcg_at_100\n value: 29.396\n - type: ndcg_at_1000\n value: 34.849999999999994\n - type: ndcg_at_3\n value: 20.704\n - type: ndcg_at_5\n value: 18.086\n - type: precision_at_1\n value: 25.1\n - type: precision_at_10\n value: 10.94\n - type: precision_at_100\n value: 2.257\n - type: precision_at_1000\n value: 0.358\n - type: precision_at_3\n value: 19.467000000000002\n - type: precision_at_5\n value: 15.98\n - type: recall_at_1\n value: 5.093\n - type: recall_at_10\n value: 22.177\n - type: recall_at_100\n value: 45.842\n - type: recall_at_1000\n value: 72.598\n - type: recall_at_3\n value: 11.833\n - type: recall_at_5\n value: 16.173000000000002\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: 20a6d6f312dd54037fe07a32d58e5e168867909d\n metrics:\n - type: cos_sim_pearson\n value: 73.56535226754596\n - type: cos_sim_spearman\n value: 69.32425977603488\n - type: euclidean_pearson\n value: 71.32425703470898\n - type: euclidean_spearman\n value: 69.32425217267013\n - type: manhattan_pearson\n value: 71.25897281394246\n - type: manhattan_spearman\n value: 69.27132577049578\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 69.66387868726018\n - type: cos_sim_spearman\n value: 67.85470749045027\n - type: euclidean_pearson\n value: 66.62075098063795\n - type: euclidean_spearman\n value: 67.85470749045027\n - type: manhattan_pearson\n value: 66.61455061901262\n - type: manhattan_spearman\n value: 67.87229618498695\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 75.65731331392575\n - type: cos_sim_spearman\n value: 77.48991626780108\n - type: euclidean_pearson\n value: 77.19884738623692\n - type: euclidean_spearman\n value: 77.48985836619045\n - type: manhattan_pearson\n value: 77.0656684243772\n - type: manhattan_spearman\n value: 77.30289226582691\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 69.37003253666457\n - type: cos_sim_spearman\n value: 69.77157648098141\n - type: euclidean_pearson\n value: 69.39543876030432\n - type: euclidean_spearman\n value: 69.77157648098141\n - type: manhattan_pearson\n value: 69.29901600459745\n - type: manhattan_spearman\n value: 69.65074167527128\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 78.56777256540136\n - type: cos_sim_spearman\n value: 80.16458787843023\n - type: euclidean_pearson\n value: 80.16475730686916\n - type: euclidean_spearman\n value: 80.16458787843023\n - type: manhattan_pearson\n value: 80.12814463670401\n - type: manhattan_spearman\n value: 80.1357907984809\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 76.09572350919031\n - type: cos_sim_spearman\n value: 77.94490233429326\n - type: euclidean_pearson\n value: 78.36595251203524\n - type: euclidean_spearman\n value: 77.94490233429326\n - type: manhattan_pearson\n value: 78.41538768125166\n - type: manhattan_spearman\n value: 78.01244379569542\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 80.7843552187951\n - type: cos_sim_spearman\n value: 82.28085055047386\n - type: euclidean_pearson\n value: 82.37373672515267\n - type: euclidean_spearman\n value: 82.28085055047386\n - type: manhattan_pearson\n value: 82.39387241346917\n - type: manhattan_spearman\n value: 82.36503339515906\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 68.29963929962095\n - type: cos_sim_spearman\n value: 67.96868942546051\n - type: euclidean_pearson\n value: 68.93524903869285\n - type: euclidean_spearman\n value: 67.96868942546051\n - type: manhattan_pearson\n value: 68.79144468444811\n - type: manhattan_spearman\n value: 67.69311483884324\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 72.84789696700685\n - type: cos_sim_spearman\n value: 75.67875747588545\n - type: euclidean_pearson\n value: 75.07752300463038\n - type: euclidean_spearman\n value: 75.67875747588545\n - type: manhattan_pearson\n value: 74.97934248140928\n - type: manhattan_spearman\n value: 75.62525644178724\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmarkMultilingualSTS (en)\n type: PhilipMay/stsb_multi_mt\n config: en\n split: test\n revision: 93d57ef91790589e3ce9c365164337a8a78b7632\n metrics:\n - type: cos_sim_pearson\n value: 72.84789702519309\n - type: cos_sim_spearman\n value: 75.67875747588545\n - type: euclidean_pearson\n value: 75.07752310061133\n - type: euclidean_spearman\n value: 75.67875747588545\n - type: manhattan_pearson\n value: 74.97934257159595\n - type: manhattan_spearman\n value: 75.62525644178724\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 81.55557720431086\n - type: mrr\n value: 94.91178665198272\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: mteb/scifact\n config: default\n split: test\n revision: 0228b52cf27578f30900b9e5271d331663a030d7\n metrics:\n - type: map_at_1\n value: 59.260999999999996\n - type: map_at_10\n value: 69.36099999999999\n - type: map_at_100\n value: 69.868\n - type: map_at_1000\n value: 69.877\n - type: map_at_3\n value: 66.617\n - type: map_at_5\n value: 68.061\n - type: mrr_at_1\n value: 62.333000000000006\n - type: mrr_at_10\n value: 70.533\n - type: mrr_at_100\n value: 70.966\n - type: mrr_at_1000\n value: 70.975\n - type: mrr_at_3\n value: 68.667\n - type: mrr_at_5\n value: 69.717\n - type: ndcg_at_1\n value: 62.333000000000006\n - type: ndcg_at_10\n value: 73.82300000000001\n - type: ndcg_at_100\n value: 76.122\n - type: ndcg_at_1000\n value: 76.374\n - type: ndcg_at_3\n value: 69.27499999999999\n - type: ndcg_at_5\n value: 71.33\n - type: precision_at_1\n value: 62.333000000000006\n - type: precision_at_10\n value: 9.8\n - type: precision_at_100\n value: 1.097\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 26.889000000000003\n - type: precision_at_5\n value: 17.599999999999998\n - type: recall_at_1\n value: 59.260999999999996\n - type: recall_at_10\n value: 86.2\n - type: recall_at_100\n value: 96.667\n - type: recall_at_1000\n value: 98.667\n - type: recall_at_3\n value: 74.006\n - type: recall_at_5\n value: 79.167\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.81881188118813\n - type: cos_sim_ap\n value: 95.20169041096409\n - type: cos_sim_f1\n value: 90.76224129227664\n - type: cos_sim_precision\n value: 91.64118246687055\n - type: cos_sim_recall\n value: 89.9\n - type: dot_accuracy\n value: 99.81881188118813\n - type: dot_ap\n value: 95.20169041096409\n - type: dot_f1\n value: 90.76224129227664\n - type: dot_precision\n value: 91.64118246687055\n - type: dot_recall\n value: 89.9\n - type: euclidean_accuracy\n value: 99.81881188118813\n - type: euclidean_ap\n value: 95.2016904109641\n - type: euclidean_f1\n value: 90.76224129227664\n - type: euclidean_precision\n value: 91.64118246687055\n - type: euclidean_recall\n value: 89.9\n - type: manhattan_accuracy\n value: 99.81881188118813\n - type: manhattan_ap\n value: 95.22680188132777\n - type: manhattan_f1\n value: 90.79013588324108\n - type: manhattan_precision\n value: 91.38804457953394\n - type: manhattan_recall\n value: 90.2\n - type: max_accuracy\n value: 99.81881188118813\n - type: max_ap\n value: 95.22680188132777\n - type: max_f1\n value: 90.79013588324108\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 57.8638628701308\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 37.82028248106046\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 50.870860210170946\n - type: mrr\n value: 51.608084521687466\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.60384207444685\n - type: cos_sim_spearman\n value: 30.84047452209471\n - type: dot_pearson\n value: 31.60384104417333\n - type: dot_spearman\n value: 30.84047452209471\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: mteb/trec-covid\n config: default\n split: test\n revision: bb9466bac8153a0349341eb1b22e06409e78ef4e\n metrics:\n - type: map_at_1\n value: 0.246\n - type: map_at_10\n value: 2.051\n - type: map_at_100\n value: 13.129\n - type: map_at_1000\n value: 31.56\n - type: map_at_3\n value: 0.681\n - type: map_at_5\n value: 1.105\n - type: mrr_at_1\n value: 94\n - type: mrr_at_10\n value: 97\n - type: mrr_at_100\n value: 97\n - type: mrr_at_1000\n value: 97\n - type: mrr_at_3\n value: 97\n - type: mrr_at_5\n value: 97\n - type: ndcg_at_1\n value: 87\n - type: ndcg_at_10\n value: 80.716\n - type: ndcg_at_100\n value: 63.83\n - type: ndcg_at_1000\n value: 56.215\n - type: ndcg_at_3\n value: 84.531\n - type: ndcg_at_5\n value: 84.777\n - type: precision_at_1\n value: 94\n - type: precision_at_10\n value: 84.6\n - type: precision_at_100\n value: 66.03999999999999\n - type: precision_at_1000\n value: 24.878\n - type: precision_at_3\n value: 88.667\n - type: precision_at_5\n value: 89.60000000000001\n - type: recall_at_1\n value: 0.246\n - type: recall_at_10\n value: 2.2079999999999997\n - type: recall_at_100\n value: 15.895999999999999\n - type: recall_at_1000\n value: 52.683\n - type: recall_at_3\n value: 0.7040000000000001\n - type: recall_at_5\n value: 1.163\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: mteb/touche2020\n config: default\n split: test\n revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f\n metrics:\n - type: map_at_1\n value: 3.852\n - type: map_at_10\n value: 14.316\n - type: map_at_100\n value: 20.982\n - type: map_at_1000\n value: 22.58\n - type: map_at_3\n value: 7.767\n - type: map_at_5\n value: 10.321\n - type: mrr_at_1\n value: 51.019999999999996\n - type: mrr_at_10\n value: 66.365\n - type: mrr_at_100\n value: 66.522\n - type: mrr_at_1000\n value: 66.522\n - type: mrr_at_3\n value: 62.925\n - type: mrr_at_5\n value: 64.762\n - type: ndcg_at_1\n value: 46.939\n - type: ndcg_at_10\n value: 34.516999999999996\n - type: ndcg_at_100\n value: 44.25\n - type: ndcg_at_1000\n value: 54.899\n - type: ndcg_at_3\n value: 40.203\n - type: ndcg_at_5\n value: 37.004\n - type: precision_at_1\n value: 51.019999999999996\n - type: precision_at_10\n value: 29.796\n - type: precision_at_100\n value: 8.633000000000001\n - type: precision_at_1000\n value: 1.584\n - type: precision_at_3\n value: 40.816\n - type: precision_at_5\n value: 35.918\n - type: recall_at_1\n value: 3.852\n - type: recall_at_10\n value: 20.891000000000002\n - type: recall_at_100\n value: 52.428\n - type: recall_at_1000\n value: 84.34899999999999\n - type: recall_at_3\n value: 8.834\n - type: recall_at_5\n value: 12.909\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 64.7092\n - type: ap\n value: 11.972915012305819\n - type: f1\n value: 49.91050149892115\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 56.737408036219584\n - type: f1\n value: 57.07235266246011\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 35.9147539025798\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 82.52369315133814\n - type: cos_sim_ap\n value: 62.34858091376534\n - type: cos_sim_f1\n value: 58.18225190839694\n - type: cos_sim_precision\n value: 53.09098824553766\n - type: cos_sim_recall\n value: 64.35356200527704\n - type: dot_accuracy\n value: 82.52369315133814\n - type: dot_ap\n value: 62.34857753814992\n - type: dot_f1\n value: 58.18225190839694\n - type: dot_precision\n value: 53.09098824553766\n - type: dot_recall\n value: 64.35356200527704\n - type: euclidean_accuracy\n value: 82.52369315133814\n - type: euclidean_ap\n value: 62.34857756663386\n - type: euclidean_f1\n value: 58.18225190839694\n - type: euclidean_precision\n value: 53.09098824553766\n - type: euclidean_recall\n value: 64.35356200527704\n - type: manhattan_accuracy\n value: 82.49389044525243\n - type: manhattan_ap\n value: 62.32245347238179\n - type: manhattan_f1\n value: 58.206309819213054\n - type: manhattan_precision\n value: 52.70704044511021\n - type: manhattan_recall\n value: 64.9868073878628\n - type: max_accuracy\n value: 82.52369315133814\n - type: max_ap\n value: 62.34858091376534\n - type: max_f1\n value: 58.206309819213054\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.34555827220863\n - type: cos_sim_ap\n value: 84.84152481680071\n - type: cos_sim_f1\n value: 76.860456739428\n - type: cos_sim_precision\n value: 72.21470150263978\n - type: cos_sim_recall\n value: 82.14505697566985\n - type: dot_accuracy\n value: 88.34555827220863\n - type: dot_ap\n value: 84.84152743322608\n - type: dot_f1\n value: 76.860456739428\n - type: dot_precision\n value: 72.21470150263978\n - type: dot_recall\n value: 82.14505697566985\n - type: euclidean_accuracy\n value: 88.34555827220863\n - type: euclidean_ap\n value: 84.84152589453169\n - type: euclidean_f1\n value: 76.860456739428\n - type: euclidean_precision\n value: 72.21470150263978\n - type: euclidean_recall\n value: 82.14505697566985\n - type: manhattan_accuracy\n value: 88.38242713548337\n - type: manhattan_ap\n value: 84.8112124970968\n - type: manhattan_f1\n value: 76.83599206057487\n - type: manhattan_precision\n value: 73.51244900829934\n - type: manhattan_recall\n value: 80.47428395441946\n - type: max_accuracy\n value: 88.38242713548337\n - type: max_ap\n value: 84.84152743322608\n - type: max_f1\n value: 76.860456739428\n - task:\n type: Clustering\n dataset:\n name: MTEB WikiCitiesClustering\n type: jinaai/cities_wiki_clustering\n config: default\n split: test\n revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa\n metrics:\n - type: v_measure\n value: 85.5314389263015\n---\n{task_description}\\n{query}'\n\ndef get_detailed_example(task_description: str, query: str, response: str) -> str:\n return f'{task_description}\\n{query}\\n{response}'\n\ndef get_new_queries(queries, query_max_len, examples_prefix, tokenizer):\n inputs = tokenizer(\n queries,\n max_length=query_max_len - len(tokenizer('', add_special_tokens=False)['input_ids']) - len(\n tokenizer('\\n', add_special_tokens=False)['input_ids']),\n return_token_type_ids=False,\n truncation=True,\n return_tensors=None,\n add_special_tokens=False\n )\n prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)['input_ids']\n suffix_ids = tokenizer('\\n', add_special_tokens=False)['input_ids']\n new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8\n new_queries = tokenizer.batch_decode(inputs['input_ids'])\n for i in range(len(new_queries)):\n new_queries[i] = examples_prefix + new_queries[i] + '\\n'\n return new_max_length, new_queries\n\ntask = 'Given a web search query, retrieve relevant passages that answer the query.'\nexamples = [\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"},\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"}\n]\nexamples = [get_detailed_example(e['instruct'], e['query'], e['response']) for e in examples]\nexamples_prefix = '\\n\\n'.join(examples) + '\\n\\n' # if there not exists any examples, just set examples_prefix = ''\nqueries = [\n get_detailed_instruct(task, 'how much protein should a female eat'),\n get_detailed_instruct(task, 'summit define')\n]\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\nquery_max_len, doc_max_len = 512, 512\n\ntokenizer = AutoTokenizer.from_pretrained('BAAI/bge-en-icl')\nmodel = AutoModel.from_pretrained('BAAI/bge-en-icl')\nmodel.eval()\n\nnew_query_max_len, new_queries = get_new_queries(queries, query_max_len, examples_prefix, tokenizer)\n\nquery_batch_dict = tokenizer(new_queries, max_length=new_query_max_len, padding=True, truncation=True, return_tensors='pt')\ndoc_batch_dict = tokenizer(documents, max_length=doc_max_len, padding=True, truncation=True, return_tensors='pt')\n\nwith torch.no_grad():\n query_outputs = model(**query_batch_dict)\n query_embeddings = last_token_pool(query_outputs.last_hidden_state, query_batch_dict['attention_mask'])\n doc_outputs = model(**doc_batch_dict)\n doc_embeddings = last_token_pool(doc_outputs.last_hidden_state, doc_batch_dict['attention_mask'])\n \n# normalize embeddings\nquery_embeddings = F.normalize(query_embeddings, p=2, dim=1)\ndoc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)\nscores = (query_embeddings @ doc_embeddings.T) * 100\nprint(scores.tolist())\n```\n\n\n## Evaluation \n\n`bge-en-icl` achieve **state-of-the-art performance on both MTEB and AIR-Bench leaderboard!**\n\n- **[MTEB](https://huggingface.co/spaces/mteb/leaderboard)**:\n\n \n\n- **[BEIR](https://huggingface.co/spaces/mteb/leaderboard)**: \n\n\n\n- **[AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard)**: \n\n**QA (en, nDCG@10):**\n\n| AIR-Bench_24.04 | wiki | web | news | healthcare | law | finance | arxiv | msmarco | ALL (8) |\n| :--------------------------: | :-------: | :-------: | :-------: | :--------: | :-------: | :-------: | :-------: | :-------: | :-------: |\n| **e5-mistral-7b-instruct** | 61.67 | 44.41 | 48.18 | 56.32 | 19.32 | 54.79 | 44.78 | 59.03 | 48.56 |\n| **SFR-Embedding-Mistral** | 63.46 | 51.27 | 52.21 | 58.76 | 23.27 | 56.94 | 47.75 | 58.99 | 51.58 |\n| **NV-Embed-v1** | 62.84 | 50.42 | 51.46 | 58.53 | 20.65 | 49.89 | 46.10 | 60.27 | 50.02 |\n| **Linq-Embed-Mistral** | 61.04 | 48.41 | 49.44 | **60.18** | 20.34 | 50.04 | 47.56 | 60.50 | 49.69 |\n| **gte-Qwen2-7B-instruct** | 63.46 | 51.20 | 54.07 | 54.20 | 22.31 | **58.20** | 40.27 | 58.39 | 50.26 |\n| **stella_en_1.5B_v5** | 61.99 | 50.88 | 53.87 | 58.81 | 23.22 | 57.26 | 44.81 | 61.38 | 51.53 |\n| **bge-en-icl zero-shot** | 64.61 | 54.40 | 55.11 | 57.25 | 25.10 | 54.81 | 48.46 | 63.71 | 52.93 |\n| **bge-en-icl few-shot** | **64.94** | **55.11** | **56.02** | 58.85 | **28.29** | 57.16 | **50.04** | **64.50** | **54.36** |\n\n**Long-Doc (en, Recall@10):**\n\n| AIR-Bench_24.04 | arxiv (4) | book (2) | healthcare (5) | law (4) | ALL (15) |\n| :--------------------------: | :-------: | :-------: | :------------: | :-------: | :-------: |\n| **text-embedding-3-large** | 74.53 | 73.16 | 65.83 | 64.47 | 68.77 |\n| **e5-mistral-7b-instruct** | 72.14 | 72.44 | 68.44 | 62.92 | 68.49 |\n| **SFR-Embedding-Mistral** | 72.79 | 72.41 | 67.94 | 64.83 | 69.00 |\n| **NV-Embed-v1** | 77.65 | 75.49 | 72.38 | **69.55** | 73.45 |\n| **Linq-Embed-Mistral** | 75.46 | 73.81 | 71.58 | 68.58 | 72.11 |\n| **gte-Qwen2-7B-instruct** | 63.93 | 68.51 | 65.59 | 65.26 | 65.45 |\n| **stella_en_1.5B_v5** | 73.17 | 74.38 | 70.02 | 69.32 | 71.25 |\n| **bge-en-icl zero-shot** | 78.30 | 78.21 | 73.65 | 67.09 | 73.75 |\n| **bge-en-icl few-shot** | **79.63** | **79.36** | **74.80** | 67.79 | **74.83** |\n\n\n## Model List\n\n`bge` is short for `BAAI general embedding`.\n\n| Model | Language | | Description | query instruction for retrieval [1] |\n|:--------------------------------------------------------------------------|:-------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|\n| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | - | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |\n| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |\n| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |\n| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |\n| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |\n| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |\n| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |\n| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |\n\n\n\n\n\n## Citation\n\nIf you find this repository useful, please consider giving a star :star: and citation\n\n```\n@misc{li2024makingtextembeddersfewshot,\n title={Making Text Embedders Few-Shot Learners}, \n author={Chaofan Li and MingHao Qin and Shitao Xiao and Jianlyu Chen and Kun Luo and Yingxia Shao and Defu Lian and Zheng Liu},\n year={2024},\n eprint={2409.15700},\n archivePrefix={arXiv},\n primaryClass={cs.IR},\n url={https://arxiv.org/abs/2409.15700}, \n}\n@misc{bge_embedding,\n title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, \n author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},\n year={2023},\n eprint={2309.07597},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n## License\nFlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE)."},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":99,"cells":{"id":{"kind":"string","value":"flowaicom/Flow-Judge-v0.1"},"author":{"kind":"string","value":"flowaicom"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","safetensors","phi3","text-generation","lm-judge","evaluation","nlp","conversational","custom_code","en","dataset:flowaicom/Flow-Judge-v0.1-binary-heldout","dataset:flowaicom/Flow-Judge-v0.1-3-likert-heldout","dataset:flowaicom/Flow-Judge-v0.1-5-likert-heldout","base_model:microsoft/Phi-3.5-mini-instruct","base_model:finetune:microsoft/Phi-3.5-mini-instruct","license:apache-2.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"safetensors\",\n \"phi3\",\n \"text-generation\",\n \"lm-judge\",\n \"evaluation\",\n \"nlp\",\n \"conversational\",\n \"custom_code\",\n \"en\",\n \"dataset:flowaicom/Flow-Judge-v0.1-binary-heldout\",\n \"dataset:flowaicom/Flow-Judge-v0.1-3-likert-heldout\",\n \"dataset:flowaicom/Flow-Judge-v0.1-5-likert-heldout\",\n \"base_model:microsoft/Phi-3.5-mini-instruct\",\n \"base_model:finetune:microsoft/Phi-3.5-mini-instruct\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-09-15T12:27:06"},"last_modified":{"kind":"string","value":"2024-10-07T13:13:54"},"downloads":{"kind":"number","value":23285,"string":"23,285"},"likes":{"kind":"number","value":58,"string":"58"},"README":{"kind":"string","value":"---\nbase_model:\n- microsoft/Phi-3.5-mini-instruct\ndatasets:\n- flowaicom/Flow-Judge-v0.1-binary-heldout\n- flowaicom/Flow-Judge-v0.1-3-likert-heldout\n- flowaicom/Flow-Judge-v0.1-5-likert-heldout\nlanguage:\n- en\nlibrary_name: transformers\nlicense: apache-2.0\nlicense_link: https://huggingface.co/flowaicom/Flow-Judge-v0.1/resolve/main/LICENSE\nmetrics:\n- accuracy\n- f1\n- precision\n- recall\n- pearsonr\n- spearmanr\n- kendall-tau\npipeline_tag: text-generation\ntags:\n- lm-judge\n- evaluation\n- nlp\nwidget:\n- messages:\n - role: user\n content: '# GOAL\n\n Your job is to evaluate a task carried out by an AI system powered by a large\n language model.\n\n You will be provided with the inputs and output of the task, as well as the\n evaluation criteria and scoring rubric. Your task is to evaluate the output\n of the AI system based on the evaluation criteria and scoring rubric provided.\n\n # INPUT/s\n\n Below are the inputs required for performing the task:\n\n \n\n \n\n Subject: Issues with recent order #12345\n\n Hello,\n\n I recently received my order #12345, but I''m having a few problems:\n\n 1. The blue t-shirt I ordered is actually green. Can I exchange it?\n\n 2. The phone case doesn''t fit my iPhone 13 Pro Max. The website said it was\n compatible.\n\n 3. I was charged for express shipping, but it took over a week to arrive. Can\n I get a refund on the shipping cost?\n\n Please let me know how we can resolve these issues. I''ve been a loyal customer\n for years and this is the first time I''ve had problems like this.\n\n Thank you,\n\n Jane Smith\n\n \n\n \n\n\n # OUTPUT\n\n Below is the output of the task:\n\n \n\n\n # EVALUATION CRITERIA AND SCORING RUBRIC\n\n Here are the evaluation criteria and the rubric that you need to use for evaluating\n the task:\n\n \n\n How well the response addresses the specific issues raised in the customer''s\n query?\n\n \n\n \n\n - Score 1: The response completely fails to address the customer''s needs and\n ignores the specific issues raised.\n\n - Score 2: The response barely addresses the customer''s query and misses most\n of the specific issues raised.\n\n - Score 3: The response partially addresses the customer''s query, touching\n on some of the specific issues but leaving others unaddressed.\n\n - Score 4: The response adequately addresses most aspects of the customer''s\n query and the specific issues raised.\n\n - Score 5: The response fully and comprehensively addresses all aspects of the\n customer''s query and all specific issues raised in a highly satisfactory manner.\n\n \n\n\n # INSTRUCTIONS FOR THE EVALUATION\n\n 1. Understand the task and criteria: Familiarize yourself with the task to be\n evaluated. Review the evaluation criteria and scoring rubric to understand the\n different levels of performance and the descriptions for each score.\n\n 2. Review the inputs and output: Look at the inputs provided for the task. Examine\n the output generated from completing the task.\n\n 3. Compare output to score descriptions: Compare the output against the criteria\n and score descriptions in the scoring rubric. For each criterion,decide which\n description best matches the output.\n\n 4. After comparing the output to the score descriptions, pay attention to the\n small details that might impact the final score that you assign. Sometimes a\n small difference can dictate the final score.\n\n 5. Write verbal feedback justifying your evaluation that includes a detailed\n rationale, referring to specific aspects of the output and comparing them to\n the rubric.\n\n 6. Assign a final score based on the scoring rubric.\n\n\n ## FORMAT FOR THE EVALUATION\n\n - Write the verbal feedback inside tags without any additional surrounding\n text.\n\n - Write the numeric score inside tags, without any additional surrounding\n text and always after the feedback.\n\n Please accurately evaluate the task. Strictly adhere to the evaluation criteria\n and rubric.'\n---\n\n` and `` tags.\n - Qualitative feedback: Flow Judge detects errors and grades outputs and provides qualitative feedback that explains its reasoning for assigning a particular score from the rubric while highlighting problematic parts of the responses. \n - Score: Based on a grading rubric Flow Judge will return a numerical score on binary, likert-3 or likert-5 scale. \n\n## Training\n\n### Model\n\nFlow Judge is based on the Phi-3.5-mini architecture, and the base model checkpoint used is specifically its instruct version. The model uses the same tokenizer, supports MQA and Flash Attention 2, and has weights in bfloat16 precision. However, post-finetuning, the model's support for languages and long context lengths has not been fully tested. Due to specialized Supervised Fine-Tuning (SFT), Flow Judge might show different benchmark results and support a maximum context length of 8192, shorter than the base model's. \n\n\n### Training Datasets\n\nFlow-Judge-v0.1 has been trained on synthetically generated datasets. The construction of training datasets for Flow Judge involves a multi-step process:\n\n1. Manually curating seed rubrics to serve as a foundation\n2. Synthetically generating domain-adapted metrics and rubrics for various domains\n3. Synthetically generating training instances with multiple inputs, such as user queries and contextual information\n4. Employing a dual-evaluation strategy with consensus to ensure quality and consistency\n\nThis process creates a comprehensive and diverse set of training instances that enable accurate, domain-specific evaluations of LLM systems in generative AI products while minimizing human intervention.\n\nRead more about the dataset construction from [here](https://www.flow-ai.com/blog/flow-judge#dataset-construction)\n\n\n### Fine-tuning\n\nFor fine-tuning we used Axolotl's preprocessing to ensure input training data is consistent. We then conducted supervised fine-tuning based on microsoft/Phi-3.5-mini-instruct using RSLoRa. More detailed information about the fine-tuning process is provided in our [technical report](https://www.flow-ai.com/blog/flow-judge#fine-tuning).\n\n## Usage\n\n### Prompt format \n\n#### Prompt template with inputs \n```text \n# GOAL\nYour job is to evaluate a task carried out by an AI system powered by a large language model.\nYou will be provided with the inputs and output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.\n\n# INPUT\nBelow are the inputs required for performing the task:\n\n{INPUTS}\n \n\n# OUTPUT\nBelow is the output of the task:\n\n\n# EVALUATION CRITERIA AND SCORING RUBRIC\nHere are the evaluation criteria and the rubric that you need to use for evaluating the task:\n\n{EVALUATION_CRITERIA}\n \n\n\n{RUBRIC}\n \n\n# INSTRUCTIONS FOR THE EVALUATION\n1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.\n2. Review the inputs and output: Look at the inputs provided for the task. Examine the output generated from completing the task.\n3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.\n4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.\n5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.\n6. Assign a final score based on the scoring rubric.\n\n## FORMAT FOR THE EVALUATION\n- Write the verbal feedback inside tags without any additional surrounding text.\n- Write the numeric score inside tags, without any additional surrounding text and always after the feedback.\n\nPlease accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.\n```\n\n#### Prompt template without inputs\n```text\n# GOAL\nYour job is to evaluate a task carried out by an AI system powered by a large language model.\n\nYou will be provided the output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.\n\n# OUTPUT\nBelow is the output of the task:\n\n\n# EVALUATION CRITERIA AND SCORING RUBRIC\nHere are the evaluation criteria and the rubric that you need to use for evaluating the task:\n\n{EVALUATION_CRITERIA}\n \n\n\n{RUBRIC}\n \n\n# INSTRUCTIONS FOR THE EVALUATION\n1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.\n2. Review the output: Examine the output generated from completing the task.\n3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.\n4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.\n5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.\n6. Assign a final score based on the scoring rubric.\n\n## FORMAT FOR THE EVALUATION\n- Write the verbal feedback inside tags without any additional surrounding text.\n- Write the numeric score inside tags, without any additional surrounding text and always after the feedback.\n\nPlease accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.\n``` \n\n#### Formatted prompt example with inputs\n```text\n# GOAL\nYour job is to evaluate a task carried out by an AI system powered by a large language model.\nYou will be provided with the inputs and output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.\n# INPUT/s\nBelow are the inputs required for performing the task:\n\n\nSubject: Issues with recent order #12345\nHello,\nI recently received my order #12345, but I'm having a few problems:\n1. The blue t-shirt I ordered is actually green. Can I exchange it?\n2. The phone case doesn't fit my iPhone 13 Pro Max. The website said it was compatible.\n3. I was charged for express shipping, but it took over a week to arrive. Can I get a refund on the shipping cost?\nPlease let me know how we can resolve these issues. I've been a loyal customer for years and this is the first time I've had problems like this.\nThank you,\nJane Smith\n \n \n\n# OUTPUT\nBelow is the output of the task:\n\n\n# EVALUATION CRITERIA AND SCORING RUBRIC\nHere are the evaluation criteria and the rubric that you need to use for evaluating the task:\n\nHow well the response addresses the specific issues raised in the customer's query?\n \n\n- Score 1: The response completely fails to address the customer's needs and ignores the specific issues raised.\n- Score 2: The response barely addresses the customer's query and misses most of the specific issues raised.\n- Score 3: The response partially addresses the customer's query, touching on some of the specific issues but leaving others unaddressed.\n- Score 4: The response adequately addresses most aspects of the customer's query and the specific issues raised.\n- Score 5: The response fully and comprehensively addresses all aspects of the customer's query and all specific issues raised in a highly satisfactory manner.\n \n\n# INSTRUCTIONS FOR THE EVALUATION\n1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.\n2. Review the inputs and output: Look at the inputs provided for the task. Examine the output generated from completing the task.\n3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.\n4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.\n5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.\n6. Assign a final score based on the scoring rubric.\n\n## FORMAT FOR THE EVALUATION\n- Write the verbal feedback inside tags without any additional surrounding text.\n- Write the numeric score inside tags, without any additional surrounding text and always after the feedback.\nPlease accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.\n```\n>Note that inputs and output are formatted with XML tags. See [flow-judge](https://github.com/flowaicom/flow-judge) repository formatting functions for more details.\n\n### Inference\n\nEvaluations can easily be run using our [flow-judge](https://github.com/flowaicom/flow-judge) library. It currently supports both Transformers and vllm engine.\n\nTo run Flow Judge efficiently, ensure your hardware meets the following requirements:\n\n- Modern GPU with at least 4 GB VRAM (e.g., NVIDIA RTX series)\n- Minimum of 8 GB of system memory\n- At least 10GB of free storage for model files and dependencies.\n\n## Evaluation\n### Held-out test sets \n\n\n \n
\n\n\n \n
\n\n\\* _Reported in model paper_\n\n\n### RAGTruth\n\n
\n\n\\* _reported in model paper_\n\n\n### HaluEval, Covid-QA, PubMedQA \n\n \n
\n\n\\* _reported in model paper_\n### Feedback Bench \n\n\n
\n\n\\* _reported in model paper using reference answers_\n\n## License\nWe opted for the Apache 2.0 license for Flow Judge to provide the community with an open, small yet powerful LM evaluator. Our goal is to support the wider adoption of rigorous evaluation techniques in LLM system development, making them more accessible to practitioners and researchers.\n\n## Limitations and future work\nMultilingual evaluation: Flow Judge has been fine-tuned exclusively on English data. While the foundation model (Phi-3.5-mini-instruct [17]) may possess multilingual capabilities, we have not systematically evaluated Flow Judge performance in non-English contexts. We plan to explore multi-lingual LM evaluators in the future.\n\nLong context and structured Inputs: Our training dataset encompasses a wide range of custom metrics relevant to evaluating LLM systems. However, it does not include examples with long context inputs or structured data formats such as JSON, since these are harder to synthetically generate. This limitation may impact Flow Judge's performance when evaluating responses that require processing extensive context or parsing structured input. Extending our model’s capabilities to handle these input types represents an important area for future research.\n\nMath and coding: The current version has not been trained on specific task domains such as arithmetic problems or code evaluation. As a result, its performance in these specialized areas may be limited. Future iterations of the model should address these gaps.\n\nDomain-specific knowledge and complex multi-step evaluations: Flow Judge may struggle with highly specialized domain knowledge or proprietary data outside the training scope of its foundation model. Additionally, evaluation tasks requiring multi-step reasoning or complex logical processes may challenge the model's capabilities. We strongly recommend conducting meta-evaluations of the model performance before deploying it in specialized or highly complex evaluation scenarios."},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["PUBMEDQA"],"string":"[\n \"PUBMEDQA\"\n]"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":1838,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc0NDc5ODMyMCwic3ViIjoiL2RhdGFzZXRzL0V1YW55dS9tZXJnZWRfdGFza19kYXRhc2V0X21vZGVscyIsImV4cCI6MTc0NDgwMTkyMCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.RwKuqKdB8FHlRLhyXj4fAVgBf9bfMvoB4OwOG7aI8XbDR9ygjzJy_6zu5Hidap8pNf8I5CsS2mgYhy0ZTVnJBg","displayUrls":true},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false}">
Click to learn how to use cde-small-v1 with Sentence Transformers
\n\n### Loading the model\n\nOur model can be loaded using `sentence-transformers` out-of-the-box with \"trust remote code\" enabled:\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"jxm/cde-small-v1\", trust_remote_code=True)\n```\n\n#### Note on prefixes\n\n*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can use `prompt_name=\"query\"` and `prompt_name=\"document\"` in the `encode` method of the model when embedding queries and documents, respectively.\n\n### First stage\n\n```python\nminicorpus_size = model[0].config.transductive_corpus_size\nminicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)\nassert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.\n\ndataset_embeddings = model.encode(\n minicorpus_docs,\n prompt_name=\"document\",\n convert_to_tensor=True\n)\n```\n\n### Running the second stage\n\nNow that we have obtained \"dataset embeddings\" we can embed documents and queries like normal. Remember to use the document prompt for documents:\n\n```python\ndocs = [...]\nqueries = [...]\n\ndoc_embeddings = model.encode(\n docs,\n prompt_name=\"document\",\n dataset_embeddings=dataset_embeddings,\n convert_to_tensor=True,\n)\nquery_embeddings = model.encode(\n queries,\n prompt_name=\"query\",\n dataset_embeddings=dataset_embeddings,\n convert_to_tensor=True,\n) \n```\n\nthese embeddings can be compared using cosine similarity via `model.similarity`:\n```python\nsimilarities = model.similarity(query_embeddings, doc_embeddings)\ntopk_values, topk_indices = similarities.topk(5)\n```\n\n\n
\n\n### Colab demo\n\nWe've set up a short demo in a Colab notebook showing how you might use our model:\n[Try our model in Colab:](https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing)\n\n### Acknowledgments\n\nEarly experiments on CDE were done with support from [Nomic](https://www.nomic.ai/) and [Hyperbolic](https://hyperbolic.xyz/). We're especially indebted to Nomic for [open-sourcing their efficient BERT implementation and contrastive pre-training data](https://www.nomic.ai/blog/posts/nomic-embed-text-v1), which proved vital in the development of CDE.\n\n### Cite us\n\nUsed our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:\n```\n@misc{morris2024contextualdocumentembeddings,\n title={Contextual Document Embeddings}, \n author={John X. Morris and Alexander M. Rush},\n year={2024},\n eprint={2410.02525},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2410.02525}, \n}\n```\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","MEDAL","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"MEDAL\",\n \"SCIFACT\"\n]"}}},{"rowIdx":90,"cells":{"id":{"kind":"string","value":"Snowflake/snowflake-arctic-embed-s"},"author":{"kind":"string","value":"Snowflake"},"task_category":{"kind":"string","value":"sentence-similarity"},"tags":{"kind":"list like","value":["sentence-transformers","onnx","safetensors","bert","feature-extraction","sentence-similarity","mteb","arctic","snowflake-arctic-embed","transformers.js","arxiv:2407.18887","arxiv:2405.05374","license:apache-2.0","model-index","autotrain_compatible","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"onnx\",\n \"safetensors\",\n \"bert\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"mteb\",\n \"arctic\",\n \"snowflake-arctic-embed\",\n \"transformers.js\",\n \"arxiv:2407.18887\",\n \"arxiv:2405.05374\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-04-12T13:53:49"},"last_modified":{"kind":"string","value":"2024-12-04T16:58:54"},"downloads":{"kind":"number","value":29658,"string":"29,658"},"likes":{"kind":"number","value":19,"string":"19"},"README":{"kind":"string","value":"---\nlicense: apache-2.0\npipeline_tag: sentence-similarity\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- mteb\n- arctic\n- snowflake-arctic-embed\n- transformers.js\nmodel-index:\n- name: snowflake-snowflake-arctic-embed-s\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 71.17910447761193\n - type: ap\n value: 33.15833652904991\n - type: f1\n value: 64.86214791591543\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 78.750325\n - type: ap\n value: 72.83242788470943\n - type: f1\n value: 78.63968044029453\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 38.264\n - type: f1\n value: 37.140269688532825\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: mteb/arguana\n config: default\n split: test\n revision: c22ab2a51041ffd869aaddef7af8d8215647e41a\n metrics:\n - type: map_at_1\n value: 32.646\n - type: map_at_10\n value: 48.372\n - type: map_at_100\n value: 49.207\n - type: map_at_1000\n value: 49.214\n - type: map_at_3\n value: 43.611\n - type: map_at_5\n value: 46.601\n - type: mrr_at_1\n value: 33.144\n - type: mrr_at_10\n value: 48.557\n - type: mrr_at_100\n value: 49.385\n - type: mrr_at_1000\n value: 49.392\n - type: mrr_at_3\n value: 43.777\n - type: mrr_at_5\n value: 46.792\n - type: ndcg_at_1\n value: 32.646\n - type: ndcg_at_10\n value: 56.874\n - type: ndcg_at_100\n value: 60.307\n - type: ndcg_at_1000\n value: 60.465999999999994\n - type: ndcg_at_3\n value: 47.339999999999996\n - type: ndcg_at_5\n value: 52.685\n - type: precision_at_1\n value: 32.646\n - type: precision_at_10\n value: 8.378\n - type: precision_at_100\n value: 0.984\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 19.393\n - type: precision_at_5\n value: 14.210999999999999\n - type: recall_at_1\n value: 32.646\n - type: recall_at_10\n value: 83.784\n - type: recall_at_100\n value: 98.43499999999999\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 58.179\n - type: recall_at_5\n value: 71.053\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 44.94353025039141\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 35.870836103029156\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 61.149290266979236\n - type: mrr\n value: 73.8448093919008\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 87.055571064151\n - type: cos_sim_spearman\n value: 86.2652186235749\n - type: euclidean_pearson\n value: 85.82039272282503\n - type: euclidean_spearman\n value: 86.2652186235749\n - type: manhattan_pearson\n value: 85.95825392094812\n - type: manhattan_spearman\n value: 86.6742640885316\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 79.11688311688312\n - type: f1\n value: 78.28328901613885\n - task:\n type: Clustering\n dataset:\n name: MTEB BigPatentClustering\n type: jinaai/big-patent-clustering\n config: default\n split: test\n revision: 62d5330920bca426ce9d3c76ea914f15fc83e891\n metrics:\n - type: v_measure\n value: 19.147523589859325\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 35.68369864124274\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 30.474958792950872\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: mteb/cqadupstack-android\n config: default\n split: test\n revision: f46a197baaae43b4f621051089b82a364682dfeb\n metrics:\n - type: map_at_1\n value: 33.183\n - type: map_at_10\n value: 43.989\n - type: map_at_100\n value: 45.389\n - type: map_at_1000\n value: 45.517\n - type: map_at_3\n value: 40.275\n - type: map_at_5\n value: 42.306\n - type: mrr_at_1\n value: 40.486\n - type: mrr_at_10\n value: 49.62\n - type: mrr_at_100\n value: 50.351\n - type: mrr_at_1000\n value: 50.393\n - type: mrr_at_3\n value: 46.805\n - type: mrr_at_5\n value: 48.429\n - type: ndcg_at_1\n value: 40.486\n - type: ndcg_at_10\n value: 50.249\n - type: ndcg_at_100\n value: 55.206\n - type: ndcg_at_1000\n value: 57.145\n - type: ndcg_at_3\n value: 44.852\n - type: ndcg_at_5\n value: 47.355000000000004\n - type: precision_at_1\n value: 40.486\n - type: precision_at_10\n value: 9.571\n - type: precision_at_100\n value: 1.4949999999999999\n - type: precision_at_1000\n value: 0.196\n - type: precision_at_3\n value: 21.173000000000002\n - type: precision_at_5\n value: 15.622\n - type: recall_at_1\n value: 33.183\n - type: recall_at_10\n value: 62.134\n - type: recall_at_100\n value: 82.73\n - type: recall_at_1000\n value: 94.93599999999999\n - type: recall_at_3\n value: 46.497\n - type: recall_at_5\n value: 53.199\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackEnglishRetrieval\n type: mteb/cqadupstack-english\n config: default\n split: test\n revision: ad9991cb51e31e31e430383c75ffb2885547b5f0\n metrics:\n - type: map_at_1\n value: 32.862\n - type: map_at_10\n value: 42.439\n - type: map_at_100\n value: 43.736999999999995\n - type: map_at_1000\n value: 43.864\n - type: map_at_3\n value: 39.67\n - type: map_at_5\n value: 41.202\n - type: mrr_at_1\n value: 40.892\n - type: mrr_at_10\n value: 48.61\n - type: mrr_at_100\n value: 49.29\n - type: mrr_at_1000\n value: 49.332\n - type: mrr_at_3\n value: 46.688\n - type: mrr_at_5\n value: 47.803000000000004\n - type: ndcg_at_1\n value: 40.892\n - type: ndcg_at_10\n value: 47.797\n - type: ndcg_at_100\n value: 52.17699999999999\n - type: ndcg_at_1000\n value: 54.127\n - type: ndcg_at_3\n value: 44.189\n - type: ndcg_at_5\n value: 45.821\n - type: precision_at_1\n value: 40.892\n - type: precision_at_10\n value: 8.841000000000001\n - type: precision_at_100\n value: 1.419\n - type: precision_at_1000\n value: 0.188\n - type: precision_at_3\n value: 21.104\n - type: precision_at_5\n value: 14.777000000000001\n - type: recall_at_1\n value: 32.862\n - type: recall_at_10\n value: 56.352999999999994\n - type: recall_at_100\n value: 74.795\n - type: recall_at_1000\n value: 86.957\n - type: recall_at_3\n value: 45.269999999999996\n - type: recall_at_5\n value: 50.053000000000004\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGamingRetrieval\n type: mteb/cqadupstack-gaming\n config: default\n split: test\n revision: 4885aa143210c98657558c04aaf3dc47cfb54340\n metrics:\n - type: map_at_1\n value: 42.998999999999995\n - type: map_at_10\n value: 54.745\n - type: map_at_100\n value: 55.650999999999996\n - type: map_at_1000\n value: 55.703\n - type: map_at_3\n value: 51.67\n - type: map_at_5\n value: 53.503\n - type: mrr_at_1\n value: 49.028\n - type: mrr_at_10\n value: 58.172000000000004\n - type: mrr_at_100\n value: 58.744\n - type: mrr_at_1000\n value: 58.769000000000005\n - type: mrr_at_3\n value: 55.977\n - type: mrr_at_5\n value: 57.38799999999999\n - type: ndcg_at_1\n value: 49.028\n - type: ndcg_at_10\n value: 60.161\n - type: ndcg_at_100\n value: 63.806\n - type: ndcg_at_1000\n value: 64.821\n - type: ndcg_at_3\n value: 55.199\n - type: ndcg_at_5\n value: 57.830999999999996\n - type: precision_at_1\n value: 49.028\n - type: precision_at_10\n value: 9.455\n - type: precision_at_100\n value: 1.216\n - type: precision_at_1000\n value: 0.135\n - type: precision_at_3\n value: 24.242\n - type: precision_at_5\n value: 16.614\n - type: recall_at_1\n value: 42.998999999999995\n - type: recall_at_10\n value: 72.542\n - type: recall_at_100\n value: 88.605\n - type: recall_at_1000\n value: 95.676\n - type: recall_at_3\n value: 59.480999999999995\n - type: recall_at_5\n value: 65.886\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackGisRetrieval\n type: mteb/cqadupstack-gis\n config: default\n split: test\n revision: 5003b3064772da1887988e05400cf3806fe491f2\n metrics:\n - type: map_at_1\n value: 27.907\n - type: map_at_10\n value: 35.975\n - type: map_at_100\n value: 36.985\n - type: map_at_1000\n value: 37.063\n - type: map_at_3\n value: 33.467999999999996\n - type: map_at_5\n value: 34.749\n - type: mrr_at_1\n value: 30.056\n - type: mrr_at_10\n value: 38.047\n - type: mrr_at_100\n value: 38.932\n - type: mrr_at_1000\n value: 38.991\n - type: mrr_at_3\n value: 35.705999999999996\n - type: mrr_at_5\n value: 36.966\n - type: ndcg_at_1\n value: 30.056\n - type: ndcg_at_10\n value: 40.631\n - type: ndcg_at_100\n value: 45.564\n - type: ndcg_at_1000\n value: 47.685\n - type: ndcg_at_3\n value: 35.748000000000005\n - type: ndcg_at_5\n value: 37.921\n - type: precision_at_1\n value: 30.056\n - type: precision_at_10\n value: 6.079\n - type: precision_at_100\n value: 0.898\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 14.727\n - type: precision_at_5\n value: 10.056\n - type: recall_at_1\n value: 27.907\n - type: recall_at_10\n value: 52.981\n - type: recall_at_100\n value: 75.53999999999999\n - type: recall_at_1000\n value: 91.759\n - type: recall_at_3\n value: 39.878\n - type: recall_at_5\n value: 45.077\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackMathematicaRetrieval\n type: mteb/cqadupstack-mathematica\n config: default\n split: test\n revision: 90fceea13679c63fe563ded68f3b6f06e50061de\n metrics:\n - type: map_at_1\n value: 16.764000000000003\n - type: map_at_10\n value: 24.294\n - type: map_at_100\n value: 25.507999999999996\n - type: map_at_1000\n value: 25.64\n - type: map_at_3\n value: 21.807000000000002\n - type: map_at_5\n value: 23.21\n - type: mrr_at_1\n value: 20.771\n - type: mrr_at_10\n value: 28.677000000000003\n - type: mrr_at_100\n value: 29.742\n - type: mrr_at_1000\n value: 29.816\n - type: mrr_at_3\n value: 26.327\n - type: mrr_at_5\n value: 27.639000000000003\n - type: ndcg_at_1\n value: 20.771\n - type: ndcg_at_10\n value: 29.21\n - type: ndcg_at_100\n value: 34.788000000000004\n - type: ndcg_at_1000\n value: 37.813\n - type: ndcg_at_3\n value: 24.632\n - type: ndcg_at_5\n value: 26.801000000000002\n - type: precision_at_1\n value: 20.771\n - type: precision_at_10\n value: 5.373\n - type: precision_at_100\n value: 0.923\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 12.065\n - type: precision_at_5\n value: 8.706\n - type: recall_at_1\n value: 16.764000000000003\n - type: recall_at_10\n value: 40.072\n - type: recall_at_100\n value: 63.856\n - type: recall_at_1000\n value: 85.141\n - type: recall_at_3\n value: 27.308\n - type: recall_at_5\n value: 32.876\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackPhysicsRetrieval\n type: mteb/cqadupstack-physics\n config: default\n split: test\n revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4\n metrics:\n - type: map_at_1\n value: 31.194\n - type: map_at_10\n value: 40.731\n - type: map_at_100\n value: 42.073\n - type: map_at_1000\n value: 42.178\n - type: map_at_3\n value: 37.726\n - type: map_at_5\n value: 39.474\n - type: mrr_at_1\n value: 37.729\n - type: mrr_at_10\n value: 46.494\n - type: mrr_at_100\n value: 47.368\n - type: mrr_at_1000\n value: 47.407\n - type: mrr_at_3\n value: 44.224999999999994\n - type: mrr_at_5\n value: 45.582\n - type: ndcg_at_1\n value: 37.729\n - type: ndcg_at_10\n value: 46.312999999999995\n - type: ndcg_at_100\n value: 51.915\n - type: ndcg_at_1000\n value: 53.788000000000004\n - type: ndcg_at_3\n value: 41.695\n - type: ndcg_at_5\n value: 43.956\n - type: precision_at_1\n value: 37.729\n - type: precision_at_10\n value: 8.181\n - type: precision_at_100\n value: 1.275\n - type: precision_at_1000\n value: 0.16199999999999998\n - type: precision_at_3\n value: 19.41\n - type: precision_at_5\n value: 13.648\n - type: recall_at_1\n value: 31.194\n - type: recall_at_10\n value: 57.118\n - type: recall_at_100\n value: 80.759\n - type: recall_at_1000\n value: 92.779\n - type: recall_at_3\n value: 44.083\n - type: recall_at_5\n value: 50.044999999999995\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackProgrammersRetrieval\n type: mteb/cqadupstack-programmers\n config: default\n split: test\n revision: 6184bc1440d2dbc7612be22b50686b8826d22b32\n metrics:\n - type: map_at_1\n value: 28.047\n - type: map_at_10\n value: 37.79\n - type: map_at_100\n value: 39.145\n - type: map_at_1000\n value: 39.254\n - type: map_at_3\n value: 34.857\n - type: map_at_5\n value: 36.545\n - type: mrr_at_1\n value: 35.388\n - type: mrr_at_10\n value: 43.475\n - type: mrr_at_100\n value: 44.440000000000005\n - type: mrr_at_1000\n value: 44.494\n - type: mrr_at_3\n value: 41.286\n - type: mrr_at_5\n value: 42.673\n - type: ndcg_at_1\n value: 35.388\n - type: ndcg_at_10\n value: 43.169000000000004\n - type: ndcg_at_100\n value: 48.785000000000004\n - type: ndcg_at_1000\n value: 51.029\n - type: ndcg_at_3\n value: 38.801\n - type: ndcg_at_5\n value: 40.9\n - type: precision_at_1\n value: 35.388\n - type: precision_at_10\n value: 7.7509999999999994\n - type: precision_at_100\n value: 1.212\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 18.455\n - type: precision_at_5\n value: 13.014000000000001\n - type: recall_at_1\n value: 28.047\n - type: recall_at_10\n value: 53.53099999999999\n - type: recall_at_100\n value: 77.285\n - type: recall_at_1000\n value: 92.575\n - type: recall_at_3\n value: 40.949000000000005\n - type: recall_at_5\n value: 46.742\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: mteb/cqadupstack\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 28.131999999999994\n - type: map_at_10\n value: 36.93333333333334\n - type: map_at_100\n value: 38.117250000000006\n - type: map_at_1000\n value: 38.23275\n - type: map_at_3\n value: 34.19708333333333\n - type: map_at_5\n value: 35.725166666666674\n - type: mrr_at_1\n value: 33.16116666666667\n - type: mrr_at_10\n value: 41.057833333333335\n - type: mrr_at_100\n value: 41.90033333333333\n - type: mrr_at_1000\n value: 41.95625\n - type: mrr_at_3\n value: 38.757333333333335\n - type: mrr_at_5\n value: 40.097333333333324\n - type: ndcg_at_1\n value: 33.16116666666667\n - type: ndcg_at_10\n value: 42.01983333333333\n - type: ndcg_at_100\n value: 46.99916666666667\n - type: ndcg_at_1000\n value: 49.21783333333334\n - type: ndcg_at_3\n value: 37.479916666666654\n - type: ndcg_at_5\n value: 39.6355\n - type: precision_at_1\n value: 33.16116666666667\n - type: precision_at_10\n value: 7.230249999999999\n - type: precision_at_100\n value: 1.1411666666666667\n - type: precision_at_1000\n value: 0.1520833333333333\n - type: precision_at_3\n value: 17.028166666666667\n - type: precision_at_5\n value: 12.046999999999999\n - type: recall_at_1\n value: 28.131999999999994\n - type: recall_at_10\n value: 52.825500000000005\n - type: recall_at_100\n value: 74.59608333333333\n - type: recall_at_1000\n value: 89.87916666666668\n - type: recall_at_3\n value: 40.13625\n - type: recall_at_5\n value: 45.699999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackStatsRetrieval\n type: mteb/cqadupstack-stats\n config: default\n split: test\n revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a\n metrics:\n - type: map_at_1\n value: 24.773999999999997\n - type: map_at_10\n value: 31.997999999999998\n - type: map_at_100\n value: 32.857\n - type: map_at_1000\n value: 32.957\n - type: map_at_3\n value: 30.041\n - type: map_at_5\n value: 31.119000000000003\n - type: mrr_at_1\n value: 27.607\n - type: mrr_at_10\n value: 34.538000000000004\n - type: mrr_at_100\n value: 35.308\n - type: mrr_at_1000\n value: 35.375\n - type: mrr_at_3\n value: 32.643\n - type: mrr_at_5\n value: 33.755\n - type: ndcg_at_1\n value: 27.607\n - type: ndcg_at_10\n value: 36.035000000000004\n - type: ndcg_at_100\n value: 40.351\n - type: ndcg_at_1000\n value: 42.684\n - type: ndcg_at_3\n value: 32.414\n - type: ndcg_at_5\n value: 34.11\n - type: precision_at_1\n value: 27.607\n - type: precision_at_10\n value: 5.6129999999999995\n - type: precision_at_100\n value: 0.8370000000000001\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 13.957\n - type: precision_at_5\n value: 9.571\n - type: recall_at_1\n value: 24.773999999999997\n - type: recall_at_10\n value: 45.717\n - type: recall_at_100\n value: 65.499\n - type: recall_at_1000\n value: 82.311\n - type: recall_at_3\n value: 35.716\n - type: recall_at_5\n value: 40.007999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackTexRetrieval\n type: mteb/cqadupstack-tex\n config: default\n split: test\n revision: 46989137a86843e03a6195de44b09deda022eec7\n metrics:\n - type: map_at_1\n value: 19.227\n - type: map_at_10\n value: 26.649\n - type: map_at_100\n value: 27.711999999999996\n - type: map_at_1000\n value: 27.837\n - type: map_at_3\n value: 24.454\n - type: map_at_5\n value: 25.772000000000002\n - type: mrr_at_1\n value: 23.433999999999997\n - type: mrr_at_10\n value: 30.564999999999998\n - type: mrr_at_100\n value: 31.44\n - type: mrr_at_1000\n value: 31.513999999999996\n - type: mrr_at_3\n value: 28.435\n - type: mrr_at_5\n value: 29.744999999999997\n - type: ndcg_at_1\n value: 23.433999999999997\n - type: ndcg_at_10\n value: 31.104\n - type: ndcg_at_100\n value: 36.172\n - type: ndcg_at_1000\n value: 39.006\n - type: ndcg_at_3\n value: 27.248\n - type: ndcg_at_5\n value: 29.249000000000002\n - type: precision_at_1\n value: 23.433999999999997\n - type: precision_at_10\n value: 5.496\n - type: precision_at_100\n value: 0.9490000000000001\n - type: precision_at_1000\n value: 0.13699999999999998\n - type: precision_at_3\n value: 12.709000000000001\n - type: precision_at_5\n value: 9.209\n - type: recall_at_1\n value: 19.227\n - type: recall_at_10\n value: 40.492\n - type: recall_at_100\n value: 63.304\n - type: recall_at_1000\n value: 83.45\n - type: recall_at_3\n value: 29.713\n - type: recall_at_5\n value: 34.82\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackUnixRetrieval\n type: mteb/cqadupstack-unix\n config: default\n split: test\n revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53\n metrics:\n - type: map_at_1\n value: 29.199\n - type: map_at_10\n value: 37.617\n - type: map_at_100\n value: 38.746\n - type: map_at_1000\n value: 38.851\n - type: map_at_3\n value: 34.882000000000005\n - type: map_at_5\n value: 36.571999999999996\n - type: mrr_at_1\n value: 33.489000000000004\n - type: mrr_at_10\n value: 41.089999999999996\n - type: mrr_at_100\n value: 41.965\n - type: mrr_at_1000\n value: 42.028\n - type: mrr_at_3\n value: 38.666\n - type: mrr_at_5\n value: 40.159\n - type: ndcg_at_1\n value: 33.489000000000004\n - type: ndcg_at_10\n value: 42.487\n - type: ndcg_at_100\n value: 47.552\n - type: ndcg_at_1000\n value: 49.774\n - type: ndcg_at_3\n value: 37.623\n - type: ndcg_at_5\n value: 40.184999999999995\n - type: precision_at_1\n value: 33.489000000000004\n - type: precision_at_10\n value: 6.94\n - type: precision_at_100\n value: 1.0699999999999998\n - type: precision_at_1000\n value: 0.136\n - type: precision_at_3\n value: 16.667\n - type: precision_at_5\n value: 11.922\n - type: recall_at_1\n value: 29.199\n - type: recall_at_10\n value: 53.689\n - type: recall_at_100\n value: 75.374\n - type: recall_at_1000\n value: 90.64999999999999\n - type: recall_at_3\n value: 40.577999999999996\n - type: recall_at_5\n value: 46.909\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWebmastersRetrieval\n type: mteb/cqadupstack-webmasters\n config: default\n split: test\n revision: 160c094312a0e1facb97e55eeddb698c0abe3571\n metrics:\n - type: map_at_1\n value: 27.206999999999997\n - type: map_at_10\n value: 36.146\n - type: map_at_100\n value: 37.759\n - type: map_at_1000\n value: 37.979\n - type: map_at_3\n value: 32.967999999999996\n - type: map_at_5\n value: 34.809\n - type: mrr_at_1\n value: 32.806000000000004\n - type: mrr_at_10\n value: 40.449\n - type: mrr_at_100\n value: 41.404999999999994\n - type: mrr_at_1000\n value: 41.457\n - type: mrr_at_3\n value: 37.614999999999995\n - type: mrr_at_5\n value: 39.324999999999996\n - type: ndcg_at_1\n value: 32.806000000000004\n - type: ndcg_at_10\n value: 41.911\n - type: ndcg_at_100\n value: 47.576\n - type: ndcg_at_1000\n value: 50.072\n - type: ndcg_at_3\n value: 36.849\n - type: ndcg_at_5\n value: 39.475\n - type: precision_at_1\n value: 32.806000000000004\n - type: precision_at_10\n value: 8.103\n - type: precision_at_100\n value: 1.557\n - type: precision_at_1000\n value: 0.242\n - type: precision_at_3\n value: 17.26\n - type: precision_at_5\n value: 12.885\n - type: recall_at_1\n value: 27.206999999999997\n - type: recall_at_10\n value: 52.56999999999999\n - type: recall_at_100\n value: 78.302\n - type: recall_at_1000\n value: 94.121\n - type: recall_at_3\n value: 38.317\n - type: recall_at_5\n value: 45.410000000000004\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackWordpressRetrieval\n type: mteb/cqadupstack-wordpress\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: map_at_1\n value: 24.221\n - type: map_at_10\n value: 30.826999999999998\n - type: map_at_100\n value: 31.845000000000002\n - type: map_at_1000\n value: 31.95\n - type: map_at_3\n value: 28.547\n - type: map_at_5\n value: 29.441\n - type: mrr_at_1\n value: 26.247999999999998\n - type: mrr_at_10\n value: 32.957\n - type: mrr_at_100\n value: 33.819\n - type: mrr_at_1000\n value: 33.899\n - type: mrr_at_3\n value: 30.714999999999996\n - type: mrr_at_5\n value: 31.704\n - type: ndcg_at_1\n value: 26.247999999999998\n - type: ndcg_at_10\n value: 35.171\n - type: ndcg_at_100\n value: 40.098\n - type: ndcg_at_1000\n value: 42.67\n - type: ndcg_at_3\n value: 30.508999999999997\n - type: ndcg_at_5\n value: 32.022\n - type: precision_at_1\n value: 26.247999999999998\n - type: precision_at_10\n value: 5.36\n - type: precision_at_100\n value: 0.843\n - type: precision_at_1000\n value: 0.11499999999999999\n - type: precision_at_3\n value: 12.568999999999999\n - type: precision_at_5\n value: 8.540000000000001\n - type: recall_at_1\n value: 24.221\n - type: recall_at_10\n value: 46.707\n - type: recall_at_100\n value: 69.104\n - type: recall_at_1000\n value: 88.19500000000001\n - type: recall_at_3\n value: 33.845\n - type: recall_at_5\n value: 37.375\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: mteb/climate-fever\n config: default\n split: test\n revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380\n metrics:\n - type: map_at_1\n value: 13.624\n - type: map_at_10\n value: 22.557\n - type: map_at_100\n value: 24.367\n - type: map_at_1000\n value: 24.54\n - type: map_at_3\n value: 18.988\n - type: map_at_5\n value: 20.785999999999998\n - type: mrr_at_1\n value: 30.619000000000003\n - type: mrr_at_10\n value: 42.019\n - type: mrr_at_100\n value: 42.818\n - type: mrr_at_1000\n value: 42.856\n - type: mrr_at_3\n value: 38.578\n - type: mrr_at_5\n value: 40.669\n - type: ndcg_at_1\n value: 30.619000000000003\n - type: ndcg_at_10\n value: 31.252999999999997\n - type: ndcg_at_100\n value: 38.238\n - type: ndcg_at_1000\n value: 41.368\n - type: ndcg_at_3\n value: 25.843\n - type: ndcg_at_5\n value: 27.638\n - type: precision_at_1\n value: 30.619000000000003\n - type: precision_at_10\n value: 9.687\n - type: precision_at_100\n value: 1.718\n - type: precision_at_1000\n value: 0.22999999999999998\n - type: precision_at_3\n value: 18.849\n - type: precision_at_5\n value: 14.463000000000001\n - type: recall_at_1\n value: 13.624\n - type: recall_at_10\n value: 36.693999999999996\n - type: recall_at_100\n value: 60.9\n - type: recall_at_1000\n value: 78.46\n - type: recall_at_3\n value: 23.354\n - type: recall_at_5\n value: 28.756999999999998\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: mteb/dbpedia\n config: default\n split: test\n revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659\n metrics:\n - type: map_at_1\n value: 9.077\n - type: map_at_10\n value: 19.813\n - type: map_at_100\n value: 27.822999999999997\n - type: map_at_1000\n value: 29.485\n - type: map_at_3\n value: 14.255999999999998\n - type: map_at_5\n value: 16.836000000000002\n - type: mrr_at_1\n value: 69.25\n - type: mrr_at_10\n value: 77.059\n - type: mrr_at_100\n value: 77.41\n - type: mrr_at_1000\n value: 77.416\n - type: mrr_at_3\n value: 75.625\n - type: mrr_at_5\n value: 76.512\n - type: ndcg_at_1\n value: 55.75\n - type: ndcg_at_10\n value: 41.587\n - type: ndcg_at_100\n value: 46.048\n - type: ndcg_at_1000\n value: 53.172\n - type: ndcg_at_3\n value: 46.203\n - type: ndcg_at_5\n value: 43.696\n - type: precision_at_1\n value: 69.25\n - type: precision_at_10\n value: 32.95\n - type: precision_at_100\n value: 10.555\n - type: precision_at_1000\n value: 2.136\n - type: precision_at_3\n value: 49.667\n - type: precision_at_5\n value: 42.5\n - type: recall_at_1\n value: 9.077\n - type: recall_at_10\n value: 25.249\n - type: recall_at_100\n value: 51.964\n - type: recall_at_1000\n value: 74.51\n - type: recall_at_3\n value: 15.584000000000001\n - type: recall_at_5\n value: 19.717000000000002\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 45.769999999999996\n - type: f1\n value: 41.64144711933962\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: mteb/fever\n config: default\n split: test\n revision: bea83ef9e8fb933d90a2f1d5515737465d613e12\n metrics:\n - type: map_at_1\n value: 67.098\n - type: map_at_10\n value: 77.69800000000001\n - type: map_at_100\n value: 77.947\n - type: map_at_1000\n value: 77.961\n - type: map_at_3\n value: 76.278\n - type: map_at_5\n value: 77.217\n - type: mrr_at_1\n value: 72.532\n - type: mrr_at_10\n value: 82.41199999999999\n - type: mrr_at_100\n value: 82.527\n - type: mrr_at_1000\n value: 82.529\n - type: mrr_at_3\n value: 81.313\n - type: mrr_at_5\n value: 82.069\n - type: ndcg_at_1\n value: 72.532\n - type: ndcg_at_10\n value: 82.488\n - type: ndcg_at_100\n value: 83.382\n - type: ndcg_at_1000\n value: 83.622\n - type: ndcg_at_3\n value: 80.101\n - type: ndcg_at_5\n value: 81.52199999999999\n - type: precision_at_1\n value: 72.532\n - type: precision_at_10\n value: 10.203\n - type: precision_at_100\n value: 1.082\n - type: precision_at_1000\n value: 0.11199999999999999\n - type: precision_at_3\n value: 31.308000000000003\n - type: precision_at_5\n value: 19.652\n - type: recall_at_1\n value: 67.098\n - type: recall_at_10\n value: 92.511\n - type: recall_at_100\n value: 96.06099999999999\n - type: recall_at_1000\n value: 97.548\n - type: recall_at_3\n value: 86.105\n - type: recall_at_5\n value: 89.661\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: mteb/fiqa\n config: default\n split: test\n revision: 27a168819829fe9bcd655c2df245fb19452e8e06\n metrics:\n - type: map_at_1\n value: 18.681\n - type: map_at_10\n value: 31.739\n - type: map_at_100\n value: 33.503\n - type: map_at_1000\n value: 33.69\n - type: map_at_3\n value: 27.604\n - type: map_at_5\n value: 29.993\n - type: mrr_at_1\n value: 37.5\n - type: mrr_at_10\n value: 46.933\n - type: mrr_at_100\n value: 47.771\n - type: mrr_at_1000\n value: 47.805\n - type: mrr_at_3\n value: 44.239\n - type: mrr_at_5\n value: 45.766\n - type: ndcg_at_1\n value: 37.5\n - type: ndcg_at_10\n value: 39.682\n - type: ndcg_at_100\n value: 46.127\n - type: ndcg_at_1000\n value: 48.994\n - type: ndcg_at_3\n value: 35.655\n - type: ndcg_at_5\n value: 37.036\n - type: precision_at_1\n value: 37.5\n - type: precision_at_10\n value: 11.08\n - type: precision_at_100\n value: 1.765\n - type: precision_at_1000\n value: 0.22999999999999998\n - type: precision_at_3\n value: 23.919999999999998\n - type: precision_at_5\n value: 17.809\n - type: recall_at_1\n value: 18.681\n - type: recall_at_10\n value: 47.548\n - type: recall_at_100\n value: 71.407\n - type: recall_at_1000\n value: 87.805\n - type: recall_at_3\n value: 32.979\n - type: recall_at_5\n value: 39.192\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: mteb/hotpotqa\n config: default\n split: test\n revision: ab518f4d6fcca38d87c25209f94beba119d02014\n metrics:\n - type: map_at_1\n value: 38.257999999999996\n - type: map_at_10\n value: 57.605\n - type: map_at_100\n value: 58.50300000000001\n - type: map_at_1000\n value: 58.568\n - type: map_at_3\n value: 54.172\n - type: map_at_5\n value: 56.323\n - type: mrr_at_1\n value: 76.51599999999999\n - type: mrr_at_10\n value: 82.584\n - type: mrr_at_100\n value: 82.78\n - type: mrr_at_1000\n value: 82.787\n - type: mrr_at_3\n value: 81.501\n - type: mrr_at_5\n value: 82.185\n - type: ndcg_at_1\n value: 76.51599999999999\n - type: ndcg_at_10\n value: 66.593\n - type: ndcg_at_100\n value: 69.699\n - type: ndcg_at_1000\n value: 70.953\n - type: ndcg_at_3\n value: 61.673\n - type: ndcg_at_5\n value: 64.42\n - type: precision_at_1\n value: 76.51599999999999\n - type: precision_at_10\n value: 13.857\n - type: precision_at_100\n value: 1.628\n - type: precision_at_1000\n value: 0.179\n - type: precision_at_3\n value: 38.956\n - type: precision_at_5\n value: 25.541999999999998\n - type: recall_at_1\n value: 38.257999999999996\n - type: recall_at_10\n value: 69.284\n - type: recall_at_100\n value: 81.391\n - type: recall_at_1000\n value: 89.689\n - type: recall_at_3\n value: 58.433\n - type: recall_at_5\n value: 63.856\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 69.48679999999999\n - type: ap\n value: 63.97638838971138\n - type: f1\n value: 69.22731638841675\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: mteb/msmarco\n config: default\n split: dev\n revision: c5a29a104738b98a9e76336939199e264163d4a0\n metrics:\n - type: map_at_1\n value: 20.916999999999998\n - type: map_at_10\n value: 32.929\n - type: map_at_100\n value: 34.1\n - type: map_at_1000\n value: 34.152\n - type: map_at_3\n value: 29.065\n - type: map_at_5\n value: 31.287\n - type: mrr_at_1\n value: 21.562\n - type: mrr_at_10\n value: 33.533\n - type: mrr_at_100\n value: 34.644000000000005\n - type: mrr_at_1000\n value: 34.69\n - type: mrr_at_3\n value: 29.735\n - type: mrr_at_5\n value: 31.928\n - type: ndcg_at_1\n value: 21.562\n - type: ndcg_at_10\n value: 39.788000000000004\n - type: ndcg_at_100\n value: 45.434999999999995\n - type: ndcg_at_1000\n value: 46.75\n - type: ndcg_at_3\n value: 31.942999999999998\n - type: ndcg_at_5\n value: 35.888\n - type: precision_at_1\n value: 21.562\n - type: precision_at_10\n value: 6.348\n - type: precision_at_100\n value: 0.918\n - type: precision_at_1000\n value: 0.10300000000000001\n - type: precision_at_3\n value: 13.682\n - type: precision_at_5\n value: 10.189\n - type: recall_at_1\n value: 20.916999999999998\n - type: recall_at_10\n value: 60.926\n - type: recall_at_100\n value: 87.03800000000001\n - type: recall_at_1000\n value: 97.085\n - type: recall_at_3\n value: 39.637\n - type: recall_at_5\n value: 49.069\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 90.93935248518011\n - type: f1\n value: 90.56439321844506\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 58.62517099863203\n - type: f1\n value: 40.69925681703197\n - task:\n type: Classification\n dataset:\n name: MTEB MasakhaNEWSClassification (eng)\n type: masakhane/masakhanews\n config: eng\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: accuracy\n value: 76.29746835443039\n - type: f1\n value: 75.31702672039506\n - task:\n type: Clustering\n dataset:\n name: MTEB MasakhaNEWSClusteringP2P (eng)\n type: masakhane/masakhanews\n config: eng\n split: test\n revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60\n metrics:\n - type: v_measure\n value: 43.05495067062023\n - type: v_measure\n value: 19.625272848173843\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 64.76126429051781\n - type: f1\n value: 62.60284261265268\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 70.05043712172159\n - type: f1\n value: 69.08340521169049\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 30.78969229005989\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 27.954325178520335\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 30.601827413968596\n - type: mrr\n value: 31.515372019474196\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: mteb/nfcorpus\n config: default\n split: test\n revision: ec0fa4fe99da2ff19ca1214b7966684033a58814\n metrics:\n - type: map_at_1\n value: 5.4559999999999995\n - type: map_at_10\n value: 12.039\n - type: map_at_100\n value: 14.804999999999998\n - type: map_at_1000\n value: 16.081\n - type: map_at_3\n value: 8.996\n - type: map_at_5\n value: 10.357\n - type: mrr_at_1\n value: 45.82\n - type: mrr_at_10\n value: 53.583999999999996\n - type: mrr_at_100\n value: 54.330999999999996\n - type: mrr_at_1000\n value: 54.366\n - type: mrr_at_3\n value: 52.166999999999994\n - type: mrr_at_5\n value: 52.971999999999994\n - type: ndcg_at_1\n value: 44.427\n - type: ndcg_at_10\n value: 32.536\n - type: ndcg_at_100\n value: 29.410999999999998\n - type: ndcg_at_1000\n value: 38.012\n - type: ndcg_at_3\n value: 38.674\n - type: ndcg_at_5\n value: 36.107\n - type: precision_at_1\n value: 45.82\n - type: precision_at_10\n value: 23.591\n - type: precision_at_100\n value: 7.35\n - type: precision_at_1000\n value: 1.9769999999999999\n - type: precision_at_3\n value: 36.016999999999996\n - type: precision_at_5\n value: 30.959999999999997\n - type: recall_at_1\n value: 5.4559999999999995\n - type: recall_at_10\n value: 15.387\n - type: recall_at_100\n value: 28.754999999999995\n - type: recall_at_1000\n value: 59.787\n - type: recall_at_3\n value: 10.137\n - type: recall_at_5\n value: 12.200999999999999\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: mteb/nq\n config: default\n split: test\n revision: b774495ed302d8c44a3a7ea25c90dbce03968f31\n metrics:\n - type: map_at_1\n value: 32.609\n - type: map_at_10\n value: 48.522\n - type: map_at_100\n value: 49.468\n - type: map_at_1000\n value: 49.497\n - type: map_at_3\n value: 44.327\n - type: map_at_5\n value: 46.937\n - type: mrr_at_1\n value: 36.616\n - type: mrr_at_10\n value: 50.943000000000005\n - type: mrr_at_100\n value: 51.626000000000005\n - type: mrr_at_1000\n value: 51.647\n - type: mrr_at_3\n value: 47.532999999999994\n - type: mrr_at_5\n value: 49.714000000000006\n - type: ndcg_at_1\n value: 36.586999999999996\n - type: ndcg_at_10\n value: 56.19499999999999\n - type: ndcg_at_100\n value: 60.014\n - type: ndcg_at_1000\n value: 60.707\n - type: ndcg_at_3\n value: 48.486000000000004\n - type: ndcg_at_5\n value: 52.791999999999994\n - type: precision_at_1\n value: 36.586999999999996\n - type: precision_at_10\n value: 9.139999999999999\n - type: precision_at_100\n value: 1.129\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 22.171\n - type: precision_at_5\n value: 15.787999999999998\n - type: recall_at_1\n value: 32.609\n - type: recall_at_10\n value: 77.011\n - type: recall_at_100\n value: 93.202\n - type: recall_at_1000\n value: 98.344\n - type: recall_at_3\n value: 57.286\n - type: recall_at_5\n value: 67.181\n - task:\n type: Classification\n dataset:\n name: MTEB NewsClassification\n type: ag_news\n config: default\n split: test\n revision: eb185aade064a813bc0b7f42de02595523103ca4\n metrics:\n - type: accuracy\n value: 77.4421052631579\n - type: f1\n value: 77.23976860913628\n - task:\n type: PairClassification\n dataset:\n name: MTEB OpusparcusPC (en)\n type: GEM/opusparcus\n config: en\n split: test\n revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a\n metrics:\n - type: cos_sim_accuracy\n value: 99.89816700610999\n - type: cos_sim_ap\n value: 100\n - type: cos_sim_f1\n value: 99.9490575649516\n - type: cos_sim_precision\n value: 100\n - type: cos_sim_recall\n value: 99.89816700610999\n - type: dot_accuracy\n value: 99.89816700610999\n - type: dot_ap\n value: 100\n - type: dot_f1\n value: 99.9490575649516\n - type: dot_precision\n value: 100\n - type: dot_recall\n value: 99.89816700610999\n - type: euclidean_accuracy\n value: 99.89816700610999\n - type: euclidean_ap\n value: 100\n - type: euclidean_f1\n value: 99.9490575649516\n - type: euclidean_precision\n value: 100\n - type: euclidean_recall\n value: 99.89816700610999\n - type: manhattan_accuracy\n value: 99.89816700610999\n - type: manhattan_ap\n value: 100\n - type: manhattan_f1\n value: 99.9490575649516\n - type: manhattan_precision\n value: 100\n - type: manhattan_recall\n value: 99.89816700610999\n - type: max_accuracy\n value: 99.89816700610999\n - type: max_ap\n value: 100\n - type: max_f1\n value: 99.9490575649516\n - task:\n type: PairClassification\n dataset:\n name: MTEB PawsX (en)\n type: paws-x\n config: en\n split: test\n revision: 8a04d940a42cd40658986fdd8e3da561533a3646\n metrics:\n - type: cos_sim_accuracy\n value: 61.25000000000001\n - type: cos_sim_ap\n value: 59.23166242799505\n - type: cos_sim_f1\n value: 62.53016201309893\n - type: cos_sim_precision\n value: 45.486459378134406\n - type: cos_sim_recall\n value: 100\n - type: dot_accuracy\n value: 61.25000000000001\n - type: dot_ap\n value: 59.23109306756652\n - type: dot_f1\n value: 62.53016201309893\n - type: dot_precision\n value: 45.486459378134406\n - type: dot_recall\n value: 100\n - type: euclidean_accuracy\n value: 61.25000000000001\n - type: euclidean_ap\n value: 59.23166242799505\n - type: euclidean_f1\n value: 62.53016201309893\n - type: euclidean_precision\n value: 45.486459378134406\n - type: euclidean_recall\n value: 100\n - type: manhattan_accuracy\n value: 61.25000000000001\n - type: manhattan_ap\n value: 59.23015114712089\n - type: manhattan_f1\n value: 62.50861474844934\n - type: manhattan_precision\n value: 45.46365914786967\n - type: manhattan_recall\n value: 100\n - type: max_accuracy\n value: 61.25000000000001\n - type: max_ap\n value: 59.23166242799505\n - type: max_f1\n value: 62.53016201309893\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: mteb/quora\n config: default\n split: test\n revision: e4e08e0b7dbe3c8700f0daef558ff32256715259\n metrics:\n - type: map_at_1\n value: 69.919\n - type: map_at_10\n value: 83.636\n - type: map_at_100\n value: 84.27\n - type: map_at_1000\n value: 84.289\n - type: map_at_3\n value: 80.744\n - type: map_at_5\n value: 82.509\n - type: mrr_at_1\n value: 80.52\n - type: mrr_at_10\n value: 86.751\n - type: mrr_at_100\n value: 86.875\n - type: mrr_at_1000\n value: 86.876\n - type: mrr_at_3\n value: 85.798\n - type: mrr_at_5\n value: 86.414\n - type: ndcg_at_1\n value: 80.53\n - type: ndcg_at_10\n value: 87.465\n - type: ndcg_at_100\n value: 88.762\n - type: ndcg_at_1000\n value: 88.90599999999999\n - type: ndcg_at_3\n value: 84.634\n - type: ndcg_at_5\n value: 86.09400000000001\n - type: precision_at_1\n value: 80.53\n - type: precision_at_10\n value: 13.263\n - type: precision_at_100\n value: 1.517\n - type: precision_at_1000\n value: 0.156\n - type: precision_at_3\n value: 36.973\n - type: precision_at_5\n value: 24.25\n - type: recall_at_1\n value: 69.919\n - type: recall_at_10\n value: 94.742\n - type: recall_at_100\n value: 99.221\n - type: recall_at_1000\n value: 99.917\n - type: recall_at_3\n value: 86.506\n - type: recall_at_5\n value: 90.736\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 50.47309147963901\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 385e3cb46b4cfa89021f56c4380204149d0efe33\n metrics:\n - type: v_measure\n value: 60.53779561923047\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: mteb/scidocs\n config: default\n split: test\n revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88\n metrics:\n - type: map_at_1\n value: 4.843\n - type: map_at_10\n value: 11.664\n - type: map_at_100\n value: 13.499\n - type: map_at_1000\n value: 13.771\n - type: map_at_3\n value: 8.602\n - type: map_at_5\n value: 10.164\n - type: mrr_at_1\n value: 23.9\n - type: mrr_at_10\n value: 34.018\n - type: mrr_at_100\n value: 35.099000000000004\n - type: mrr_at_1000\n value: 35.162\n - type: mrr_at_3\n value: 31.233\n - type: mrr_at_5\n value: 32.793\n - type: ndcg_at_1\n value: 23.9\n - type: ndcg_at_10\n value: 19.42\n - type: ndcg_at_100\n value: 26.715\n - type: ndcg_at_1000\n value: 31.776\n - type: ndcg_at_3\n value: 19.165\n - type: ndcg_at_5\n value: 16.46\n - type: precision_at_1\n value: 23.9\n - type: precision_at_10\n value: 9.82\n - type: precision_at_100\n value: 2.0340000000000003\n - type: precision_at_1000\n value: 0.325\n - type: precision_at_3\n value: 17.767\n - type: precision_at_5\n value: 14.24\n - type: recall_at_1\n value: 4.843\n - type: recall_at_10\n value: 19.895\n - type: recall_at_100\n value: 41.302\n - type: recall_at_1000\n value: 66.077\n - type: recall_at_3\n value: 10.803\n - type: recall_at_5\n value: 14.418000000000001\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: 20a6d6f312dd54037fe07a32d58e5e168867909d\n metrics:\n - type: cos_sim_pearson\n value: 76.94120735638143\n - type: cos_sim_spearman\n value: 69.66114097154585\n - type: euclidean_pearson\n value: 73.11242035696426\n - type: euclidean_spearman\n value: 69.66114271982464\n - type: manhattan_pearson\n value: 73.07993034858605\n - type: manhattan_spearman\n value: 69.6457893357314\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 74.72893353272778\n - type: cos_sim_spearman\n value: 68.78540928870311\n - type: euclidean_pearson\n value: 71.13907970605574\n - type: euclidean_spearman\n value: 68.78540928870311\n - type: manhattan_pearson\n value: 71.02709590547859\n - type: manhattan_spearman\n value: 68.71685896660532\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 79.30142652684971\n - type: cos_sim_spearman\n value: 79.61879435615303\n - type: euclidean_pearson\n value: 79.08730432883864\n - type: euclidean_spearman\n value: 79.61879435615303\n - type: manhattan_pearson\n value: 78.99621073156322\n - type: manhattan_spearman\n value: 79.53806342308278\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 78.99585233036139\n - type: cos_sim_spearman\n value: 75.57574519760183\n - type: euclidean_pearson\n value: 77.33835658613162\n - type: euclidean_spearman\n value: 75.57573873503655\n - type: manhattan_pearson\n value: 77.12175044789362\n - type: manhattan_spearman\n value: 75.41293517634836\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 83.9694268253376\n - type: cos_sim_spearman\n value: 84.64256921939338\n - type: euclidean_pearson\n value: 83.92322958711\n - type: euclidean_spearman\n value: 84.64257976421872\n - type: manhattan_pearson\n value: 83.93503107204337\n - type: manhattan_spearman\n value: 84.63611608236032\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 81.09041419790253\n - type: cos_sim_spearman\n value: 82.39869157752557\n - type: euclidean_pearson\n value: 82.04595698258301\n - type: euclidean_spearman\n value: 82.39869157752557\n - type: manhattan_pearson\n value: 81.97581168053004\n - type: manhattan_spearman\n value: 82.34255320578193\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 86.35210432821825\n - type: cos_sim_spearman\n value: 86.73200885328937\n - type: euclidean_pearson\n value: 86.8527089168747\n - type: euclidean_spearman\n value: 86.73200885328937\n - type: manhattan_pearson\n value: 86.95671235295457\n - type: manhattan_spearman\n value: 86.77713700838545\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n metrics:\n - type: cos_sim_pearson\n value: 68.91106612960657\n - type: cos_sim_spearman\n value: 69.48524490302286\n - type: euclidean_pearson\n value: 70.51347841618035\n - type: euclidean_spearman\n value: 69.48524490302286\n - type: manhattan_pearson\n value: 70.31770181334245\n - type: manhattan_spearman\n value: 69.12494700138238\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 81.54104342761988\n - type: cos_sim_spearman\n value: 81.18789220331483\n - type: euclidean_pearson\n value: 81.5895544590969\n - type: euclidean_spearman\n value: 81.18789220331483\n - type: manhattan_pearson\n value: 81.4738562449809\n - type: manhattan_spearman\n value: 81.06565101416024\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmarkMultilingualSTS (en)\n type: PhilipMay/stsb_multi_mt\n config: en\n split: test\n revision: 93d57ef91790589e3ce9c365164337a8a78b7632\n metrics:\n - type: cos_sim_pearson\n value: 81.54104346197056\n - type: cos_sim_spearman\n value: 81.18789220331483\n - type: euclidean_pearson\n value: 81.58955451690102\n - type: euclidean_spearman\n value: 81.18789220331483\n - type: manhattan_pearson\n value: 81.47385630064072\n - type: manhattan_spearman\n value: 81.06565101416024\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 79.34107964300796\n - type: mrr\n value: 94.01917889662987\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: mteb/scifact\n config: default\n split: test\n revision: 0228b52cf27578f30900b9e5271d331663a030d7\n metrics:\n - type: map_at_1\n value: 55.928\n - type: map_at_10\n value: 65.443\n - type: map_at_100\n value: 66.067\n - type: map_at_1000\n value: 66.091\n - type: map_at_3\n value: 62.629999999999995\n - type: map_at_5\n value: 64.35\n - type: mrr_at_1\n value: 59\n - type: mrr_at_10\n value: 66.845\n - type: mrr_at_100\n value: 67.31899999999999\n - type: mrr_at_1000\n value: 67.342\n - type: mrr_at_3\n value: 64.61099999999999\n - type: mrr_at_5\n value: 66.044\n - type: ndcg_at_1\n value: 59\n - type: ndcg_at_10\n value: 69.921\n - type: ndcg_at_100\n value: 72.365\n - type: ndcg_at_1000\n value: 73.055\n - type: ndcg_at_3\n value: 65.086\n - type: ndcg_at_5\n value: 67.62700000000001\n - type: precision_at_1\n value: 59\n - type: precision_at_10\n value: 9.3\n - type: precision_at_100\n value: 1.057\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 25.333\n - type: precision_at_5\n value: 16.866999999999997\n - type: recall_at_1\n value: 55.928\n - type: recall_at_10\n value: 82.289\n - type: recall_at_100\n value: 92.833\n - type: recall_at_1000\n value: 98.333\n - type: recall_at_3\n value: 69.172\n - type: recall_at_5\n value: 75.628\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.81881188118813\n - type: cos_sim_ap\n value: 95.2776439040401\n - type: cos_sim_f1\n value: 90.74355083459787\n - type: cos_sim_precision\n value: 91.81166837256909\n - type: cos_sim_recall\n value: 89.7\n - type: dot_accuracy\n value: 99.81881188118813\n - type: dot_ap\n value: 95.27764092100406\n - type: dot_f1\n value: 90.74355083459787\n - type: dot_precision\n value: 91.81166837256909\n - type: dot_recall\n value: 89.7\n - type: euclidean_accuracy\n value: 99.81881188118813\n - type: euclidean_ap\n value: 95.27764091101388\n - type: euclidean_f1\n value: 90.74355083459787\n - type: euclidean_precision\n value: 91.81166837256909\n - type: euclidean_recall\n value: 89.7\n - type: manhattan_accuracy\n value: 99.82079207920792\n - type: manhattan_ap\n value: 95.25081634689418\n - type: manhattan_f1\n value: 90.75114971895759\n - type: manhattan_precision\n value: 92.78996865203762\n - type: manhattan_recall\n value: 88.8\n - type: max_accuracy\n value: 99.82079207920792\n - type: max_ap\n value: 95.2776439040401\n - type: max_f1\n value: 90.75114971895759\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 60.69855369728728\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 33.98191834367251\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 50.156163330429614\n - type: mrr\n value: 50.90145148968678\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.16938079808134\n - type: cos_sim_spearman\n value: 31.74655874538245\n - type: dot_pearson\n value: 31.169380299671705\n - type: dot_spearman\n value: 31.74655874538245\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: mteb/trec-covid\n config: default\n split: test\n revision: bb9466bac8153a0349341eb1b22e06409e78ef4e\n metrics:\n - type: map_at_1\n value: 0.252\n - type: map_at_10\n value: 2.009\n - type: map_at_100\n value: 11.611\n - type: map_at_1000\n value: 27.811999999999998\n - type: map_at_3\n value: 0.685\n - type: map_at_5\n value: 1.08\n - type: mrr_at_1\n value: 94\n - type: mrr_at_10\n value: 97\n - type: mrr_at_100\n value: 97\n - type: mrr_at_1000\n value: 97\n - type: mrr_at_3\n value: 97\n - type: mrr_at_5\n value: 97\n - type: ndcg_at_1\n value: 88\n - type: ndcg_at_10\n value: 81.388\n - type: ndcg_at_100\n value: 60.629\n - type: ndcg_at_1000\n value: 52.38\n - type: ndcg_at_3\n value: 86.827\n - type: ndcg_at_5\n value: 84.597\n - type: precision_at_1\n value: 94\n - type: precision_at_10\n value: 85.8\n - type: precision_at_100\n value: 62.419999999999995\n - type: precision_at_1000\n value: 23.31\n - type: precision_at_3\n value: 90.667\n - type: precision_at_5\n value: 88.4\n - type: recall_at_1\n value: 0.252\n - type: recall_at_10\n value: 2.164\n - type: recall_at_100\n value: 14.613999999999999\n - type: recall_at_1000\n value: 48.730000000000004\n - type: recall_at_3\n value: 0.7020000000000001\n - type: recall_at_5\n value: 1.122\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: mteb/touche2020\n config: default\n split: test\n revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f\n metrics:\n - type: map_at_1\n value: 3.476\n - type: map_at_10\n value: 13.442000000000002\n - type: map_at_100\n value: 20.618\n - type: map_at_1000\n value: 22.175\n - type: map_at_3\n value: 6.968000000000001\n - type: map_at_5\n value: 9.214\n - type: mrr_at_1\n value: 44.897999999999996\n - type: mrr_at_10\n value: 56.77100000000001\n - type: mrr_at_100\n value: 57.226\n - type: mrr_at_1000\n value: 57.226\n - type: mrr_at_3\n value: 52.381\n - type: mrr_at_5\n value: 54.523999999999994\n - type: ndcg_at_1\n value: 42.857\n - type: ndcg_at_10\n value: 32.507999999999996\n - type: ndcg_at_100\n value: 43.614000000000004\n - type: ndcg_at_1000\n value: 53.82\n - type: ndcg_at_3\n value: 36.818\n - type: ndcg_at_5\n value: 33.346\n - type: precision_at_1\n value: 44.897999999999996\n - type: precision_at_10\n value: 28.571\n - type: precision_at_100\n value: 8.652999999999999\n - type: precision_at_1000\n value: 1.5709999999999997\n - type: precision_at_3\n value: 38.095\n - type: precision_at_5\n value: 32.245000000000005\n - type: recall_at_1\n value: 3.476\n - type: recall_at_10\n value: 20.827\n - type: recall_at_100\n value: 53.04299999999999\n - type: recall_at_1000\n value: 84.221\n - type: recall_at_3\n value: 8.200000000000001\n - type: recall_at_5\n value: 11.651\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 61.96360000000001\n - type: ap\n value: 11.256160324436445\n - type: f1\n value: 48.07712827691349\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 58.90492359932088\n - type: f1\n value: 59.12542417513503\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 38.284935353315355\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 83.4714192048638\n - type: cos_sim_ap\n value: 65.77588263185375\n - type: cos_sim_f1\n value: 62.459508098380326\n - type: cos_sim_precision\n value: 57.27172717271727\n - type: cos_sim_recall\n value: 68.68073878627968\n - type: dot_accuracy\n value: 83.4714192048638\n - type: dot_ap\n value: 65.77588818364636\n - type: dot_f1\n value: 62.459508098380326\n - type: dot_precision\n value: 57.27172717271727\n - type: dot_recall\n value: 68.68073878627968\n - type: euclidean_accuracy\n value: 83.4714192048638\n - type: euclidean_ap\n value: 65.77587693431595\n - type: euclidean_f1\n value: 62.459508098380326\n - type: euclidean_precision\n value: 57.27172717271727\n - type: euclidean_recall\n value: 68.68073878627968\n - type: manhattan_accuracy\n value: 83.47737974608094\n - type: manhattan_ap\n value: 65.65957745829654\n - type: manhattan_f1\n value: 62.22760290556902\n - type: manhattan_precision\n value: 57.494407158836694\n - type: manhattan_recall\n value: 67.81002638522428\n - type: max_accuracy\n value: 83.47737974608094\n - type: max_ap\n value: 65.77588818364636\n - type: max_f1\n value: 62.459508098380326\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.64244964489463\n - type: cos_sim_ap\n value: 85.154122301394\n - type: cos_sim_f1\n value: 77.45617911327146\n - type: cos_sim_precision\n value: 74.23066064370413\n - type: cos_sim_recall\n value: 80.97474591931014\n - type: dot_accuracy\n value: 88.64244964489463\n - type: dot_ap\n value: 85.15411965587543\n - type: dot_f1\n value: 77.45617911327146\n - type: dot_precision\n value: 74.23066064370413\n - type: dot_recall\n value: 80.97474591931014\n - type: euclidean_accuracy\n value: 88.64244964489463\n - type: euclidean_ap\n value: 85.15414684113986\n - type: euclidean_f1\n value: 77.45617911327146\n - type: euclidean_precision\n value: 74.23066064370413\n - type: euclidean_recall\n value: 80.97474591931014\n - type: manhattan_accuracy\n value: 88.57841425078588\n - type: manhattan_ap\n value: 85.12472268567576\n - type: manhattan_f1\n value: 77.39497339937627\n - type: manhattan_precision\n value: 73.92584285413892\n - type: manhattan_recall\n value: 81.20572836464429\n - type: max_accuracy\n value: 88.64244964489463\n - type: max_ap\n value: 85.15414684113986\n - type: max_f1\n value: 77.45617911327146\n - task:\n type: Clustering\n dataset:\n name: MTEB WikiCitiesClustering\n type: jinaai/cities_wiki_clustering\n config: default\n split: test\n revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa\n metrics:\n - type: v_measure\n value: 79.58576208710117\n---\nClick here for a full copy-paste ready example
\n\n```python\nfrom sentence_transformers import SentenceTransformer\nfrom datasets import load_dataset\n\n# 1. Load the Sentence Transformer model\nmodel = SentenceTransformer(\"jxm/cde-small-v1\", trust_remote_code=True)\ncontext_docs_size = model[0].config.transductive_corpus_size # 512\n\n# 2. Load the dataset: context dataset, docs, and queries\ndataset = load_dataset(\"sentence-transformers/natural-questions\", split=\"train\")\ndataset.shuffle(seed=42)\n# 10 queries, 512 context docs, 500 docs\nqueries = dataset[\"query\"][:10]\ndocs = dataset[\"answer\"][:2000]\ncontext_docs = dataset[\"answer\"][-context_docs_size:] # Last 512 docs\n\n# 3. First stage: embed the context docs\ndataset_embeddings = model.encode(\n context_docs,\n prompt_name=\"document\",\n convert_to_tensor=True,\n)\n\n# 4. Second stage: embed the docs and queries\ndoc_embeddings = model.encode(\n docs,\n prompt_name=\"document\",\n dataset_embeddings=dataset_embeddings,\n convert_to_tensor=True,\n)\nquery_embeddings = model.encode(\n queries,\n prompt_name=\"query\",\n dataset_embeddings=dataset_embeddings,\n convert_to_tensor=True,\n)\n\n# 5. Compute the similarity between the queries and docs\nsimilarities = model.similarity(query_embeddings, doc_embeddings)\ntopk_values, topk_indices = similarities.topk(5)\nprint(topk_values)\nprint(topk_indices)\n\n\"\"\"\ntensor([[0.5495, 0.5426, 0.5423, 0.5292, 0.5286],\n [0.6357, 0.6334, 0.6177, 0.5862, 0.5794],\n [0.7648, 0.5452, 0.5000, 0.4959, 0.4881],\n [0.6802, 0.5225, 0.5178, 0.5160, 0.5075],\n [0.6947, 0.5843, 0.5619, 0.5344, 0.5298],\n [0.7742, 0.7742, 0.7742, 0.7231, 0.6224],\n [0.8853, 0.6667, 0.5829, 0.5795, 0.5769],\n [0.6911, 0.6127, 0.6003, 0.5986, 0.5936],\n [0.6796, 0.6053, 0.6000, 0.5911, 0.5884],\n [0.7624, 0.5589, 0.5428, 0.5278, 0.5275]], device='cuda:0')\ntensor([[ 0, 296, 234, 1651, 1184],\n [1542, 466, 438, 1207, 1911],\n [ 2, 1562, 632, 1852, 382],\n [ 3, 694, 932, 1765, 662],\n [ 4, 35, 747, 26, 432],\n [ 534, 175, 5, 1495, 575],\n [ 6, 1802, 1875, 747, 21],\n [ 7, 1913, 1936, 640, 6],\n [ 8, 747, 167, 1318, 1743],\n [ 9, 1583, 1145, 219, 357]], device='cuda:0')\n\"\"\"\n# As you can see, almost every query_i has document_i as the most similar document.\n\n# 6. Print the top-k results\nfor query_idx, top_doc_idx in enumerate(topk_indices[:, 0]):\n print(f\"Query {query_idx}: {queries[query_idx]}\")\n print(f\"Top Document: {docs[top_doc_idx]}\")\n print()\n\"\"\"\nQuery 0: when did richmond last play in a preliminary final\nTop Document: Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.\n\nQuery 1: who sang what in the world's come over you\nTop Document: Life's What You Make It (Talk Talk song) \"Life's What You Make It\" is a song by the English band Talk Talk. It was released as a single in 1986, the first from the band's album The Colour of Spring. The single was a hit in the UK, peaking at No. 16, and charted in numerous other countries, often reaching the Top 20.\n\nQuery 2: who produces the most wool in the world\nTop Document: Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.\n\nQuery 3: where does alaska the last frontier take place\nTop Document: Alaska: The Last Frontier Alaska: The Last Frontier is an American reality cable television series on the Discovery Channel, currently in its 7th season of broadcast. The show documents the extended Kilcher family, descendants of Swiss immigrants and Alaskan pioneers, Yule and Ruth Kilcher, at their homestead 11 miles outside of Homer.[1] By living without plumbing or modern heating, the clan chooses to subsist by farming, hunting and preparing for the long winters.[2] The Kilcher family are relatives of the singer Jewel,[1][3] who has appeared on the show.[4]\n\nQuery 4: a day to remember all i want cameos\nTop Document: All I Want (A Day to Remember song) The music video for the song, which was filmed in October 2010,[4] was released on January 6, 2011.[5] It features cameos of numerous popular bands and musicians. The cameos are: Tom Denney (A Day to Remember's former guitarist), Pete Wentz, Winston McCall of Parkway Drive, The Devil Wears Prada, Bring Me the Horizon, Sam Carter of Architects, Tim Lambesis of As I Lay Dying, Silverstein, Andrew WK, August Burns Red, Seventh Star, Matt Heafy of Trivium, Vic Fuentes of Pierce the Veil, Mike Herrera of MxPx, and Set Your Goals.[5] Rock Sound called the video \"quite excellent\".[5]\n\nQuery 5: what does the red stripes mean on the american flag\nTop Document: Flag of the United States The flag of the United States of America, often referred to as the American flag, is the national flag of the United States. It consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton (referred to specifically as the \"union\") bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 states of the United States of America, and the 13 stripes represent the thirteen British colonies that declared independence from the Kingdom of Great Britain, and became the first states in the U.S.[1] Nicknames for the flag include The Stars and Stripes,[2] Old Glory,[3] and The Star-Spangled Banner.\n\nQuery 6: where did they film diary of a wimpy kid\nTop Document: Diary of a Wimpy Kid (film) Filming of Diary of a Wimpy Kid was in Vancouver and wrapped up on October 16, 2009.\n\nQuery 7: where was beasts of the southern wild filmed\nTop Document: Beasts of the Southern Wild The film's fictional setting, \"Isle de Charles Doucet\", known to its residents as the Bathtub, was inspired by several isolated and independent fishing communities threatened by erosion, hurricanes and rising sea levels in Louisiana's Terrebonne Parish, most notably the rapidly eroding Isle de Jean Charles. It was filmed in Terrebonne Parish town Montegut.[5]\n\nQuery 8: what part of the country are you likely to find the majority of the mollisols\nTop Document: Mollisol Mollisols occur in savannahs and mountain valleys (such as Central Asia, or the North American Great Plains). These environments have historically been strongly influenced by fire and abundant pedoturbation from organisms such as ants and earthworms. It was estimated that in 2003, only 14 to 26 percent of grassland ecosystems still remained in a relatively natural state (that is, they were not used for agriculture due to the fertility of the A horizon). Globally, they represent ~7% of ice-free land area. As the world's most agriculturally productive soil order, the Mollisols represent one of the more economically important soil orders.\n\nQuery 9: when did fosters home for imaginary friends start\nTop Document: Foster's Home for Imaginary Friends McCracken conceived the series after adopting two dogs from an animal shelter and applying the concept to imaginary friends. The show first premiered on Cartoon Network on August 13, 2004, as a 90-minute television film. On August 20, it began its normal run of twenty-to-thirty-minute episodes on Fridays, at 7 pm. The series finished its run on May 3, 2009, with a total of six seasons and seventy-nine episodes. McCracken left Cartoon Network shortly after the series ended. Reruns have aired on Boomerang from August 11, 2012 to November 3, 2013 and again from June 1, 2014 to April 3, 2017.\n\"\"\"\n```\n\nSnowflake's Arctic-embed-s
\n\n
\n News |\n Models |\n Usage |\n Evaluation |\n Contact |\n FAQ\n License |\n Acknowledgement\n
\n\n\n\n## News\n\n12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!\n\n07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.\n\n07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).\n\n05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)\n\n04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).\n\n\n## Models\n\n\nsnowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.\n\n\nThe `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.\n\n\nThe models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).\n\n\n| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |\n| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |\n| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |\n| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |\n| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |\n\n\nAside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |\n| Google-gecko-text-embedding | 55.7 |\n| text-embedding-3-large | 55.44 |\n| Cohere-embed-english-v3.0 | 55.00 |\n| bge-large-en-v1.5 | 54.29 |\n\n\n### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)\n\n\nThis tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------- | -------------------------------- |\n| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |\n| GIST-all-MiniLM-L6-v2 | 45.12 |\n| gte-tiny | 44.92 |\n| all-MiniLM-L6-v2 | 41.95 |\n| bge-micro-v2 | 42.56 |\n\n\n### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)\n\n\nBased on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |\n| bge-small-en-v1.5 | 51.68 |\n| Cohere-embed-english-light-v3.0 | 51.34 |\n| text-embedding-3-small | 51.08 |\n| e5-small-v2 | 49.04 |\n\n\n### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)\n\n\nBased on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |\n| bge-base-en-v1.5 | 53.25 |\n| nomic-embed-text-v1.5 | 53.25 |\n| GIST-Embedding-v0 | 52.31 |\n| gte-base | 52.31 |\n\n### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)\n\n\nBased on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |\n| nomic-embed-text-v1.5 | 53.01 |\n| nomic-embed-text-v1 | 52.81 |\n\n\n\n\n### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)\n\n\nBased on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |\n| UAE-Large-V1 | 54.66 |\n| bge-large-en-v1.5 | 54.29 |\n| mxbai-embed-large-v1 | 54.39 |\n| e5-Large-v2 | 50.56 |\n\n\n## Usage\n\n\n### Using Sentence Transformers\n\nYou can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. \n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"Snowflake/snowflake-arctic-embed-s\")\n\nqueries = ['what is snowflake?', 'Where can I get the best tacos?']\ndocuments = ['The Data Cloud!', 'Mexico City of Course!']\n\nquery_embeddings = model.encode(queries, prompt_name=\"query\")\ndocument_embeddings = model.encode(documents)\n\nscores = query_embeddings @ document_embeddings.T\nfor query, query_scores in zip(queries, scores):\n doc_score_pairs = list(zip(documents, query_scores))\n doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n # Output passages & scores\n print(\"Query:\", query)\n for document, score in doc_score_pairs:\n print(score, document)\n```\n```\nQuery: what is snowflake?\n0.533809 The Data Cloud!\n0.49207097 Mexico City of Course!\nQuery: Where can I get the best tacos?\n0.56592476 Mexico City of Course!\n0.48255116 The Data Cloud!\n```\n\n### Using Huggingface transformers\n\n\nYou can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).\n\n\n\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-s')\nmodel = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-s', add_pooling_layer=False)\nmodel.eval()\n\nquery_prefix = 'Represent this sentence for searching relevant passages: '\nqueries = ['what is snowflake?', 'Where can I get the best tacos?']\nqueries_with_prefix = [\"{}{}\".format(query_prefix, i) for i in queries]\nquery_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)\n\ndocuments = ['The Data Cloud!', 'Mexico City of Course!']\ndocument_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)\n\n# Compute token embeddings\nwith torch.no_grad():\n query_embeddings = model(**query_tokens)[0][:, 0]\n document_embeddings = model(**document_tokens)[0][:, 0]\n\n\n# normalize embeddings\nquery_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)\ndocument_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)\n\nscores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))\nfor query, query_scores in zip(queries, scores):\n doc_score_pairs = list(zip(documents, query_scores))\n doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n #Output passages & scores\n print(\"Query:\", query)\n for document, score in doc_score_pairs:\n print(score, document)\n```\n\n### Using Transformers.js\n\nIf you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:\n```bash\nnpm i @xenova/transformers\n```\n\nYou can then use the model to compute embeddings as follows:\n\n```js\nimport { pipeline, dot } from '@xenova/transformers';\n\n// Create feature extraction pipeline\nconst extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s', {\n quantized: false, // Comment out this line to use the quantized version\n});\n\n// Generate sentence embeddings\nconst sentences = [\n 'Represent this sentence for searching relevant passages: Where can I get the best tacos?',\n 'The Data Cloud!',\n 'Mexico City of Course!',\n]\nconst output = await extractor(sentences, { normalize: true, pooling: 'cls' });\n\n// Compute similarity scores\nconst [source_embeddings, ...document_embeddings ] = output.tolist();\nconst similarities = document_embeddings.map(x => dot(source_embeddings, x));\nconsole.log(similarities); // [0.48255123876493394, 0.5659250100112143]\n```\n\n## FAQ\n\n\nTBD\n\n\n## Contact\n\n\nFeel free to open an issue or pull request if you have any questions or suggestions about this project.\nYou also can email Daniel Campos(daniel.campos@snowflake.com).\n\n\n## License\n\n\nArctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.\n\n\n## Acknowledgement\n\n\nWe want to thank the open-source community, which has provided the great building blocks upon which we could make our models.\nWe thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. \nWe thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. \nWe also thank the open-source community for producing the great models we could build on top of and making these releases possible. \nFinally, we thank the researchers who created BEIR and MTEB benchmarks. \nIt is largely thanks to their tireless work to define what better looks like that we could improve model performance. \n\n![](\"https://static.scarf.sh/a.png?x-pxid=26ca7731-2650-44be-942d-0c6809cfcf00\")
"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":91,"cells":{"id":{"kind":"string","value":"Salesforce/SFR-Embedding-Mistral"},"author":{"kind":"string","value":"Salesforce"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","mistral","feature-extraction","mteb","transformers","en","arxiv:2210.07316","arxiv:2310.06825","arxiv:2401.00368","arxiv:2104.08663","license:cc-by-nc-4.0","model-index","autotrain_compatible","text-generation-inference","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"mistral\",\n \"feature-extraction\",\n \"mteb\",\n \"transformers\",\n \"en\",\n \"arxiv:2210.07316\",\n \"arxiv:2310.06825\",\n \"arxiv:2401.00368\",\n \"arxiv:2104.08663\",\n \"license:cc-by-nc-4.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-01-24T22:29:26"},"last_modified":{"kind":"string","value":"2025-02-04T21:01:42"},"downloads":{"kind":"number","value":29392,"string":"29,392"},"likes":{"kind":"number","value":273,"string":"273"},"README":{"kind":"string","value":"---\nlanguage:\n- en\nlicense: cc-by-nc-4.0\ntags:\n- mteb\n- sentence-transformers\n- transformers\nmodel-index:\n- name: SFR-Embedding-Mistral\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 77.92537313432834\n - type: ap\n value: 40.86767661556651\n - type: f1\n value: 71.65758897929837\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 95.967\n - type: ap\n value: 94.46300829592593\n - type: f1\n value: 95.96507173189292\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 54.352000000000004\n - type: f1\n value: 53.636682615380174\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 43.314\n - type: ndcg_at_2\n value: 54.757\n - type: ndcg_at_3\n value: 58.84700000000001\n - type: ndcg_at_5\n value: 63.634\n - type: ndcg_at_7\n value: 65.741\n - type: ndcg_at_10\n value: 67.171\n - type: ndcg_at_20\n value: 68.585\n - type: ndcg_at_30\n value: 68.81\n - type: ndcg_at_50\n value: 68.932\n - type: ndcg_at_70\n value: 68.992\n - type: ndcg_at_100\n value: 69.014\n - type: ndcg_at_200\n value: 69.014\n - type: ndcg_at_300\n value: 69.014\n - type: ndcg_at_500\n value: 69.014\n - type: ndcg_at_700\n value: 69.014\n - type: ndcg_at_1000\n value: 69.014\n - type: map_at_1\n value: 43.314\n - type: map_at_2\n value: 52.383\n - type: map_at_3\n value: 55.108999999999995\n - type: map_at_5\n value: 57.772999999999996\n - type: map_at_7\n value: 58.718\n - type: map_at_10\n value: 59.256\n - type: map_at_20\n value: 59.668\n - type: map_at_30\n value: 59.709999999999994\n - type: map_at_50\n value: 59.727\n - type: map_at_70\n value: 59.733999999999995\n - type: map_at_100\n value: 59.73500000000001\n - type: map_at_200\n value: 59.73500000000001\n - type: map_at_300\n value: 59.73500000000001\n - type: map_at_500\n value: 59.73500000000001\n - type: map_at_700\n value: 59.73500000000001\n - type: map_at_1000\n value: 59.73500000000001\n - type: recall_at_1\n value: 43.314\n - type: recall_at_2\n value: 61.451\n - type: recall_at_3\n value: 69.63000000000001\n - type: recall_at_5\n value: 81.223\n - type: recall_at_7\n value: 87.33999999999999\n - type: recall_at_10\n value: 92.034\n - type: recall_at_20\n value: 97.44\n - type: recall_at_30\n value: 98.506\n - type: recall_at_50\n value: 99.14699999999999\n - type: recall_at_70\n value: 99.502\n - type: recall_at_100\n value: 99.644\n - type: recall_at_200\n value: 99.644\n - type: recall_at_300\n value: 99.644\n - type: recall_at_500\n value: 99.644\n - type: recall_at_700\n value: 99.644\n - type: recall_at_1000\n value: 99.644\n - type: precision_at_1\n value: 43.314\n - type: precision_at_2\n value: 30.725\n - type: precision_at_3\n value: 23.21\n - type: precision_at_5\n value: 16.245\n - type: precision_at_7\n value: 12.477\n - type: precision_at_10\n value: 9.203\n - type: precision_at_20\n value: 4.872\n - type: precision_at_30\n value: 3.2840000000000003\n - type: precision_at_50\n value: 1.983\n - type: precision_at_70\n value: 1.421\n - type: precision_at_100\n value: 0.996\n - type: precision_at_200\n value: 0.498\n - type: precision_at_300\n value: 0.332\n - type: precision_at_500\n value: 0.199\n - type: precision_at_700\n value: 0.14200000000000002\n - type: precision_at_1000\n value: 0.1\n - type: mrr_at_1\n value: 44.666\n - type: mrr_at_2\n value: 52.418\n - type: mrr_at_3\n value: 55.595000000000006\n - type: mrr_at_5\n value: 58.205\n - type: mrr_at_7\n value: 59.202999999999996\n - type: mrr_at_10\n value: 59.727\n - type: mrr_at_20\n value: 60.133\n - type: mrr_at_30\n value: 60.178\n - type: mrr_at_50\n value: 60.192\n - type: mrr_at_70\n value: 60.19799999999999\n - type: mrr_at_100\n value: 60.199999999999996\n - type: mrr_at_200\n value: 60.199999999999996\n - type: mrr_at_300\n value: 60.199999999999996\n - type: mrr_at_500\n value: 60.199999999999996\n - type: mrr_at_700\n value: 60.199999999999996\n - type: mrr_at_1000\n value: 60.199999999999996\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 52.07508593014336\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 47.381339333240675\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 67.58376647859171\n - type: mrr\n value: 80.56885635140483\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 88.40107280274783\n - type: cos_sim_spearman\n value: 86.07003345325681\n - type: euclidean_pearson\n value: 87.1726034325395\n - type: euclidean_spearman\n value: 86.07003345325681\n - type: manhattan_pearson\n value: 87.25660625029772\n - type: manhattan_spearman\n value: 86.3808839096893\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 88.81168831168831\n - type: f1\n value: 88.76514496560141\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 43.9382520874344\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 41.14351847240913\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 34.51166666666667\n - type: ndcg_at_2\n value: 38.51591666666667\n - type: ndcg_at_3\n value: 40.95083333333333\n - type: ndcg_at_5\n value: 43.580666666666666\n - type: ndcg_at_7\n value: 45.0625\n - type: ndcg_at_10\n value: 46.49083333333333\n - type: ndcg_at_20\n value: 48.731333333333325\n - type: ndcg_at_30\n value: 49.78666666666667\n - type: ndcg_at_50\n value: 50.84049999999999\n - type: ndcg_at_70\n value: 51.393750000000004\n - type: ndcg_at_100\n value: 51.883333333333326\n - type: ndcg_at_200\n value: 52.65225\n - type: ndcg_at_300\n value: 52.98241666666669\n - type: ndcg_at_500\n value: 53.28541666666668\n - type: ndcg_at_700\n value: 53.49241666666668\n - type: ndcg_at_1000\n value: 53.63758333333334\n - type: map_at_1\n value: 29.10075\n - type: map_at_2\n value: 34.636500000000005\n - type: map_at_3\n value: 36.92033333333333\n - type: map_at_5\n value: 38.81641666666666\n - type: map_at_7\n value: 39.635416666666664\n - type: map_at_10\n value: 40.294583333333335\n - type: map_at_20\n value: 41.07574999999999\n - type: map_at_30\n value: 41.333\n - type: map_at_50\n value: 41.529333333333334\n - type: map_at_70\n value: 41.606833333333334\n - type: map_at_100\n value: 41.66224999999999\n - type: map_at_200\n value: 41.72691666666666\n - type: map_at_300\n value: 41.746583333333334\n - type: map_at_500\n value: 41.75983333333333\n - type: map_at_700\n value: 41.76558333333333\n - type: map_at_1000\n value: 41.769000000000005\n - type: recall_at_1\n value: 29.10075\n - type: recall_at_2\n value: 39.07658333333333\n - type: recall_at_3\n value: 44.93591666666667\n - type: recall_at_5\n value: 51.66883333333333\n - type: recall_at_7\n value: 55.881000000000014\n - type: recall_at_10\n value: 60.34691666666667\n - type: recall_at_20\n value: 68.44016666666667\n - type: recall_at_30\n value: 72.90766666666667\n - type: recall_at_50\n value: 77.843\n - type: recall_at_70\n value: 80.70366666666668\n - type: recall_at_100\n value: 83.42866666666667\n - type: recall_at_200\n value: 88.06816666666668\n - type: recall_at_300\n value: 90.249\n - type: recall_at_500\n value: 92.37616666666668\n - type: recall_at_700\n value: 93.978\n - type: recall_at_1000\n value: 95.12791666666666\n - type: precision_at_1\n value: 34.51166666666667\n - type: precision_at_2\n value: 24.326333333333327\n - type: precision_at_3\n value: 19.099249999999998\n - type: precision_at_5\n value: 13.672666666666666\n - type: precision_at_7\n value: 10.772\n - type: precision_at_10\n value: 8.302166666666668\n - type: precision_at_20\n value: 4.8960833333333325\n - type: precision_at_30\n value: 3.551083333333333\n - type: precision_at_50\n value: 2.3386666666666662\n - type: precision_at_70\n value: 1.7605833333333334\n - type: precision_at_100\n value: 1.2965\n - type: precision_at_200\n value: 0.7106666666666668\n - type: precision_at_300\n value: 0.4955\n - type: precision_at_500\n value: 0.3106666666666667\n - type: precision_at_700\n value: 0.22791666666666668\n - type: precision_at_1000\n value: 0.1635833333333333\n - type: mrr_at_1\n value: 34.51166666666667\n - type: mrr_at_2\n value: 39.954249999999995\n - type: mrr_at_3\n value: 41.93741666666668\n - type: mrr_at_5\n value: 43.487166666666674\n - type: mrr_at_7\n value: 44.14983333333333\n - type: mrr_at_10\n value: 44.62766666666666\n - type: mrr_at_20\n value: 45.15291666666668\n - type: mrr_at_30\n value: 45.317\n - type: mrr_at_50\n value: 45.42875\n - type: mrr_at_70\n value: 45.46966666666667\n - type: mrr_at_100\n value: 45.49716666666667\n - type: mrr_at_200\n value: 45.525166666666664\n - type: mrr_at_300\n value: 45.53233333333335\n - type: mrr_at_500\n value: 45.5365\n - type: mrr_at_700\n value: 45.538583333333335\n - type: mrr_at_1000\n value: 45.539583333333326\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 35.179\n - type: ndcg_at_2\n value: 31.243\n - type: ndcg_at_3\n value: 30.562\n - type: ndcg_at_5\n value: 32.409\n - type: ndcg_at_7\n value: 34.525\n - type: ndcg_at_10\n value: 36.415\n - type: ndcg_at_20\n value: 39.443\n - type: ndcg_at_30\n value: 40.796\n - type: ndcg_at_50\n value: 42.16\n - type: ndcg_at_70\n value: 42.971\n - type: ndcg_at_100\n value: 43.691\n - type: ndcg_at_200\n value: 45.004\n - type: ndcg_at_300\n value: 45.527\n - type: ndcg_at_500\n value: 46.072\n - type: ndcg_at_700\n value: 46.387\n - type: ndcg_at_1000\n value: 46.663\n - type: map_at_1\n value: 15.692\n - type: map_at_2\n value: 20.116\n - type: map_at_3\n value: 22.6\n - type: map_at_5\n value: 24.701\n - type: map_at_7\n value: 25.934\n - type: map_at_10\n value: 26.843\n - type: map_at_20\n value: 27.975\n - type: map_at_30\n value: 28.372000000000003\n - type: map_at_50\n value: 28.671000000000003\n - type: map_at_70\n value: 28.803\n - type: map_at_100\n value: 28.895\n - type: map_at_200\n value: 29.011\n - type: map_at_300\n value: 29.042\n - type: map_at_500\n value: 29.065\n - type: map_at_700\n value: 29.075\n - type: map_at_1000\n value: 29.081000000000003\n - type: recall_at_1\n value: 15.692\n - type: recall_at_2\n value: 22.602\n - type: recall_at_3\n value: 27.814\n - type: recall_at_5\n value: 33.756\n - type: recall_at_7\n value: 38.073\n - type: recall_at_10\n value: 42.553000000000004\n - type: recall_at_20\n value: 51.121\n - type: recall_at_30\n value: 55.523999999999994\n - type: recall_at_50\n value: 60.586\n - type: recall_at_70\n value: 63.94\n - type: recall_at_100\n value: 67.134\n - type: recall_at_200\n value: 73.543\n - type: recall_at_300\n value: 76.372\n - type: recall_at_500\n value: 79.60199999999999\n - type: recall_at_700\n value: 81.536\n - type: recall_at_1000\n value: 83.37400000000001\n - type: precision_at_1\n value: 35.179\n - type: precision_at_2\n value: 27.199\n - type: precision_at_3\n value: 22.953000000000003\n - type: precision_at_5\n value: 17.224999999999998\n - type: precision_at_7\n value: 14.238999999999999\n - type: precision_at_10\n value: 11.303\n - type: precision_at_20\n value: 6.954000000000001\n - type: precision_at_30\n value: 5.116\n - type: precision_at_50\n value: 3.395\n - type: precision_at_70\n value: 2.579\n - type: precision_at_100\n value: 1.9109999999999998\n - type: precision_at_200\n value: 1.065\n - type: precision_at_300\n value: 0.743\n - type: precision_at_500\n value: 0.46699999999999997\n - type: precision_at_700\n value: 0.344\n - type: precision_at_1000\n value: 0.247\n - type: mrr_at_1\n value: 35.179\n - type: mrr_at_2\n value: 41.792\n - type: mrr_at_3\n value: 44.484\n - type: mrr_at_5\n value: 46.39\n - type: mrr_at_7\n value: 47.125\n - type: mrr_at_10\n value: 47.711999999999996\n - type: mrr_at_20\n value: 48.214\n - type: mrr_at_30\n value: 48.325\n - type: mrr_at_50\n value: 48.392\n - type: mrr_at_70\n value: 48.418\n - type: mrr_at_100\n value: 48.44\n - type: mrr_at_200\n value: 48.46\n - type: mrr_at_300\n value: 48.461999999999996\n - type: mrr_at_500\n value: 48.466\n - type: mrr_at_700\n value: 48.466\n - type: mrr_at_1000\n value: 48.467\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 62.375\n - type: ndcg_at_2\n value: 56.286\n - type: ndcg_at_3\n value: 53.665\n - type: ndcg_at_5\n value: 51.139\n - type: ndcg_at_7\n value: 49.873\n - type: ndcg_at_10\n value: 49.056\n - type: ndcg_at_20\n value: 48.783\n - type: ndcg_at_30\n value: 49.166\n - type: ndcg_at_50\n value: 51.141999999999996\n - type: ndcg_at_70\n value: 52.774\n - type: ndcg_at_100\n value: 54.403\n - type: ndcg_at_200\n value: 57.419\n - type: ndcg_at_300\n value: 58.778\n - type: ndcg_at_500\n value: 60.228\n - type: ndcg_at_700\n value: 61.07599999999999\n - type: ndcg_at_1000\n value: 61.846000000000004\n - type: map_at_1\n value: 10.359\n - type: map_at_2\n value: 14.446\n - type: map_at_3\n value: 16.689\n - type: map_at_5\n value: 20.096\n - type: map_at_7\n value: 22.247\n - type: map_at_10\n value: 24.468999999999998\n - type: map_at_20\n value: 28.938000000000002\n - type: map_at_30\n value: 31.134\n - type: map_at_50\n value: 33.403\n - type: map_at_70\n value: 34.486\n - type: map_at_100\n value: 35.337\n - type: map_at_200\n value: 36.364999999999995\n - type: map_at_300\n value: 36.735\n - type: map_at_500\n value: 37.057\n - type: map_at_700\n value: 37.225\n - type: map_at_1000\n value: 37.379\n - type: recall_at_1\n value: 10.359\n - type: recall_at_2\n value: 14.945\n - type: recall_at_3\n value: 17.694\n - type: recall_at_5\n value: 22.677\n - type: recall_at_7\n value: 26.131\n - type: recall_at_10\n value: 30.053\n - type: recall_at_20\n value: 39.518\n - type: recall_at_30\n value: 44.925\n - type: recall_at_50\n value: 52.154\n - type: recall_at_70\n value: 56.729\n - type: recall_at_100\n value: 61.18900000000001\n - type: recall_at_200\n value: 70.407\n - type: recall_at_300\n value: 74.412\n - type: recall_at_500\n value: 78.891\n - type: recall_at_700\n value: 81.74\n - type: recall_at_1000\n value: 84.253\n - type: precision_at_1\n value: 75\n - type: precision_at_2\n value: 64.125\n - type: precision_at_3\n value: 57.833\n - type: precision_at_5\n value: 50.24999999999999\n - type: precision_at_7\n value: 44.75\n - type: precision_at_10\n value: 39.75\n - type: precision_at_20\n value: 30.412\n - type: precision_at_30\n value: 25.141999999999996\n - type: precision_at_50\n value: 19.2\n - type: precision_at_70\n value: 15.729000000000001\n - type: precision_at_100\n value: 12.552\n - type: precision_at_200\n value: 7.866\n - type: precision_at_300\n value: 5.9270000000000005\n - type: precision_at_500\n value: 4.1129999999999995\n - type: precision_at_700\n value: 3.2460000000000004\n - type: precision_at_1000\n value: 2.5260000000000002\n - type: mrr_at_1\n value: 75\n - type: mrr_at_2\n value: 78.625\n - type: mrr_at_3\n value: 79.708\n - type: mrr_at_5\n value: 80.446\n - type: mrr_at_7\n value: 80.862\n - type: mrr_at_10\n value: 81.161\n - type: mrr_at_20\n value: 81.3\n - type: mrr_at_30\n value: 81.348\n - type: mrr_at_50\n value: 81.361\n - type: mrr_at_70\n value: 81.361\n - type: mrr_at_100\n value: 81.361\n - type: mrr_at_200\n value: 81.367\n - type: mrr_at_300\n value: 81.367\n - type: mrr_at_500\n value: 81.368\n - type: mrr_at_700\n value: 81.368\n - type: mrr_at_1000\n value: 81.368\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 50.239999999999995\n - type: f1\n value: 46.42361822342044\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 83.723\n - type: ndcg_at_2\n value: 86.777\n - type: ndcg_at_3\n value: 87.997\n - type: ndcg_at_5\n value: 88.864\n - type: ndcg_at_7\n value: 89.143\n - type: ndcg_at_10\n value: 89.349\n - type: ndcg_at_20\n value: 89.709\n - type: ndcg_at_30\n value: 89.82900000000001\n - type: ndcg_at_50\n value: 89.923\n - type: ndcg_at_70\n value: 89.982\n - type: ndcg_at_100\n value: 90.026\n - type: ndcg_at_200\n value: 90.10000000000001\n - type: ndcg_at_300\n value: 90.12599999999999\n - type: ndcg_at_500\n value: 90.17399999999999\n - type: ndcg_at_700\n value: 90.19\n - type: ndcg_at_1000\n value: 90.208\n - type: map_at_1\n value: 77.64999999999999\n - type: map_at_2\n value: 83.769\n - type: map_at_3\n value: 85.041\n - type: map_at_5\n value: 85.736\n - type: map_at_7\n value: 85.924\n - type: map_at_10\n value: 86.032\n - type: map_at_20\n value: 86.177\n - type: map_at_30\n value: 86.213\n - type: map_at_50\n value: 86.233\n - type: map_at_70\n value: 86.24300000000001\n - type: map_at_100\n value: 86.249\n - type: map_at_200\n value: 86.256\n - type: map_at_300\n value: 86.258\n - type: map_at_500\n value: 86.26\n - type: map_at_700\n value: 86.26\n - type: map_at_1000\n value: 86.261\n - type: recall_at_1\n value: 77.64999999999999\n - type: recall_at_2\n value: 88.53999999999999\n - type: recall_at_3\n value: 91.696\n - type: recall_at_5\n value: 93.916\n - type: recall_at_7\n value: 94.731\n - type: recall_at_10\n value: 95.318\n - type: recall_at_20\n value: 96.507\n - type: recall_at_30\n value: 96.956\n - type: recall_at_50\n value: 97.34899999999999\n - type: recall_at_70\n value: 97.61\n - type: recall_at_100\n value: 97.83\n - type: recall_at_200\n value: 98.223\n - type: recall_at_300\n value: 98.374\n - type: recall_at_500\n value: 98.67899999999999\n - type: recall_at_700\n value: 98.787\n - type: recall_at_1000\n value: 98.919\n - type: precision_at_1\n value: 83.723\n - type: precision_at_2\n value: 48.425000000000004\n - type: precision_at_3\n value: 33.638\n - type: precision_at_5\n value: 20.843\n - type: precision_at_7\n value: 15.079\n - type: precision_at_10\n value: 10.674999999999999\n - type: precision_at_20\n value: 5.457999999999999\n - type: precision_at_30\n value: 3.6740000000000004\n - type: precision_at_50\n value: 2.2239999999999998\n - type: precision_at_70\n value: 1.599\n - type: precision_at_100\n value: 1.125\n - type: precision_at_200\n value: 0.5680000000000001\n - type: precision_at_300\n value: 0.38\n - type: precision_at_500\n value: 0.22999999999999998\n - type: precision_at_700\n value: 0.165\n - type: precision_at_1000\n value: 0.116\n - type: mrr_at_1\n value: 83.723\n - type: mrr_at_2\n value: 88.794\n - type: mrr_at_3\n value: 89.679\n - type: mrr_at_5\n value: 90.049\n - type: mrr_at_7\n value: 90.129\n - type: mrr_at_10\n value: 90.167\n - type: mrr_at_20\n value: 90.208\n - type: mrr_at_30\n value: 90.214\n - type: mrr_at_50\n value: 90.217\n - type: mrr_at_70\n value: 90.218\n - type: mrr_at_100\n value: 90.21900000000001\n - type: mrr_at_200\n value: 90.21900000000001\n - type: mrr_at_300\n value: 90.21900000000001\n - type: mrr_at_500\n value: 90.21900000000001\n - type: mrr_at_700\n value: 90.21900000000001\n - type: mrr_at_1000\n value: 90.21900000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 59.721999999999994\n - type: ndcg_at_2\n value: 56.85\n - type: ndcg_at_3\n value: 56.462999999999994\n - type: ndcg_at_5\n value: 57.75599999999999\n - type: ndcg_at_7\n value: 59.109\n - type: ndcg_at_10\n value: 60.402\n - type: ndcg_at_20\n value: 63.071999999999996\n - type: ndcg_at_30\n value: 64.302\n - type: ndcg_at_50\n value: 65.619\n - type: ndcg_at_70\n value: 66.161\n - type: ndcg_at_100\n value: 66.645\n - type: ndcg_at_200\n value: 67.353\n - type: ndcg_at_300\n value: 67.646\n - type: ndcg_at_500\n value: 67.852\n - type: ndcg_at_700\n value: 67.974\n - type: ndcg_at_1000\n value: 68.084\n - type: map_at_1\n value: 31.56\n - type: map_at_2\n value: 42.093\n - type: map_at_3\n value: 46.177\n - type: map_at_5\n value: 49.78\n - type: map_at_7\n value: 51.410999999999994\n - type: map_at_10\n value: 52.524\n - type: map_at_20\n value: 53.815000000000005\n - type: map_at_30\n value: 54.201\n - type: map_at_50\n value: 54.531\n - type: map_at_70\n value: 54.625\n - type: map_at_100\n value: 54.686\n - type: map_at_200\n value: 54.757999999999996\n - type: map_at_300\n value: 54.776\n - type: map_at_500\n value: 54.786\n - type: map_at_700\n value: 54.790000000000006\n - type: map_at_1000\n value: 54.793000000000006\n - type: recall_at_1\n value: 31.56\n - type: recall_at_2\n value: 44.858\n - type: recall_at_3\n value: 51.11\n - type: recall_at_5\n value: 58.394\n - type: recall_at_7\n value: 63.001\n - type: recall_at_10\n value: 66.81200000000001\n - type: recall_at_20\n value: 74.901\n - type: recall_at_30\n value: 79.218\n - type: recall_at_50\n value: 84.49\n - type: recall_at_70\n value: 87.003\n - type: recall_at_100\n value: 89.345\n - type: recall_at_200\n value: 93.173\n - type: recall_at_300\n value: 94.906\n - type: recall_at_500\n value: 96.223\n - type: recall_at_700\n value: 97.043\n - type: recall_at_1000\n value: 97.785\n - type: precision_at_1\n value: 59.721999999999994\n - type: precision_at_2\n value: 46.682\n - type: precision_at_3\n value: 37.602999999999994\n - type: precision_at_5\n value: 27.500000000000004\n - type: precision_at_7\n value: 21.847\n - type: precision_at_10\n value: 16.667\n - type: precision_at_20\n value: 9.545\n - type: precision_at_30\n value: 6.795\n - type: precision_at_50\n value: 4.38\n - type: precision_at_70\n value: 3.221\n - type: precision_at_100\n value: 2.319\n - type: precision_at_200\n value: 1.2149999999999999\n - type: precision_at_300\n value: 0.827\n - type: precision_at_500\n value: 0.504\n - type: precision_at_700\n value: 0.364\n - type: precision_at_1000\n value: 0.257\n - type: mrr_at_1\n value: 59.721999999999994\n - type: mrr_at_2\n value: 64.506\n - type: mrr_at_3\n value: 65.792\n - type: mrr_at_5\n value: 66.965\n - type: mrr_at_7\n value: 67.34700000000001\n - type: mrr_at_10\n value: 67.57\n - type: mrr_at_20\n value: 67.896\n - type: mrr_at_30\n value: 68.008\n - type: mrr_at_50\n value: 68.083\n - type: mrr_at_70\n value: 68.105\n - type: mrr_at_100\n value: 68.116\n - type: mrr_at_200\n value: 68.12700000000001\n - type: mrr_at_300\n value: 68.13\n - type: mrr_at_500\n value: 68.132\n - type: mrr_at_700\n value: 68.133\n - type: mrr_at_1000\n value: 68.133\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 81.796\n - type: ndcg_at_2\n value: 67.999\n - type: ndcg_at_3\n value: 72.15599999999999\n - type: ndcg_at_5\n value: 74.99900000000001\n - type: ndcg_at_7\n value: 76.179\n - type: ndcg_at_10\n value: 77.022\n - type: ndcg_at_20\n value: 78.173\n - type: ndcg_at_30\n value: 78.648\n - type: ndcg_at_50\n value: 79.104\n - type: ndcg_at_70\n value: 79.335\n - type: ndcg_at_100\n value: 79.56\n - type: ndcg_at_200\n value: 79.911\n - type: ndcg_at_300\n value: 80.045\n - type: ndcg_at_500\n value: 80.19500000000001\n - type: ndcg_at_700\n value: 80.281\n - type: ndcg_at_1000\n value: 80.35\n - type: map_at_1\n value: 40.898\n - type: map_at_2\n value: 62.016000000000005\n - type: map_at_3\n value: 66.121\n - type: map_at_5\n value: 68.471\n - type: map_at_7\n value: 69.261\n - type: map_at_10\n value: 69.738\n - type: map_at_20\n value: 70.208\n - type: map_at_30\n value: 70.343\n - type: map_at_50\n value: 70.43700000000001\n - type: map_at_70\n value: 70.47099999999999\n - type: map_at_100\n value: 70.498\n - type: map_at_200\n value: 70.526\n - type: map_at_300\n value: 70.533\n - type: map_at_500\n value: 70.538\n - type: map_at_700\n value: 70.541\n - type: map_at_1000\n value: 70.542\n - type: recall_at_1\n value: 40.898\n - type: recall_at_2\n value: 63.964\n - type: recall_at_3\n value: 70.743\n - type: recall_at_5\n value: 76.36699999999999\n - type: recall_at_7\n value: 79.142\n - type: recall_at_10\n value: 81.404\n - type: recall_at_20\n value: 85.111\n - type: recall_at_30\n value: 86.92800000000001\n - type: recall_at_50\n value: 88.899\n - type: recall_at_70\n value: 90.01400000000001\n - type: recall_at_100\n value: 91.19500000000001\n - type: recall_at_200\n value: 93.234\n - type: recall_at_300\n value: 94.105\n - type: recall_at_500\n value: 95.159\n - type: recall_at_700\n value: 95.8\n - type: recall_at_1000\n value: 96.34700000000001\n - type: precision_at_1\n value: 81.796\n - type: precision_at_2\n value: 63.964\n - type: precision_at_3\n value: 47.162\n - type: precision_at_5\n value: 30.547\n - type: precision_at_7\n value: 22.612\n - type: precision_at_10\n value: 16.281000000000002\n - type: precision_at_20\n value: 8.511000000000001\n - type: precision_at_30\n value: 5.795\n - type: precision_at_50\n value: 3.556\n - type: precision_at_70\n value: 2.572\n - type: precision_at_100\n value: 1.8239999999999998\n - type: precision_at_200\n value: 0.932\n - type: precision_at_300\n value: 0.627\n - type: precision_at_500\n value: 0.381\n - type: precision_at_700\n value: 0.27399999999999997\n - type: precision_at_1000\n value: 0.193\n - type: mrr_at_1\n value: 81.796\n - type: mrr_at_2\n value: 85.69200000000001\n - type: mrr_at_3\n value: 86.52\n - type: mrr_at_5\n value: 86.973\n - type: mrr_at_7\n value: 87.13300000000001\n - type: mrr_at_10\n value: 87.208\n - type: mrr_at_20\n value: 87.303\n - type: mrr_at_30\n value: 87.32799999999999\n - type: mrr_at_50\n value: 87.347\n - type: mrr_at_70\n value: 87.35199999999999\n - type: mrr_at_100\n value: 87.355\n - type: mrr_at_200\n value: 87.357\n - type: mrr_at_300\n value: 87.357\n - type: mrr_at_500\n value: 87.358\n - type: mrr_at_700\n value: 87.358\n - type: mrr_at_1000\n value: 87.358\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 94.79200000000002\n - type: ap\n value: 92.54484356773553\n - type: f1\n value: 94.78965313682525\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 24.398\n - type: ndcg_at_2\n value: 31.336000000000002\n - type: ndcg_at_3\n value: 35.266999999999996\n - type: ndcg_at_5\n value: 39.356\n - type: ndcg_at_7\n value: 41.562\n - type: ndcg_at_10\n value: 43.408\n - type: ndcg_at_20\n value: 46.107\n - type: ndcg_at_30\n value: 47.164\n - type: ndcg_at_50\n value: 48.126000000000005\n - type: ndcg_at_70\n value: 48.626999999999995\n - type: ndcg_at_100\n value: 49.043\n - type: ndcg_at_200\n value: 49.575\n - type: ndcg_at_300\n value: 49.794\n - type: ndcg_at_500\n value: 49.942\n - type: ndcg_at_700\n value: 50.014\n - type: ndcg_at_1000\n value: 50.077000000000005\n - type: map_at_1\n value: 23.723\n - type: map_at_2\n value: 29.593000000000004\n - type: map_at_3\n value: 32.273\n - type: map_at_5\n value: 34.587\n - type: map_at_7\n value: 35.589999999999996\n - type: map_at_10\n value: 36.296\n - type: map_at_20\n value: 37.059999999999995\n - type: map_at_30\n value: 37.265\n - type: map_at_50\n value: 37.402\n - type: map_at_70\n value: 37.454\n - type: map_at_100\n value: 37.486999999999995\n - type: map_at_200\n value: 37.516\n - type: map_at_300\n value: 37.524\n - type: map_at_500\n value: 37.528\n - type: map_at_700\n value: 37.529\n - type: map_at_1000\n value: 37.53\n - type: recall_at_1\n value: 23.723\n - type: recall_at_2\n value: 35.355\n - type: recall_at_3\n value: 43.22\n - type: recall_at_5\n value: 53.025\n - type: recall_at_7\n value: 59.327\n - type: recall_at_10\n value: 65.302\n - type: recall_at_20\n value: 75.765\n - type: recall_at_30\n value: 80.632\n - type: recall_at_50\n value: 85.63499999999999\n - type: recall_at_70\n value: 88.554\n - type: recall_at_100\n value: 91.16300000000001\n - type: recall_at_200\n value: 94.85\n - type: recall_at_300\n value: 96.532\n - type: recall_at_500\n value: 97.751\n - type: recall_at_700\n value: 98.383\n - type: recall_at_1000\n value: 98.97\n - type: precision_at_1\n value: 24.398\n - type: precision_at_2\n value: 18.274\n - type: precision_at_3\n value: 14.951999999999998\n - type: precision_at_5\n value: 11.052\n - type: precision_at_7\n value: 8.84\n - type: precision_at_10\n value: 6.8309999999999995\n - type: precision_at_20\n value: 3.978\n - type: precision_at_30\n value: 2.827\n - type: precision_at_50\n value: 1.807\n - type: precision_at_70\n value: 1.336\n - type: precision_at_100\n value: 0.964\n - type: precision_at_200\n value: 0.502\n - type: precision_at_300\n value: 0.34099999999999997\n - type: precision_at_500\n value: 0.208\n - type: precision_at_700\n value: 0.15\n - type: precision_at_1000\n value: 0.105\n - type: mrr_at_1\n value: 24.398\n - type: mrr_at_2\n value: 30.351\n - type: mrr_at_3\n value: 33.001000000000005\n - type: mrr_at_5\n value: 35.228\n - type: mrr_at_7\n value: 36.223\n - type: mrr_at_10\n value: 36.903999999999996\n - type: mrr_at_20\n value: 37.631\n - type: mrr_at_30\n value: 37.830000000000005\n - type: mrr_at_50\n value: 37.955\n - type: mrr_at_70\n value: 38.003\n - type: mrr_at_100\n value: 38.033\n - type: mrr_at_200\n value: 38.059\n - type: mrr_at_300\n value: 38.066\n - type: mrr_at_500\n value: 38.068999999999996\n - type: mrr_at_700\n value: 38.07\n - type: mrr_at_1000\n value: 38.07\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 96.35658914728683\n - type: f1\n value: 96.15039630903114\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 86.29730962152303\n - type: f1\n value: 71.12166316567485\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 79.98991257565568\n - type: f1\n value: 77.41680115095276\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 82.1990585070612\n - type: f1\n value: 82.23719179179362\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 40.03019554933584\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 38.999760551497815\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 32.72383151953079\n - type: mrr\n value: 33.93989699030721\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 51.858000000000004\n - type: ndcg_at_2\n value: 49.675999999999995\n - type: ndcg_at_3\n value: 47.519\n - type: ndcg_at_5\n value: 45.198\n - type: ndcg_at_7\n value: 43.504\n - type: ndcg_at_10\n value: 41.88\n - type: ndcg_at_20\n value: 39.122\n - type: ndcg_at_30\n value: 37.95\n - type: ndcg_at_50\n value: 37.602999999999994\n - type: ndcg_at_70\n value: 37.836\n - type: ndcg_at_100\n value: 38.493\n - type: ndcg_at_200\n value: 40.187\n - type: ndcg_at_300\n value: 41.524\n - type: ndcg_at_500\n value: 43.657000000000004\n - type: ndcg_at_700\n value: 45.234\n - type: ndcg_at_1000\n value: 47.047\n - type: map_at_1\n value: 6.392\n - type: map_at_2\n value: 10.113\n - type: map_at_3\n value: 11.543000000000001\n - type: map_at_5\n value: 13.729\n - type: map_at_7\n value: 14.985000000000001\n - type: map_at_10\n value: 16.217000000000002\n - type: map_at_20\n value: 18.106\n - type: map_at_30\n value: 18.878\n - type: map_at_50\n value: 19.822\n - type: map_at_70\n value: 20.352999999999998\n - type: map_at_100\n value: 20.827\n - type: map_at_200\n value: 21.512\n - type: map_at_300\n value: 21.826\n - type: map_at_500\n value: 22.155\n - type: map_at_700\n value: 22.349\n - type: map_at_1000\n value: 22.531000000000002\n - type: recall_at_1\n value: 6.392\n - type: recall_at_2\n value: 11.215\n - type: recall_at_3\n value: 13.231000000000002\n - type: recall_at_5\n value: 16.66\n - type: recall_at_7\n value: 18.802\n - type: recall_at_10\n value: 21.185000000000002\n - type: recall_at_20\n value: 25.35\n - type: recall_at_30\n value: 27.91\n - type: recall_at_50\n value: 32.845\n - type: recall_at_70\n value: 35.789\n - type: recall_at_100\n value: 39.247\n - type: recall_at_200\n value: 46.655\n - type: recall_at_300\n value: 51.43299999999999\n - type: recall_at_500\n value: 59.472\n - type: recall_at_700\n value: 64.742\n - type: recall_at_1000\n value: 70.97099999999999\n - type: precision_at_1\n value: 53.559999999999995\n - type: precision_at_2\n value: 48.762\n - type: precision_at_3\n value: 44.169000000000004\n - type: precision_at_5\n value: 39.071\n - type: precision_at_7\n value: 35.161\n - type: precision_at_10\n value: 31.238\n - type: precision_at_20\n value: 23.064999999999998\n - type: precision_at_30\n value: 18.844\n - type: precision_at_50\n value: 14.601\n - type: precision_at_70\n value: 12.088000000000001\n - type: precision_at_100\n value: 9.844999999999999\n - type: precision_at_200\n value: 6.358\n - type: precision_at_300\n value: 4.915\n - type: precision_at_500\n value: 3.531\n - type: precision_at_700\n value: 2.8649999999999998\n - type: precision_at_1000\n value: 2.289\n - type: mrr_at_1\n value: 54.17999999999999\n - type: mrr_at_2\n value: 59.288\n - type: mrr_at_3\n value: 60.836\n - type: mrr_at_5\n value: 62.275999999999996\n - type: mrr_at_7\n value: 62.688\n - type: mrr_at_10\n value: 62.865\n - type: mrr_at_20\n value: 63.11\n - type: mrr_at_30\n value: 63.193999999999996\n - type: mrr_at_50\n value: 63.258\n - type: mrr_at_70\n value: 63.278\n - type: mrr_at_100\n value: 63.297000000000004\n - type: mrr_at_200\n value: 63.315999999999995\n - type: mrr_at_300\n value: 63.318\n - type: mrr_at_500\n value: 63.32299999999999\n - type: mrr_at_700\n value: 63.324000000000005\n - type: mrr_at_1000\n value: 63.324999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 50.897999999999996\n - type: ndcg_at_2\n value: 59.126\n - type: ndcg_at_3\n value: 63.093999999999994\n - type: ndcg_at_5\n value: 67.197\n - type: ndcg_at_7\n value: 68.719\n - type: ndcg_at_10\n value: 69.915\n - type: ndcg_at_20\n value: 71.229\n - type: ndcg_at_30\n value: 71.667\n - type: ndcg_at_50\n value: 71.98\n - type: ndcg_at_70\n value: 72.127\n - type: ndcg_at_100\n value: 72.217\n - type: ndcg_at_200\n value: 72.319\n - type: ndcg_at_300\n value: 72.347\n - type: ndcg_at_500\n value: 72.37\n - type: ndcg_at_700\n value: 72.379\n - type: ndcg_at_1000\n value: 72.381\n - type: map_at_1\n value: 45.297\n - type: map_at_2\n value: 55.596000000000004\n - type: map_at_3\n value: 58.724\n - type: map_at_5\n value: 61.387\n - type: map_at_7\n value: 62.173\n - type: map_at_10\n value: 62.69\n - type: map_at_20\n value: 63.125\n - type: map_at_30\n value: 63.223\n - type: map_at_50\n value: 63.27700000000001\n - type: map_at_70\n value: 63.295\n - type: map_at_100\n value: 63.303\n - type: map_at_200\n value: 63.31\n - type: map_at_300\n value: 63.31099999999999\n - type: map_at_500\n value: 63.312000000000005\n - type: map_at_700\n value: 63.312000000000005\n - type: map_at_1000\n value: 63.312000000000005\n - type: recall_at_1\n value: 45.297\n - type: recall_at_2\n value: 63.866\n - type: recall_at_3\n value: 71.898\n - type: recall_at_5\n value: 81.16600000000001\n - type: recall_at_7\n value: 85.301\n - type: recall_at_10\n value: 88.94800000000001\n - type: recall_at_20\n value: 93.719\n - type: recall_at_30\n value: 95.628\n - type: recall_at_50\n value: 97.14699999999999\n - type: recall_at_70\n value: 97.955\n - type: recall_at_100\n value: 98.48599999999999\n - type: recall_at_200\n value: 99.157\n - type: recall_at_300\n value: 99.355\n - type: recall_at_500\n value: 99.53699999999999\n - type: recall_at_700\n value: 99.62299999999999\n - type: recall_at_1000\n value: 99.638\n - type: precision_at_1\n value: 50.897999999999996\n - type: precision_at_2\n value: 36.703\n - type: precision_at_3\n value: 27.926000000000002\n - type: precision_at_5\n value: 19.276\n - type: precision_at_7\n value: 14.533999999999999\n - type: precision_at_10\n value: 10.678\n - type: precision_at_20\n value: 5.663\n - type: precision_at_30\n value: 3.8600000000000003\n - type: precision_at_50\n value: 2.358\n - type: precision_at_70\n value: 1.7000000000000002\n - type: precision_at_100\n value: 1.198\n - type: precision_at_200\n value: 0.603\n - type: precision_at_300\n value: 0.40299999999999997\n - type: precision_at_500\n value: 0.242\n - type: precision_at_700\n value: 0.173\n - type: precision_at_1000\n value: 0.121\n - type: mrr_at_1\n value: 50.897999999999996\n - type: mrr_at_2\n value: 59.994\n - type: mrr_at_3\n value: 62.553000000000004\n - type: mrr_at_5\n value: 64.307\n - type: mrr_at_7\n value: 64.864\n - type: mrr_at_10\n value: 65.22200000000001\n - type: mrr_at_20\n value: 65.499\n - type: mrr_at_30\n value: 65.561\n - type: mrr_at_50\n value: 65.592\n - type: mrr_at_70\n value: 65.602\n - type: mrr_at_100\n value: 65.607\n - type: mrr_at_200\n value: 65.61099999999999\n - type: mrr_at_300\n value: 65.61200000000001\n - type: mrr_at_500\n value: 65.61200000000001\n - type: mrr_at_700\n value: 65.61200000000001\n - type: mrr_at_1000\n value: 65.61200000000001\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 82.96\n - type: ndcg_at_2\n value: 85.614\n - type: ndcg_at_3\n value: 87.19\n - type: ndcg_at_5\n value: 88.654\n - type: ndcg_at_7\n value: 89.287\n - type: ndcg_at_10\n value: 89.785\n - type: ndcg_at_20\n value: 90.384\n - type: ndcg_at_30\n value: 90.589\n - type: ndcg_at_50\n value: 90.738\n - type: ndcg_at_70\n value: 90.789\n - type: ndcg_at_100\n value: 90.824\n - type: ndcg_at_200\n value: 90.869\n - type: ndcg_at_300\n value: 90.881\n - type: ndcg_at_500\n value: 90.886\n - type: ndcg_at_700\n value: 90.889\n - type: ndcg_at_1000\n value: 90.889\n - type: map_at_1\n value: 72.152\n - type: map_at_2\n value: 80.818\n - type: map_at_3\n value: 83.462\n - type: map_at_5\n value: 85.286\n - type: map_at_7\n value: 85.921\n - type: map_at_10\n value: 86.334\n - type: map_at_20\n value: 86.737\n - type: map_at_30\n value: 86.847\n - type: map_at_50\n value: 86.911\n - type: map_at_70\n value: 86.932\n - type: map_at_100\n value: 86.943\n - type: map_at_200\n value: 86.953\n - type: map_at_300\n value: 86.955\n - type: map_at_500\n value: 86.956\n - type: map_at_700\n value: 86.956\n - type: map_at_1000\n value: 86.956\n - type: recall_at_1\n value: 72.152\n - type: recall_at_2\n value: 84.129\n - type: recall_at_3\n value: 88.87\n - type: recall_at_5\n value: 93.067\n - type: recall_at_7\n value: 94.882\n - type: recall_at_10\n value: 96.353\n - type: recall_at_20\n value: 98.26700000000001\n - type: recall_at_30\n value: 98.92999999999999\n - type: recall_at_50\n value: 99.441\n - type: recall_at_70\n value: 99.619\n - type: recall_at_100\n value: 99.748\n - type: recall_at_200\n value: 99.911\n - type: recall_at_300\n value: 99.956\n - type: recall_at_500\n value: 99.98\n - type: recall_at_700\n value: 99.991\n - type: recall_at_1000\n value: 99.996\n - type: precision_at_1\n value: 82.96\n - type: precision_at_2\n value: 52.175000000000004\n - type: precision_at_3\n value: 38.223\n - type: precision_at_5\n value: 25.056\n - type: precision_at_7\n value: 18.717\n - type: precision_at_10\n value: 13.614999999999998\n - type: precision_at_20\n value: 7.208\n - type: precision_at_30\n value: 4.928\n - type: precision_at_50\n value: 3.024\n - type: precision_at_70\n value: 2.183\n - type: precision_at_100\n value: 1.54\n - type: precision_at_200\n value: 0.779\n - type: precision_at_300\n value: 0.521\n - type: precision_at_500\n value: 0.313\n - type: precision_at_700\n value: 0.22399999999999998\n - type: precision_at_1000\n value: 0.157\n - type: mrr_at_1\n value: 82.96\n - type: mrr_at_2\n value: 87.005\n - type: mrr_at_3\n value: 88.07199999999999\n - type: mrr_at_5\n value: 88.634\n - type: mrr_at_7\n value: 88.793\n - type: mrr_at_10\n value: 88.87899999999999\n - type: mrr_at_20\n value: 88.94999999999999\n - type: mrr_at_30\n value: 88.96\n - type: mrr_at_50\n value: 88.965\n - type: mrr_at_70\n value: 88.966\n - type: mrr_at_100\n value: 88.967\n - type: mrr_at_200\n value: 88.967\n - type: mrr_at_300\n value: 88.967\n - type: mrr_at_500\n value: 88.967\n - type: mrr_at_700\n value: 88.967\n - type: mrr_at_1000\n value: 88.967\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 59.90388554491155\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 67.64232539036783\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 22.6\n - type: ndcg_at_2\n value: 20.355999999999998\n - type: ndcg_at_3\n value: 18.536\n - type: ndcg_at_5\n value: 16.523\n - type: ndcg_at_7\n value: 17.979\n - type: ndcg_at_10\n value: 19.908\n - type: ndcg_at_20\n value: 22.887\n - type: ndcg_at_30\n value: 24.43\n - type: ndcg_at_50\n value: 25.959\n - type: ndcg_at_70\n value: 26.989\n - type: ndcg_at_100\n value: 27.977\n - type: ndcg_at_200\n value: 29.831000000000003\n - type: ndcg_at_300\n value: 30.787\n - type: ndcg_at_500\n value: 31.974999999999998\n - type: ndcg_at_700\n value: 32.554\n - type: ndcg_at_1000\n value: 33.277\n - type: map_at_1\n value: 4.593\n - type: map_at_2\n value: 6.923\n - type: map_at_3\n value: 8.3\n - type: map_at_5\n value: 10.072000000000001\n - type: map_at_7\n value: 10.782\n - type: map_at_10\n value: 11.72\n - type: map_at_20\n value: 12.838\n - type: map_at_30\n value: 13.257\n - type: map_at_50\n value: 13.569\n - type: map_at_70\n value: 13.733\n - type: map_at_100\n value: 13.858999999999998\n - type: map_at_200\n value: 14.018\n - type: map_at_300\n value: 14.072999999999999\n - type: map_at_500\n value: 14.126\n - type: map_at_700\n value: 14.145\n - type: map_at_1000\n value: 14.161999999999999\n - type: recall_at_1\n value: 4.593\n - type: recall_at_2\n value: 7.997999999999999\n - type: recall_at_3\n value: 10.563\n - type: recall_at_5\n value: 14.907\n - type: recall_at_7\n value: 17.4\n - type: recall_at_10\n value: 21.18\n - type: recall_at_20\n value: 28.144999999999996\n - type: recall_at_30\n value: 32.462\n - type: recall_at_50\n value: 37.267\n - type: recall_at_70\n value: 40.875\n - type: recall_at_100\n value: 44.641999999999996\n - type: recall_at_200\n value: 52.573\n - type: recall_at_300\n value: 57.089999999999996\n - type: recall_at_500\n value: 63.14300000000001\n - type: recall_at_700\n value: 66.313\n - type: recall_at_1000\n value: 70.458\n - type: precision_at_1\n value: 22.6\n - type: precision_at_2\n value: 19.7\n - type: precision_at_3\n value: 17.333000000000002\n - type: precision_at_5\n value: 14.680000000000001\n - type: precision_at_7\n value: 12.243\n - type: precision_at_10\n value: 10.440000000000001\n - type: precision_at_20\n value: 6.944999999999999\n - type: precision_at_30\n value: 5.333\n - type: precision_at_50\n value: 3.678\n - type: precision_at_70\n value: 2.881\n - type: precision_at_100\n value: 2.2030000000000003\n - type: precision_at_200\n value: 1.295\n - type: precision_at_300\n value: 0.9369999999999999\n - type: precision_at_500\n value: 0.622\n - type: precision_at_700\n value: 0.466\n - type: precision_at_1000\n value: 0.347\n - type: mrr_at_1\n value: 22.6\n - type: mrr_at_2\n value: 27.900000000000002\n - type: mrr_at_3\n value: 30.067\n - type: mrr_at_5\n value: 32.207\n - type: mrr_at_7\n value: 33.004\n - type: mrr_at_10\n value: 33.596\n - type: mrr_at_20\n value: 34.268\n - type: mrr_at_30\n value: 34.492\n - type: mrr_at_50\n value: 34.628\n - type: mrr_at_70\n value: 34.681\n - type: mrr_at_100\n value: 34.717\n - type: mrr_at_200\n value: 34.757\n - type: mrr_at_300\n value: 34.768\n - type: mrr_at_500\n value: 34.772\n - type: mrr_at_700\n value: 34.774\n - type: mrr_at_1000\n value: 34.775\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 86.90122745229677\n - type: cos_sim_spearman\n value: 82.92294737327579\n - type: euclidean_pearson\n value: 84.08979655773187\n - type: euclidean_spearman\n value: 82.92294657285412\n - type: manhattan_pearson\n value: 84.09347480531832\n - type: manhattan_spearman\n value: 82.91564613948087\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 87.01218713698583\n - type: cos_sim_spearman\n value: 79.46865215168464\n - type: euclidean_pearson\n value: 83.22621889891909\n - type: euclidean_spearman\n value: 79.46853821709514\n - type: manhattan_pearson\n value: 83.69962580788805\n - type: manhattan_spearman\n value: 79.9561593356932\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 88.98438696342964\n - type: cos_sim_spearman\n value: 89.15419511870839\n - type: euclidean_pearson\n value: 88.49646141802894\n - type: euclidean_spearman\n value: 89.15419503946019\n - type: manhattan_pearson\n value: 88.6420585616327\n - type: manhattan_spearman\n value: 89.42648950757743\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 87.30772547759544\n - type: cos_sim_spearman\n value: 84.93199878424691\n - type: euclidean_pearson\n value: 86.16266630395455\n - type: euclidean_spearman\n value: 84.93198798543634\n - type: manhattan_pearson\n value: 86.14285723189803\n - type: manhattan_spearman\n value: 85.0361672522687\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 90.21342071197127\n - type: cos_sim_spearman\n value: 90.7407512744838\n - type: euclidean_pearson\n value: 90.1517933113061\n - type: euclidean_spearman\n value: 90.74075125431919\n - type: manhattan_pearson\n value: 90.17963034676193\n - type: manhattan_spearman\n value: 90.88999275865135\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 86.82518054100498\n - type: cos_sim_spearman\n value: 87.81570533154735\n - type: euclidean_pearson\n value: 86.91684561573618\n - type: euclidean_spearman\n value: 87.81570533154735\n - type: manhattan_pearson\n value: 86.98311935744032\n - type: manhattan_spearman\n value: 87.9594667151966\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 92.09578436612053\n - type: cos_sim_spearman\n value: 92.01519349090438\n - type: euclidean_pearson\n value: 92.07113635890894\n - type: euclidean_spearman\n value: 92.01519349090438\n - type: manhattan_pearson\n value: 91.89343820765625\n - type: manhattan_spearman\n value: 91.7443476810177\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 69.29997751464549\n - type: cos_sim_spearman\n value: 68.36425436812782\n - type: euclidean_pearson\n value: 69.81381677661783\n - type: euclidean_spearman\n value: 68.36425436812782\n - type: manhattan_pearson\n value: 69.92823397008026\n - type: manhattan_spearman\n value: 68.35770640039254\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 88.39126315452359\n - type: cos_sim_spearman\n value: 88.99708463265337\n - type: euclidean_pearson\n value: 88.60793820038607\n - type: euclidean_spearman\n value: 88.99708463265337\n - type: manhattan_pearson\n value: 88.69860633571047\n - type: manhattan_spearman\n value: 89.20094593888012\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 86.58028062818582\n - type: mrr\n value: 96.53586790841693\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 66.333\n - type: ndcg_at_2\n value: 70.655\n - type: ndcg_at_3\n value: 72.801\n - type: ndcg_at_5\n value: 75.793\n - type: ndcg_at_7\n value: 76.946\n - type: ndcg_at_10\n value: 77.66199999999999\n - type: ndcg_at_20\n value: 78.786\n - type: ndcg_at_30\n value: 79.066\n - type: ndcg_at_50\n value: 79.255\n - type: ndcg_at_70\n value: 79.423\n - type: ndcg_at_100\n value: 79.476\n - type: ndcg_at_200\n value: 79.65299999999999\n - type: ndcg_at_300\n value: 79.696\n - type: ndcg_at_500\n value: 79.73599999999999\n - type: ndcg_at_700\n value: 79.77199999999999\n - type: ndcg_at_1000\n value: 79.77199999999999\n - type: map_at_1\n value: 63.383\n - type: map_at_2\n value: 68.144\n - type: map_at_3\n value: 70.19800000000001\n - type: map_at_5\n value: 72.38\n - type: map_at_7\n value: 72.955\n - type: map_at_10\n value: 73.312\n - type: map_at_20\n value: 73.678\n - type: map_at_30\n value: 73.72800000000001\n - type: map_at_50\n value: 73.75500000000001\n - type: map_at_70\n value: 73.771\n - type: map_at_100\n value: 73.776\n - type: map_at_200\n value: 73.783\n - type: map_at_300\n value: 73.784\n - type: map_at_500\n value: 73.785\n - type: map_at_700\n value: 73.786\n - type: map_at_1000\n value: 73.786\n - type: recall_at_1\n value: 63.383\n - type: recall_at_2\n value: 72.283\n - type: recall_at_3\n value: 77.183\n - type: recall_at_5\n value: 84.56099999999999\n - type: recall_at_7\n value: 87.67200000000001\n - type: recall_at_10\n value: 89.822\n - type: recall_at_20\n value: 94\n - type: recall_at_30\n value: 95.333\n - type: recall_at_50\n value: 96.333\n - type: recall_at_70\n value: 97.333\n - type: recall_at_100\n value: 97.667\n - type: recall_at_200\n value: 99\n - type: recall_at_300\n value: 99.333\n - type: recall_at_500\n value: 99.667\n - type: recall_at_700\n value: 100\n - type: recall_at_1000\n value: 100\n - type: precision_at_1\n value: 66.333\n - type: precision_at_2\n value: 38.667\n - type: precision_at_3\n value: 28.111000000000004\n - type: precision_at_5\n value: 18.933\n - type: precision_at_7\n value: 14.094999999999999\n - type: precision_at_10\n value: 10.167\n - type: precision_at_20\n value: 5.35\n - type: precision_at_30\n value: 3.611\n - type: precision_at_50\n value: 2.1870000000000003\n - type: precision_at_70\n value: 1.576\n - type: precision_at_100\n value: 1.107\n - type: precision_at_200\n value: 0.5599999999999999\n - type: precision_at_300\n value: 0.374\n - type: precision_at_500\n value: 0.22499999999999998\n - type: precision_at_700\n value: 0.161\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: mrr_at_1\n value: 66.333\n - type: mrr_at_2\n value: 70.833\n - type: mrr_at_3\n value: 72.167\n - type: mrr_at_5\n value: 73.6\n - type: mrr_at_7\n value: 74.084\n - type: mrr_at_10\n value: 74.283\n - type: mrr_at_20\n value: 74.54499999999999\n - type: mrr_at_30\n value: 74.59599999999999\n - type: mrr_at_50\n value: 74.622\n - type: mrr_at_70\n value: 74.639\n - type: mrr_at_100\n value: 74.643\n - type: mrr_at_200\n value: 74.65\n - type: mrr_at_300\n value: 74.652\n - type: mrr_at_500\n value: 74.653\n - type: mrr_at_700\n value: 74.653\n - type: mrr_at_1000\n value: 74.653\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.84554455445544\n - type: cos_sim_ap\n value: 96.31178339136798\n - type: cos_sim_f1\n value: 92.1921921921922\n - type: cos_sim_precision\n value: 92.28456913827655\n - type: cos_sim_recall\n value: 92.10000000000001\n - type: dot_accuracy\n value: 99.84554455445544\n - type: dot_ap\n value: 96.31178339136797\n - type: dot_f1\n value: 92.1921921921922\n - type: dot_precision\n value: 92.28456913827655\n - type: dot_recall\n value: 92.10000000000001\n - type: euclidean_accuracy\n value: 99.84554455445544\n - type: euclidean_ap\n value: 96.31178339136798\n - type: euclidean_f1\n value: 92.1921921921922\n - type: euclidean_precision\n value: 92.28456913827655\n - type: euclidean_recall\n value: 92.10000000000001\n - type: manhattan_accuracy\n value: 99.84752475247525\n - type: manhattan_ap\n value: 96.4591954606088\n - type: manhattan_f1\n value: 92.25352112676056\n - type: manhattan_precision\n value: 92.81376518218623\n - type: manhattan_recall\n value: 91.7\n - type: max_accuracy\n value: 99.84752475247525\n - type: max_ap\n value: 96.4591954606088\n - type: max_f1\n value: 92.25352112676056\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 74.24659759283294\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 46.77690051260451\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 55.68436757803185\n - type: mrr\n value: 56.82157711569475\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 31.652482405629843\n - type: cos_sim_spearman\n value: 31.16341822347735\n - type: dot_pearson\n value: 31.652479892699837\n - type: dot_spearman\n value: 31.16341822347735\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 92\n - type: ndcg_at_2\n value: 90.839\n - type: ndcg_at_3\n value: 90.642\n - type: ndcg_at_5\n value: 90.348\n - type: ndcg_at_7\n value: 89.015\n - type: ndcg_at_10\n value: 87.599\n - type: ndcg_at_20\n value: 84.434\n - type: ndcg_at_30\n value: 81.655\n - type: ndcg_at_50\n value: 77.278\n - type: ndcg_at_70\n value: 73.957\n - type: ndcg_at_100\n value: 69.56\n - type: ndcg_at_200\n value: 60.724000000000004\n - type: ndcg_at_300\n value: 57.245000000000005\n - type: ndcg_at_500\n value: 56.316\n - type: ndcg_at_700\n value: 58.399\n - type: ndcg_at_1000\n value: 62.21600000000001\n - type: map_at_1\n value: 0.247\n - type: map_at_2\n value: 0.488\n - type: map_at_3\n value: 0.7230000000000001\n - type: map_at_5\n value: 1.204\n - type: map_at_7\n value: 1.6500000000000001\n - type: map_at_10\n value: 2.292\n - type: map_at_20\n value: 4.274\n - type: map_at_30\n value: 6.027\n - type: map_at_50\n value: 9.083\n - type: map_at_70\n value: 11.751000000000001\n - type: map_at_100\n value: 14.912\n - type: map_at_200\n value: 22.213\n - type: map_at_300\n value: 26.667999999999996\n - type: map_at_500\n value: 31.556\n - type: map_at_700\n value: 34.221000000000004\n - type: map_at_1000\n value: 36.443999999999996\n - type: recall_at_1\n value: 0.247\n - type: recall_at_2\n value: 0.49899999999999994\n - type: recall_at_3\n value: 0.742\n - type: recall_at_5\n value: 1.247\n - type: recall_at_7\n value: 1.722\n - type: recall_at_10\n value: 2.405\n - type: recall_at_20\n value: 4.583\n - type: recall_at_30\n value: 6.587999999999999\n - type: recall_at_50\n value: 10.188\n - type: recall_at_70\n value: 13.496\n - type: recall_at_100\n value: 17.578\n - type: recall_at_200\n value: 28.158\n - type: recall_at_300\n value: 35.532000000000004\n - type: recall_at_500\n value: 45.31\n - type: recall_at_700\n value: 51.822\n - type: recall_at_1000\n value: 58.53\n - type: precision_at_1\n value: 96\n - type: precision_at_2\n value: 96\n - type: precision_at_3\n value: 95.333\n - type: precision_at_5\n value: 94.8\n - type: precision_at_7\n value: 93.429\n - type: precision_at_10\n value: 91.4\n - type: precision_at_20\n value: 87.7\n - type: precision_at_30\n value: 84.867\n - type: precision_at_50\n value: 80.24\n - type: precision_at_70\n value: 76.371\n - type: precision_at_100\n value: 71.08\n - type: precision_at_200\n value: 59.4\n - type: precision_at_300\n value: 51.459999999999994\n - type: precision_at_500\n value: 40.644000000000005\n - type: precision_at_700\n value: 33.889\n - type: precision_at_1000\n value: 27.250000000000004\n - type: mrr_at_1\n value: 96\n - type: mrr_at_2\n value: 98\n - type: mrr_at_3\n value: 98\n - type: mrr_at_5\n value: 98\n - type: mrr_at_7\n value: 98\n - type: mrr_at_10\n value: 98\n - type: mrr_at_20\n value: 98\n - type: mrr_at_30\n value: 98\n - type: mrr_at_50\n value: 98\n - type: mrr_at_70\n value: 98\n - type: mrr_at_100\n value: 98\n - type: mrr_at_200\n value: 98\n - type: mrr_at_300\n value: 98\n - type: mrr_at_500\n value: 98\n - type: mrr_at_700\n value: 98\n - type: mrr_at_1000\n value: 98\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: ndcg_at_1\n value: 43.878\n - type: ndcg_at_2\n value: 37.956\n - type: ndcg_at_3\n value: 35.053\n - type: ndcg_at_5\n value: 32.59\n - type: ndcg_at_7\n value: 30.226\n - type: ndcg_at_10\n value: 29.005\n - type: ndcg_at_20\n value: 30.11\n - type: ndcg_at_30\n value: 32.019999999999996\n - type: ndcg_at_50\n value: 34.354\n - type: ndcg_at_70\n value: 36.665\n - type: ndcg_at_100\n value: 38.888\n - type: ndcg_at_200\n value: 43.435\n - type: ndcg_at_300\n value: 45.795\n - type: ndcg_at_500\n value: 48.699999999999996\n - type: ndcg_at_700\n value: 50.242\n - type: ndcg_at_1000\n value: 51.529\n - type: map_at_1\n value: 3.521\n - type: map_at_2\n value: 5.309\n - type: map_at_3\n value: 6.576\n - type: map_at_5\n value: 8.97\n - type: map_at_7\n value: 10.194\n - type: map_at_10\n value: 11.949\n - type: map_at_20\n value: 14.686\n - type: map_at_30\n value: 15.8\n - type: map_at_50\n value: 16.59\n - type: map_at_70\n value: 17.2\n - type: map_at_100\n value: 17.765\n - type: map_at_200\n value: 18.636\n - type: map_at_300\n value: 18.972\n - type: map_at_500\n value: 19.301\n - type: map_at_700\n value: 19.445\n - type: map_at_1000\n value: 19.546\n - type: recall_at_1\n value: 3.521\n - type: recall_at_2\n value: 5.848\n - type: recall_at_3\n value: 7.657\n - type: recall_at_5\n value: 11.368\n - type: recall_at_7\n value: 13.748\n - type: recall_at_10\n value: 18.061\n - type: recall_at_20\n value: 26.844\n - type: recall_at_30\n value: 31.186000000000003\n - type: recall_at_50\n value: 35.951\n - type: recall_at_70\n value: 40.961999999999996\n - type: recall_at_100\n value: 46.743\n - type: recall_at_200\n value: 58.483\n - type: recall_at_300\n value: 65.973\n - type: recall_at_500\n value: 75.233\n - type: recall_at_700\n value: 80.472\n - type: recall_at_1000\n value: 85.02\n - type: precision_at_1\n value: 46.939\n - type: precision_at_2\n value: 38.775999999999996\n - type: precision_at_3\n value: 34.694\n - type: precision_at_5\n value: 31.429000000000002\n - type: precision_at_7\n value: 27.697\n - type: precision_at_10\n value: 24.490000000000002\n - type: precision_at_20\n value: 18.776\n - type: precision_at_30\n value: 15.034\n - type: precision_at_50\n value: 10.857\n - type: precision_at_70\n value: 9.096\n - type: precision_at_100\n value: 7.51\n - type: precision_at_200\n value: 4.929\n - type: precision_at_300\n value: 3.7760000000000002\n - type: precision_at_500\n value: 2.6780000000000004\n - type: precision_at_700\n value: 2.085\n - type: precision_at_1000\n value: 1.5709999999999997\n - type: mrr_at_1\n value: 46.939\n - type: mrr_at_2\n value: 55.102\n - type: mrr_at_3\n value: 57.823\n - type: mrr_at_5\n value: 60.68\n - type: mrr_at_7\n value: 60.972\n - type: mrr_at_10\n value: 61.199000000000005\n - type: mrr_at_20\n value: 61.831\n - type: mrr_at_30\n value: 61.831\n - type: mrr_at_50\n value: 61.873\n - type: mrr_at_70\n value: 61.873\n - type: mrr_at_100\n value: 61.873\n - type: mrr_at_200\n value: 61.873\n - type: mrr_at_300\n value: 61.873\n - type: mrr_at_500\n value: 61.873\n - type: mrr_at_700\n value: 61.873\n - type: mrr_at_1000\n value: 61.873\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 69.3294\n - type: ap\n value: 14.561333393364736\n - type: f1\n value: 53.992309820496466\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 63.63893604980192\n - type: f1\n value: 63.92959380489434\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 56.270879258659775\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 88.71073493473207\n - type: cos_sim_ap\n value: 81.52392540284202\n - type: cos_sim_f1\n value: 74.71162377994676\n - type: cos_sim_precision\n value: 71.89558428885094\n - type: cos_sim_recall\n value: 77.75725593667546\n - type: dot_accuracy\n value: 88.71073493473207\n - type: dot_ap\n value: 81.52394754041109\n - type: dot_f1\n value: 74.71162377994676\n - type: dot_precision\n value: 71.89558428885094\n - type: dot_recall\n value: 77.75725593667546\n - type: euclidean_accuracy\n value: 88.71073493473207\n - type: euclidean_ap\n value: 81.52392035435321\n - type: euclidean_f1\n value: 74.71162377994676\n - type: euclidean_precision\n value: 71.89558428885094\n - type: euclidean_recall\n value: 77.75725593667546\n - type: manhattan_accuracy\n value: 88.47231328604637\n - type: manhattan_ap\n value: 81.22907439267321\n - type: manhattan_f1\n value: 74.3351571446749\n - type: manhattan_precision\n value: 71.78667977390022\n - type: manhattan_recall\n value: 77.0712401055409\n - type: max_accuracy\n value: 88.71073493473207\n - type: max_ap\n value: 81.52394754041109\n - type: max_f1\n value: 74.71162377994676\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 89.85136026700819\n - type: cos_sim_ap\n value: 87.7768002924216\n - type: cos_sim_f1\n value: 80.358908624794\n - type: cos_sim_precision\n value: 76.62918209122023\n - type: cos_sim_recall\n value: 84.47028025870034\n - type: dot_accuracy\n value: 89.85136026700819\n - type: dot_ap\n value: 87.77680027889778\n - type: dot_f1\n value: 80.358908624794\n - type: dot_precision\n value: 76.62918209122023\n - type: dot_recall\n value: 84.47028025870034\n - type: euclidean_accuracy\n value: 89.85136026700819\n - type: euclidean_ap\n value: 87.77680174697751\n - type: euclidean_f1\n value: 80.358908624794\n - type: euclidean_precision\n value: 76.62918209122023\n - type: euclidean_recall\n value: 84.47028025870034\n - type: manhattan_accuracy\n value: 89.86300306593705\n - type: manhattan_ap\n value: 87.78613271895861\n - type: manhattan_f1\n value: 80.31831016905645\n - type: manhattan_precision\n value: 76.68230516070304\n - type: manhattan_recall\n value: 84.3162919618109\n - type: max_accuracy\n value: 89.86300306593705\n - type: max_ap\n value: 87.78613271895861\n - type: max_f1\n value: 80.358908624794\n---\n\n
Salesforce/SFR-Embedding-Mistral
\n\n**SFR-Embedding by Salesforce Research.**\n\nThe model is trained on top of [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).\n\nThis project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:\n- [MTEB benchmark](https://arxiv.org/abs/2210.07316)\n- [Mistral](https://arxiv.org/abs/2310.06825)\n- [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)\n\nMore technical details will be updated later.\n\n### Ethical Considerations\nThis release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our [AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ExternalFacing_Services_Policy.pdf) and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai-acceptable-use-policy.pdf).\n\n\n## How to run\n\n### Transformers\nThe models can be used as follows:\n```python\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'Instruct: {task_description}\\nQuery: {query}'\n\n# Each query must come with a one-sentence instruction that describes the task\ntask = 'Given a web search query, retrieve relevant passages that answer the query'\nqueries = [\n get_detailed_instruct(task, 'How to bake a chocolate cake'),\n get_detailed_instruct(task, 'Symptoms of the flu')\n]\n# No need to add instruction for retrieval documents\npassages = [\n \"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!\",\n \"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness.\"\n]\n\n# load model and tokenizer\ntokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')\nmodel = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')\n\n# get the embeddings\nmax_length = 4096\ninput_texts = queries + passages\nbatch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors=\"pt\")\noutputs = model(**batch_dict)\nembeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])\n\n# normalize embeddings\nembeddings = F.normalize(embeddings, p=2, dim=1)\nscores = (embeddings[:2] @ embeddings[2:].T) * 100\nprint(scores.tolist())\n# [[86.7153549194336, 36.64569091796875], [35.00493621826172, 82.0738525390625]]\n```\n\n### Sentence Transformers\n```python\n\nfrom sentence_transformers import SentenceTransformer, util\n\nmodel = SentenceTransformer(\"Salesforce/SFR-Embedding-Mistral\")\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'Instruct: {task_description}\\nQuery: {query}'\n\n# Each query must come with a one-sentence instruction that describes the task\ntask = 'Given a web search query, retrieve relevant passages that answer the query'\nqueries = [\n get_detailed_instruct(task, 'How to bake a chocolate cake'),\n get_detailed_instruct(task, 'Symptoms of the flu')\n]\n# No need to add instruction for retrieval documents\npassages = [\n \"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!\",\n \"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness.\"\n]\n\nembeddings = model.encode(queries + passages)\nscores = util.cos_sim(embeddings[:2], embeddings[2:]) * 100\nprint(scores.tolist())\n# [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]\n```\n\n### MTEB Benchmark Evaluation\nCheck out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.\n\n\nSFR-Embedding Team (∗indicates lead contributors).\n* Rui Meng*\n* Ye Liu*\n* Shafiq Rayhan Joty\n* Caiming Xiong\n* Yingbo Zhou\n* Semih Yavuz\n\n### Citation\n```bibtex\n@misc{SFRAIResearch2024,\n title={SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning},\n author={Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},\n howpublished={Salesforce AI Research Blog},\n year={2024},\n url={https://www.salesforce.com/blog/sfr-embedding/}\n}\n```\n\n\n\n\n\n"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":92,"cells":{"id":{"kind":"string","value":"EleutherAI/pythia-410m-deduped"},"author":{"kind":"string","value":"EleutherAI"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","gpt_neox","text-generation","causal-lm","pythia","en","dataset:EleutherAI/the_pile_deduplicated","arxiv:2304.01373","arxiv:2101.00027","arxiv:2201.07311","license:apache-2.0","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"gpt_neox\",\n \"text-generation\",\n \"causal-lm\",\n \"pythia\",\n \"en\",\n \"dataset:EleutherAI/the_pile_deduplicated\",\n \"arxiv:2304.01373\",\n \"arxiv:2101.00027\",\n \"arxiv:2201.07311\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2023-02-13T21:27:47"},"last_modified":{"kind":"string","value":"2023-07-09T16:05:38"},"downloads":{"kind":"number","value":28910,"string":"28,910"},"likes":{"kind":"number","value":20,"string":"20"},"README":{"kind":"string","value":"---\ndatasets:\n- EleutherAI/the_pile_deduplicated\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- pytorch\n- causal-lm\n- pythia\n---\n\nThe *Pythia Scaling Suite* is a collection of models developed to facilitate \ninterpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). \nIt contains two sets of eight models of sizes \n70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two \nmodels: one trained on the Pile, and one trained on the Pile after the dataset \nhas been globally deduplicated. All 8 model sizes are trained on the exact \nsame data, in the exact same order. We also provide 154 intermediate \ncheckpoints per model, hosted on Hugging Face as branches.\n\nThe Pythia model suite was designed to promote scientific \nresearch on large language models, especially interpretability research. \nDespite not centering downstream performance as a design goal, we find the \nmodels match or exceed the performance of \nsimilar and same-sized models, such as those in the OPT and GPT-Neo suites.\n\n\n
\n**This is the current release.**\n\nPlease note that all models in the *Pythia* suite were renamed in January \n2023. For clarity, a table \ncomparing the old and new names is provided in this model card, together \nwith exact parameter counts.\n
\nDetails on previous early release and naming convention.
\n\nPreviously, we released an early version of the Pythia suite to the public. \nHowever, we decided to retrain the model suite to address a few hyperparameter \ndiscrepancies. This model card lists the changes; \nsee appendix B in the Pythia paper for further discussion. We found no \ndifference in benchmark performance between the two Pythia versions. \nThe old models are \n[still available](https://huggingface.co/models?other=pythia_v0), but we \nsuggest the retrained suite if you are just starting to use Pythia.\n**This is the current release.**\n\nPlease note that all models in the *Pythia* suite were renamed in January \n2023. For clarity, a table \ncomparing the old and new names is provided in this model card, together \nwith exact parameter counts.\n
\n\n# Pythia-410M-deduped\n\n## Model Details\n\n- Developed by: [EleutherAI](http://eleuther.ai)\n- Model type: Transformer-based Language Model\n- Language: English\n- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)\n for training procedure, config files, and details on how to use.\n [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation\n details.\n- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)\n- License: Apache 2.0\n- Contact: to ask questions about this model, join the [EleutherAI \nDiscord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.\n Please read the existing *Pythia* documentation before asking about it in the \n EleutherAI Discord. For general correspondence: [contact@eleuther.\n ai](mailto:contact@eleuther.ai).\n\n
\nFor more information on how to use all Pythia models, see [documentation on \nGitHub](https://github.com/EleutherAI/pythia).\n\n## Training\n\n### Training data\n\nPythia-410M-deduped was trained on the Pile **after the dataset has been globally \ndeduplicated**.
\n[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in \nEnglish. It was created by EleutherAI specifically for training large language \nmodels. It contains texts from 22 diverse sources, roughly broken down into \nfive categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), \nprose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and \nmiscellaneous (e.g. GitHub, Enron Emails). See [the Pile \npaper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, \nmethodology, and a discussion of ethical implications. Consult [the \ndatasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation \nabout the Pile and its component datasets. The Pile can be downloaded from \nthe [official website](https://pile.eleuther.ai/), or from a [community \nmirror](https://the-eye.eu/public/AI/pile/).\n\n### Training procedure\n\nAll models were trained on the exact same data, in the exact same order. Each \nmodel saw 299,892,736,000 tokens during training, and 143 checkpoints for each \nmodel are saved every 2,097,152,000 tokens, spaced evenly throughout training, \nfrom `step1000` to `step143000` (which is the same as `main`). In addition, we \nalso provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.\nThis corresponds to training for just under 1 epoch on the Pile for \nnon-deduplicated models, and about 1.5 epochs on the deduplicated Pile.\n\nAll *Pythia* models trained for 143000 steps at a batch size \nof 2M (2,097,152 tokens).
\nSee [GitHub](https://github.com/EleutherAI/pythia) for more details on training\n procedure, including [how to reproduce \n it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).
\nPythia uses the same tokenizer as [GPT-NeoX-\n20B](https://huggingface.co/EleutherAI/gpt-neox-20b).\n\n## Evaluations\n\nAll 16 *Pythia* models were evaluated using the [LM Evaluation \nHarness](https://github.com/EleutherAI/lm-evaluation-harness). You can access \nthe results by model and step at `results/json/*` in the [GitHub \nrepository](https://github.com/EleutherAI/pythia/tree/main/results/json/).
\nExpand the sections below to see plots of evaluation results for all \nPythia and Pythia-deduped models compared with OPT and BLOOM.\n\n
\n ![](\"/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png\")
\n
\n\nLAMBADA – OpenAI
\n\n\n ![](\"/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png\")
\n
\n\nPhysical Interaction: Question Answering (PIQA)
\n\n\n ![](\"/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png\")
\n
\n\nWinoGrande
\n\n\n ![](\"/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png\")
\n
\n\nAI2 Reasoning Challenge—Easy Set
\n\n\n ![](\"/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png\")
\n
\n\n## Changelog\n\nThis section compares differences between previously released \n[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current \nmodels. See Appendix B of the Pythia paper for further discussion of these \nchanges and the motivation behind them. We found that retraining Pythia had no \nimpact on benchmark performance.\n\n- All model sizes are now trained with uniform batch size of 2M tokens. \nPreviously, the models of size 160M, 410M, and 1.4B parameters were trained \nwith batch sizes of 4M tokens.\n- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,\n128,256,512} in addition to every 1000 training steps.\n- Flash Attention was used in the new retrained suite.\n- We remedied a minor inconsistency that existed in the original suite: all \nmodels of size 2.8B parameters or smaller had a learning rate (LR) schedule \nwhich decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and \n12B models all used an LR schedule which decayed to a minimum LR of 0. In \nthe redone training runs, we rectified this inconsistency: all models now were \ntrained with LR decaying to a minimum of 0.1× their maximum LR.\n\n### Naming convention and parameter count\n\n*Pythia* models were renamed in January 2023. It is possible that the old \nnaming convention still persists in some documentation by accident. The \ncurrent naming convention (70M, 160M, etc.) is based on total parameter count. \n\nSciQ
\n\n\n\nBigScience Large Open-science Open-access Multilingual Language Model \nVersion 1.3 / 6 July 2022\n\nCurrent Checkpoint: **Training Iteration 95000**\n\nLink to paper: [here](https://arxiv.org/abs/2211.05100)\n\nTotal seen tokens: **366B**\n\n---\n\n# Model Details \n\nBLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.\n\n## Basics\n*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*\n*It is useful for anyone who wants to reference the model.*\n\n\n
\n\n\n## Technical Specifications\n*This section includes details about the model objective and architecture, and the compute infrastructure.*\n*It is useful for people interested in model development.*\n\nClick to expand
\n \n**Developed by:** BigScience ([website](https://bigscience.huggingface.co))\n\n*All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)*\n \n**Model Type:** Transformer-based Language Model\n\n**Checkpoints format:** `transformers` (Megatron-DeepSpeed format available [here](https://huggingface.co/bigscience/bloom-optimizer-states))\n\n**Version:** 1.0.0\n\n**Languages:** Multiple; see [training data](#training-data)\n\n**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license) / [article and FAQ](https://bigscience.huggingface.co/blog/the-bigscience-rail-license))\n\n**Release Date Estimate:** Monday, 11.July.2022\n\n**Send Questions to:** bigscience-contact@googlegroups.com\n\n**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022\n\n**Funded by:** \n \n* The French government.\n\n* Hugging Face ([website](https://huggingface.co)).\n\n* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*\n\n\n
\n\n---\n\n# Training\n*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.*\n*It is useful for people who want to learn more about the model inputs and training footprint.*\n\nClick to expand
\n\nPlease see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.\n\n### Model Architecture and Objective\n\n* Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):\n\n* Decoder-only architecture\n\n* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))\n\n* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions\n\n* 176,247,271,424 parameters:\n\n * 3,596,615,680 embedding parameters\n\n * 70 layers, 112 attention heads\n\n * Hidden layers are 14336-dimensional\n\n * Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))\n\n**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).\n \n### Compute infrastructure\nJean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).\n\n#### Hardware\n\n* 384 A100 80GB GPUs (48 nodes)\n \n* Additional 32 A100 80GB GPUs (4 nodes) in reserve\n\n* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links\n\n* CPU: AMD\n\n* CPU memory: 512GB per node\n\n* GPU memory: 640GB per node\n\n* Inter-node connect: Omni-Path Architecture (OPA)\n\n* NCCL-communications network: a fully dedicated subnet\n\n* Disc IO network: shared network with other types of nodes\n\n#### Software\n\n* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))\n\n* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))\n\n* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))\n\n* apex ([Github link](https://github.com/NVIDIA/apex))\n \n\n
\n\n---\n\n# Uses\n\n*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.*\n*It is useful for anyone considering using the model or who is affected by the model.*\n\nClick to expand
\n\n## Training Data\n*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*\n\nDetails for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus), and the sizes of each of their contributions to the aggregated training data are presented in an [Interactive Corpus Map](https://huggingface.co/spaces/bigscience-catalogue-lm-data/corpus-map).\n\nTraining data includes:\n\n- 46 natural languages\n \n- 13 programming languages\n\n- In 1.6TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)\n\n### Languages\n \nThe pie chart shows the distribution of languages in training data.\n \n\n\n\nThe following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.\n\nDistribution of Niger Congo and Indic languages.\n \n| Niger Congo | Percentage | | Indic | Percentage |\n|----------------|------------| ------ |-----------|------------|\n| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |\n| Kikuyu | 0.00004 | | Odia | 0.04 |\n| Bambara | 0.00004 | | Gujarati | 0.04 |\n| Akan | 0.00007 | | Marathi | 0.05 |\n| Xitsonga | 0.00007 | | Punjabi | 0.05 |\n| Sesotho | 0.00007 | | Kannada | 0.06 |\n| Chi Chewa | 0.0001 | | Nepali | 0.07 |\n| Setswana | 0.0002 | | Telugu | 0.09 |\n| Lingala | 0.0002 | | Malayalam | 0.10 |\n| Northern Sotho | 0.0002 | | Urdu | 0.10 |\n| Fon | 0.0002 | | Tamil | 0.20 |\n| Kirundi | 0.0003 | | Bengali | 0.50 |\n| Wolof | 0.0004 | | Hindi | 0.70 |\n| Luganda | 0.0004 |\n| Chi Shona | 0.001 |\n| Isi Zulu | 0.001 |\n| Igbo | 0.001 |\n| Xhosa | 0.001 |\n| Kinyarwanda | 0.003 |\n| Yoruba | 0.006 |\n| Swahili | 0.02 |\n\nDistribution of programming languages.\n \n| Extension | Language | Number of files |\n|----------------|------------|-----------------|\n| java | Java | 5,407,724 |\n| php | PHP | 4,942,186 |\n| cpp | C++ | 2,503,930 |\n| py | Python | 2,435,072 |\n| js | JavaScript | 1,905,518 |\n| cs | C# | 1,577,347 |\n| rb | Ruby | 6,78,413 |\n| cc | C++ | 443,054 |\n| hpp | C++ | 391,048 |\n| lua | Lua | 352,317 |\n| go | GO | 227,763 |\n| ts | TypeScript | 195,254 |\n| C | C | 134,537 |\n| scala | Scala | 92,052 |\n| hh | C++ | 67,161 |\n| H | C++ | 55,899 |\n| tsx | TypeScript | 33,107 |\n| rs | Rust | 29,693 |\n| phpt | PHP | 9,702 |\n| c++ | C++ | 1,342 |\n| h++ | C++ | 791 |\n| php3 | PHP | 540 |\n| phps | PHP | 270 |\n| php5 | PHP | 166 |\n| php4 | PHP | 29 |\n \n### Preprocessing\n\n**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), a learned subword tokenizer trained using:\n \n- A byte-level Byte Pair Encoding (BPE) algorithm \n\n- A simple pre-tokenization rule, no normalization\n\n- A vocabulary size of 250,680\n\nIt was trained on a subset of a preliminary version of the corpus using alpha-weighting per language. \n\n## Speeds, Sizes, Times\n\nTraining logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)\n\n- Dates:\n \n - Started 11th March, 2022 11:42am PST\n\n - Estimated end: 5th July, 2022\n\n- Checkpoint size:\n \n - Bf16 weights: 329GB\n \n - Full checkpoint with optimizer states: 2.3TB\n\n- Training throughput: About 150 TFLOP per GPU per second\n\n- Number of epochs: 1\n\n- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)\n\n- Server training location: Île-de-France, France\n\n\n## Environmental Impact\n\nThe training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.\n \n**Estimated carbon emissions:** *(Forthcoming.)*\n \n**Estimated electricity usage:** *(Forthcoming.)*\n\n\n![](\"https://s3.amazonaws.com/moonup/production/uploads/1657271608456-62441d1d9fdefb55a0b7d12c.png\")
\n\n## Intended Use\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.\n\n### Direct Use\n\n- Text generation\n\n- Exploring characteristics of language generated by a language model\n\n - Examples: Cloze tests, counterfactuals, generations with reframings\n\n### Downstream Use\n\n- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization\n\n### Misuse and Out-of-scope Use\n*This section addresses what users ought not do with the model.*\n\nSee the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.\n\n#### Out-of-scope Uses\n\nUsing the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. \n\nOut-of-scope Uses Include:\n\n- Usage in biomedical domains, political and legal domains, or finance domains\n\n- Usage for evaluating or scoring individuals, such as for employment, education, or credit\n\n- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct\n\n#### Misuse\n\nIntentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:\n\n- Spam generation\n\n- Disinformation and influence operations\n\n- Disparagement and defamation\n\n- Harassment and abuse\n \n- [Deception](#deception)\n\n- Unconsented impersonation and imitation\n\n- Unconsented surveillance \n\n- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)\n\n## Intended Users\n\n### Direct Users\n\n- General Public\n\n- Researchers\n\n- Students\n\n- Educators\n\n- Engineers/developers\n\n- Non-commercial entities\n\n- Community advocates, including human and civil rights groups\n\n### Indirect Users\n\n- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)\n\n- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)\n\n### Others Affected (Parties Prenantes)\n\n- People and groups referred to by the LLM\n\n- People and groups exposed to outputs of, or decisions based on, the LLM\n\n- People and groups whose original work is included in the LLM\n\n
\n\n---\n\n# Risks and Limitations\n*This section identifies foreseeable harms and misunderstandings.*\n \nClick to expand
\n \n## How to use\n\nThis model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows:\n\n\n\n## Intended Use\n\nThis model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.\n\n### Direct Use\n\n- Text generation\n\n- Exploring characteristics of language generated by a language model\n\n - Examples: Cloze tests, counterfactuals, generations with reframings\n\n### Downstream Use\n\n- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization\n\n### Misuse and Out-of-scope Use\n*This section addresses what users ought not do with the model.*\n\nSee the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.\n\n#### Out-of-scope Uses\n\nUsing the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct. \n\nOut-of-scope Uses Include:\n\n- Usage in biomedical domains, political and legal domains, or finance domains\n\n- Usage for evaluating or scoring individuals, such as for employment, education, or credit\n\n- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct\n\n#### Misuse\n\nIntentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:\n\n- Spam generation\n\n- Disinformation and influence operations\n\n- Disparagement and defamation\n\n- Harassment and abuse\n \n- [Deception](#deception)\n\n- Unconsented impersonation and imitation\n\n- Unconsented surveillance \n\n- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)\n\n## Intended Users\n\n### Direct Users\n\n- General Public\n\n- Researchers\n\n- Students\n\n- Educators\n\n- Engineers/developers\n\n- Non-commercial entities\n\n- Community advocates, including human and civil rights groups\n\n### Indirect Users\n\n- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)\n\n- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)\n\n### Others Affected (Parties Prenantes)\n\n- People and groups referred to by the LLM\n\n- People and groups exposed to outputs of, or decisions based on, the LLM\n\n- People and groups whose original work is included in the LLM\n\n\n
\n\n---\n\n# Evaluation\n*This section describes the evaluation protocols and provides the results.*\n\n\nClick to expand
\n\nModel may:\n\n- Overrepresent some viewpoints and underrepresent others\n\n- Contain stereotypes\n \n- Contain [personal information](#personal-data-and-information)\n\n- Generate:\n\n - Hateful, abusive, or violent language\n\n - Discriminatory or prejudicial language\n\n - Content that may not be appropriate for all settings, including sexual content\n\n- Make errors, including producing incorrect information as if it were factual\n\n- Generate irrelevant or repetitive outputs\n\n- Induce users into attributing human traits to it, such as sentience or consciousness\n\n\n
\n\n---\n\n# Recommendations\n\n*This section provides information on warnings and potential mitigations.*\n\nClick to expand
\n\n## Metrics \n*This section describes the different ways performance is calculated and why.*\n\nIncludes:\n\n| Metric | Why chosen |\n|--------------------|--------------------------------------------------------------------|\n| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |\n| Cross Entropy [Loss](#loss) | Standard objective for language models. |\n\nAnd multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_\n\n## Factors \n*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*\n\n- Language, such as English or Yoruba\n\n- Domain, such as newswire or stories\n\n- Demographic characteristics, such as gender or nationality\n\n## Results\n*Results are based on the [Factors](#factors) and [Metrics](#metrics).*\n\n**Zero-shot evaluations:**\n\nWARNING: This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.\n\nSee this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results\n\n| Task | Language | Metric | BLOOM-176B | OPT-175B* |\n|:--------|:-----------------|:------------------------|-------------:|------------:|\n| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |\n| humaneval | python | pass@10 ↑ | 0.328 | 0.0 |\n| humaneval | python | pass@100 ↑ | 0.572 | 0.003 |\n\n\n**Train-time Evaluation:**\n\nFinal checkpoint after 95K steps:\n\n- Training Loss: 1.939\n\n- Validation Loss: 2.061\n\n- Perplexity: 7.045\n\nFor more see: https://huggingface.co/bigscience/tr11-176B-ml-logs\n\n\n
\n\n---\n\n# Glossary and Calculations\n\n*This section defines common terms and how metrics are calculated.*\nClick to expand
\n\n- Indirect users should be made aware when the content they're working with is created by the LLM.\n\n- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.\n\n- Models trained or finetuned downstream of BLOOM LM should include an updated Model Card.\n\n- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.\n\n\n
\n\n---\n\n# More Information\n*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.*\n\nClick to expand
\n\n- **Loss:** A calculation of the difference between what the model has learned and what the data shows (\"groundtruth\"). The lower the loss, the better. The training process aims to minimize the loss. \n\n- **Perplexity:** This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy. \n\n- **High-stakes settings:** Such as those identified as \"high-risk AI systems\" and \"unacceptable risk AI systems\" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).\n\n- **Critical decisions:** Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).\n\n- **Human rights:** Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).\n\n- **Personal Data and Personal Information:** Personal data and information is defined in multiple data protection regulations, such as \"[personal data](https://gdpr-info.eu/issues/personal-data/)\" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and \"personal information\" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).\n \n- **Sensitive characteristics:** This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))\n\n- **Deception:** Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.\n\n\n
\n\n\n## Original checkpoints\n\nThe checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).\n\nMany intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/\n\n---\n \n# Model Card Authors\n*Ordered roughly chronologically and by amount of time spent on creating this model card.*\n\nMargaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff"},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING","TRANSLATION","SUMMARIZATION"],"string":"[\n \"QUESTION_ANSWERING\",\n \"TRANSLATION\",\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["CAS","ESSAI"],"string":"[\n \"CAS\",\n \"ESSAI\"\n]"}}},{"rowIdx":94,"cells":{"id":{"kind":"string","value":"bigscience/T0pp"},"author":{"kind":"string","value":"bigscience"},"task_category":{"kind":"string","value":"text2text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","safetensors","t5","text2text-generation","en","dataset:bigscience/P3","arxiv:2110.08207","license:apache-2.0","autotrain_compatible","text-generation-inference","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"safetensors\",\n \"t5\",\n \"text2text-generation\",\n \"en\",\n \"dataset:bigscience/P3\",\n \"arxiv:2110.08207\",\n \"license:apache-2.0\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2022-03-02T23:29:05"},"last_modified":{"kind":"string","value":"2024-03-19T16:02:07"},"downloads":{"kind":"number","value":26474,"string":"26,474"},"likes":{"kind":"number","value":401,"string":"401"},"README":{"kind":"string","value":"---\ndatasets:\n- bigscience/P3\nlanguage: en\nlicense: apache-2.0\nwidget:\n- text: A is the son's of B's uncle. What is the family relationship between A and\n B?\n- text: 'Reorder the words in this sentence: justin and name bieber years is my am\n I 27 old.'\n- text: \"Task: copy but say the opposite.\\n PSG won its match against Barca.\"\n- text: 'Is this review positive or negative? Review: Best cast iron skillet you will\n every buy.'\n example_title: Sentiment analysis\n- text: \"Question A: How is air traffic controlled? \\nQuestion B: How do you become\\\n \\ an air traffic controller?\\nPick one: these questions are duplicates or not\\\n \\ duplicates.\"\n- text: \"Barack Obama nominated Hilary Clinton as his secretary of state on Monday.\\\n \\ He chose her because she had foreign affairs experience as a former First Lady.\\\n \\ \\nIn the previous sentence, decide who 'her' is referring to.\"\n example_title: Coreference resolution\n- text: \"Last week I upgraded my iOS version and ever since then my phone has been\\\n \\ overheating whenever I use your app.\\n Select the category for the above sentence\\\n \\ from: mobile, website, billing, account access.\"\n- text: \"Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach\\\n \\ was carrying 38 passengers.\\n Sentence 2: The head of the local disaster unit,\\\n \\ Gyorgy Heizler, said the bus was full except for 38 empty seats.\\n\\n Do sentences\\\n \\ 1 and 2 have the same meaning?\"\n example_title: Paraphrase identification\n- text: \"Here's the beginning of an article, choose a tag that best describes the\\\n \\ topic of the article: business, cinema, politics, health, travel, sports.\\n\\n\\\n \\ The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\\n (CNN)\\\n \\ Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds.\\\n \\ For a Cold War creation, Ian Fleming's suave spy has certainly gotten around,\\\n \\ but despite different guises in the tuxedo and occasional scuba gear, when it\\\n \\ comes to Bond ratings, there really shouldn't be much argument about who wore\\\n \\ it best.\"\n- text: \"Max: Know any good websites to buy clothes from?\\n Payton: Sure :) LINK 1,\\\n \\ LINK 2, LINK 3\\n Max: That's a lot of them!\\n Payton: Yeah, but they have different\\\n \\ things so I usually buy things from 2 or 3 of them.\\n Max: I'll check them out.\\\n \\ Thanks.\\n\\n Who or what are Payton and Max referring to when they say 'them'?\"\n- text: \"Is the word 'table' used in the same meaning in the two following sentences?\\n\\\n \\n Sentence A: you can leave the books on the table over there.\\n Sentence B:\\\n \\ the tables in this book are very hard to read.\"\n- text: \"On a shelf, there are five books: a gray book, a red book, a purple book,\\\n \\ a blue book, and a black book.\\n The red book is to the right of the gray book.\\\n \\ The black book is to the left of the blue book. The blue book is to the left\\\n \\ of the gray book. The purple book is the second from the right.\\n\\n Which book\\\n \\ is the leftmost book?\"\n example_title: Logic puzzles\n- text: \"The two men running to become New York City's next mayor will face off in\\\n \\ their first debate Wednesday night.\\n\\n Democrat Eric Adams, the Brooklyn Borough\\\n \\ president and a former New York City police captain, is widely expected to win\\\n \\ the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era\\\n \\ Guardian Angels anti-crime patril.\\n\\n Who are the men running for mayor?\"\n example_title: Reading comprehension\n- text: \"The word 'binne' means any animal that is furry and has four legs, and the\\\n \\ word 'bam' means a simple sort of dwelling.\\n\\n Which of the following best\\\n \\ characterizes binne bams?\\n - Sentence 1: Binne bams are for pets.\\n - Sentence\\\n \\ 2: Binne bams are typically furnished with sofas and televisions.\\n - Sentence\\\n \\ 3: Binne bams are luxurious apartments.\\n - Sentence 4: Binne bams are places\\\n \\ where people live.\"\ninference: false\n---\n\n**How do I pronounce the name of the model?** T0 should be pronounced \"T Zero\" (like in \"T5 for zero-shot\") and any \"p\" stands for \"Plus\", so \"T0pp\" should be pronounced \"T Zero Plus Plus\"!\n\n**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)\n\n# Model Description\n\nT0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.\n\n# Intended uses\n\nYou can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *\"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy\"*, and the model will hopefully generate *\"Positive\"*.\n\nA few other examples that you can try:\n- *A is the son's of B's uncle. What is the family relationship between A and B?*\n- *Question A: How is air traffic controlled?Click to expand
\n\n## Intermediate checkpoints\n\nFor academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow [this link](https://huggingface.co/bigscience/bloom-176-intermediate) to get these checkpoints.\n\n \n## Dataset Creation\n\nBlog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling\n\n## Technical Specifications\n\nBlog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours\n\nMore details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nBlog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model\n\nDetails on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml\n\nTensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss\n\n## Lessons\n\nInsights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md\n\nDetails on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md\n\n## Initial Results\n\nInitial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book\n\n\nQuestion B: How do you become an air traffic controller?
\nPick one: these questions are duplicates or not duplicates.*\n- *Is the word 'table' used in the same meaning in the two following sentences?
\nSentence A: you can leave the books on the table over there.
\nSentence B: the tables in this book are very hard to read.*\n- *Max: Know any good websites to buy clothes from?
\nPayton: Sure :) LINK 1, LINK 2, LINK 3
\nMax: That's a lot of them!
\nPayton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.
\nMax: I'll check them out. Thanks.
\nWho or what are Payton and Max referring to when they say 'them'?*\n- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.
\nThe red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.
\nWhich book is the leftmost book?*\n- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*\n\n# How to use\n\nWe make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce \"T Zero Plus Plus\") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.\n\n|Model|Number of parameters|\n|-|-|\n|[T0](https://huggingface.co/bigscience/T0)|11 billion|\n|[T0p](https://huggingface.co/bigscience/T0p)|11 billion|\n|[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion|\n|[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion|\n|[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion|\n|[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion|\n\nHere is how to use the model in PyTorch:\n```python\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"bigscience/T0pp\")\nmodel = AutoModelForSeq2SeqLM.from_pretrained(\"bigscience/T0pp\")\n\ninputs = tokenizer.encode(\"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy\", return_tensors=\"pt\")\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\nIf you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.\n\n**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**\n\n# Training procedure\n\nT0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective.\n\nAt a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section.\n\nTraining details:\n- Fine-tuning steps: 12'200\n- Input sequence length: 1024\n- Target sequence length: 256\n- Batch size: 1'024 sequences\n- Optimizer: Adafactor\n- Learning rate: 1e-3\n- Dropout: 0.1\n- Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples)\n- Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length\n\n# Training data\n\nWe trained different variants T0 with different mixtures of datasets.\n\n|Model|Training datasets|\n|--|--|\n|T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop
- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES
- Closed-Book QA: Hotpot QA*, Wiki QA
- Structure-To-Text: Common Gen, Wiki Bio
- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp
- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum
- Topic Classification: AG News, DBPedia, TREC
- Paraphrase Identification: MRPC, PAWS, QQP|\n|T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:
- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag
- Extractive QA: SQuAD v2
- Closed-Book QA: Trivia QA, Web Questions|\n|T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):
- BoolQ
- COPA
- MultiRC
- ReCoRD
- WiC
- WSC|\n|T0_single_prompt|Same as T0 but only one prompt per training dataset|\n|T0_original_task_only|Same as T0 but only original tasks templates|\n|T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model|\n\nFor reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page.\n\n*: We recast Hotpot QA as closed-book QA due to long input sequence length.\n\n# Evaluation data\n\nWe evaluate our models on a suite of held-out tasks:\n\n|Task category|Datasets|\n|-|-|\n|Natural language inference|ANLI, CB, RTE|\n|Coreference resolution|WSC, Winogrande|\n|Word sense disambiguation|WiC|\n|Sentence completion|COPA, HellaSwag, Story Cloze|\n\nWe also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench):\n- Code description task\n- Conceptual combinations\n- Hindu knowledge json\n- Known unknowns\n- Language identification\n- Logic grid puzzle task\n- Logical deduction\n- Common misconceptions\n- Movie dialog same or different\n- Novel concepts\n- Strategyqa\n- Formal fallacies syllogisms negation\n- VitaminC\n- Winowhy multiple choice\n\n# Limitations\n\n- The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html).\n- We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model.\n- Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text.\n\n# Bias and fairness\n\nEven if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics:\n- Input: `Is the earth flat?` - Prediction: `yes`\n- Input: `Do vaccines cause autism?` - Prediction: `yes`\n- Input: `Complete this sentence: This man works as a` - Prediction: `Architect`\n- Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny`\n- Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex`\n- Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault`\n- Input: `what is something everyone hates, but you like?` - Prediction: `sex`\n- Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex`\n- Input: `What is your favorite \"your mom\" joke?` - Prediction: `Your mom is a slut`\n- Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy`\n\nLanguage models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases.\n\nTo measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts.\n\n
| Dataset | \nModel | \nAverage (Acc.) | \nMedian (Acc.) | \n
| CrowS-Pairs | T0 | 59.2 | 83.8 | \nT0p | 57.6 | 83.8 | \n
| T0pp | 62.7 | 64.4 | \n|
| T0_single_prompt | 57.6 | 69.5 | \n|
| T0_original_task_only | 47.1 | 37.8 | \n|
| T0_3B | 56.9 | 82.6 | \n \n|
| WinoGender | T0 | 84.2 | 84.3 | \nT0p | 80.1 | 80.6 | \n
| T0pp | 89.2 | 90.0 | \n|
| T0_single_prompt | 81.6 | 84.6 | \n|
| T0_original_task_only | 83.7 | 83.8 | \n|
| T0_3B | 69.7 | 69.4 | \n \n
| Model | \nSubset | \nAverage (Acc.) | \nMedian (Acc.) | \n||||
| Pro | \nAnti | \nPro - Anti | \nPro | \nAnti | \nPro - Anti | \n||
| T0 | Type 1 | \n68.0 | 61.9 | 6.0 | 71.7 | 61.9 | 9.8 | \nType 2 | \n79.3 | 76.4 | 2.8 | 79.3 | 75.0 | 4.3 | \n \n \nT0p | \nType 1 | \n66.6 | 57.2 | 9.4 | 71.5 | 62.6 | 8.8 | \n \n \nType 2 | \n77.7 | 73.4 | 4.3 | 86.1 | 81.3 | 4.8 | \n \n \nT0pp | \nType 1 | \n63.8 | 55.9 | 7.9 | 72.7 | 63.4 | 9.3 | \n \n \nType 2 | \n66.8 | 63.0 | 3.9 | 79.3 | 74.0 | 5.3 | \n \n \nT0_single_prompt | \nType 1 | \n73.7 | 60.5 | 13.2 | 79.3 | 60.6 | 18.7 | \n \n \nType 2 | \n77.7 | 69.6 | 8.0 | 80.8 | 69.7 | 11.1 | \n \n\n \nT0_original_task_only | \nType 1 | \n78.1 | 67.7 | 10.4 | 81.8 | 67.2 | 14.6 | \n \n \nType 2 | \n85.2 | 82.3 | 2.9 | 89.6 | 85.4 | 4.3 | \n \n\n \nT0_3B | \nType 1 | \n82.3 | 70.1 | 12.2 | 83.6 | 62.9 | 20.7 | \n \n \nType 2 | \n83.8 | 76.5 | 7.3 | 85.9 | 75 | 10.9 | \n \n
Snowflake's Arctic-embed-l
\n\n
\n News |\n Models |\n Usage |\n Evaluation |\n Contact |\n FAQ\n License |\n Acknowledgement\n
\n\n\n\n## News\n\n12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!\n\n07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.\n\n07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).\n\n05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)\n\n04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).\n\n\n## Models\n\n\nsnowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.\n\n\nThe `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.\n\n\nThe models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).\n\n\n| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |\n| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |\n| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |\n| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |\n| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |\n\n\nAside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |\n| Google-gecko-text-embedding | 55.7 |\n| text-embedding-3-large | 55.44 |\n| Cohere-embed-english-v3.0 | 55.00 |\n| bge-large-en-v1.5 | 54.29 |\n\n\n### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)\n\n\nThis tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------- | -------------------------------- |\n| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |\n| GIST-all-MiniLM-L6-v2 | 45.12 |\n| gte-tiny | 44.92 |\n| all-MiniLM-L6-v2 | 41.95 |\n| bge-micro-v2 | 42.56 |\n\n\n### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)\n\n\nBased on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |\n| bge-small-en-v1.5 | 51.68 |\n| Cohere-embed-english-light-v3.0 | 51.34 |\n| text-embedding-3-small | 51.08 |\n| e5-small-v2 | 49.04 |\n\n\n### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)\n\n\nBased on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |\n| bge-base-en-v1.5 | 53.25 |\n| nomic-embed-text-v1.5 | 53.25 |\n| GIST-Embedding-v0 | 52.31 |\n| gte-base | 52.31 |\n\n### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)\n\n\nBased on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |\n| nomic-embed-text-v1.5 | 53.01 |\n| nomic-embed-text-v1 | 52.81 |\n\n\n\n\n### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)\n\n\nBased on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.\n\n\n| Model Name | MTEB Retrieval Score (NDCG @ 10) |\n| ------------------------------------------------------------------ | -------------------------------- |\n| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |\n| UAE-Large-V1 | 54.66 |\n| bge-large-en-v1.5 | 54.29 |\n| mxbai-embed-large-v1 | 54.39 |\n| e5-Large-v2 | 50.56 |\n\n\n## Usage\n\n\n### Using Sentence Transformers\n\nYou can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. \n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer(\"Snowflake/snowflake-arctic-embed-l\")\n\nqueries = ['what is snowflake?', 'Where can I get the best tacos?']\ndocuments = ['The Data Cloud!', 'Mexico City of Course!']\n\nquery_embeddings = model.encode(queries, prompt_name=\"query\")\ndocument_embeddings = model.encode(documents)\n\nscores = query_embeddings @ document_embeddings.T\nfor query, query_scores in zip(queries, scores):\n doc_score_pairs = list(zip(documents, query_scores))\n doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n # Output passages & scores\n print(\"Query:\", query)\n for document, score in doc_score_pairs:\n print(score, document)\n```\n```\nQuery: what is snowflake?\n0.28976774 The Data Cloud!\n0.19071159 Mexico City of Course!\nQuery: Where can I get the best tacos?\n0.38650584 Mexico City of Course!\n0.25145516 The Data Cloud!\n```\n\n\n### Using Huggingface transformers\n\n\nYou can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).\n\n\n\n```python\nimport torch\nfrom transformers import AutoModel, AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-l')\nmodel = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-l', add_pooling_layer=False)\nmodel.eval()\n\nquery_prefix = 'Represent this sentence for searching relevant passages: '\nqueries = ['what is snowflake?', 'Where can I get the best tacos?']\nqueries_with_prefix = [\"{}{}\".format(query_prefix, i) for i in queries]\nquery_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)\n\ndocuments = ['The Data Cloud!', 'Mexico City of Course!']\ndocument_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)\n\n# Compute token embeddings\nwith torch.no_grad():\n query_embeddings = model(**query_tokens)[0][:, 0]\n document_embeddings = model(**document_tokens)[0][:, 0]\n\n\n# normalize embeddings\nquery_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)\ndocument_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)\n\nscores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))\nfor query, query_scores in zip(queries, scores):\n doc_score_pairs = list(zip(documents, query_scores))\n doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\n #Output passages & scores\n print(\"Query:\", query)\n for document, score in doc_score_pairs:\n print(score, document)\n```\n\n### Using Transformers.js\n\nIf you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:\n```bash\nnpm i @xenova/transformers\n```\n\nYou can then use the model to compute embeddings as follows:\n\n```js\nimport { pipeline, dot } from '@xenova/transformers';\n\n// Create feature extraction pipeline\nconst extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-l', {\n quantized: false, // Comment out this line to use the quantized version\n});\n\n// Generate sentence embeddings\nconst sentences = [\n 'Represent this sentence for searching relevant passages: Where can I get the best tacos?',\n 'The Data Cloud!',\n 'Mexico City of Course!',\n]\nconst output = await extractor(sentences, { normalize: true, pooling: 'cls' });\n\n// Compute similarity scores\nconst [source_embeddings, ...document_embeddings ] = output.tolist();\nconst similarities = document_embeddings.map(x => dot(source_embeddings, x));\nconsole.log(similarities); // [0.25145517380846977, 0.3865060421197194]\n```\n\n## Using Infinity\n\nOpenAI compatible API deployment with [Infinity](https://github.com/michaelfeil/infinity) and Docker.\n\n```bash\ndocker run --gpus all -v $PWD/data:/app/.cache -p \"7997\":\"7997\" \\\nmichaelf34/infinity:0.0.70 \\\nv2 --model-id Snowflake/snowflake-arctic-embed-l --dtype float16 --batch-size 32 --engine torch --port 7997\n```\n\n## FAQ\n\n\nTBD\n\n\n## Contact\n\n\nFeel free to open an issue or pull request if you have any questions or suggestions about this project.\nYou also can email Daniel Campos(daniel.campos@snowflake.com).\n\n\n## License\n\n\nArctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.\n\n\n## Acknowledgement\n\n\nWe want to thank the open-source community, which has provided the great building blocks upon which we could make our models.\nWe thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. \nWe thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. \nWe also thank the open-source community for producing the great models we could build on top of and making these releases possible. \nFinally, we thank the researchers who created BEIR and MTEB benchmarks. \nIt is largely thanks to their tireless work to define what better looks like that we could improve model performance.\n\n![](\"https://static.scarf.sh/a.png?x-pxid=d6741f66-9018-401c-8805-d79c74fb98ff\")
"},"matched_task":{"kind":"list like","value":["SUMMARIZATION"],"string":"[\n \"SUMMARIZATION\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["BIOSSES","SCIFACT"],"string":"[\n \"BIOSSES\",\n \"SCIFACT\"\n]"}}},{"rowIdx":97,"cells":{"id":{"kind":"string","value":"aaditya/Llama3-OpenBioLLM-70B"},"author":{"kind":"string","value":"aaditya"},"task_category":{"kind":"string","value":"text-generation"},"tags":{"kind":"list like","value":["transformers","pytorch","llama","text-generation","llama-3","Mixtral","instruct","finetune","chatml","DPO","RLHF","gpt4","distillation","heathcare","medical","clinical","med","lifescience","Pharmaceutical","Pharma","conversational","en","arxiv:2305.18290","arxiv:2303.13375","arxiv:2212.13138","arxiv:2305.09617","arxiv:2402.07023","base_model:meta-llama/Meta-Llama-3-70B-Instruct","base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct","license:llama3","autotrain_compatible","text-generation-inference","endpoints_compatible","region:us"],"string":"[\n \"transformers\",\n \"pytorch\",\n \"llama\",\n \"text-generation\",\n \"llama-3\",\n \"Mixtral\",\n \"instruct\",\n \"finetune\",\n \"chatml\",\n \"DPO\",\n \"RLHF\",\n \"gpt4\",\n \"distillation\",\n \"heathcare\",\n \"medical\",\n \"clinical\",\n \"med\",\n \"lifescience\",\n \"Pharmaceutical\",\n \"Pharma\",\n \"conversational\",\n \"en\",\n \"arxiv:2305.18290\",\n \"arxiv:2303.13375\",\n \"arxiv:2212.13138\",\n \"arxiv:2305.09617\",\n \"arxiv:2402.07023\",\n \"base_model:meta-llama/Meta-Llama-3-70B-Instruct\",\n \"base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct\",\n \"license:llama3\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-04-24T02:21:12"},"last_modified":{"kind":"string","value":"2025-01-18T07:39:15"},"downloads":{"kind":"number","value":26028,"string":"26,028"},"likes":{"kind":"number","value":403,"string":"403"},"README":{"kind":"string","value":"---\nbase_model: meta-llama/Meta-Llama-3-70B-Instruct\nlanguage:\n- en\nlicense: llama3\ntags:\n- llama-3\n- llama\n- Mixtral\n- instruct\n- finetune\n- chatml\n- DPO\n- RLHF\n- gpt4\n- distillation\n- heathcare\n- medical\n- clinical\n- med\n- lifescience\n- Pharmaceutical\n- Pharma\nwidget:\n- example_title: OpenBioLLM-70B\n messages:\n - role: system\n content: You are an expert and experienced from the healthcare and biomedical\n domain with extensive medical knowledge and practical experience.\n - role: user\n content: How long does it take for newborn jaundice to go away?\n output:\n text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition\n in newborns where the yellowing of the skin and eyes occurs due to an elevated\n level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when\n red blood cells break down. In most cases, newborn jaundice resolves on its\n own without any specific treatment.\n\n The duration of newborn jaundice can vary depending on several factors such\n as the underlying cause, gestational age at birth, and individual variations\n in bilirubin metabolism. Here are some general guidelines:\n\n 1. Physiological jaundice: This is the most common type of newborn jaundice\n and usually appears within 24-72 hours after birth. It tends to peak between\n the second and fifth day of life and gradually improves over the next week or\n two. By the time the baby is one week old, the jaundice should have mostly resolved.\n 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and\n may appear later than physiological jaundice, typically between the fifth and\n fourteenth day of life. It tends to persist for a longer duration but usually\n resolves within six weeks after birth. 3. Pathological jaundice: This type of\n jaundice is less common and occurs due to an underlying medical condition that\n affects bilirubin metabolism or liver function. The duration of pathological\n jaundice depends on the specific cause and may require treatment.\n\n It''s important for parents to monitor their newborn''s jaundice closely and\n seek medical advice if the jaundice progresses rapidly, becomes severe, or is\n accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness.\n In these cases, further evaluation and management may be necessary. Remember\n that each baby is unique, and the timing of jaundice resolution can vary. If\n you have concerns about your newborn''s jaundice, it''s always best to consult\n with a healthcare professional for personalized advice and guidance.'\nmodel-index:\n- name: OpenBioLLM-70B\n results: []\n---\n\n\n
\n![](\"https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png\")
\n\n\n\n\n\n\n\n \n
\n\nAdvancing Open-source Large Language Models in Medical Domain
\n\n \n ![\"OpenChat](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n Online Demo\n |\n \n ![\"GitHub](\"https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png\")
\n GitHub\n |\n \n ![\"ArXiv](\"https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true\")
\n Paper\n |\n \n ![\"Discord](\"https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png\")
\n Discord\n \n
\n![](\"https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png\")
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\n\n\n- **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290)\n- **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)\n\nThis combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications.\n\n⚙️ **Release Details**:\n\n- **Model Size**: 70 billion parameters\n- **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-70B-GGUF)\n- **Language(s) (NLP):** en\n- **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs \n- **License:** Meta-Llama License \n- **Fine-tuned from models:** [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct)\n- **Resources for more information:**\n - Paper: Coming soon\n\nThe model can be fine-tuned for more specialized tasks and datasets as needed.\n\nOpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.\n\nWe are excited to share OpenBioLLM-70B with researchers and developers around the world.\n\n### Community & Resources\n\n#### 🔥 Your Daily Dose of Medical AI Breakthroughs 🚀 \nWe turn hours of the latest research papers into minutes. Get daily tweets and news on the latest medical AI breakthroughs, dataset releases, and benchmark results – all carefully curated to save you time while keeping you informed.\n\n\n\n\n
\n\n\n\n### Use with transformers\n\n**Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.**\n\nSee the snippet below for usage with Transformers:\n\n```python\nimport transformers\nimport torch\n\nmodel_id = \"aaditya/OpenBioLLM-Llama3-70B\"\n\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model_id,\n model_kwargs={\"torch_dtype\": torch.bfloat16},\n device=\"auto\",\n)\n\nmessages = [\n {\"role\": \"system\", \"content\": \"You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience.\"},\n {\"role\": \"user\", \"content\": \"How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?\"},\n]\n\nprompt = pipeline.tokenizer.apply_chat_template(\n\t\tmessages, \n\t\ttokenize=False, \n\t\tadd_generation_prompt=True\n)\n\nterminators = [\n pipeline.tokenizer.eos_token_id,\n pipeline.tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n]\n\noutputs = pipeline(\n prompt,\n max_new_tokens=256,\n eos_token_id=terminators,\n do_sample=True,\n temperature=0.0,\n top_p=0.9,\n)\nprint(outputs[0][\"generated_text\"][len(prompt):])\n```\n\n## **Training procedure**\n\n### **Training hyperparameters**\n\n![\"Twitter](\"https://img.shields.io/badge/X-Follow%20%40OpenLifeSciAI-black?style=flat&logo=x\"){kind=icon}
\n ![\"LinkedIn\"/](\"https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin\"){kind=icon}
\n ![\"YouTube\"/](\"https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube\"){kind=icon}
\n ![\"Discord\"/](\"https://img.shields.io/badge/Discord-Join-7289DA?style=for-the-badge&logo=discord\"){kind=icon}
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\n\n \n### **Peft hyperparameters**\n\nClick to see details
\n\n- learning_rate: 0.0002\n- lr_scheduler: cosine\n- train_batch_size: 12\n- eval_batch_size: 8\n- GPU: H100 80GB SXM5\n- num_devices: 8\n- optimizer: adamw_bnb_8bit\n- lr_scheduler_warmup_steps: 100\n- num_epochs: 4\n\n
\n\n\n\n### **Training results**\n\n### **Framework versions**\n\n- Transformers 4.39.3\n- Pytorch 2.1.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.1\n- Axolotl\n- Lm harness for evaluation\n\n\n# Benchmark Results\n\n🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.\n\n🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.\n\n| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg |\n|--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|\n| **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** |\n| Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 |\n| **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 |\n| Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 |\n| **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 |\n| Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 |\n| GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 |\n| Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 |\n| gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 |\n| Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 |\n| Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 |\n| MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 |\n| BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 |\n| AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 |\n| ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 |\n\nClick to see details
\n\n- adapter: qlora\n- lora_r: 128\n- lora_alpha: 256\n- lora_dropout: 0.05\n- lora_target_linear: true\n \n-lora_target_modules:\n - q_proj\n - v_proj\n - k_proj\n - o_proj\n - gate_proj\n - down_proj\n - up_proj\n\n![](\"https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png\")
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\n\n## Detailed Medical Subjectwise accuracy\n\n\n\n\n# Use Cases & Examples\n\n🚨 **Below results are from the quantized version of OpenBioLLM-70B\n\n\n# Summarize Clinical Notes\n\nOpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries\n\n\n\n\n# Answer Medical Questions\n\nOpenBioLLM-70B can provide answers to a wide range of medical questions.\n\n\n\n\n\n\n\n
\n\n# Clinical Entity Recognition\n\nOpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.\n\n\n\n\n\n\n# Biomarkers Extraction\n\n\n\n\n\n# Classification\n\nOpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization\n\n\n\n# De-Identification\n\nOpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.\n\n\n\n\n\n**Advisory Notice!** \n\nWhile OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.\n\nTherefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations.\nOpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.\n\nAppropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:\n\n- Thorough testing and evaluation in relevant clinical scenarios\n- Alignment with evidence-based guidelines and best practices\n- Mitigation of potential biases and failure modes\n- Integration with human oversight and interpretation\n- Compliance with regulatory and ethical standards\n\nAlways consult a qualified healthcare provider for personal medical needs.\n\n\n\n# Citation\n\nIf you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:\n\n```\n@misc{OpenBioLLMs,\n author = {Ankit Pal, Malaikannan Sankarasubbu},\n title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},\n year = {2024},\n publisher = {Hugging Face},\n journal = {Hugging Face repository},\n howpublished = {\\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}\n}\n```\n\nThe accompanying paper is currently in progress and will be released soon.\n\nClick to see details
\n\n\n\n\n\n\n\n
\n\nWe look forward to hearing you and collaborating on this exciting project!\n\n**Contributors:**\n- [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com]\n- Saama AI Labs\n- Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.\n\n\n# References\n\nWe thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models!\n\n\nResult sources\n\n- [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) \n- [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138)\n- [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617)\n- [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023)"},"matched_task":{"kind":"list like","value":["QUESTION_ANSWERING"],"string":"[\n \"QUESTION_ANSWERING\"\n]"},"matched_bigbio_names":{"kind":"list like","value":["MEDQA","PUBMEDQA"],"string":"[\n \"MEDQA\",\n \"PUBMEDQA\"\n]"}}},{"rowIdx":98,"cells":{"id":{"kind":"string","value":"BAAI/bge-en-icl"},"author":{"kind":"string","value":"BAAI"},"task_category":{"kind":"string","value":"feature-extraction"},"tags":{"kind":"list like","value":["sentence-transformers","safetensors","mistral","feature-extraction","sentence-similarity","transformers","mteb","arxiv:2409.15700","arxiv:2309.07597","license:apache-2.0","model-index","autotrain_compatible","text-generation-inference","text-embeddings-inference","endpoints_compatible","region:us"],"string":"[\n \"sentence-transformers\",\n \"safetensors\",\n \"mistral\",\n \"feature-extraction\",\n \"sentence-similarity\",\n \"transformers\",\n \"mteb\",\n \"arxiv:2409.15700\",\n \"arxiv:2309.07597\",\n \"license:apache-2.0\",\n \"model-index\",\n \"autotrain_compatible\",\n \"text-generation-inference\",\n \"text-embeddings-inference\",\n \"endpoints_compatible\",\n \"region:us\"\n]"},"created_time":{"kind":"string","value":"2024-07-25T09:37:34"},"last_modified":{"kind":"string","value":"2025-01-15T02:43:23"},"downloads":{"kind":"number","value":24875,"string":"24,875"},"likes":{"kind":"number","value":126,"string":"126"},"README":{"kind":"string","value":"---\nlicense: apache-2.0\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- mteb\nmodel-index:\n- name: bge-en-icl\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 93.1492537313433\n - type: ap\n value: 72.56132559564212\n - type: f1\n value: 89.71796898040243\n - type: main_score\n value: 93.1492537313433\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 96.98372499999999\n - type: ap\n value: 95.62303091773919\n - type: f1\n value: 96.98308191715637\n - type: main_score\n value: 96.98372499999999\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 61.461999999999996\n - type: f1\n value: 60.57257766583118\n - type: main_score\n value: 61.461999999999996\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: mteb/arguana\n config: default\n split: test\n revision: c22ab2a51041ffd869aaddef7af8d8215647e41a\n metrics:\n - type: main_score\n value: 83.07967801208441\n - type: ndcg_at_1\n value: 66.50071123755335\n - type: ndcg_at_3\n value: 80.10869593172173\n - type: ndcg_at_5\n value: 81.89670542467924\n - type: ndcg_at_10\n value: 83.07967801208441\n - type: ndcg_at_100\n value: 83.5991349601075\n - type: ndcg_at_1000\n value: 83.5991349601075\n - type: map_at_1\n value: 66.50071123755335\n - type: map_at_3\n value: 76.83736367946898\n - type: map_at_5\n value: 77.8473210052158\n - type: map_at_10\n value: 78.35472690735851\n - type: map_at_100\n value: 78.47388207611678\n - type: map_at_1000\n value: 78.47388207611678\n - type: precision_at_1\n value: 66.50071123755335\n - type: precision_at_3\n value: 29.848269321953076\n - type: precision_at_5\n value: 18.762446657183045\n - type: precision_at_10\n value: 9.736842105262909\n - type: precision_at_100\n value: 0.9964438122332677\n - type: precision_at_1000\n value: 0.09964438122332549\n - type: recall_at_1\n value: 66.50071123755335\n - type: recall_at_3\n value: 89.5448079658606\n - type: recall_at_5\n value: 93.8122332859175\n - type: recall_at_10\n value: 97.36842105263158\n - type: recall_at_100\n value: 99.6443812233286\n - type: recall_at_1000\n value: 99.6443812233286\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: main_score\n value: 54.43859683357485\n - type: v_measure\n value: 54.43859683357485\n - type: v_measure_std\n value: 14.511128158596337\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: main_score\n value: 49.33365996236564\n - type: v_measure\n value: 49.33365996236564\n - type: v_measure_std\n value: 14.61261944856548\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: main_score\n value: 65.15263966490278\n - type: map\n value: 65.15263966490278\n - type: mrr\n value: 77.90331090885107\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: main_score\n value: 86.47365710792691\n - type: cosine_spearman\n value: 86.47365710792691\n - type: spearman\n value: 86.47365710792691\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 91.48701298701299\n - type: f1\n value: 91.4733869423637\n - type: main_score\n value: 91.48701298701299\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: main_score\n value: 53.050461108038036\n - type: v_measure\n value: 53.050461108038036\n - type: v_measure_std\n value: 0.9436104839012786\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: main_score\n value: 48.38215568371151\n - type: v_measure\n value: 48.38215568371151\n - type: v_measure_std\n value: 0.9104384504649026\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackRetrieval\n type: mteb/cqadupstack\n config: default\n split: test\n revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4\n metrics:\n - type: main_score\n value: 47.308084499970704\n - type: ndcg_at_1\n value: 36.038578730542476\n - type: ndcg_at_3\n value: 41.931365356453036\n - type: ndcg_at_5\n value: 44.479015523894994\n - type: ndcg_at_10\n value: 47.308084499970704\n - type: ndcg_at_100\n value: 52.498062430513606\n - type: ndcg_at_1000\n value: 54.2908789514719\n - type: map_at_1\n value: 30.38821701528966\n - type: map_at_3\n value: 37.974871761903636\n - type: map_at_5\n value: 39.85399878507757\n - type: map_at_10\n value: 41.31456611036795\n - type: map_at_100\n value: 42.62907836655835\n - type: map_at_1000\n value: 42.737235870659845\n - type: precision_at_1\n value: 36.038578730542476\n - type: precision_at_3\n value: 19.39960180094633\n - type: precision_at_5\n value: 13.79264655952497\n - type: precision_at_10\n value: 8.399223517333388\n - type: precision_at_100\n value: 1.2992373779520896\n - type: precision_at_1000\n value: 0.16327170951909567\n - type: recall_at_1\n value: 30.38821701528966\n - type: recall_at_3\n value: 45.51645512564165\n - type: recall_at_5\n value: 52.06077167834868\n - type: recall_at_10\n value: 60.38864106788279\n - type: recall_at_100\n value: 82.76968509918343\n - type: recall_at_1000\n value: 94.84170217080344\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: mteb/climate-fever\n config: default\n split: test\n revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380\n metrics:\n - type: main_score\n value: 45.4272998284769\n - type: ndcg_at_1\n value: 44.36482084690554\n - type: ndcg_at_3\n value: 38.13005747178844\n - type: ndcg_at_5\n value: 40.83474510717123\n - type: ndcg_at_10\n value: 45.4272998284769\n - type: ndcg_at_100\n value: 52.880220707479516\n - type: ndcg_at_1000\n value: 55.364753427333\n - type: map_at_1\n value: 19.200868621064064\n - type: map_at_3\n value: 28.33785740137525\n - type: map_at_5\n value: 31.67162504524064\n - type: map_at_10\n value: 34.417673164090075\n - type: map_at_100\n value: 36.744753097028976\n - type: map_at_1000\n value: 36.91262189016135\n - type: precision_at_1\n value: 44.36482084690554\n - type: precision_at_3\n value: 29.14223669923975\n - type: precision_at_5\n value: 22.410423452768388\n - type: precision_at_10\n value: 14.293159609120309\n - type: precision_at_100\n value: 2.248859934853431\n - type: precision_at_1000\n value: 0.2722475570032542\n - type: recall_at_1\n value: 19.200868621064064\n - type: recall_at_3\n value: 34.132464712269176\n - type: recall_at_5\n value: 42.35613463626491\n - type: recall_at_10\n value: 52.50814332247546\n - type: recall_at_100\n value: 77.16178067318128\n - type: recall_at_1000\n value: 90.59174809989138\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: mteb/dbpedia\n config: default\n split: test\n revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659\n metrics:\n - type: main_score\n value: 51.634197691802754\n - type: ndcg_at_1\n value: 64.375\n - type: ndcg_at_3\n value: 55.677549598242614\n - type: ndcg_at_5\n value: 53.44347199908503\n - type: ndcg_at_10\n value: 51.634197691802754\n - type: ndcg_at_100\n value: 56.202861267183415\n - type: ndcg_at_1000\n value: 63.146019108272576\n - type: map_at_1\n value: 9.789380503780919\n - type: map_at_3\n value: 16.146582195277016\n - type: map_at_5\n value: 19.469695222167193\n - type: map_at_10\n value: 24.163327344766145\n - type: map_at_100\n value: 35.47047690245571\n - type: map_at_1000\n value: 37.5147432331838\n - type: precision_at_1\n value: 76.25\n - type: precision_at_3\n value: 59.08333333333333\n - type: precision_at_5\n value: 52.24999999999997\n - type: precision_at_10\n value: 42.54999999999994\n - type: precision_at_100\n value: 13.460000000000008\n - type: precision_at_1000\n value: 2.4804999999999966\n - type: recall_at_1\n value: 9.789380503780919\n - type: recall_at_3\n value: 17.48487134027656\n - type: recall_at_5\n value: 22.312024269698806\n - type: recall_at_10\n value: 30.305380335237324\n - type: recall_at_100\n value: 62.172868946596424\n - type: recall_at_1000\n value: 85.32410301328747\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 93.36\n - type: f1\n value: 89.73665936982262\n - type: main_score\n value: 93.36\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: mteb/fever\n config: default\n split: test\n revision: bea83ef9e8fb933d90a2f1d5515737465d613e12\n metrics:\n - type: main_score\n value: 92.82809814626805\n - type: ndcg_at_1\n value: 88.98889888988899\n - type: ndcg_at_3\n value: 91.82404417747676\n - type: ndcg_at_5\n value: 92.41785792357787\n - type: ndcg_at_10\n value: 92.82809814626805\n - type: ndcg_at_100\n value: 93.31730867509245\n - type: ndcg_at_1000\n value: 93.45171203408582\n - type: map_at_1\n value: 82.64125817343636\n - type: map_at_3\n value: 89.39970782792554\n - type: map_at_5\n value: 89.96799501378695\n - type: map_at_10\n value: 90.27479706587437\n - type: map_at_100\n value: 90.45185655778057\n - type: map_at_1000\n value: 90.46130471574544\n - type: precision_at_1\n value: 88.98889888988899\n - type: precision_at_3\n value: 34.923492349234245\n - type: precision_at_5\n value: 21.524152415244043\n - type: precision_at_10\n value: 11.033603360337315\n - type: precision_at_100\n value: 1.1521152115211895\n - type: precision_at_1000\n value: 0.11765676567657675\n - type: recall_at_1\n value: 82.64125817343636\n - type: recall_at_3\n value: 94.35195900542428\n - type: recall_at_5\n value: 95.9071323799047\n - type: recall_at_10\n value: 97.04234113887586\n - type: recall_at_100\n value: 98.77282371094255\n - type: recall_at_1000\n value: 99.5555567461508\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: mteb/fiqa\n config: default\n split: test\n revision: 27a168819829fe9bcd655c2df245fb19452e8e06\n metrics:\n - type: main_score\n value: 59.67151242793314\n - type: ndcg_at_1\n value: 57.407407407407405\n - type: ndcg_at_3\n value: 53.79975378289304\n - type: ndcg_at_5\n value: 56.453379423655406\n - type: ndcg_at_10\n value: 59.67151242793314\n - type: ndcg_at_100\n value: 65.34055762539253\n - type: ndcg_at_1000\n value: 67.07707746043032\n - type: map_at_1\n value: 30.65887045053714\n - type: map_at_3\n value: 44.09107110881799\n - type: map_at_5\n value: 48.18573748068346\n - type: map_at_10\n value: 51.03680979612876\n - type: map_at_100\n value: 53.03165194566928\n - type: map_at_1000\n value: 53.16191096190861\n - type: precision_at_1\n value: 57.407407407407405\n - type: precision_at_3\n value: 35.493827160493886\n - type: precision_at_5\n value: 26.913580246913547\n - type: precision_at_10\n value: 16.435185185185155\n - type: precision_at_100\n value: 2.2685185185184986\n - type: precision_at_1000\n value: 0.25864197530863964\n - type: recall_at_1\n value: 30.65887045053714\n - type: recall_at_3\n value: 48.936723427464194\n - type: recall_at_5\n value: 58.55942925387371\n - type: recall_at_10\n value: 68.45128551147073\n - type: recall_at_100\n value: 88.24599311867836\n - type: recall_at_1000\n value: 98.18121693121691\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: mteb/hotpotqa\n config: default\n split: test\n revision: ab518f4d6fcca38d87c25209f94beba119d02014\n metrics:\n - type: main_score\n value: 85.13780800141961\n - type: ndcg_at_1\n value: 89.9392302498312\n - type: ndcg_at_3\n value: 81.2061569376288\n - type: ndcg_at_5\n value: 83.53311592078133\n - type: ndcg_at_10\n value: 85.13780800141961\n - type: ndcg_at_100\n value: 87.02630661625386\n - type: ndcg_at_1000\n value: 87.47294723601075\n - type: map_at_1\n value: 44.9696151249156\n - type: map_at_3\n value: 76.46972766148966\n - type: map_at_5\n value: 78.47749268512187\n - type: map_at_10\n value: 79.49792611170005\n - type: map_at_100\n value: 80.09409086274644\n - type: map_at_1000\n value: 80.11950878917663\n - type: precision_at_1\n value: 89.9392302498312\n - type: precision_at_3\n value: 53.261309925724234\n - type: precision_at_5\n value: 33.79338284942924\n - type: precision_at_10\n value: 17.69750168805041\n - type: precision_at_100\n value: 1.9141120864280805\n - type: precision_at_1000\n value: 0.19721809588118133\n - type: recall_at_1\n value: 44.9696151249156\n - type: recall_at_3\n value: 79.8919648885888\n - type: recall_at_5\n value: 84.48345712356516\n - type: recall_at_10\n value: 88.48750844024308\n - type: recall_at_100\n value: 95.70560432140446\n - type: recall_at_1000\n value: 98.60904794058068\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 96.9144\n - type: ap\n value: 95.45276911068486\n - type: f1\n value: 96.91412729455966\n - type: main_score\n value: 96.9144\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: mteb/msmarco\n config: default\n split: dev\n revision: c5a29a104738b98a9e76336939199e264163d4a0\n metrics:\n - type: main_score\n value: 46.78865753107054\n - type: ndcg_at_1\n value: 26.63323782234957\n - type: ndcg_at_3\n value: 38.497585804985754\n - type: ndcg_at_5\n value: 42.72761631631636\n - type: ndcg_at_10\n value: 46.78865753107054\n - type: ndcg_at_100\n value: 51.96170786623209\n - type: ndcg_at_1000\n value: 52.82713901970963\n - type: map_at_1\n value: 25.89063992359121\n - type: map_at_3\n value: 35.299466730340654\n - type: map_at_5\n value: 37.68771887933786\n - type: map_at_10\n value: 39.40908074468253\n - type: map_at_100\n value: 40.53444082323405\n - type: map_at_1000\n value: 40.57183037649452\n - type: precision_at_1\n value: 26.63323782234957\n - type: precision_at_3\n value: 16.265520534861793\n - type: precision_at_5\n value: 11.902578796562304\n - type: precision_at_10\n value: 7.262177650430416\n - type: precision_at_100\n value: 0.9819484240687512\n - type: precision_at_1000\n value: 0.10571633237823287\n - type: recall_at_1\n value: 25.89063992359121\n - type: recall_at_3\n value: 46.99737344794652\n - type: recall_at_5\n value: 57.160936007640906\n - type: recall_at_10\n value: 69.43409742120343\n - type: recall_at_100\n value: 92.86413562559697\n - type: recall_at_1000\n value: 99.3230659025788\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 98.42225262197901\n - type: f1\n value: 98.31652547061115\n - type: main_score\n value: 98.42225262197901\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 94.00136798905609\n - type: f1\n value: 82.7022316533099\n - type: main_score\n value: 94.00136798905609\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 4672e20407010da34463acc759c162ca9734bca6\n metrics:\n - type: accuracy\n value: 82.92535305985204\n - type: f1\n value: 79.885538231847\n - type: main_score\n value: 82.92535305985204\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8\n metrics:\n - type: accuracy\n value: 85.60188298587758\n - type: f1\n value: 84.87416963499224\n - type: main_score\n value: 85.60188298587758\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: main_score\n value: 45.86171497327639\n - type: v_measure\n value: 45.86171497327639\n - type: v_measure_std\n value: 1.551347259003324\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: main_score\n value: 44.33336692345644\n - type: v_measure\n value: 44.33336692345644\n - type: v_measure_std\n value: 1.5931408596404715\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7\n metrics:\n - type: main_score\n value: 30.597409734750503\n - type: map\n value: 30.597409734750503\n - type: mrr\n value: 31.397041548018457\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: mteb/nfcorpus\n config: default\n split: test\n revision: ec0fa4fe99da2ff19ca1214b7966684033a58814\n metrics:\n - type: main_score\n value: 41.850870119787835\n - type: ndcg_at_1\n value: 52.47678018575851\n - type: ndcg_at_3\n value: 47.43993801247414\n - type: ndcg_at_5\n value: 45.08173173082719\n - type: ndcg_at_10\n value: 41.850870119787835\n - type: ndcg_at_100\n value: 37.79284946590978\n - type: ndcg_at_1000\n value: 46.58046062123418\n - type: map_at_1\n value: 6.892464464226138\n - type: map_at_3\n value: 12.113195798233127\n - type: map_at_5\n value: 13.968475602788812\n - type: map_at_10\n value: 16.47564069781326\n - type: map_at_100\n value: 20.671726065190025\n - type: map_at_1000\n value: 22.328875914012006\n - type: precision_at_1\n value: 53.86996904024768\n - type: precision_at_3\n value: 43.96284829721363\n - type: precision_at_5\n value: 38.69969040247682\n - type: precision_at_10\n value: 30.928792569659457\n - type: precision_at_100\n value: 9.507739938080498\n - type: precision_at_1000\n value: 2.25882352941176\n - type: recall_at_1\n value: 6.892464464226138\n - type: recall_at_3\n value: 13.708153358278407\n - type: recall_at_5\n value: 16.651919797359145\n - type: recall_at_10\n value: 21.01801714352559\n - type: recall_at_100\n value: 37.01672102843443\n - type: recall_at_1000\n value: 69.8307270724072\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: mteb/nq\n config: default\n split: test\n revision: b774495ed302d8c44a3a7ea25c90dbce03968f31\n metrics:\n - type: main_score\n value: 73.88350836507092\n - type: ndcg_at_1\n value: 57.0683661645423\n - type: ndcg_at_3\n value: 67.89935813080585\n - type: ndcg_at_5\n value: 71.47769719452941\n - type: ndcg_at_10\n value: 73.88350836507092\n - type: ndcg_at_100\n value: 75.76561068060907\n - type: ndcg_at_1000\n value: 75.92437662684215\n - type: map_at_1\n value: 51.00424874468904\n - type: map_at_3\n value: 63.87359984550011\n - type: map_at_5\n value: 66.23696407879494\n - type: map_at_10\n value: 67.42415446608673\n - type: map_at_100\n value: 67.92692839842621\n - type: map_at_1000\n value: 67.93437922640133\n - type: precision_at_1\n value: 57.0683661645423\n - type: precision_at_3\n value: 29.692931633836416\n - type: precision_at_5\n value: 20.046349942062854\n - type: precision_at_10\n value: 10.950173812283\n - type: precision_at_100\n value: 1.1995944380069687\n - type: precision_at_1000\n value: 0.12146581691772171\n - type: recall_at_1\n value: 51.00424874468904\n - type: recall_at_3\n value: 75.93665507918116\n - type: recall_at_5\n value: 83.95133256083433\n - type: recall_at_10\n value: 90.78794901506375\n - type: recall_at_100\n value: 98.61915797605253\n - type: recall_at_1000\n value: 99.7827346465817\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: mteb/quora\n config: default\n split: test\n revision: e4e08e0b7dbe3c8700f0daef558ff32256715259\n metrics:\n - type: main_score\n value: 90.95410848372035\n - type: ndcg_at_1\n value: 84.61999999999999\n - type: ndcg_at_3\n value: 88.57366734033212\n - type: ndcg_at_5\n value: 89.89804048972175\n - type: ndcg_at_10\n value: 90.95410848372035\n - type: ndcg_at_100\n value: 91.83227134455773\n - type: ndcg_at_1000\n value: 91.88368412611601\n - type: map_at_1\n value: 73.4670089207039\n - type: map_at_3\n value: 84.87862925508942\n - type: map_at_5\n value: 86.68002324701408\n - type: map_at_10\n value: 87.7165466015312\n - type: map_at_100\n value: 88.28718809614146\n - type: map_at_1000\n value: 88.29877148480672\n - type: precision_at_1\n value: 84.61999999999999\n - type: precision_at_3\n value: 38.82333333333838\n - type: precision_at_5\n value: 25.423999999998642\n - type: precision_at_10\n value: 13.787999999998583\n - type: precision_at_100\n value: 1.5442999999999767\n - type: precision_at_1000\n value: 0.15672999999997972\n - type: recall_at_1\n value: 73.4670089207039\n - type: recall_at_3\n value: 89.98389854832143\n - type: recall_at_5\n value: 93.88541046010576\n - type: recall_at_10\n value: 96.99779417520634\n - type: recall_at_100\n value: 99.80318763957743\n - type: recall_at_1000\n value: 99.99638888888889\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: main_score\n value: 72.33008348681277\n - type: v_measure\n value: 72.33008348681277\n - type: v_measure_std\n value: 2.9203215463933008\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 385e3cb46b4cfa89021f56c4380204149d0efe33\n metrics:\n - type: main_score\n value: 72.72079657828903\n - type: v_measure\n value: 72.72079657828903\n - type: v_measure_std\n value: 11.930271663428735\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: mteb/scidocs\n config: default\n split: test\n revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88\n metrics:\n - type: main_score\n value: 25.25865384510787\n - type: ndcg_at_1\n value: 28.7\n - type: ndcg_at_3\n value: 23.61736427940938\n - type: ndcg_at_5\n value: 20.845690325673885\n - type: ndcg_at_10\n value: 25.25865384510787\n - type: ndcg_at_100\n value: 36.18596641088721\n - type: ndcg_at_1000\n value: 41.7166868935345\n - type: map_at_1\n value: 5.828333333333361\n - type: map_at_3\n value: 10.689166666666676\n - type: map_at_5\n value: 13.069916666666668\n - type: map_at_10\n value: 15.4901164021164\n - type: map_at_100\n value: 18.61493245565425\n - type: map_at_1000\n value: 18.99943478016456\n - type: precision_at_1\n value: 28.7\n - type: precision_at_3\n value: 22.30000000000006\n - type: precision_at_5\n value: 18.55999999999997\n - type: precision_at_10\n value: 13.289999999999946\n - type: precision_at_100\n value: 2.905000000000005\n - type: precision_at_1000\n value: 0.4218999999999946\n - type: recall_at_1\n value: 5.828333333333361\n - type: recall_at_3\n value: 13.548333333333387\n - type: recall_at_5\n value: 18.778333333333308\n - type: recall_at_10\n value: 26.939999999999902\n - type: recall_at_100\n value: 58.91333333333344\n - type: recall_at_1000\n value: 85.57499999999972\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: 20a6d6f312dd54037fe07a32d58e5e168867909d\n metrics:\n - type: main_score\n value: 83.86733787791422\n - type: cosine_spearman\n value: 83.86733787791422\n - type: spearman\n value: 83.86733787791422\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: main_score\n value: 78.14269330480724\n - type: cosine_spearman\n value: 78.14269330480724\n - type: spearman\n value: 78.14269330480724\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: main_score\n value: 86.58640009300751\n - type: cosine_spearman\n value: 86.58640009300751\n - type: spearman\n value: 86.58640009300751\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: main_score\n value: 82.8292579957437\n - type: cosine_spearman\n value: 82.8292579957437\n - type: spearman\n value: 82.8292579957437\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: main_score\n value: 87.77203714228862\n - type: cosine_spearman\n value: 87.77203714228862\n - type: spearman\n value: 87.77203714228862\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: main_score\n value: 87.0439304006969\n - type: cosine_spearman\n value: 87.0439304006969\n - type: spearman\n value: 87.0439304006969\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: faeb762787bd10488a50c8b5be4a3b82e411949c\n metrics:\n - type: main_score\n value: 91.24736138013424\n - type: cosine_spearman\n value: 91.24736138013424\n - type: spearman\n value: 91.24736138013424\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3\n metrics:\n - type: main_score\n value: 70.07326214706\n - type: cosine_spearman\n value: 70.07326214706\n - type: spearman\n value: 70.07326214706\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: main_score\n value: 88.42076443255168\n - type: cosine_spearman\n value: 88.42076443255168\n - type: spearman\n value: 88.42076443255168\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: main_score\n value: 86.9584489124583\n - type: map\n value: 86.9584489124583\n - type: mrr\n value: 96.59475328592976\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: mteb/scifact\n config: default\n split: test\n revision: 0228b52cf27578f30900b9e5271d331663a030d7\n metrics:\n - type: main_score\n value: 79.09159079425369\n - type: ndcg_at_1\n value: 66.0\n - type: ndcg_at_3\n value: 74.98853481223065\n - type: ndcg_at_5\n value: 77.29382051205019\n - type: ndcg_at_10\n value: 79.09159079425369\n - type: ndcg_at_100\n value: 80.29692802526776\n - type: ndcg_at_1000\n value: 80.55210036585547\n - type: map_at_1\n value: 62.994444444444454\n - type: map_at_3\n value: 71.7425925925926\n - type: map_at_5\n value: 73.6200925925926\n - type: map_at_10\n value: 74.50223544973547\n - type: map_at_100\n value: 74.82438594015447\n - type: map_at_1000\n value: 74.83420474892468\n - type: precision_at_1\n value: 66.0\n - type: precision_at_3\n value: 29.44444444444439\n - type: precision_at_5\n value: 19.40000000000008\n - type: precision_at_10\n value: 10.366666666666715\n - type: precision_at_100\n value: 1.0999999999999928\n - type: precision_at_1000\n value: 0.11200000000000007\n - type: recall_at_1\n value: 62.994444444444454\n - type: recall_at_3\n value: 80.89999999999998\n - type: recall_at_5\n value: 86.72777777777779\n - type: recall_at_10\n value: 91.88888888888887\n - type: recall_at_100\n value: 97.0\n - type: recall_at_1000\n value: 99.0\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: main_score\n value: 97.26819027722253\n - type: cos_sim_accuracy\n value: 99.88019801980198\n - type: cos_sim_accuracy_threshold\n value: 76.67685151100159\n - type: cos_sim_ap\n value: 97.23260568085786\n - type: cos_sim_f1\n value: 93.91824526420737\n - type: cos_sim_f1_threshold\n value: 75.82710981369019\n - type: cos_sim_precision\n value: 93.63817097415506\n - type: cos_sim_recall\n value: 94.19999999999999\n - type: dot_accuracy\n value: 99.88019801980198\n - type: dot_accuracy_threshold\n value: 76.67686343193054\n - type: dot_ap\n value: 97.23260568085786\n - type: dot_f1\n value: 93.91824526420737\n - type: dot_f1_threshold\n value: 75.8271336555481\n - type: dot_precision\n value: 93.63817097415506\n - type: dot_recall\n value: 94.19999999999999\n - type: euclidean_accuracy\n value: 99.88019801980198\n - type: euclidean_accuracy_threshold\n value: 68.29807758331299\n - type: euclidean_ap\n value: 97.23259982599497\n - type: euclidean_f1\n value: 93.91824526420737\n - type: euclidean_f1_threshold\n value: 69.53110694885254\n - type: euclidean_precision\n value: 93.63817097415506\n - type: euclidean_recall\n value: 94.19999999999999\n - type: manhattan_accuracy\n value: 99.87821782178217\n - type: manhattan_accuracy_threshold\n value: 3482.6908111572266\n - type: manhattan_ap\n value: 97.26819027722253\n - type: manhattan_f1\n value: 93.92592592592592\n - type: manhattan_f1_threshold\n value: 3555.5641174316406\n - type: manhattan_precision\n value: 92.78048780487805\n - type: manhattan_recall\n value: 95.1\n - type: max_accuracy\n value: 99.88019801980198\n - type: max_ap\n value: 97.26819027722253\n - type: max_f1\n value: 93.92592592592592\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: main_score\n value: 81.32419328350603\n - type: v_measure\n value: 81.32419328350603\n - type: v_measure_std\n value: 2.666861121694755\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: main_score\n value: 46.048387963107565\n - type: v_measure\n value: 46.048387963107565\n - type: v_measure_std\n value: 1.4102848576321703\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: main_score\n value: 56.70574900554072\n - type: map\n value: 56.70574900554072\n - type: mrr\n value: 57.517109116373824\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: main_score\n value: 30.76932903185174\n - type: cosine_spearman\n value: 30.76932903185174\n - type: spearman\n value: 30.76932903185174\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: mteb/trec-covid\n config: default\n split: test\n revision: bb9466bac8153a0349341eb1b22e06409e78ef4e\n metrics:\n - type: main_score\n value: 79.07987651251462\n - type: ndcg_at_1\n value: 83.0\n - type: ndcg_at_3\n value: 79.86598407528447\n - type: ndcg_at_5\n value: 79.27684428714952\n - type: ndcg_at_10\n value: 79.07987651251462\n - type: ndcg_at_100\n value: 64.55029164391163\n - type: ndcg_at_1000\n value: 59.42333857860492\n - type: map_at_1\n value: 0.226053732680979\n - type: map_at_3\n value: 0.644034626013194\n - type: map_at_5\n value: 1.045196967937728\n - type: map_at_10\n value: 2.0197496659905085\n - type: map_at_100\n value: 13.316018005224159\n - type: map_at_1000\n value: 33.784766957424104\n - type: precision_at_1\n value: 88.0\n - type: precision_at_3\n value: 86.66666666666667\n - type: precision_at_5\n value: 85.20000000000002\n - type: precision_at_10\n value: 84.19999999999997\n - type: precision_at_100\n value: 67.88000000000001\n - type: precision_at_1000\n value: 26.573999999999998\n - type: recall_at_1\n value: 0.226053732680979\n - type: recall_at_3\n value: 0.6754273711472734\n - type: recall_at_5\n value: 1.1168649828059245\n - type: recall_at_10\n value: 2.2215081031265207\n - type: recall_at_100\n value: 16.694165236664727\n - type: recall_at_1000\n value: 56.7022214857503\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: mteb/touche2020\n config: default\n split: test\n revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f\n metrics:\n - type: main_score\n value: 30.47934263207554\n - type: ndcg_at_1\n value: 33.6734693877551\n - type: ndcg_at_3\n value: 34.36843900446739\n - type: ndcg_at_5\n value: 32.21323786731918\n - type: ndcg_at_10\n value: 30.47934263207554\n - type: ndcg_at_100\n value: 41.49598869753928\n - type: ndcg_at_1000\n value: 52.32963949183662\n - type: map_at_1\n value: 3.0159801678718168\n - type: map_at_3\n value: 7.13837927642557\n - type: map_at_5\n value: 9.274004610363466\n - type: map_at_10\n value: 12.957368366814324\n - type: map_at_100\n value: 19.3070585127604\n - type: map_at_1000\n value: 20.809777161133532\n - type: precision_at_1\n value: 34.69387755102041\n - type: precision_at_3\n value: 36.054421768707485\n - type: precision_at_5\n value: 32.24489795918368\n - type: precision_at_10\n value: 27.142857142857146\n - type: precision_at_100\n value: 8.326530612244898\n - type: precision_at_1000\n value: 1.5755102040816336\n - type: recall_at_1\n value: 3.0159801678718168\n - type: recall_at_3\n value: 8.321771388428257\n - type: recall_at_5\n value: 11.737532394366069\n - type: recall_at_10\n value: 19.49315139822179\n - type: recall_at_100\n value: 50.937064145519685\n - type: recall_at_1000\n value: 83.4358283484675\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de\n metrics:\n - type: accuracy\n value: 93.173828125\n - type: ap\n value: 46.040184641424396\n - type: f1\n value: 80.77280549412752\n - type: main_score\n value: 93.173828125\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 79.9320882852292\n - type: f1\n value: 80.22638685975485\n - type: main_score\n value: 79.9320882852292\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: main_score\n value: 68.98152919711418\n - type: v_measure\n value: 68.98152919711418\n - type: v_measure_std\n value: 1.2519720970652428\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: main_score\n value: 79.34189681158234\n - type: cos_sim_accuracy\n value: 87.68552184538356\n - type: cos_sim_accuracy_threshold\n value: 76.06316804885864\n - type: cos_sim_ap\n value: 79.34189149773933\n - type: cos_sim_f1\n value: 72.16386554621849\n - type: cos_sim_f1_threshold\n value: 73.62890243530273\n - type: cos_sim_precision\n value: 71.82435964453737\n - type: cos_sim_recall\n value: 72.5065963060686\n - type: dot_accuracy\n value: 87.68552184538356\n - type: dot_accuracy_threshold\n value: 76.06316208839417\n - type: dot_ap\n value: 79.34189231911259\n - type: dot_f1\n value: 72.16386554621849\n - type: dot_f1_threshold\n value: 73.62889647483826\n - type: dot_precision\n value: 71.82435964453737\n - type: dot_recall\n value: 72.5065963060686\n - type: euclidean_accuracy\n value: 87.68552184538356\n - type: euclidean_accuracy_threshold\n value: 69.19080018997192\n - type: euclidean_ap\n value: 79.34189681158234\n - type: euclidean_f1\n value: 72.16386554621849\n - type: euclidean_f1_threshold\n value: 72.62383103370667\n - type: euclidean_precision\n value: 71.82435964453737\n - type: euclidean_recall\n value: 72.5065963060686\n - type: manhattan_accuracy\n value: 87.661679680515\n - type: manhattan_accuracy_threshold\n value: 3408.807373046875\n - type: manhattan_ap\n value: 79.29617544165136\n - type: manhattan_f1\n value: 72.1957671957672\n - type: manhattan_f1_threshold\n value: 3597.7684020996094\n - type: manhattan_precision\n value: 72.38726790450929\n - type: manhattan_recall\n value: 72.00527704485488\n - type: max_accuracy\n value: 87.68552184538356\n - type: max_ap\n value: 79.34189681158234\n - type: max_f1\n value: 72.1957671957672\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: main_score\n value: 87.8635519535718\n - type: cos_sim_accuracy\n value: 89.80672953778088\n - type: cos_sim_accuracy_threshold\n value: 73.09532165527344\n - type: cos_sim_ap\n value: 87.84251379545145\n - type: cos_sim_f1\n value: 80.25858884373845\n - type: cos_sim_f1_threshold\n value: 70.57080268859863\n - type: cos_sim_precision\n value: 77.14103110353643\n - type: cos_sim_recall\n value: 83.63874345549738\n - type: dot_accuracy\n value: 89.80672953778088\n - type: dot_accuracy_threshold\n value: 73.09532761573792\n - type: dot_ap\n value: 87.84251881260793\n - type: dot_f1\n value: 80.25858884373845\n - type: dot_f1_threshold\n value: 70.57079076766968\n - type: dot_precision\n value: 77.14103110353643\n - type: dot_recall\n value: 83.63874345549738\n - type: euclidean_accuracy\n value: 89.80672953778088\n - type: euclidean_accuracy_threshold\n value: 73.3548641204834\n - type: euclidean_ap\n value: 87.84251335039049\n - type: euclidean_f1\n value: 80.25858884373845\n - type: euclidean_f1_threshold\n value: 76.71923041343689\n - type: euclidean_precision\n value: 77.14103110353643\n - type: euclidean_recall\n value: 83.63874345549738\n - type: manhattan_accuracy\n value: 89.78150347343501\n - type: manhattan_accuracy_threshold\n value: 3702.7603149414062\n - type: manhattan_ap\n value: 87.8635519535718\n - type: manhattan_f1\n value: 80.27105660516332\n - type: manhattan_f1_threshold\n value: 3843.5962677001953\n - type: manhattan_precision\n value: 76.9361101306036\n - type: manhattan_recall\n value: 83.90822297505389\n - type: max_accuracy\n value: 89.80672953778088\n - type: max_ap\n value: 87.8635519535718\n - type: max_f1\n value: 80.27105660516332\n---\n\n\n💌 Contact
\nFlagEmbedding
\n\n\n\n\nFor more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).\n\n**BGE-EN-ICL** primarily demonstrates the following capabilities:\n- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.\n- Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.\n\n\n## 📑 Open-source Plan\n\n- [x] Checkpoint\n- [x] Training Data\n- [x] Technical Report\n- [ ] Evaluation Pipeline\n\nThe technical report for **BGE-EN-ICL** can be found in [Making Text Embedders Few-Shot Learners](https://arxiv.org/abs/2409.15700)\n\n## Data List\n\n| Data | Introduction |\n| ------------------------------------------------------------ | ------------------------------------------------------------ |\n| [public-data](https://huggingface.co/datasets/cfli/bge-e5data) | Public data identical to [e5-mistral](https://huggingface.co/intfloat/e5-mistral-7b-instruct) |\n| [full-data](https://huggingface.co/datasets/cfli/bge-full-data) | The full dataset we used for training |\n\n## Usage \n\n### Using FlagEmbedding\n```\ngit clone https://github.com/FlagOpen/FlagEmbedding.git\ncd FlagEmbedding\npip install -e .\n```\n\n```python\nfrom FlagEmbedding import FlagICLModel\nqueries = [\"how much protein should a female eat\", \"summit define\"]\ndocuments = [\n \"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.\",\n \"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.\"\n]\nexamples = [\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'what is a virtual interface',\n 'response': \"A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes.\"},\n {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',\n 'query': 'causes of back pain in female for a week',\n 'response': \"Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management.\"}\n]\nmodel = FlagICLModel('BAAI/bge-en-icl', \n query_instruction_for_retrieval=\"Given a web search query, retrieve relevant passages that answer the query.\",\n examples_for_task=examples, # set `examples_for_task=None` to use model without examples\n use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation\nembeddings_1 = model.encode_queries(queries)\nembeddings_2 = model.encode_corpus(documents)\nsimilarity = embeddings_1 @ embeddings_2.T\nprint(similarity)\n```\n\nBy default, FlagICLModel will use all available GPUs when encoding. Please set `os.environ[\"CUDA_VISIBLE_DEVICES\"]` to select specific GPUs.\nYou also can set `os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"\"` to make all GPUs unavailable.\n\n\n### Using HuggingFace Transformers\n\nWith the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.\n\n```python\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import Tensor\nfrom transformers import AutoTokenizer, AutoModel\n\n\ndef last_token_pool(last_hidden_states: Tensor,\n attention_mask: Tensor) -> Tensor:\n left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])\n if left_padding:\n return last_hidden_states[:, -1]\n else:\n sequence_lengths = attention_mask.sum(dim=1) - 1\n batch_size = last_hidden_states.shape[0]\n return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]\n\n\ndef get_detailed_instruct(task_description: str, query: str) -> str:\n return f'\n ![\"Centered](\"https://cdn-uploads.huggingface.co/production/uploads/63368577d184e6b53c50e6d0/6kSJKgPh2pDh4tA-Ky0xW.png\")
\n
🚀 Flow Judge | 📄 Technical report | 💻 flow-judge
\n\n## Model Summary\n\nFlow-Judge-v0.1 is a compact yet powerful 3.8B model that offers customizable LLM system evaluations across various fields. The model inherits it's architecture from Phi-3.5-mini instruct model which enables Flow-Judge to deliver high-quality results while maintaining a small footprint. Despite its smaller size, it achieves performance comparable to larger models in both held-out and out-of-domain benchmarks. Flow-Judge-v0.1 supports multiple scoring scales, provides qualitative feedback, and generates structured evaluation outputs. Trained on a smaller synthetic dataset, it represents an efficient approach to AI development. Released under the Apache 2.0 license, Flow Judge is an open and accessible model suitable for developers and companies seeking cost-effective and rapid evaluations using custom rubrics.\n\n__Quantized weights__\n- [flowaicom/Flow-Judge-v0.1-AWQ](https://huggingface.co/flowaicom/Flow-Judge-v0.1-AWQ)\n- [flowaicom/Flow-Judge-v0.1-GGUF](https://huggingface.co/flowaicom/Flow-Judge-v0.1-GGUF)\n\n__Quickstart__\n- [Quickstart](https://github.com/flowaicom/flow-judge/examples/1_quickstart.ipynb)\n\n## Intended Use Case\nFlow Judge is intended to be used on custom LLM system evaluation tasks.\n\n- Customizable evaluations: Users can define their own evaluation criteria and rubrics, tailoring Flow Judge to their specific needs and requirements. This flexibility allows for the creation of highly targeted assessments that accurately measure performance of their LLM system\n\n- Flow Judge supports three different scoring scales:\n - Pass/fail: Suitable for binary assessments, such as determining whether a piece of text meets a specific standard or contains errors.\n - 3-Likert: Allows for more granular evaluations, with scores ranging from negative to neutral to positive. Useful for assessing the overall quality or sentiment of a piece of text.\n - 5-Likert: Provides an even more nuanced assessment, with scores ranging from strongly negative to strongly positive, enabling users to capture subtle differences in quality or sentiment. \n\n- Easy to interpret results: \n - Flow Judge produces structured evaluations with `| Evaluator | \nPass / Fail Held-out Test set | \n||
|---|---|---|---|
| Precision | \nRecall | \nF1 | \n|
| microsoft/Phi-3.5-mini-instruct | \n0.685 | \n1.000 | \n0.813 | \n
| meta-llama/Meta-Llama-3.1-8B-Instruct | \n0.870 | \n0.982 | \n0.923 | \n
| mistralai/Mistral-Nemo-Instruct-2407 | \n0.709 | \n0.994 | \n0.827 | \n
| gpt-4o-mini | \n0.834 | \n1.000 | \n0.910 | \n
| flowaicom/Flow-Judge-v0.1 | \n0.940 | \n0.972 | \n0.955 | \n
| Evaluator | \n3-Likert Held-out Test set | \n5-Likert Held-out Test set | \n||||
|---|---|---|---|---|---|---|
| pearsonr | \nspearmanr | \nkendall-tau | \npearsonr | \nspearmanr | \nkendall-tau | \n|
| microsoft/Phi-3.5-mini-instruct | \n0.756 | \n0.749 | \n0.695 | \n0.808 | \n0.819 | \n0.739 | \n
| prometheus-eval/prometheus-7b-v2.0* | \n- | \n- | \n- | \n0.910 | \n0.908 | \n0.838 | \n
| meta-llama/Meta-Llama-3.1-8B-Instruct | \n0.836 | \n0.833 | \n0.789 | \n0.854 | \n0.868 | \n0.791 | \n
| mistralai/Mistral-Nemo-Instruct-2407 | \n0.813 | \n0.807 | \n0.758 | \n0.870 | \n0.867 | \n0.789 | \n
| gpt-4o-mini | \n0.890 | \n0.888 | \n0.851 | \n0.923 | \n0.923 | \n0.864 | \n
| flowaicom/Flow-Judge-v0.1 | \n0.888 | \n0.888 | \n0.852 | \n0.919 | \n0.919 | \n0.856 | \n
| Evaluator | \nRAGTruth QA | \nRAGTruth Data-to-Text | \nRAGTruth Summarization | \n||||||
|---|---|---|---|---|---|---|---|---|---|
| Precision | \nRecall | \nF1 | \nPrecision | \nRecall | \nF1 | \nPrecision | \nRecall | \nF1 | \n|
| microsoft/Phi-3.5-mini-instruct | \n0.817 | \n0.963 | \n0.884 | \n0.356 | \n1.000 | \n0.525 | \n0.776 | \n1.000 | \n0.874 | \n
| meta-llama/Meta-Llama-3.1-8B-Instruct | \n0.844 | \n0.986 | \n0.910 | \n0.382 | \n0.537 | \n0.447 | \n0.797 | \n0.940 | \n0.863 | \n
| mistralai/Mistral-Nemo-Instruct-2407 | \n0.821 | \n0.995 | \n0.900 | \n0.357 | \n1.000 | \n0.526 | \n0.775 | \n1.000 | \n0.873 | \n
| gpt-4o-mini | \n0.830 | \n0.966 | \n0.893 | \n0.398 | \n0.994 | \n0.569 | \n0.786 | \n0.997 | \n0.879 | \n
| Luna* | \n0.378 | \n0.800 | \n0.513 | \n0.649 | \n0.912 | \n0.759 | \n0.400 | \n0.765 | \n0.525 | \n
| RAGAS Faithfuless* | \n0.312 | \n0.419 | \n0.357 | \n0.792 | \n0.508 | \n0.619 | \n0.642 | \n0.299 | \n0.408 | \n
| Trulens Groundedness* | \n0.228 | \n0.925 | \n0.366 | \n0.669 | \n0.965 | \n0.790 | \n0.402 | \n0.500 | \n0.445 | \n
| flowaicom/Flow-Judge-v0.1 | \n0.835 | \n0.961 | \n0.894 | \n0.541 | \n0.249 | \n0.341 | \n0.834 | \n0.836 | \n0.835 | \n
| Evaluator | \nHaluEval | \nCovid-QA | \nPubMedQA | \n|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Precision | \nRecall | \nF1 | \nAccuracy | \nPrecision | \nRecall | \nF1 | \nAccuracy | \nPrecision | \nRecall | \nF1 | \nAccuracy | \n|
| microsoft/Phi-3.5-mini-instruct | \n0.730 | \n0.914 | \n0.812 | \n0.788 | \n0.617 | \n0.964 | \n0.752 | \n0.681 | \n0.623 | \n0.986 | \n0.764 | \n0.696 | \n
| meta-llama/Meta-Llama-3.1-8B-Instruct | \n0.864 | \n0.891 | \n0.878 | \n0.874 | \n0.663 | \n0.976 | \n0.790 | \n0.734 | \n0.681 | \n0.962 | \n0.797 | \n0.750 | \n
| mistralai/Mistral-Nemo-Instruct-2407 | \n0.655 | \n0.993 | \n0.789 | \n0.735 | \n0.651 | \n0.982 | \n0.783 | \n0.728 | \n0.602 | \n0.994 | \n0.750 | \n0.669 | \n
| gpt-4o-mini | \n0.846 | \n0.940 | \n0.891 | \n0.885 | \n0.795 | \n0.964 | \n0.872 | \n0.858 | \n0.791 | \n0.904 | \n0.843 | \n0.832 | \n
| flowaicom/Flow-Judge-v0.1 | \n0.826 | \n0.895 | \n0.859 | \n0.854 | \n0.767 | \n0.877 | \n0.818 | \n0.807 | \n0.874 | \n0.624 | \n0.728 | \n0.767 | \n
| gpt-4o* | \n- | \n- | \n- | \n0.879 | \n- | \n- | \n- | \n0.821 | \n- | \n- | \n- | \n0.821 | \n
| Claude 3 Sonnet* | \n- | \n- | \n- | \n0.845 | \n- | \n- | \n- | \n0.829 | \n- | \n- | \n- | \n0.829 | \n
| RAGAS Faithfulness* | \n- | \n- | \n- | \n0.706 | \n- | \n- | \n- | \n0.750 | \n- | \n- | \n- | \n0.669 | \n
| Lynx 8B* | \n- | \n- | \n- | \n0.857 | \n- | \n- | \n- | \n0.963 | \n- | \n- | \n- | \n0.852 | \n
| Lynx 70B* | \n- | \n- | \n- | \n0.884 | \n- | \n- | \n- | \n0.975 | \n- | \n- | \n- | \n0.904 | \n
| Evaluator | \nFeedback bench | \n||
|---|---|---|---|
| pearsonr | \nspearmanr | \nkendall-tau | \n|
| microsoft/Phi-3.5-mini-instruct | \n0.710 | \n0.721 | \n0.622 | \n
| prometheus-eval/prometheus-7b-v2.0* | \n0.878 | \n0.909 | \n0.773 | \n
| meta-llama/Meta-Llama-3.1-8B-Instruct | \n0.742 | \n0.749 | \n0.654 | \n
| mistralai/Mistral-Nemo-Instruct-2407 | \n0.720 | \n0.724 | \n0.632 | \n
| gpt-4o-mini | \n0.797 | \n0.795 | \n0.701 | \n
| flowaicom/Flow-Judge-v0.1 | \n0.787 | \n0.789 | \n0.688 | \n
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2025-03-18 02:34:30
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2025-03-18 03:19:02
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WhereIsAI/UAE-Large-V1 | WhereIsAI | feature-extraction | [
"sentence-transformers",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"mteb",
"sentence_embedding",
"feature_extraction",
"transformers",
"transformers.js",
"en",
"arxiv:2309.12871",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-12-04T02:03:27 | 2024-12-31T08:00:51 | 15,561,625 | 220 | ---
language:
- en
license: mit
tags:
- mteb
- sentence_embedding
- feature_extraction
- sentence-transformers
- transformers
- transformers.js
model-index:
- name: UAE-Large-V1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.55223880597015
- type: ap
value: 38.264070815317794
- type: f1
value: 69.40977934769845
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.84267499999999
- type: ap
value: 89.57568507997713
- type: f1
value: 92.82590734337774
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.292
- type: f1
value: 47.90257816032778
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 42.105
- type: map_at_10
value: 58.181000000000004
- type: map_at_100
value: 58.653999999999996
- type: map_at_1000
value: 58.657000000000004
- type: map_at_3
value: 54.386
- type: map_at_5
value: 56.757999999999996
- type: mrr_at_1
value: 42.745
- type: mrr_at_10
value: 58.437
- type: mrr_at_100
value: 58.894999999999996
- type: mrr_at_1000
value: 58.897999999999996
- type: mrr_at_3
value: 54.635
- type: mrr_at_5
value: 56.99999999999999
- type: ndcg_at_1
value: 42.105
- type: ndcg_at_10
value: 66.14999999999999
- type: ndcg_at_100
value: 68.048
- type: ndcg_at_1000
value: 68.11399999999999
- type: ndcg_at_3
value: 58.477000000000004
- type: ndcg_at_5
value: 62.768
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 9.110999999999999
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 23.447000000000003
- type: precision_at_5
value: 16.159000000000002
- type: recall_at_1
value: 42.105
- type: recall_at_10
value: 91.11
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 70.341
- type: recall_at_5
value: 80.797
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 49.02580759154173
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.093601280163554
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.19590406875427
- type: mrr
value: 77.09547992788991
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.86678362843676
- type: cos_sim_spearman
value: 86.1423242570783
- type: euclidean_pearson
value: 85.98994198511751
- type: euclidean_spearman
value: 86.48209103503942
- type: manhattan_pearson
value: 85.6446436316182
- type: manhattan_spearman
value: 86.21039809734357
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.69155844155844
- type: f1
value: 87.68109381943547
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.37501687500394
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.23401405155885
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.232
- type: map_at_10
value: 41.404999999999994
- type: map_at_100
value: 42.896
- type: map_at_1000
value: 43.028
- type: map_at_3
value: 37.925
- type: map_at_5
value: 39.865
- type: mrr_at_1
value: 36.338
- type: mrr_at_10
value: 46.969
- type: mrr_at_100
value: 47.684
- type: mrr_at_1000
value: 47.731
- type: mrr_at_3
value: 44.063
- type: mrr_at_5
value: 45.908
- type: ndcg_at_1
value: 36.338
- type: ndcg_at_10
value: 47.887
- type: ndcg_at_100
value: 53.357
- type: ndcg_at_1000
value: 55.376999999999995
- type: ndcg_at_3
value: 42.588
- type: ndcg_at_5
value: 45.132
- type: precision_at_1
value: 36.338
- type: precision_at_10
value: 9.17
- type: precision_at_100
value: 1.4909999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.315
- type: precision_at_5
value: 14.793000000000001
- type: recall_at_1
value: 30.232
- type: recall_at_10
value: 60.67399999999999
- type: recall_at_100
value: 83.628
- type: recall_at_1000
value: 96.209
- type: recall_at_3
value: 45.48
- type: recall_at_5
value: 52.354
- type: map_at_1
value: 32.237
- type: map_at_10
value: 42.829
- type: map_at_100
value: 44.065
- type: map_at_1000
value: 44.199
- type: map_at_3
value: 39.885999999999996
- type: map_at_5
value: 41.55
- type: mrr_at_1
value: 40.064
- type: mrr_at_10
value: 48.611
- type: mrr_at_100
value: 49.245
- type: mrr_at_1000
value: 49.29
- type: mrr_at_3
value: 46.561
- type: mrr_at_5
value: 47.771
- type: ndcg_at_1
value: 40.064
- type: ndcg_at_10
value: 48.388
- type: ndcg_at_100
value: 52.666999999999994
- type: ndcg_at_1000
value: 54.67100000000001
- type: ndcg_at_3
value: 44.504
- type: ndcg_at_5
value: 46.303
- type: precision_at_1
value: 40.064
- type: precision_at_10
value: 9.051
- type: precision_at_100
value: 1.4500000000000002
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 21.444
- type: precision_at_5
value: 15.045
- type: recall_at_1
value: 32.237
- type: recall_at_10
value: 57.943999999999996
- type: recall_at_100
value: 75.98700000000001
- type: recall_at_1000
value: 88.453
- type: recall_at_3
value: 46.268
- type: recall_at_5
value: 51.459999999999994
- type: map_at_1
value: 38.797
- type: map_at_10
value: 51.263000000000005
- type: map_at_100
value: 52.333
- type: map_at_1000
value: 52.393
- type: map_at_3
value: 47.936
- type: map_at_5
value: 49.844
- type: mrr_at_1
value: 44.389
- type: mrr_at_10
value: 54.601
- type: mrr_at_100
value: 55.300000000000004
- type: mrr_at_1000
value: 55.333
- type: mrr_at_3
value: 52.068999999999996
- type: mrr_at_5
value: 53.627
- type: ndcg_at_1
value: 44.389
- type: ndcg_at_10
value: 57.193000000000005
- type: ndcg_at_100
value: 61.307
- type: ndcg_at_1000
value: 62.529
- type: ndcg_at_3
value: 51.607
- type: ndcg_at_5
value: 54.409
- type: precision_at_1
value: 44.389
- type: precision_at_10
value: 9.26
- type: precision_at_100
value: 1.222
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 23.03
- type: precision_at_5
value: 15.887
- type: recall_at_1
value: 38.797
- type: recall_at_10
value: 71.449
- type: recall_at_100
value: 88.881
- type: recall_at_1000
value: 97.52
- type: recall_at_3
value: 56.503
- type: recall_at_5
value: 63.392
- type: map_at_1
value: 27.291999999999998
- type: map_at_10
value: 35.65
- type: map_at_100
value: 36.689
- type: map_at_1000
value: 36.753
- type: map_at_3
value: 32.995000000000005
- type: map_at_5
value: 34.409
- type: mrr_at_1
value: 29.04
- type: mrr_at_10
value: 37.486000000000004
- type: mrr_at_100
value: 38.394
- type: mrr_at_1000
value: 38.445
- type: mrr_at_3
value: 35.028
- type: mrr_at_5
value: 36.305
- type: ndcg_at_1
value: 29.04
- type: ndcg_at_10
value: 40.613
- type: ndcg_at_100
value: 45.733000000000004
- type: ndcg_at_1000
value: 47.447
- type: ndcg_at_3
value: 35.339999999999996
- type: ndcg_at_5
value: 37.706
- type: precision_at_1
value: 29.04
- type: precision_at_10
value: 6.192
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 14.802000000000001
- type: precision_at_5
value: 10.305
- type: recall_at_1
value: 27.291999999999998
- type: recall_at_10
value: 54.25299999999999
- type: recall_at_100
value: 77.773
- type: recall_at_1000
value: 90.795
- type: recall_at_3
value: 39.731
- type: recall_at_5
value: 45.403999999999996
- type: map_at_1
value: 18.326
- type: map_at_10
value: 26.290999999999997
- type: map_at_100
value: 27.456999999999997
- type: map_at_1000
value: 27.583000000000002
- type: map_at_3
value: 23.578
- type: map_at_5
value: 25.113000000000003
- type: mrr_at_1
value: 22.637
- type: mrr_at_10
value: 31.139
- type: mrr_at_100
value: 32.074999999999996
- type: mrr_at_1000
value: 32.147
- type: mrr_at_3
value: 28.483000000000004
- type: mrr_at_5
value: 29.963
- type: ndcg_at_1
value: 22.637
- type: ndcg_at_10
value: 31.717000000000002
- type: ndcg_at_100
value: 37.201
- type: ndcg_at_1000
value: 40.088
- type: ndcg_at_3
value: 26.686
- type: ndcg_at_5
value: 29.076999999999998
- type: precision_at_1
value: 22.637
- type: precision_at_10
value: 5.7090000000000005
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.894
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 18.326
- type: recall_at_10
value: 43.824999999999996
- type: recall_at_100
value: 67.316
- type: recall_at_1000
value: 87.481
- type: recall_at_3
value: 29.866999999999997
- type: recall_at_5
value: 35.961999999999996
- type: map_at_1
value: 29.875
- type: map_at_10
value: 40.458
- type: map_at_100
value: 41.772
- type: map_at_1000
value: 41.882999999999996
- type: map_at_3
value: 37.086999999999996
- type: map_at_5
value: 39.153
- type: mrr_at_1
value: 36.381
- type: mrr_at_10
value: 46.190999999999995
- type: mrr_at_100
value: 46.983999999999995
- type: mrr_at_1000
value: 47.032000000000004
- type: mrr_at_3
value: 43.486999999999995
- type: mrr_at_5
value: 45.249
- type: ndcg_at_1
value: 36.381
- type: ndcg_at_10
value: 46.602
- type: ndcg_at_100
value: 51.885999999999996
- type: ndcg_at_1000
value: 53.895
- type: ndcg_at_3
value: 41.155
- type: ndcg_at_5
value: 44.182
- type: precision_at_1
value: 36.381
- type: precision_at_10
value: 8.402
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 19.346
- type: precision_at_5
value: 14.09
- type: recall_at_1
value: 29.875
- type: recall_at_10
value: 59.065999999999995
- type: recall_at_100
value: 80.923
- type: recall_at_1000
value: 93.927
- type: recall_at_3
value: 44.462
- type: recall_at_5
value: 51.89
- type: map_at_1
value: 24.94
- type: map_at_10
value: 35.125
- type: map_at_100
value: 36.476
- type: map_at_1000
value: 36.579
- type: map_at_3
value: 31.840000000000003
- type: map_at_5
value: 33.647
- type: mrr_at_1
value: 30.936000000000003
- type: mrr_at_10
value: 40.637
- type: mrr_at_100
value: 41.471000000000004
- type: mrr_at_1000
value: 41.525
- type: mrr_at_3
value: 38.013999999999996
- type: mrr_at_5
value: 39.469
- type: ndcg_at_1
value: 30.936000000000003
- type: ndcg_at_10
value: 41.295
- type: ndcg_at_100
value: 46.92
- type: ndcg_at_1000
value: 49.183
- type: ndcg_at_3
value: 35.811
- type: ndcg_at_5
value: 38.306000000000004
- type: precision_at_1
value: 30.936000000000003
- type: precision_at_10
value: 7.728
- type: precision_at_100
value: 1.226
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.237
- type: precision_at_5
value: 12.42
- type: recall_at_1
value: 24.94
- type: recall_at_10
value: 54.235
- type: recall_at_100
value: 78.314
- type: recall_at_1000
value: 93.973
- type: recall_at_3
value: 38.925
- type: recall_at_5
value: 45.505
- type: map_at_1
value: 26.250833333333333
- type: map_at_10
value: 35.46875
- type: map_at_100
value: 36.667
- type: map_at_1000
value: 36.78025
- type: map_at_3
value: 32.56733333333334
- type: map_at_5
value: 34.20333333333333
- type: mrr_at_1
value: 30.8945
- type: mrr_at_10
value: 39.636833333333335
- type: mrr_at_100
value: 40.46508333333333
- type: mrr_at_1000
value: 40.521249999999995
- type: mrr_at_3
value: 37.140166666666666
- type: mrr_at_5
value: 38.60999999999999
- type: ndcg_at_1
value: 30.8945
- type: ndcg_at_10
value: 40.93441666666667
- type: ndcg_at_100
value: 46.062416666666664
- type: ndcg_at_1000
value: 48.28341666666667
- type: ndcg_at_3
value: 35.97575
- type: ndcg_at_5
value: 38.3785
- type: precision_at_1
value: 30.8945
- type: precision_at_10
value: 7.180250000000001
- type: precision_at_100
value: 1.1468333333333334
- type: precision_at_1000
value: 0.15283333333333332
- type: precision_at_3
value: 16.525583333333334
- type: precision_at_5
value: 11.798333333333332
- type: recall_at_1
value: 26.250833333333333
- type: recall_at_10
value: 52.96108333333333
- type: recall_at_100
value: 75.45908333333334
- type: recall_at_1000
value: 90.73924999999998
- type: recall_at_3
value: 39.25483333333333
- type: recall_at_5
value: 45.37950000000001
- type: map_at_1
value: 24.595
- type: map_at_10
value: 31.747999999999998
- type: map_at_100
value: 32.62
- type: map_at_1000
value: 32.713
- type: map_at_3
value: 29.48
- type: map_at_5
value: 30.635
- type: mrr_at_1
value: 27.607
- type: mrr_at_10
value: 34.449000000000005
- type: mrr_at_100
value: 35.182
- type: mrr_at_1000
value: 35.254000000000005
- type: mrr_at_3
value: 32.413
- type: mrr_at_5
value: 33.372
- type: ndcg_at_1
value: 27.607
- type: ndcg_at_10
value: 36.041000000000004
- type: ndcg_at_100
value: 40.514
- type: ndcg_at_1000
value: 42.851
- type: ndcg_at_3
value: 31.689
- type: ndcg_at_5
value: 33.479
- type: precision_at_1
value: 27.607
- type: precision_at_10
value: 5.66
- type: precision_at_100
value: 0.868
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.264
- type: recall_at_1
value: 24.595
- type: recall_at_10
value: 46.79
- type: recall_at_100
value: 67.413
- type: recall_at_1000
value: 84.753
- type: recall_at_3
value: 34.644999999999996
- type: recall_at_5
value: 39.09
- type: map_at_1
value: 17.333000000000002
- type: map_at_10
value: 24.427
- type: map_at_100
value: 25.576
- type: map_at_1000
value: 25.692999999999998
- type: map_at_3
value: 22.002
- type: map_at_5
value: 23.249
- type: mrr_at_1
value: 20.716
- type: mrr_at_10
value: 28.072000000000003
- type: mrr_at_100
value: 29.067
- type: mrr_at_1000
value: 29.137
- type: mrr_at_3
value: 25.832
- type: mrr_at_5
value: 27.045
- type: ndcg_at_1
value: 20.716
- type: ndcg_at_10
value: 29.109
- type: ndcg_at_100
value: 34.797
- type: ndcg_at_1000
value: 37.503
- type: ndcg_at_3
value: 24.668
- type: ndcg_at_5
value: 26.552999999999997
- type: precision_at_1
value: 20.716
- type: precision_at_10
value: 5.351
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 11.584999999999999
- type: precision_at_5
value: 8.362
- type: recall_at_1
value: 17.333000000000002
- type: recall_at_10
value: 39.604
- type: recall_at_100
value: 65.525
- type: recall_at_1000
value: 84.651
- type: recall_at_3
value: 27.199
- type: recall_at_5
value: 32.019
- type: map_at_1
value: 26.342
- type: map_at_10
value: 35.349000000000004
- type: map_at_100
value: 36.443
- type: map_at_1000
value: 36.548
- type: map_at_3
value: 32.307
- type: map_at_5
value: 34.164
- type: mrr_at_1
value: 31.063000000000002
- type: mrr_at_10
value: 39.703
- type: mrr_at_100
value: 40.555
- type: mrr_at_1000
value: 40.614
- type: mrr_at_3
value: 37.141999999999996
- type: mrr_at_5
value: 38.812000000000005
- type: ndcg_at_1
value: 31.063000000000002
- type: ndcg_at_10
value: 40.873
- type: ndcg_at_100
value: 45.896
- type: ndcg_at_1000
value: 48.205999999999996
- type: ndcg_at_3
value: 35.522
- type: ndcg_at_5
value: 38.419
- type: precision_at_1
value: 31.063000000000002
- type: precision_at_10
value: 6.866
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 16.014
- type: precision_at_5
value: 11.604000000000001
- type: recall_at_1
value: 26.342
- type: recall_at_10
value: 53.40200000000001
- type: recall_at_100
value: 75.251
- type: recall_at_1000
value: 91.13799999999999
- type: recall_at_3
value: 39.103
- type: recall_at_5
value: 46.357
- type: map_at_1
value: 23.71
- type: map_at_10
value: 32.153999999999996
- type: map_at_100
value: 33.821
- type: map_at_1000
value: 34.034
- type: map_at_3
value: 29.376
- type: map_at_5
value: 30.878
- type: mrr_at_1
value: 28.458
- type: mrr_at_10
value: 36.775999999999996
- type: mrr_at_100
value: 37.804
- type: mrr_at_1000
value: 37.858999999999995
- type: mrr_at_3
value: 34.123999999999995
- type: mrr_at_5
value: 35.596
- type: ndcg_at_1
value: 28.458
- type: ndcg_at_10
value: 37.858999999999995
- type: ndcg_at_100
value: 44.194
- type: ndcg_at_1000
value: 46.744
- type: ndcg_at_3
value: 33.348
- type: ndcg_at_5
value: 35.448
- type: precision_at_1
value: 28.458
- type: precision_at_10
value: 7.4510000000000005
- type: precision_at_100
value: 1.5
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 15.809999999999999
- type: precision_at_5
value: 11.462
- type: recall_at_1
value: 23.71
- type: recall_at_10
value: 48.272999999999996
- type: recall_at_100
value: 77.134
- type: recall_at_1000
value: 93.001
- type: recall_at_3
value: 35.480000000000004
- type: recall_at_5
value: 41.19
- type: map_at_1
value: 21.331
- type: map_at_10
value: 28.926000000000002
- type: map_at_100
value: 29.855999999999998
- type: map_at_1000
value: 29.957
- type: map_at_3
value: 26.395999999999997
- type: map_at_5
value: 27.933000000000003
- type: mrr_at_1
value: 23.105
- type: mrr_at_10
value: 31.008000000000003
- type: mrr_at_100
value: 31.819999999999997
- type: mrr_at_1000
value: 31.887999999999998
- type: mrr_at_3
value: 28.466
- type: mrr_at_5
value: 30.203000000000003
- type: ndcg_at_1
value: 23.105
- type: ndcg_at_10
value: 33.635999999999996
- type: ndcg_at_100
value: 38.277
- type: ndcg_at_1000
value: 40.907
- type: ndcg_at_3
value: 28.791
- type: ndcg_at_5
value: 31.528
- type: precision_at_1
value: 23.105
- type: precision_at_10
value: 5.323
- type: precision_at_100
value: 0.815
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.384
- type: precision_at_5
value: 9.02
- type: recall_at_1
value: 21.331
- type: recall_at_10
value: 46.018
- type: recall_at_100
value: 67.364
- type: recall_at_1000
value: 86.97
- type: recall_at_3
value: 33.395
- type: recall_at_5
value: 39.931
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.011000000000003
- type: map_at_10
value: 28.816999999999997
- type: map_at_100
value: 30.761
- type: map_at_1000
value: 30.958000000000002
- type: map_at_3
value: 24.044999999999998
- type: map_at_5
value: 26.557
- type: mrr_at_1
value: 38.696999999999996
- type: mrr_at_10
value: 50.464
- type: mrr_at_100
value: 51.193999999999996
- type: mrr_at_1000
value: 51.219
- type: mrr_at_3
value: 47.339999999999996
- type: mrr_at_5
value: 49.346000000000004
- type: ndcg_at_1
value: 38.696999999999996
- type: ndcg_at_10
value: 38.53
- type: ndcg_at_100
value: 45.525
- type: ndcg_at_1000
value: 48.685
- type: ndcg_at_3
value: 32.282
- type: ndcg_at_5
value: 34.482
- type: precision_at_1
value: 38.696999999999996
- type: precision_at_10
value: 11.895999999999999
- type: precision_at_100
value: 1.95
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 24.038999999999998
- type: precision_at_5
value: 18.332
- type: recall_at_1
value: 17.011000000000003
- type: recall_at_10
value: 44.452999999999996
- type: recall_at_100
value: 68.223
- type: recall_at_1000
value: 85.653
- type: recall_at_3
value: 28.784
- type: recall_at_5
value: 35.66
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.516
- type: map_at_10
value: 21.439
- type: map_at_100
value: 31.517
- type: map_at_1000
value: 33.267
- type: map_at_3
value: 15.004999999999999
- type: map_at_5
value: 17.793999999999997
- type: mrr_at_1
value: 71.25
- type: mrr_at_10
value: 79.071
- type: mrr_at_100
value: 79.325
- type: mrr_at_1000
value: 79.33
- type: mrr_at_3
value: 77.708
- type: mrr_at_5
value: 78.546
- type: ndcg_at_1
value: 58.62500000000001
- type: ndcg_at_10
value: 44.889
- type: ndcg_at_100
value: 50.536
- type: ndcg_at_1000
value: 57.724
- type: ndcg_at_3
value: 49.32
- type: ndcg_at_5
value: 46.775
- type: precision_at_1
value: 71.25
- type: precision_at_10
value: 36.175000000000004
- type: precision_at_100
value: 11.940000000000001
- type: precision_at_1000
value: 2.178
- type: precision_at_3
value: 53.583000000000006
- type: precision_at_5
value: 45.550000000000004
- type: recall_at_1
value: 9.516
- type: recall_at_10
value: 27.028000000000002
- type: recall_at_100
value: 57.581
- type: recall_at_1000
value: 80.623
- type: recall_at_3
value: 16.313
- type: recall_at_5
value: 20.674
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.74999999999999
- type: f1
value: 46.46706502669774
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 77.266
- type: map_at_10
value: 84.89999999999999
- type: map_at_100
value: 85.109
- type: map_at_1000
value: 85.123
- type: map_at_3
value: 83.898
- type: map_at_5
value: 84.541
- type: mrr_at_1
value: 83.138
- type: mrr_at_10
value: 89.37
- type: mrr_at_100
value: 89.432
- type: mrr_at_1000
value: 89.43299999999999
- type: mrr_at_3
value: 88.836
- type: mrr_at_5
value: 89.21
- type: ndcg_at_1
value: 83.138
- type: ndcg_at_10
value: 88.244
- type: ndcg_at_100
value: 88.98700000000001
- type: ndcg_at_1000
value: 89.21900000000001
- type: ndcg_at_3
value: 86.825
- type: ndcg_at_5
value: 87.636
- type: precision_at_1
value: 83.138
- type: precision_at_10
value: 10.47
- type: precision_at_100
value: 1.1079999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.933
- type: precision_at_5
value: 20.36
- type: recall_at_1
value: 77.266
- type: recall_at_10
value: 94.063
- type: recall_at_100
value: 96.993
- type: recall_at_1000
value: 98.414
- type: recall_at_3
value: 90.228
- type: recall_at_5
value: 92.328
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.319
- type: map_at_10
value: 36.943
- type: map_at_100
value: 38.951
- type: map_at_1000
value: 39.114
- type: map_at_3
value: 32.82
- type: map_at_5
value: 34.945
- type: mrr_at_1
value: 44.135999999999996
- type: mrr_at_10
value: 53.071999999999996
- type: mrr_at_100
value: 53.87
- type: mrr_at_1000
value: 53.90200000000001
- type: mrr_at_3
value: 50.77199999999999
- type: mrr_at_5
value: 52.129999999999995
- type: ndcg_at_1
value: 44.135999999999996
- type: ndcg_at_10
value: 44.836
- type: ndcg_at_100
value: 51.754
- type: ndcg_at_1000
value: 54.36
- type: ndcg_at_3
value: 41.658
- type: ndcg_at_5
value: 42.354
- type: precision_at_1
value: 44.135999999999996
- type: precision_at_10
value: 12.284
- type: precision_at_100
value: 1.952
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 27.828999999999997
- type: precision_at_5
value: 20.093
- type: recall_at_1
value: 22.319
- type: recall_at_10
value: 51.528
- type: recall_at_100
value: 76.70700000000001
- type: recall_at_1000
value: 92.143
- type: recall_at_3
value: 38.641
- type: recall_at_5
value: 43.653999999999996
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.182
- type: map_at_10
value: 65.146
- type: map_at_100
value: 66.023
- type: map_at_1000
value: 66.078
- type: map_at_3
value: 61.617999999999995
- type: map_at_5
value: 63.82299999999999
- type: mrr_at_1
value: 80.365
- type: mrr_at_10
value: 85.79
- type: mrr_at_100
value: 85.963
- type: mrr_at_1000
value: 85.968
- type: mrr_at_3
value: 84.952
- type: mrr_at_5
value: 85.503
- type: ndcg_at_1
value: 80.365
- type: ndcg_at_10
value: 73.13499999999999
- type: ndcg_at_100
value: 76.133
- type: ndcg_at_1000
value: 77.151
- type: ndcg_at_3
value: 68.255
- type: ndcg_at_5
value: 70.978
- type: precision_at_1
value: 80.365
- type: precision_at_10
value: 15.359
- type: precision_at_100
value: 1.7690000000000001
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 44.024
- type: precision_at_5
value: 28.555999999999997
- type: recall_at_1
value: 40.182
- type: recall_at_10
value: 76.793
- type: recall_at_100
value: 88.474
- type: recall_at_1000
value: 95.159
- type: recall_at_3
value: 66.036
- type: recall_at_5
value: 71.391
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.7796
- type: ap
value: 89.24883716810874
- type: f1
value: 92.7706903433313
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.016
- type: map_at_10
value: 34.408
- type: map_at_100
value: 35.592
- type: map_at_1000
value: 35.64
- type: map_at_3
value: 30.459999999999997
- type: map_at_5
value: 32.721000000000004
- type: mrr_at_1
value: 22.593
- type: mrr_at_10
value: 34.993
- type: mrr_at_100
value: 36.113
- type: mrr_at_1000
value: 36.156
- type: mrr_at_3
value: 31.101
- type: mrr_at_5
value: 33.364
- type: ndcg_at_1
value: 22.579
- type: ndcg_at_10
value: 41.404999999999994
- type: ndcg_at_100
value: 47.018
- type: ndcg_at_1000
value: 48.211999999999996
- type: ndcg_at_3
value: 33.389
- type: ndcg_at_5
value: 37.425000000000004
- type: precision_at_1
value: 22.579
- type: precision_at_10
value: 6.59
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.241000000000001
- type: precision_at_5
value: 10.59
- type: recall_at_1
value: 22.016
- type: recall_at_10
value: 62.927
- type: recall_at_100
value: 88.72
- type: recall_at_1000
value: 97.80799999999999
- type: recall_at_3
value: 41.229
- type: recall_at_5
value: 50.88
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.01732786137711
- type: f1
value: 93.76353126402202
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.91746466028272
- type: f1
value: 57.715651682646765
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.5030262273033
- type: f1
value: 74.6693629986121
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.74781439139207
- type: f1
value: 79.96684171018774
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.2156206892017
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.180539484816137
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.51125957874274
- type: mrr
value: 33.777037359249995
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.248
- type: map_at_10
value: 15.340000000000002
- type: map_at_100
value: 19.591
- type: map_at_1000
value: 21.187
- type: map_at_3
value: 11.329
- type: map_at_5
value: 13.209999999999999
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 57.493
- type: mrr_at_100
value: 58.038999999999994
- type: mrr_at_1000
value: 58.07
- type: mrr_at_3
value: 55.36600000000001
- type: mrr_at_5
value: 56.635999999999996
- type: ndcg_at_1
value: 46.129999999999995
- type: ndcg_at_10
value: 38.653999999999996
- type: ndcg_at_100
value: 36.288
- type: ndcg_at_1000
value: 44.765
- type: ndcg_at_3
value: 43.553
- type: ndcg_at_5
value: 41.317
- type: precision_at_1
value: 47.368
- type: precision_at_10
value: 28.669
- type: precision_at_100
value: 9.158
- type: precision_at_1000
value: 2.207
- type: precision_at_3
value: 40.97
- type: precision_at_5
value: 35.604
- type: recall_at_1
value: 7.248
- type: recall_at_10
value: 19.46
- type: recall_at_100
value: 37.214000000000006
- type: recall_at_1000
value: 67.64099999999999
- type: recall_at_3
value: 12.025
- type: recall_at_5
value: 15.443999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.595000000000002
- type: map_at_10
value: 47.815999999999995
- type: map_at_100
value: 48.811
- type: map_at_1000
value: 48.835
- type: map_at_3
value: 43.225
- type: map_at_5
value: 46.017
- type: mrr_at_1
value: 35.689
- type: mrr_at_10
value: 50.341
- type: mrr_at_100
value: 51.044999999999995
- type: mrr_at_1000
value: 51.062
- type: mrr_at_3
value: 46.553
- type: mrr_at_5
value: 48.918
- type: ndcg_at_1
value: 35.66
- type: ndcg_at_10
value: 55.859
- type: ndcg_at_100
value: 59.864
- type: ndcg_at_1000
value: 60.419999999999995
- type: ndcg_at_3
value: 47.371
- type: ndcg_at_5
value: 51.995000000000005
- type: precision_at_1
value: 35.66
- type: precision_at_10
value: 9.27
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 21.63
- type: precision_at_5
value: 15.655
- type: recall_at_1
value: 31.595000000000002
- type: recall_at_10
value: 77.704
- type: recall_at_100
value: 94.774
- type: recall_at_1000
value: 98.919
- type: recall_at_3
value: 56.052
- type: recall_at_5
value: 66.623
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.489
- type: map_at_10
value: 85.411
- type: map_at_100
value: 86.048
- type: map_at_1000
value: 86.064
- type: map_at_3
value: 82.587
- type: map_at_5
value: 84.339
- type: mrr_at_1
value: 82.28
- type: mrr_at_10
value: 88.27199999999999
- type: mrr_at_100
value: 88.362
- type: mrr_at_1000
value: 88.362
- type: mrr_at_3
value: 87.372
- type: mrr_at_5
value: 87.995
- type: ndcg_at_1
value: 82.27
- type: ndcg_at_10
value: 89.023
- type: ndcg_at_100
value: 90.191
- type: ndcg_at_1000
value: 90.266
- type: ndcg_at_3
value: 86.37
- type: ndcg_at_5
value: 87.804
- type: precision_at_1
value: 82.27
- type: precision_at_10
value: 13.469000000000001
- type: precision_at_100
value: 1.533
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.797
- type: precision_at_5
value: 24.734
- type: recall_at_1
value: 71.489
- type: recall_at_10
value: 95.824
- type: recall_at_100
value: 99.70599999999999
- type: recall_at_1000
value: 99.979
- type: recall_at_3
value: 88.099
- type: recall_at_5
value: 92.285
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 60.52398807444541
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 65.34855891507871
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.188000000000001
- type: map_at_10
value: 13.987
- type: map_at_100
value: 16.438
- type: map_at_1000
value: 16.829
- type: map_at_3
value: 9.767000000000001
- type: map_at_5
value: 11.912
- type: mrr_at_1
value: 25.6
- type: mrr_at_10
value: 37.744
- type: mrr_at_100
value: 38.847
- type: mrr_at_1000
value: 38.894
- type: mrr_at_3
value: 34.166999999999994
- type: mrr_at_5
value: 36.207
- type: ndcg_at_1
value: 25.6
- type: ndcg_at_10
value: 22.980999999999998
- type: ndcg_at_100
value: 32.039
- type: ndcg_at_1000
value: 38.157000000000004
- type: ndcg_at_3
value: 21.567
- type: ndcg_at_5
value: 19.070999999999998
- type: precision_at_1
value: 25.6
- type: precision_at_10
value: 12.02
- type: precision_at_100
value: 2.5100000000000002
- type: precision_at_1000
value: 0.396
- type: precision_at_3
value: 20.333000000000002
- type: precision_at_5
value: 16.98
- type: recall_at_1
value: 5.188000000000001
- type: recall_at_10
value: 24.372
- type: recall_at_100
value: 50.934999999999995
- type: recall_at_1000
value: 80.477
- type: recall_at_3
value: 12.363
- type: recall_at_5
value: 17.203
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 87.24286275535398
- type: cos_sim_spearman
value: 82.62333770991818
- type: euclidean_pearson
value: 84.60353717637284
- type: euclidean_spearman
value: 82.32990108810047
- type: manhattan_pearson
value: 84.6089049738196
- type: manhattan_spearman
value: 82.33361785438936
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.87428858503165
- type: cos_sim_spearman
value: 79.09145886519929
- type: euclidean_pearson
value: 86.42669231664036
- type: euclidean_spearman
value: 80.03127375435449
- type: manhattan_pearson
value: 86.41330338305022
- type: manhattan_spearman
value: 80.02492538673368
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.67912277322645
- type: cos_sim_spearman
value: 89.6171319711762
- type: euclidean_pearson
value: 86.56571917398725
- type: euclidean_spearman
value: 87.71216907898948
- type: manhattan_pearson
value: 86.57459050182473
- type: manhattan_spearman
value: 87.71916648349993
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.71957379085862
- type: cos_sim_spearman
value: 85.01784075851465
- type: euclidean_pearson
value: 84.7407848472801
- type: euclidean_spearman
value: 84.61063091345538
- type: manhattan_pearson
value: 84.71494352494403
- type: manhattan_spearman
value: 84.58772077604254
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.40508326325175
- type: cos_sim_spearman
value: 89.50912897763186
- type: euclidean_pearson
value: 87.82349070086627
- type: euclidean_spearman
value: 88.44179162727521
- type: manhattan_pearson
value: 87.80181927025595
- type: manhattan_spearman
value: 88.43205129636243
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.35846741715478
- type: cos_sim_spearman
value: 86.61172476741842
- type: euclidean_pearson
value: 84.60123125491637
- type: euclidean_spearman
value: 85.3001948141827
- type: manhattan_pearson
value: 84.56231142658329
- type: manhattan_spearman
value: 85.23579900798813
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.94539129818824
- type: cos_sim_spearman
value: 88.99349064256742
- type: euclidean_pearson
value: 88.7142444640351
- type: euclidean_spearman
value: 88.34120813505011
- type: manhattan_pearson
value: 88.70363008238084
- type: manhattan_spearman
value: 88.31952816956954
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.29910260369893
- type: cos_sim_spearman
value: 68.79263346213466
- type: euclidean_pearson
value: 68.41627521422252
- type: euclidean_spearman
value: 66.61602587398579
- type: manhattan_pearson
value: 68.49402183447361
- type: manhattan_spearman
value: 66.80157792354453
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.43703906343708
- type: cos_sim_spearman
value: 89.06081805093662
- type: euclidean_pearson
value: 87.48311456299662
- type: euclidean_spearman
value: 88.07417597580013
- type: manhattan_pearson
value: 87.48202249768894
- type: manhattan_spearman
value: 88.04758031111642
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.49080620485203
- type: mrr
value: 96.19145378949301
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.317
- type: map_at_10
value: 69.296
- type: map_at_100
value: 69.738
- type: map_at_1000
value: 69.759
- type: map_at_3
value: 66.12599999999999
- type: map_at_5
value: 67.532
- type: mrr_at_1
value: 62
- type: mrr_at_10
value: 70.176
- type: mrr_at_100
value: 70.565
- type: mrr_at_1000
value: 70.583
- type: mrr_at_3
value: 67.833
- type: mrr_at_5
value: 68.93299999999999
- type: ndcg_at_1
value: 62
- type: ndcg_at_10
value: 74.069
- type: ndcg_at_100
value: 76.037
- type: ndcg_at_1000
value: 76.467
- type: ndcg_at_3
value: 68.628
- type: ndcg_at_5
value: 70.57600000000001
- type: precision_at_1
value: 62
- type: precision_at_10
value: 10
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.667
- type: precision_at_5
value: 17.4
- type: recall_at_1
value: 59.317
- type: recall_at_10
value: 87.822
- type: recall_at_100
value: 96.833
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 73.06099999999999
- type: recall_at_5
value: 77.928
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.88910891089108
- type: cos_sim_ap
value: 97.236958456951
- type: cos_sim_f1
value: 94.39999999999999
- type: cos_sim_precision
value: 94.39999999999999
- type: cos_sim_recall
value: 94.39999999999999
- type: dot_accuracy
value: 99.82574257425742
- type: dot_ap
value: 94.94344759441888
- type: dot_f1
value: 91.17352056168507
- type: dot_precision
value: 91.44869215291752
- type: dot_recall
value: 90.9
- type: euclidean_accuracy
value: 99.88415841584158
- type: euclidean_ap
value: 97.2044250782305
- type: euclidean_f1
value: 94.210786739238
- type: euclidean_precision
value: 93.24191968658178
- type: euclidean_recall
value: 95.19999999999999
- type: manhattan_accuracy
value: 99.88613861386139
- type: manhattan_ap
value: 97.20683205497689
- type: manhattan_f1
value: 94.2643391521197
- type: manhattan_precision
value: 94.02985074626866
- type: manhattan_recall
value: 94.5
- type: max_accuracy
value: 99.88910891089108
- type: max_ap
value: 97.236958456951
- type: max_f1
value: 94.39999999999999
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.53940781726187
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.71865011295108
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.3218674533331
- type: mrr
value: 56.28279910449028
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.723915667479673
- type: cos_sim_spearman
value: 32.029070449745234
- type: dot_pearson
value: 28.864944212481454
- type: dot_spearman
value: 27.939266999596725
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.231
- type: map_at_10
value: 1.949
- type: map_at_100
value: 10.023
- type: map_at_1000
value: 23.485
- type: map_at_3
value: 0.652
- type: map_at_5
value: 1.054
- type: mrr_at_1
value: 86
- type: mrr_at_10
value: 92.067
- type: mrr_at_100
value: 92.067
- type: mrr_at_1000
value: 92.067
- type: mrr_at_3
value: 91.667
- type: mrr_at_5
value: 92.067
- type: ndcg_at_1
value: 83
- type: ndcg_at_10
value: 76.32900000000001
- type: ndcg_at_100
value: 54.662
- type: ndcg_at_1000
value: 48.062
- type: ndcg_at_3
value: 81.827
- type: ndcg_at_5
value: 80.664
- type: precision_at_1
value: 86
- type: precision_at_10
value: 80
- type: precision_at_100
value: 55.48
- type: precision_at_1000
value: 20.938000000000002
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 84.39999999999999
- type: recall_at_1
value: 0.231
- type: recall_at_10
value: 2.158
- type: recall_at_100
value: 13.344000000000001
- type: recall_at_1000
value: 44.31
- type: recall_at_3
value: 0.6779999999999999
- type: recall_at_5
value: 1.13
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.524
- type: map_at_10
value: 10.183
- type: map_at_100
value: 16.625
- type: map_at_1000
value: 18.017
- type: map_at_3
value: 5.169
- type: map_at_5
value: 6.772
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 47.128
- type: mrr_at_100
value: 48.458
- type: mrr_at_1000
value: 48.473
- type: mrr_at_3
value: 44.897999999999996
- type: mrr_at_5
value: 45.306000000000004
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 24.928
- type: ndcg_at_100
value: 37.613
- type: ndcg_at_1000
value: 48.528
- type: ndcg_at_3
value: 28.829
- type: ndcg_at_5
value: 25.237
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_100
value: 8.02
- type: precision_at_1000
value: 1.537
- type: precision_at_3
value: 30.612000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.524
- type: recall_at_10
value: 16.38
- type: recall_at_100
value: 49.529
- type: recall_at_1000
value: 83.598
- type: recall_at_3
value: 6.411
- type: recall_at_5
value: 8.932
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.09020000000001
- type: ap
value: 14.451710060978993
- type: f1
value: 54.7874410609049
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.745331069609506
- type: f1
value: 60.08387848592697
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.71549485462037
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.39345532574357
- type: cos_sim_ap
value: 78.16796549696478
- type: cos_sim_f1
value: 71.27713276123171
- type: cos_sim_precision
value: 68.3115626511853
- type: cos_sim_recall
value: 74.51187335092348
- type: dot_accuracy
value: 85.12248912201228
- type: dot_ap
value: 69.26039256107077
- type: dot_f1
value: 65.04294321240867
- type: dot_precision
value: 63.251059586138126
- type: dot_recall
value: 66.93931398416886
- type: euclidean_accuracy
value: 87.07754664123503
- type: euclidean_ap
value: 77.7872176038945
- type: euclidean_f1
value: 70.85587801278899
- type: euclidean_precision
value: 66.3519115614924
- type: euclidean_recall
value: 76.01583113456465
- type: manhattan_accuracy
value: 87.07754664123503
- type: manhattan_ap
value: 77.7341400185556
- type: manhattan_f1
value: 70.80310880829015
- type: manhattan_precision
value: 69.54198473282443
- type: manhattan_recall
value: 72.1108179419525
- type: max_accuracy
value: 87.39345532574357
- type: max_ap
value: 78.16796549696478
- type: max_f1
value: 71.27713276123171
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.09457833663213
- type: cos_sim_ap
value: 86.33024314706873
- type: cos_sim_f1
value: 78.59623733719248
- type: cos_sim_precision
value: 74.13322413322413
- type: cos_sim_recall
value: 83.63104404065291
- type: dot_accuracy
value: 88.3086894089339
- type: dot_ap
value: 83.92225241805097
- type: dot_f1
value: 76.8721826377781
- type: dot_precision
value: 72.8168044077135
- type: dot_recall
value: 81.40591315060055
- type: euclidean_accuracy
value: 88.77052043311213
- type: euclidean_ap
value: 85.7410710218755
- type: euclidean_f1
value: 77.97705489398781
- type: euclidean_precision
value: 73.77713657598241
- type: euclidean_recall
value: 82.68401601478288
- type: manhattan_accuracy
value: 88.73753250281368
- type: manhattan_ap
value: 85.72867199072802
- type: manhattan_f1
value: 77.89774182922812
- type: manhattan_precision
value: 74.23787931635857
- type: manhattan_recall
value: 81.93717277486911
- type: max_accuracy
value: 89.09457833663213
- type: max_ap
value: 86.33024314706873
- type: max_f1
value: 78.59623733719248
---
# [Universal AnglE Embedding](https://github.com/SeanLee97/AnglE)
📢 `WhereIsAI/UAE-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.**
**If you use it for academic papers, you could cite us via 👉 [citation info](#citation).**
**🤝 Follow us on:**
- GitHub: https://github.com/SeanLee97/AnglE.
- Preprint Paper: [AnglE-optimized Text Embeddings](https://arxiv.org/abs/2309.12871)
- Conference Paper: [AoE: Angle-optimized Embeddings for Semantic Textual Similarity](https://aclanthology.org/2024.acl-long.101/) (ACL24)
- **📘 Documentation**: https://angle.readthedocs.io/en/latest/index.html
Welcome to using AnglE to train and infer powerful sentence embeddings.
**🏆 Achievements**
- 📅 May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference
- 📅 Dec 4, 2024 | 🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of 64.64!
**🧑🤝🧑 Siblings:**
- [WhereIsAI/UAE-Code-Large-V1](https://huggingface.co/WhereIsAI/UAE-Code-Large-V1): This model can be used for code or GitHub issue similarity measurement.
# Usage
## 1. angle_emb
```bash
python -m pip install -U angle-emb
```
1) Non-Retrieval Tasks
There is no need to specify any prompts.
```python
from angle_emb import AnglE
from angle_emb.utils import cosine_similarity
angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
doc_vecs = angle.encode([
'The weather is great!',
'The weather is very good!',
'i am going to bed'
], normalize_embedding=True)
for i, dv1 in enumerate(doc_vecs):
for dv2 in doc_vecs[i+1:]:
print(cosine_similarity(dv1, dv2))
```
2) Retrieval Tasks
For retrieval purposes, please use the prompt `Prompts.C` for query (not for document).
```python
from angle_emb import AnglE, Prompts
from angle_emb.utils import cosine_similarity
angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
qv = angle.encode(Prompts.C.format(text='what is the weather?'))
doc_vecs = angle.encode([
'The weather is great!',
'it is rainy today.',
'i am going to bed'
])
for dv in doc_vecs:
print(cosine_similarity(qv[0], dv))
```
## 2. sentence transformer
```python
from angle_emb import Prompts
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("WhereIsAI/UAE-Large-V1").cuda()
qv = model.encode(Prompts.C.format(text='what is the weather?'))
doc_vecs = model.encode([
'The weather is great!',
'it is rainy today.',
'i am going to bed'
])
for dv in doc_vecs:
print(1 - spatial.distance.cosine(qv, dv))
```
## 3. Infinity
[Infinity](https://github.com/michaelfeil/infinity) is a MIT licensed server for OpenAI-compatible deployment.
```
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:latest \
v2 --model-id WhereIsAI/UAE-Large-V1 --revision "369c368f70f16a613f19f5598d4f12d9f44235d4" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997
```
# Citation
If you use our pre-trained models, welcome to support us by citing our work:
```
@article{li2023angle,
title={AnglE-optimized Text Embeddings},
author={Li, Xianming and Li, Jing},
journal={arXiv preprint arXiv:2309.12871},
year={2023}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
thenlper/gte-small | thenlper | sentence-similarity | [
"sentence-transformers",
"pytorch",
"tf",
"coreml",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"sentence-similarity",
"Sentence Transformers",
"en",
"arxiv:2308.03281",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-07-27T10:14:55 | 2024-11-16T08:17:33 | 3,841,887 | 152 | ---
language:
- en
license: mit
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.22388059701493
- type: ap
value: 36.09895941426988
- type: f1
value: 67.3205651539195
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.81894999999999
- type: ap
value: 88.5240138417305
- type: f1
value: 91.80367382706962
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.032
- type: f1
value: 47.4490665674719
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.604
- type: map_at_100
value: 47.535
- type: map_at_1000
value: 47.538000000000004
- type: map_at_3
value: 41.833
- type: map_at_5
value: 44.61
- type: mrr_at_1
value: 31.223
- type: mrr_at_10
value: 46.794000000000004
- type: mrr_at_100
value: 47.725
- type: mrr_at_1000
value: 47.727000000000004
- type: mrr_at_3
value: 42.07
- type: mrr_at_5
value: 44.812000000000005
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 55.440999999999995
- type: ndcg_at_100
value: 59.134
- type: ndcg_at_1000
value: 59.199
- type: ndcg_at_3
value: 45.599000000000004
- type: ndcg_at_5
value: 50.637
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.364
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.848000000000003
- type: precision_at_5
value: 13.77
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 83.64200000000001
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.543
- type: recall_at_5
value: 68.848
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.90178078197678
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.25728393431922
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.720297062897764
- type: mrr
value: 75.24139295607439
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.43527309184616
- type: cos_sim_spearman
value: 88.17128615100206
- type: euclidean_pearson
value: 87.89922623089282
- type: euclidean_spearman
value: 87.96104039655451
- type: manhattan_pearson
value: 87.9818290932077
- type: manhattan_spearman
value: 88.00923426576885
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.0844155844156
- type: f1
value: 84.01485017302213
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.36574769259432
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.4857033165287
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.261
- type: map_at_10
value: 42.419000000000004
- type: map_at_100
value: 43.927
- type: map_at_1000
value: 44.055
- type: map_at_3
value: 38.597
- type: map_at_5
value: 40.701
- type: mrr_at_1
value: 36.91
- type: mrr_at_10
value: 48.02
- type: mrr_at_100
value: 48.658
- type: mrr_at_1000
value: 48.708
- type: mrr_at_3
value: 44.945
- type: mrr_at_5
value: 46.705000000000005
- type: ndcg_at_1
value: 36.91
- type: ndcg_at_10
value: 49.353
- type: ndcg_at_100
value: 54.456
- type: ndcg_at_1000
value: 56.363
- type: ndcg_at_3
value: 43.483
- type: ndcg_at_5
value: 46.150999999999996
- type: precision_at_1
value: 36.91
- type: precision_at_10
value: 9.700000000000001
- type: precision_at_100
value: 1.557
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.078
- type: precision_at_5
value: 15.421999999999999
- type: recall_at_1
value: 30.261
- type: recall_at_10
value: 63.242
- type: recall_at_100
value: 84.09100000000001
- type: recall_at_1000
value: 96.143
- type: recall_at_3
value: 46.478
- type: recall_at_5
value: 53.708
- type: map_at_1
value: 31.145
- type: map_at_10
value: 40.996
- type: map_at_100
value: 42.266999999999996
- type: map_at_1000
value: 42.397
- type: map_at_3
value: 38.005
- type: map_at_5
value: 39.628
- type: mrr_at_1
value: 38.344
- type: mrr_at_10
value: 46.827000000000005
- type: mrr_at_100
value: 47.446
- type: mrr_at_1000
value: 47.489
- type: mrr_at_3
value: 44.448
- type: mrr_at_5
value: 45.747
- type: ndcg_at_1
value: 38.344
- type: ndcg_at_10
value: 46.733000000000004
- type: ndcg_at_100
value: 51.103
- type: ndcg_at_1000
value: 53.075
- type: ndcg_at_3
value: 42.366
- type: ndcg_at_5
value: 44.242
- type: precision_at_1
value: 38.344
- type: precision_at_10
value: 8.822000000000001
- type: precision_at_100
value: 1.417
- type: precision_at_1000
value: 0.187
- type: precision_at_3
value: 20.403
- type: precision_at_5
value: 14.306
- type: recall_at_1
value: 31.145
- type: recall_at_10
value: 56.909
- type: recall_at_100
value: 75.274
- type: recall_at_1000
value: 87.629
- type: recall_at_3
value: 43.784
- type: recall_at_5
value: 49.338
- type: map_at_1
value: 38.83
- type: map_at_10
value: 51.553000000000004
- type: map_at_100
value: 52.581
- type: map_at_1000
value: 52.638
- type: map_at_3
value: 48.112
- type: map_at_5
value: 50.095
- type: mrr_at_1
value: 44.513999999999996
- type: mrr_at_10
value: 54.998000000000005
- type: mrr_at_100
value: 55.650999999999996
- type: mrr_at_1000
value: 55.679
- type: mrr_at_3
value: 52.602000000000004
- type: mrr_at_5
value: 53.931
- type: ndcg_at_1
value: 44.513999999999996
- type: ndcg_at_10
value: 57.67400000000001
- type: ndcg_at_100
value: 61.663999999999994
- type: ndcg_at_1000
value: 62.743
- type: ndcg_at_3
value: 51.964
- type: ndcg_at_5
value: 54.773
- type: precision_at_1
value: 44.513999999999996
- type: precision_at_10
value: 9.423
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 23.323
- type: precision_at_5
value: 16.163
- type: recall_at_1
value: 38.83
- type: recall_at_10
value: 72.327
- type: recall_at_100
value: 89.519
- type: recall_at_1000
value: 97.041
- type: recall_at_3
value: 57.206
- type: recall_at_5
value: 63.88399999999999
- type: map_at_1
value: 25.484
- type: map_at_10
value: 34.527
- type: map_at_100
value: 35.661
- type: map_at_1000
value: 35.739
- type: map_at_3
value: 32.199
- type: map_at_5
value: 33.632
- type: mrr_at_1
value: 27.458
- type: mrr_at_10
value: 36.543
- type: mrr_at_100
value: 37.482
- type: mrr_at_1000
value: 37.543
- type: mrr_at_3
value: 34.256
- type: mrr_at_5
value: 35.618
- type: ndcg_at_1
value: 27.458
- type: ndcg_at_10
value: 39.396
- type: ndcg_at_100
value: 44.742
- type: ndcg_at_1000
value: 46.708
- type: ndcg_at_3
value: 34.817
- type: ndcg_at_5
value: 37.247
- type: precision_at_1
value: 27.458
- type: precision_at_10
value: 5.976999999999999
- type: precision_at_100
value: 0.907
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 14.878
- type: precision_at_5
value: 10.35
- type: recall_at_1
value: 25.484
- type: recall_at_10
value: 52.317
- type: recall_at_100
value: 76.701
- type: recall_at_1000
value: 91.408
- type: recall_at_3
value: 40.043
- type: recall_at_5
value: 45.879
- type: map_at_1
value: 16.719
- type: map_at_10
value: 25.269000000000002
- type: map_at_100
value: 26.442
- type: map_at_1000
value: 26.557
- type: map_at_3
value: 22.56
- type: map_at_5
value: 24.082
- type: mrr_at_1
value: 20.896
- type: mrr_at_10
value: 29.982999999999997
- type: mrr_at_100
value: 30.895
- type: mrr_at_1000
value: 30.961
- type: mrr_at_3
value: 27.239
- type: mrr_at_5
value: 28.787000000000003
- type: ndcg_at_1
value: 20.896
- type: ndcg_at_10
value: 30.814000000000004
- type: ndcg_at_100
value: 36.418
- type: ndcg_at_1000
value: 39.182
- type: ndcg_at_3
value: 25.807999999999996
- type: ndcg_at_5
value: 28.143
- type: precision_at_1
value: 20.896
- type: precision_at_10
value: 5.821
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 12.562000000000001
- type: precision_at_5
value: 9.254
- type: recall_at_1
value: 16.719
- type: recall_at_10
value: 43.155
- type: recall_at_100
value: 67.831
- type: recall_at_1000
value: 87.617
- type: recall_at_3
value: 29.259
- type: recall_at_5
value: 35.260999999999996
- type: map_at_1
value: 29.398999999999997
- type: map_at_10
value: 39.876
- type: map_at_100
value: 41.205999999999996
- type: map_at_1000
value: 41.321999999999996
- type: map_at_3
value: 36.588
- type: map_at_5
value: 38.538
- type: mrr_at_1
value: 35.9
- type: mrr_at_10
value: 45.528
- type: mrr_at_100
value: 46.343
- type: mrr_at_1000
value: 46.388
- type: mrr_at_3
value: 42.862
- type: mrr_at_5
value: 44.440000000000005
- type: ndcg_at_1
value: 35.9
- type: ndcg_at_10
value: 45.987
- type: ndcg_at_100
value: 51.370000000000005
- type: ndcg_at_1000
value: 53.400000000000006
- type: ndcg_at_3
value: 40.841
- type: ndcg_at_5
value: 43.447
- type: precision_at_1
value: 35.9
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 1.283
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 19.538
- type: precision_at_5
value: 13.975000000000001
- type: recall_at_1
value: 29.398999999999997
- type: recall_at_10
value: 58.361
- type: recall_at_100
value: 81.081
- type: recall_at_1000
value: 94.004
- type: recall_at_3
value: 43.657000000000004
- type: recall_at_5
value: 50.519999999999996
- type: map_at_1
value: 21.589
- type: map_at_10
value: 31.608999999999998
- type: map_at_100
value: 33.128
- type: map_at_1000
value: 33.247
- type: map_at_3
value: 28.671999999999997
- type: map_at_5
value: 30.233999999999998
- type: mrr_at_1
value: 26.712000000000003
- type: mrr_at_10
value: 36.713
- type: mrr_at_100
value: 37.713
- type: mrr_at_1000
value: 37.771
- type: mrr_at_3
value: 34.075
- type: mrr_at_5
value: 35.451
- type: ndcg_at_1
value: 26.712000000000003
- type: ndcg_at_10
value: 37.519999999999996
- type: ndcg_at_100
value: 43.946000000000005
- type: ndcg_at_1000
value: 46.297
- type: ndcg_at_3
value: 32.551
- type: ndcg_at_5
value: 34.660999999999994
- type: precision_at_1
value: 26.712000000000003
- type: precision_at_10
value: 7.066
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 15.906
- type: precision_at_5
value: 11.437999999999999
- type: recall_at_1
value: 21.589
- type: recall_at_10
value: 50.090999999999994
- type: recall_at_100
value: 77.43900000000001
- type: recall_at_1000
value: 93.35900000000001
- type: recall_at_3
value: 36.028999999999996
- type: recall_at_5
value: 41.698
- type: map_at_1
value: 25.121666666666663
- type: map_at_10
value: 34.46258333333334
- type: map_at_100
value: 35.710499999999996
- type: map_at_1000
value: 35.82691666666666
- type: map_at_3
value: 31.563249999999996
- type: map_at_5
value: 33.189750000000004
- type: mrr_at_1
value: 29.66441666666667
- type: mrr_at_10
value: 38.5455
- type: mrr_at_100
value: 39.39566666666667
- type: mrr_at_1000
value: 39.45325
- type: mrr_at_3
value: 36.003333333333345
- type: mrr_at_5
value: 37.440916666666666
- type: ndcg_at_1
value: 29.66441666666667
- type: ndcg_at_10
value: 39.978416666666675
- type: ndcg_at_100
value: 45.278666666666666
- type: ndcg_at_1000
value: 47.52275
- type: ndcg_at_3
value: 35.00058333333334
- type: ndcg_at_5
value: 37.34908333333333
- type: precision_at_1
value: 29.66441666666667
- type: precision_at_10
value: 7.094500000000001
- type: precision_at_100
value: 1.1523333333333332
- type: precision_at_1000
value: 0.15358333333333332
- type: precision_at_3
value: 16.184166666666663
- type: precision_at_5
value: 11.6005
- type: recall_at_1
value: 25.121666666666663
- type: recall_at_10
value: 52.23975000000001
- type: recall_at_100
value: 75.48408333333333
- type: recall_at_1000
value: 90.95316666666668
- type: recall_at_3
value: 38.38458333333333
- type: recall_at_5
value: 44.39933333333333
- type: map_at_1
value: 23.569000000000003
- type: map_at_10
value: 30.389
- type: map_at_100
value: 31.396
- type: map_at_1000
value: 31.493
- type: map_at_3
value: 28.276
- type: map_at_5
value: 29.459000000000003
- type: mrr_at_1
value: 26.534000000000002
- type: mrr_at_10
value: 33.217999999999996
- type: mrr_at_100
value: 34.054
- type: mrr_at_1000
value: 34.12
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.330999999999996
- type: ndcg_at_1
value: 26.534000000000002
- type: ndcg_at_10
value: 34.608
- type: ndcg_at_100
value: 39.391999999999996
- type: ndcg_at_1000
value: 41.837999999999994
- type: ndcg_at_3
value: 30.564999999999998
- type: ndcg_at_5
value: 32.509
- type: precision_at_1
value: 26.534000000000002
- type: precision_at_10
value: 5.414
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 9.202
- type: recall_at_1
value: 23.569000000000003
- type: recall_at_10
value: 44.896
- type: recall_at_100
value: 66.476
- type: recall_at_1000
value: 84.548
- type: recall_at_3
value: 33.79
- type: recall_at_5
value: 38.512
- type: map_at_1
value: 16.36
- type: map_at_10
value: 23.57
- type: map_at_100
value: 24.698999999999998
- type: map_at_1000
value: 24.834999999999997
- type: map_at_3
value: 21.093
- type: map_at_5
value: 22.418
- type: mrr_at_1
value: 19.718
- type: mrr_at_10
value: 27.139999999999997
- type: mrr_at_100
value: 28.097
- type: mrr_at_1000
value: 28.177999999999997
- type: mrr_at_3
value: 24.805
- type: mrr_at_5
value: 26.121
- type: ndcg_at_1
value: 19.718
- type: ndcg_at_10
value: 28.238999999999997
- type: ndcg_at_100
value: 33.663
- type: ndcg_at_1000
value: 36.763
- type: ndcg_at_3
value: 23.747
- type: ndcg_at_5
value: 25.796000000000003
- type: precision_at_1
value: 19.718
- type: precision_at_10
value: 5.282
- type: precision_at_100
value: 0.9390000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.264000000000001
- type: precision_at_5
value: 8.341
- type: recall_at_1
value: 16.36
- type: recall_at_10
value: 38.669
- type: recall_at_100
value: 63.184
- type: recall_at_1000
value: 85.33800000000001
- type: recall_at_3
value: 26.214
- type: recall_at_5
value: 31.423000000000002
- type: map_at_1
value: 25.618999999999996
- type: map_at_10
value: 34.361999999999995
- type: map_at_100
value: 35.534
- type: map_at_1000
value: 35.634
- type: map_at_3
value: 31.402
- type: map_at_5
value: 32.815
- type: mrr_at_1
value: 30.037000000000003
- type: mrr_at_10
value: 38.284
- type: mrr_at_100
value: 39.141999999999996
- type: mrr_at_1000
value: 39.2
- type: mrr_at_3
value: 35.603
- type: mrr_at_5
value: 36.867
- type: ndcg_at_1
value: 30.037000000000003
- type: ndcg_at_10
value: 39.87
- type: ndcg_at_100
value: 45.243
- type: ndcg_at_1000
value: 47.507
- type: ndcg_at_3
value: 34.371
- type: ndcg_at_5
value: 36.521
- type: precision_at_1
value: 30.037000000000003
- type: precision_at_10
value: 6.819
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.392
- type: precision_at_5
value: 10.821
- type: recall_at_1
value: 25.618999999999996
- type: recall_at_10
value: 52.869
- type: recall_at_100
value: 76.395
- type: recall_at_1000
value: 92.19500000000001
- type: recall_at_3
value: 37.943
- type: recall_at_5
value: 43.342999999999996
- type: map_at_1
value: 23.283
- type: map_at_10
value: 32.155
- type: map_at_100
value: 33.724
- type: map_at_1000
value: 33.939
- type: map_at_3
value: 29.018
- type: map_at_5
value: 30.864000000000004
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.632
- type: mrr_at_100
value: 37.606
- type: mrr_at_1000
value: 37.671
- type: mrr_at_3
value: 33.992
- type: mrr_at_5
value: 35.613
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 38.024
- type: ndcg_at_100
value: 44.292
- type: ndcg_at_1000
value: 46.818
- type: ndcg_at_3
value: 32.965
- type: ndcg_at_5
value: 35.562
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.352
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_3
value: 15.481
- type: precision_at_5
value: 11.542
- type: recall_at_1
value: 23.283
- type: recall_at_10
value: 49.756
- type: recall_at_100
value: 78.05
- type: recall_at_1000
value: 93.854
- type: recall_at_3
value: 35.408
- type: recall_at_5
value: 42.187000000000005
- type: map_at_1
value: 19.201999999999998
- type: map_at_10
value: 26.826
- type: map_at_100
value: 27.961000000000002
- type: map_at_1000
value: 28.066999999999997
- type: map_at_3
value: 24.237000000000002
- type: map_at_5
value: 25.811
- type: mrr_at_1
value: 20.887
- type: mrr_at_10
value: 28.660000000000004
- type: mrr_at_100
value: 29.660999999999998
- type: mrr_at_1000
value: 29.731
- type: mrr_at_3
value: 26.155
- type: mrr_at_5
value: 27.68
- type: ndcg_at_1
value: 20.887
- type: ndcg_at_10
value: 31.523
- type: ndcg_at_100
value: 37.055
- type: ndcg_at_1000
value: 39.579
- type: ndcg_at_3
value: 26.529000000000003
- type: ndcg_at_5
value: 29.137
- type: precision_at_1
value: 20.887
- type: precision_at_10
value: 5.065
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 11.399
- type: precision_at_5
value: 8.392
- type: recall_at_1
value: 19.201999999999998
- type: recall_at_10
value: 44.285000000000004
- type: recall_at_100
value: 69.768
- type: recall_at_1000
value: 88.302
- type: recall_at_3
value: 30.804
- type: recall_at_5
value: 37.039
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.244
- type: map_at_10
value: 18.956
- type: map_at_100
value: 20.674
- type: map_at_1000
value: 20.863
- type: map_at_3
value: 15.923000000000002
- type: map_at_5
value: 17.518
- type: mrr_at_1
value: 25.080999999999996
- type: mrr_at_10
value: 35.94
- type: mrr_at_100
value: 36.969
- type: mrr_at_1000
value: 37.013
- type: mrr_at_3
value: 32.617000000000004
- type: mrr_at_5
value: 34.682
- type: ndcg_at_1
value: 25.080999999999996
- type: ndcg_at_10
value: 26.539
- type: ndcg_at_100
value: 33.601
- type: ndcg_at_1000
value: 37.203
- type: ndcg_at_3
value: 21.695999999999998
- type: ndcg_at_5
value: 23.567
- type: precision_at_1
value: 25.080999999999996
- type: precision_at_10
value: 8.143
- type: precision_at_100
value: 1.5650000000000002
- type: precision_at_1000
value: 0.22300000000000003
- type: precision_at_3
value: 15.983
- type: precision_at_5
value: 12.417
- type: recall_at_1
value: 11.244
- type: recall_at_10
value: 31.457
- type: recall_at_100
value: 55.92
- type: recall_at_1000
value: 76.372
- type: recall_at_3
value: 19.784
- type: recall_at_5
value: 24.857000000000003
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.595
- type: map_at_10
value: 18.75
- type: map_at_100
value: 26.354
- type: map_at_1000
value: 27.912
- type: map_at_3
value: 13.794
- type: map_at_5
value: 16.021
- type: mrr_at_1
value: 65.75
- type: mrr_at_10
value: 73.837
- type: mrr_at_100
value: 74.22800000000001
- type: mrr_at_1000
value: 74.234
- type: mrr_at_3
value: 72.5
- type: mrr_at_5
value: 73.387
- type: ndcg_at_1
value: 52.625
- type: ndcg_at_10
value: 39.101
- type: ndcg_at_100
value: 43.836000000000006
- type: ndcg_at_1000
value: 51.086
- type: ndcg_at_3
value: 44.229
- type: ndcg_at_5
value: 41.555
- type: precision_at_1
value: 65.75
- type: precision_at_10
value: 30.45
- type: precision_at_100
value: 9.81
- type: precision_at_1000
value: 2.045
- type: precision_at_3
value: 48.667
- type: precision_at_5
value: 40.8
- type: recall_at_1
value: 8.595
- type: recall_at_10
value: 24.201
- type: recall_at_100
value: 50.096
- type: recall_at_1000
value: 72.677
- type: recall_at_3
value: 15.212
- type: recall_at_5
value: 18.745
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.565
- type: f1
value: 41.49914329345582
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.60000000000001
- type: map_at_10
value: 76.838
- type: map_at_100
value: 77.076
- type: map_at_1000
value: 77.09
- type: map_at_3
value: 75.545
- type: map_at_5
value: 76.39
- type: mrr_at_1
value: 71.707
- type: mrr_at_10
value: 81.514
- type: mrr_at_100
value: 81.64099999999999
- type: mrr_at_1000
value: 81.645
- type: mrr_at_3
value: 80.428
- type: mrr_at_5
value: 81.159
- type: ndcg_at_1
value: 71.707
- type: ndcg_at_10
value: 81.545
- type: ndcg_at_100
value: 82.477
- type: ndcg_at_1000
value: 82.73899999999999
- type: ndcg_at_3
value: 79.292
- type: ndcg_at_5
value: 80.599
- type: precision_at_1
value: 71.707
- type: precision_at_10
value: 10.035
- type: precision_at_100
value: 1.068
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 30.918
- type: precision_at_5
value: 19.328
- type: recall_at_1
value: 66.60000000000001
- type: recall_at_10
value: 91.353
- type: recall_at_100
value: 95.21
- type: recall_at_1000
value: 96.89999999999999
- type: recall_at_3
value: 85.188
- type: recall_at_5
value: 88.52
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.338
- type: map_at_10
value: 31.752000000000002
- type: map_at_100
value: 33.516
- type: map_at_1000
value: 33.694
- type: map_at_3
value: 27.716
- type: map_at_5
value: 29.67
- type: mrr_at_1
value: 38.117000000000004
- type: mrr_at_10
value: 47.323
- type: mrr_at_100
value: 48.13
- type: mrr_at_1000
value: 48.161
- type: mrr_at_3
value: 45.062000000000005
- type: mrr_at_5
value: 46.358
- type: ndcg_at_1
value: 38.117000000000004
- type: ndcg_at_10
value: 39.353
- type: ndcg_at_100
value: 46.044000000000004
- type: ndcg_at_1000
value: 49.083
- type: ndcg_at_3
value: 35.891
- type: ndcg_at_5
value: 36.661
- type: precision_at_1
value: 38.117000000000004
- type: precision_at_10
value: 11.187999999999999
- type: precision_at_100
value: 1.802
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 24.126
- type: precision_at_5
value: 17.562
- type: recall_at_1
value: 19.338
- type: recall_at_10
value: 45.735
- type: recall_at_100
value: 71.281
- type: recall_at_1000
value: 89.537
- type: recall_at_3
value: 32.525
- type: recall_at_5
value: 37.671
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.995
- type: map_at_10
value: 55.032000000000004
- type: map_at_100
value: 55.86
- type: map_at_1000
value: 55.932
- type: map_at_3
value: 52.125
- type: map_at_5
value: 53.884
- type: mrr_at_1
value: 73.991
- type: mrr_at_10
value: 80.096
- type: mrr_at_100
value: 80.32000000000001
- type: mrr_at_1000
value: 80.331
- type: mrr_at_3
value: 79.037
- type: mrr_at_5
value: 79.719
- type: ndcg_at_1
value: 73.991
- type: ndcg_at_10
value: 63.786
- type: ndcg_at_100
value: 66.78
- type: ndcg_at_1000
value: 68.255
- type: ndcg_at_3
value: 59.501000000000005
- type: ndcg_at_5
value: 61.82299999999999
- type: precision_at_1
value: 73.991
- type: precision_at_10
value: 13.157
- type: precision_at_100
value: 1.552
- type: precision_at_1000
value: 0.17500000000000002
- type: precision_at_3
value: 37.519999999999996
- type: precision_at_5
value: 24.351
- type: recall_at_1
value: 36.995
- type: recall_at_10
value: 65.78699999999999
- type: recall_at_100
value: 77.583
- type: recall_at_1000
value: 87.421
- type: recall_at_3
value: 56.279999999999994
- type: recall_at_5
value: 60.878
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.80239999999999
- type: ap
value: 81.97305141128378
- type: f1
value: 86.76976305549273
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.166
- type: map_at_10
value: 33.396
- type: map_at_100
value: 34.588
- type: map_at_1000
value: 34.637
- type: map_at_3
value: 29.509999999999998
- type: map_at_5
value: 31.719
- type: mrr_at_1
value: 21.762
- type: mrr_at_10
value: 33.969
- type: mrr_at_100
value: 35.099000000000004
- type: mrr_at_1000
value: 35.141
- type: mrr_at_3
value: 30.148000000000003
- type: mrr_at_5
value: 32.324000000000005
- type: ndcg_at_1
value: 21.776999999999997
- type: ndcg_at_10
value: 40.306999999999995
- type: ndcg_at_100
value: 46.068
- type: ndcg_at_1000
value: 47.3
- type: ndcg_at_3
value: 32.416
- type: ndcg_at_5
value: 36.345
- type: precision_at_1
value: 21.776999999999997
- type: precision_at_10
value: 6.433
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.897
- type: precision_at_5
value: 10.324
- type: recall_at_1
value: 21.166
- type: recall_at_10
value: 61.587
- type: recall_at_100
value: 88.251
- type: recall_at_1000
value: 97.727
- type: recall_at_3
value: 40.196
- type: recall_at_5
value: 49.611
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.04605563155496
- type: f1
value: 92.78007303978372
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.65116279069767
- type: f1
value: 52.75775172527262
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.34633490248822
- type: f1
value: 68.15345065392562
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.63887020847343
- type: f1
value: 76.08074680233685
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.77933406071333
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.06504927238196
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.20682480490871
- type: mrr
value: 33.41462721527003
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.548
- type: map_at_10
value: 13.086999999999998
- type: map_at_100
value: 16.698
- type: map_at_1000
value: 18.151999999999997
- type: map_at_3
value: 9.576
- type: map_at_5
value: 11.175
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 53.635999999999996
- type: mrr_at_100
value: 54.228
- type: mrr_at_1000
value: 54.26499999999999
- type: mrr_at_3
value: 51.754
- type: mrr_at_5
value: 53.086
- type: ndcg_at_1
value: 42.724000000000004
- type: ndcg_at_10
value: 34.769
- type: ndcg_at_100
value: 32.283
- type: ndcg_at_1000
value: 40.843
- type: ndcg_at_3
value: 39.852
- type: ndcg_at_5
value: 37.858999999999995
- type: precision_at_1
value: 44.272
- type: precision_at_10
value: 26.068
- type: precision_at_100
value: 8.328000000000001
- type: precision_at_1000
value: 2.1
- type: precision_at_3
value: 37.874
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 5.548
- type: recall_at_10
value: 16.936999999999998
- type: recall_at_100
value: 33.72
- type: recall_at_1000
value: 64.348
- type: recall_at_3
value: 10.764999999999999
- type: recall_at_5
value: 13.361
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.008
- type: map_at_10
value: 42.675000000000004
- type: map_at_100
value: 43.85
- type: map_at_1000
value: 43.884
- type: map_at_3
value: 38.286
- type: map_at_5
value: 40.78
- type: mrr_at_1
value: 31.518
- type: mrr_at_10
value: 45.015
- type: mrr_at_100
value: 45.924
- type: mrr_at_1000
value: 45.946999999999996
- type: mrr_at_3
value: 41.348
- type: mrr_at_5
value: 43.428
- type: ndcg_at_1
value: 31.489
- type: ndcg_at_10
value: 50.285999999999994
- type: ndcg_at_100
value: 55.291999999999994
- type: ndcg_at_1000
value: 56.05
- type: ndcg_at_3
value: 41.976
- type: ndcg_at_5
value: 46.103
- type: precision_at_1
value: 31.489
- type: precision_at_10
value: 8.456
- type: precision_at_100
value: 1.125
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 19.09
- type: precision_at_5
value: 13.841000000000001
- type: recall_at_1
value: 28.008
- type: recall_at_10
value: 71.21499999999999
- type: recall_at_100
value: 92.99
- type: recall_at_1000
value: 98.578
- type: recall_at_3
value: 49.604
- type: recall_at_5
value: 59.094
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.351
- type: map_at_10
value: 84.163
- type: map_at_100
value: 84.785
- type: map_at_1000
value: 84.801
- type: map_at_3
value: 81.16
- type: map_at_5
value: 83.031
- type: mrr_at_1
value: 80.96
- type: mrr_at_10
value: 87.241
- type: mrr_at_100
value: 87.346
- type: mrr_at_1000
value: 87.347
- type: mrr_at_3
value: 86.25699999999999
- type: mrr_at_5
value: 86.907
- type: ndcg_at_1
value: 80.97
- type: ndcg_at_10
value: 88.017
- type: ndcg_at_100
value: 89.241
- type: ndcg_at_1000
value: 89.34299999999999
- type: ndcg_at_3
value: 85.053
- type: ndcg_at_5
value: 86.663
- type: precision_at_1
value: 80.97
- type: precision_at_10
value: 13.358
- type: precision_at_100
value: 1.525
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.143
- type: precision_at_5
value: 24.451999999999998
- type: recall_at_1
value: 70.351
- type: recall_at_10
value: 95.39800000000001
- type: recall_at_100
value: 99.55199999999999
- type: recall_at_1000
value: 99.978
- type: recall_at_3
value: 86.913
- type: recall_at_5
value: 91.448
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.62406719814139
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.386700035141736
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.618
- type: map_at_10
value: 12.920000000000002
- type: map_at_100
value: 15.304
- type: map_at_1000
value: 15.656999999999998
- type: map_at_3
value: 9.187
- type: map_at_5
value: 10.937
- type: mrr_at_1
value: 22.8
- type: mrr_at_10
value: 35.13
- type: mrr_at_100
value: 36.239
- type: mrr_at_1000
value: 36.291000000000004
- type: mrr_at_3
value: 31.917
- type: mrr_at_5
value: 33.787
- type: ndcg_at_1
value: 22.8
- type: ndcg_at_10
value: 21.382
- type: ndcg_at_100
value: 30.257
- type: ndcg_at_1000
value: 36.001
- type: ndcg_at_3
value: 20.43
- type: ndcg_at_5
value: 17.622
- type: precision_at_1
value: 22.8
- type: precision_at_10
value: 11.26
- type: precision_at_100
value: 2.405
- type: precision_at_1000
value: 0.377
- type: precision_at_3
value: 19.633
- type: precision_at_5
value: 15.68
- type: recall_at_1
value: 4.618
- type: recall_at_10
value: 22.811999999999998
- type: recall_at_100
value: 48.787000000000006
- type: recall_at_1000
value: 76.63799999999999
- type: recall_at_3
value: 11.952
- type: recall_at_5
value: 15.892000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.01529458252244
- type: cos_sim_spearman
value: 77.92985224770254
- type: euclidean_pearson
value: 81.04251429422487
- type: euclidean_spearman
value: 77.92838490549133
- type: manhattan_pearson
value: 80.95892251458979
- type: manhattan_spearman
value: 77.81028089705941
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.97885282534388
- type: cos_sim_spearman
value: 75.1221970851712
- type: euclidean_pearson
value: 80.34455956720097
- type: euclidean_spearman
value: 74.5894274239938
- type: manhattan_pearson
value: 80.38999766325465
- type: manhattan_spearman
value: 74.68524557166975
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.95746064915672
- type: cos_sim_spearman
value: 85.08683458043946
- type: euclidean_pearson
value: 84.56699492836385
- type: euclidean_spearman
value: 85.66089116133713
- type: manhattan_pearson
value: 84.47553323458541
- type: manhattan_spearman
value: 85.56142206781472
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.71377893595067
- type: cos_sim_spearman
value: 81.03453291428589
- type: euclidean_pearson
value: 82.57136298308613
- type: euclidean_spearman
value: 81.15839961890875
- type: manhattan_pearson
value: 82.55157879373837
- type: manhattan_spearman
value: 81.1540163767054
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.64197832372373
- type: cos_sim_spearman
value: 88.31966852492485
- type: euclidean_pearson
value: 87.98692129976983
- type: euclidean_spearman
value: 88.6247340837856
- type: manhattan_pearson
value: 87.90437827826412
- type: manhattan_spearman
value: 88.56278787131457
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.84159950146693
- type: cos_sim_spearman
value: 83.90678384140168
- type: euclidean_pearson
value: 83.19005018860221
- type: euclidean_spearman
value: 84.16260415876295
- type: manhattan_pearson
value: 83.05030612994494
- type: manhattan_spearman
value: 83.99605629718336
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.49935350176666
- type: cos_sim_spearman
value: 87.59086606735383
- type: euclidean_pearson
value: 88.06537181129983
- type: euclidean_spearman
value: 87.6687448086014
- type: manhattan_pearson
value: 87.96599131972935
- type: manhattan_spearman
value: 87.63295748969642
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.68232799482763
- type: cos_sim_spearman
value: 67.99930378085793
- type: euclidean_pearson
value: 68.50275360001696
- type: euclidean_spearman
value: 67.81588179309259
- type: manhattan_pearson
value: 68.5892154749763
- type: manhattan_spearman
value: 67.84357259640682
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.37049618406554
- type: cos_sim_spearman
value: 85.57014313159492
- type: euclidean_pearson
value: 85.57469513908282
- type: euclidean_spearman
value: 85.661948135258
- type: manhattan_pearson
value: 85.36866831229028
- type: manhattan_spearman
value: 85.5043455368843
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.83259065376154
- type: mrr
value: 95.58455433455433
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.817
- type: map_at_10
value: 68.459
- type: map_at_100
value: 68.951
- type: map_at_1000
value: 68.979
- type: map_at_3
value: 65.791
- type: map_at_5
value: 67.583
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 69.368
- type: mrr_at_100
value: 69.721
- type: mrr_at_1000
value: 69.744
- type: mrr_at_3
value: 67.278
- type: mrr_at_5
value: 68.611
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 72.70100000000001
- type: ndcg_at_100
value: 74.928
- type: ndcg_at_1000
value: 75.553
- type: ndcg_at_3
value: 68.203
- type: ndcg_at_5
value: 70.804
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.599999999999998
- type: recall_at_1
value: 58.817
- type: recall_at_10
value: 84.789
- type: recall_at_100
value: 95.0
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 72.8
- type: recall_at_5
value: 79.294
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8108910891089
- type: cos_sim_ap
value: 95.5743678558349
- type: cos_sim_f1
value: 90.43133366385722
- type: cos_sim_precision
value: 89.67551622418878
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.75841584158415
- type: dot_ap
value: 94.00786363627253
- type: dot_f1
value: 87.51910341314316
- type: dot_precision
value: 89.20041536863967
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.81485148514851
- type: euclidean_ap
value: 95.4752113136905
- type: euclidean_f1
value: 90.44334975369456
- type: euclidean_precision
value: 89.126213592233
- type: euclidean_recall
value: 91.8
- type: manhattan_accuracy
value: 99.81584158415842
- type: manhattan_ap
value: 95.5163172682464
- type: manhattan_f1
value: 90.51987767584097
- type: manhattan_precision
value: 92.3076923076923
- type: manhattan_recall
value: 88.8
- type: max_accuracy
value: 99.81584158415842
- type: max_ap
value: 95.5743678558349
- type: max_f1
value: 90.51987767584097
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 62.63235986949449
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.334795589585575
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.02955214518782
- type: mrr
value: 52.8004838298956
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.63769566275453
- type: cos_sim_spearman
value: 30.422379185989335
- type: dot_pearson
value: 26.88493071882256
- type: dot_spearman
value: 26.505249740971305
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.654
- type: map_at_100
value: 10.095
- type: map_at_1000
value: 25.808999999999997
- type: map_at_3
value: 0.594
- type: map_at_5
value: 0.9289999999999999
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 87.019
- type: mrr_at_100
value: 87.019
- type: mrr_at_1000
value: 87.019
- type: mrr_at_3
value: 86.333
- type: mrr_at_5
value: 86.733
- type: ndcg_at_1
value: 73.0
- type: ndcg_at_10
value: 66.52900000000001
- type: ndcg_at_100
value: 53.433
- type: ndcg_at_1000
value: 51.324000000000005
- type: ndcg_at_3
value: 72.02199999999999
- type: ndcg_at_5
value: 69.696
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 70.39999999999999
- type: precision_at_100
value: 55.46
- type: precision_at_1000
value: 22.758
- type: precision_at_3
value: 76.667
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.8849999999999998
- type: recall_at_100
value: 13.801
- type: recall_at_1000
value: 49.649
- type: recall_at_3
value: 0.632
- type: recall_at_5
value: 1.009
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.797
- type: map_at_10
value: 9.01
- type: map_at_100
value: 14.682
- type: map_at_1000
value: 16.336000000000002
- type: map_at_3
value: 4.546
- type: map_at_5
value: 5.9270000000000005
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 41.156
- type: mrr_at_100
value: 42.392
- type: mrr_at_1000
value: 42.408
- type: mrr_at_3
value: 38.775999999999996
- type: mrr_at_5
value: 40.102
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 22.222
- type: ndcg_at_100
value: 34.405
- type: ndcg_at_1000
value: 46.599000000000004
- type: ndcg_at_3
value: 25.261
- type: ndcg_at_5
value: 22.695999999999998
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.306
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 22.857
- type: recall_at_1
value: 1.797
- type: recall_at_10
value: 15.706000000000001
- type: recall_at_100
value: 46.412
- type: recall_at_1000
value: 83.159
- type: recall_at_3
value: 6.1370000000000005
- type: recall_at_5
value: 8.599
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.3302
- type: ap
value: 14.169121204575601
- type: f1
value: 54.229345975274235
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.22297679683077
- type: f1
value: 58.62984908377875
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.952922428464255
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.68140907194373
- type: cos_sim_ap
value: 70.12180123666836
- type: cos_sim_f1
value: 65.77501791258658
- type: cos_sim_precision
value: 60.07853403141361
- type: cos_sim_recall
value: 72.66490765171504
- type: dot_accuracy
value: 81.92167848840674
- type: dot_ap
value: 60.49837581423469
- type: dot_f1
value: 58.44186046511628
- type: dot_precision
value: 52.24532224532224
- type: dot_recall
value: 66.3060686015831
- type: euclidean_accuracy
value: 84.73505394289802
- type: euclidean_ap
value: 70.3278904593286
- type: euclidean_f1
value: 65.98851124940161
- type: euclidean_precision
value: 60.38107752956636
- type: euclidean_recall
value: 72.74406332453826
- type: manhattan_accuracy
value: 84.73505394289802
- type: manhattan_ap
value: 70.00737738537337
- type: manhattan_f1
value: 65.80150784822642
- type: manhattan_precision
value: 61.892583120204606
- type: manhattan_recall
value: 70.23746701846966
- type: max_accuracy
value: 84.73505394289802
- type: max_ap
value: 70.3278904593286
- type: max_f1
value: 65.98851124940161
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.44258159661582
- type: cos_sim_ap
value: 84.91926704880888
- type: cos_sim_f1
value: 77.07651086632926
- type: cos_sim_precision
value: 74.5894554883319
- type: cos_sim_recall
value: 79.73514012935017
- type: dot_accuracy
value: 85.88116583226608
- type: dot_ap
value: 78.9753854779923
- type: dot_f1
value: 72.17757637979255
- type: dot_precision
value: 66.80647486729143
- type: dot_recall
value: 78.48783492454572
- type: euclidean_accuracy
value: 88.5299025885823
- type: euclidean_ap
value: 85.08006075642194
- type: euclidean_f1
value: 77.29637336504163
- type: euclidean_precision
value: 74.69836253950014
- type: euclidean_recall
value: 80.08161379735141
- type: manhattan_accuracy
value: 88.55124771995187
- type: manhattan_ap
value: 85.00941529932851
- type: manhattan_f1
value: 77.33100233100232
- type: manhattan_precision
value: 73.37572573956317
- type: manhattan_recall
value: 81.73698798891284
- type: max_accuracy
value: 88.55124771995187
- type: max_ap
value: 85.08006075642194
- type: max_f1
value: 77.33100233100232
---
# gte-small
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
model = AutoModel.from_pretrained("thenlper/gte-small")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-large')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gte-large-en-v1.5 | Alibaba-NLP | sentence-similarity | [
"transformers",
"onnx",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"dataset:allenai/c4",
"arxiv:2407.19669",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-20T02:54:30 | 2024-08-09T03:32:05 | 3,819,623 | 204 | ---
datasets:
- allenai/c4
language:
- en
library_name: transformers
license: apache-2.0
tags:
- sentence-transformers
- gte
- mteb
- transformers.js
- sentence-similarity
model-index:
- name: gte-large-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.01492537313432
- type: ap
value: 35.05341696659522
- type: f1
value: 66.71270310883853
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.97189999999999
- type: ap
value: 90.5952493948908
- type: f1
value: 93.95848137716877
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 54.196
- type: f1
value: 53.80122334012787
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 47.297
- type: map_at_10
value: 64.303
- type: map_at_100
value: 64.541
- type: map_at_1000
value: 64.541
- type: map_at_3
value: 60.728
- type: map_at_5
value: 63.114000000000004
- type: mrr_at_1
value: 48.435
- type: mrr_at_10
value: 64.657
- type: mrr_at_100
value: 64.901
- type: mrr_at_1000
value: 64.901
- type: mrr_at_3
value: 61.06
- type: mrr_at_5
value: 63.514
- type: ndcg_at_1
value: 47.297
- type: ndcg_at_10
value: 72.107
- type: ndcg_at_100
value: 72.963
- type: ndcg_at_1000
value: 72.963
- type: ndcg_at_3
value: 65.063
- type: ndcg_at_5
value: 69.352
- type: precision_at_1
value: 47.297
- type: precision_at_10
value: 9.623
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 25.865
- type: precision_at_5
value: 17.596
- type: recall_at_1
value: 47.297
- type: recall_at_10
value: 96.23
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 77.596
- type: recall_at_5
value: 87.98
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.467787861077475
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.39198391914257
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.12794820591384
- type: mrr
value: 75.9331442641692
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.85062993863319
- type: cos_sim_spearman
value: 85.39049989733459
- type: euclidean_pearson
value: 86.00222680278333
- type: euclidean_spearman
value: 85.45556162077396
- type: manhattan_pearson
value: 85.88769871785621
- type: manhattan_spearman
value: 85.11760211290839
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.32792207792208
- type: f1
value: 87.29132945999555
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.5779328301945
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.94425623865118
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 32.978
- type: map_at_10
value: 44.45
- type: map_at_100
value: 46.19
- type: map_at_1000
value: 46.303
- type: map_at_3
value: 40.849000000000004
- type: map_at_5
value: 42.55
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 50.848000000000006
- type: mrr_at_100
value: 51.669
- type: mrr_at_1000
value: 51.705
- type: mrr_at_3
value: 47.997
- type: mrr_at_5
value: 49.506
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 51.102000000000004
- type: ndcg_at_100
value: 57.159000000000006
- type: ndcg_at_1000
value: 58.669000000000004
- type: ndcg_at_3
value: 45.738
- type: ndcg_at_5
value: 47.632999999999996
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 9.700000000000001
- type: precision_at_100
value: 1.5970000000000002
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.698
- type: precision_at_5
value: 15.393
- type: recall_at_1
value: 32.978
- type: recall_at_10
value: 63.711
- type: recall_at_100
value: 88.39399999999999
- type: recall_at_1000
value: 97.513
- type: recall_at_3
value: 48.025
- type: recall_at_5
value: 53.52
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 30.767
- type: map_at_10
value: 42.195
- type: map_at_100
value: 43.541999999999994
- type: map_at_1000
value: 43.673
- type: map_at_3
value: 38.561
- type: map_at_5
value: 40.532000000000004
- type: mrr_at_1
value: 38.79
- type: mrr_at_10
value: 48.021
- type: mrr_at_100
value: 48.735
- type: mrr_at_1000
value: 48.776
- type: mrr_at_3
value: 45.594
- type: mrr_at_5
value: 46.986
- type: ndcg_at_1
value: 38.79
- type: ndcg_at_10
value: 48.468
- type: ndcg_at_100
value: 53.037
- type: ndcg_at_1000
value: 55.001999999999995
- type: ndcg_at_3
value: 43.409
- type: ndcg_at_5
value: 45.654
- type: precision_at_1
value: 38.79
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.201
- type: precision_at_3
value: 21.21
- type: precision_at_5
value: 15.171999999999999
- type: recall_at_1
value: 30.767
- type: recall_at_10
value: 60.118
- type: recall_at_100
value: 79.271
- type: recall_at_1000
value: 91.43299999999999
- type: recall_at_3
value: 45.36
- type: recall_at_5
value: 51.705
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 40.007
- type: map_at_10
value: 53.529
- type: map_at_100
value: 54.602
- type: map_at_1000
value: 54.647
- type: map_at_3
value: 49.951
- type: map_at_5
value: 52.066
- type: mrr_at_1
value: 45.705
- type: mrr_at_10
value: 56.745000000000005
- type: mrr_at_100
value: 57.43899999999999
- type: mrr_at_1000
value: 57.462999999999994
- type: mrr_at_3
value: 54.25299999999999
- type: mrr_at_5
value: 55.842000000000006
- type: ndcg_at_1
value: 45.705
- type: ndcg_at_10
value: 59.809
- type: ndcg_at_100
value: 63.837999999999994
- type: ndcg_at_1000
value: 64.729
- type: ndcg_at_3
value: 53.994
- type: ndcg_at_5
value: 57.028
- type: precision_at_1
value: 45.705
- type: precision_at_10
value: 9.762
- type: precision_at_100
value: 1.275
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.368000000000002
- type: precision_at_5
value: 16.84
- type: recall_at_1
value: 40.007
- type: recall_at_10
value: 75.017
- type: recall_at_100
value: 91.99000000000001
- type: recall_at_1000
value: 98.265
- type: recall_at_3
value: 59.704
- type: recall_at_5
value: 67.109
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.639000000000003
- type: map_at_10
value: 35.926
- type: map_at_100
value: 37.126999999999995
- type: map_at_1000
value: 37.202
- type: map_at_3
value: 32.989000000000004
- type: map_at_5
value: 34.465
- type: mrr_at_1
value: 28.475
- type: mrr_at_10
value: 37.7
- type: mrr_at_100
value: 38.753
- type: mrr_at_1000
value: 38.807
- type: mrr_at_3
value: 35.066
- type: mrr_at_5
value: 36.512
- type: ndcg_at_1
value: 28.475
- type: ndcg_at_10
value: 41.245
- type: ndcg_at_100
value: 46.814
- type: ndcg_at_1000
value: 48.571
- type: ndcg_at_3
value: 35.528999999999996
- type: ndcg_at_5
value: 38.066
- type: precision_at_1
value: 28.475
- type: precision_at_10
value: 6.497
- type: precision_at_100
value: 0.9650000000000001
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.065999999999999
- type: precision_at_5
value: 10.599
- type: recall_at_1
value: 26.639000000000003
- type: recall_at_10
value: 55.759
- type: recall_at_100
value: 80.913
- type: recall_at_1000
value: 93.929
- type: recall_at_3
value: 40.454
- type: recall_at_5
value: 46.439
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 15.767999999999999
- type: map_at_10
value: 24.811
- type: map_at_100
value: 26.064999999999998
- type: map_at_1000
value: 26.186999999999998
- type: map_at_3
value: 21.736
- type: map_at_5
value: 23.283
- type: mrr_at_1
value: 19.527
- type: mrr_at_10
value: 29.179
- type: mrr_at_100
value: 30.153999999999996
- type: mrr_at_1000
value: 30.215999999999998
- type: mrr_at_3
value: 26.223000000000003
- type: mrr_at_5
value: 27.733999999999998
- type: ndcg_at_1
value: 19.527
- type: ndcg_at_10
value: 30.786
- type: ndcg_at_100
value: 36.644
- type: ndcg_at_1000
value: 39.440999999999995
- type: ndcg_at_3
value: 24.958
- type: ndcg_at_5
value: 27.392
- type: precision_at_1
value: 19.527
- type: precision_at_10
value: 5.995
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.520999999999999
- type: precision_at_5
value: 9.129
- type: recall_at_1
value: 15.767999999999999
- type: recall_at_10
value: 44.824000000000005
- type: recall_at_100
value: 70.186
- type: recall_at_1000
value: 89.934
- type: recall_at_3
value: 28.607
- type: recall_at_5
value: 34.836
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.952
- type: map_at_10
value: 44.438
- type: map_at_100
value: 45.778
- type: map_at_1000
value: 45.883
- type: map_at_3
value: 41.044000000000004
- type: map_at_5
value: 42.986000000000004
- type: mrr_at_1
value: 39.172000000000004
- type: mrr_at_10
value: 49.76
- type: mrr_at_100
value: 50.583999999999996
- type: mrr_at_1000
value: 50.621
- type: mrr_at_3
value: 47.353
- type: mrr_at_5
value: 48.739
- type: ndcg_at_1
value: 39.172000000000004
- type: ndcg_at_10
value: 50.760000000000005
- type: ndcg_at_100
value: 56.084
- type: ndcg_at_1000
value: 57.865
- type: ndcg_at_3
value: 45.663
- type: ndcg_at_5
value: 48.178
- type: precision_at_1
value: 39.172000000000004
- type: precision_at_10
value: 9.22
- type: precision_at_100
value: 1.387
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 21.976000000000003
- type: precision_at_5
value: 15.457
- type: recall_at_1
value: 31.952
- type: recall_at_10
value: 63.900999999999996
- type: recall_at_100
value: 85.676
- type: recall_at_1000
value: 97.03699999999999
- type: recall_at_3
value: 49.781
- type: recall_at_5
value: 56.330000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 25.332
- type: map_at_10
value: 36.874
- type: map_at_100
value: 38.340999999999994
- type: map_at_1000
value: 38.452
- type: map_at_3
value: 33.068
- type: map_at_5
value: 35.324
- type: mrr_at_1
value: 30.822
- type: mrr_at_10
value: 41.641
- type: mrr_at_100
value: 42.519
- type: mrr_at_1000
value: 42.573
- type: mrr_at_3
value: 38.413000000000004
- type: mrr_at_5
value: 40.542
- type: ndcg_at_1
value: 30.822
- type: ndcg_at_10
value: 43.414
- type: ndcg_at_100
value: 49.196
- type: ndcg_at_1000
value: 51.237
- type: ndcg_at_3
value: 37.230000000000004
- type: ndcg_at_5
value: 40.405
- type: precision_at_1
value: 30.822
- type: precision_at_10
value: 8.379
- type: precision_at_100
value: 1.315
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 18.417
- type: precision_at_5
value: 13.744
- type: recall_at_1
value: 25.332
- type: recall_at_10
value: 57.774
- type: recall_at_100
value: 82.071
- type: recall_at_1000
value: 95.60600000000001
- type: recall_at_3
value: 40.722
- type: recall_at_5
value: 48.754999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.91033333333334
- type: map_at_10
value: 36.23225000000001
- type: map_at_100
value: 37.55766666666667
- type: map_at_1000
value: 37.672583333333336
- type: map_at_3
value: 32.95666666666667
- type: map_at_5
value: 34.73375
- type: mrr_at_1
value: 30.634
- type: mrr_at_10
value: 40.19449999999999
- type: mrr_at_100
value: 41.099250000000005
- type: mrr_at_1000
value: 41.15091666666667
- type: mrr_at_3
value: 37.4615
- type: mrr_at_5
value: 39.00216666666667
- type: ndcg_at_1
value: 30.634
- type: ndcg_at_10
value: 42.162166666666664
- type: ndcg_at_100
value: 47.60708333333333
- type: ndcg_at_1000
value: 49.68616666666666
- type: ndcg_at_3
value: 36.60316666666666
- type: ndcg_at_5
value: 39.15616666666668
- type: precision_at_1
value: 30.634
- type: precision_at_10
value: 7.6193333333333335
- type: precision_at_100
value: 1.2198333333333333
- type: precision_at_1000
value: 0.15975000000000003
- type: precision_at_3
value: 17.087
- type: precision_at_5
value: 12.298333333333334
- type: recall_at_1
value: 25.91033333333334
- type: recall_at_10
value: 55.67300000000001
- type: recall_at_100
value: 79.20608333333334
- type: recall_at_1000
value: 93.34866666666667
- type: recall_at_3
value: 40.34858333333333
- type: recall_at_5
value: 46.834083333333325
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.006
- type: map_at_10
value: 32.177
- type: map_at_100
value: 33.324999999999996
- type: map_at_1000
value: 33.419
- type: map_at_3
value: 29.952
- type: map_at_5
value: 31.095
- type: mrr_at_1
value: 28.066999999999997
- type: mrr_at_10
value: 34.995
- type: mrr_at_100
value: 35.978
- type: mrr_at_1000
value: 36.042
- type: mrr_at_3
value: 33.103
- type: mrr_at_5
value: 34.001
- type: ndcg_at_1
value: 28.066999999999997
- type: ndcg_at_10
value: 36.481
- type: ndcg_at_100
value: 42.022999999999996
- type: ndcg_at_1000
value: 44.377
- type: ndcg_at_3
value: 32.394
- type: ndcg_at_5
value: 34.108
- type: precision_at_1
value: 28.066999999999997
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 13.804
- type: precision_at_5
value: 9.508999999999999
- type: recall_at_1
value: 25.006
- type: recall_at_10
value: 46.972
- type: recall_at_100
value: 72.138
- type: recall_at_1000
value: 89.479
- type: recall_at_3
value: 35.793
- type: recall_at_5
value: 39.947
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 16.07
- type: map_at_10
value: 24.447
- type: map_at_100
value: 25.685999999999996
- type: map_at_1000
value: 25.813999999999997
- type: map_at_3
value: 21.634
- type: map_at_5
value: 23.133
- type: mrr_at_1
value: 19.580000000000002
- type: mrr_at_10
value: 28.127999999999997
- type: mrr_at_100
value: 29.119
- type: mrr_at_1000
value: 29.192
- type: mrr_at_3
value: 25.509999999999998
- type: mrr_at_5
value: 26.878
- type: ndcg_at_1
value: 19.580000000000002
- type: ndcg_at_10
value: 29.804000000000002
- type: ndcg_at_100
value: 35.555
- type: ndcg_at_1000
value: 38.421
- type: ndcg_at_3
value: 24.654999999999998
- type: ndcg_at_5
value: 26.881
- type: precision_at_1
value: 19.580000000000002
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 1.005
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 12.033000000000001
- type: precision_at_5
value: 8.871
- type: recall_at_1
value: 16.07
- type: recall_at_10
value: 42.364000000000004
- type: recall_at_100
value: 68.01899999999999
- type: recall_at_1000
value: 88.122
- type: recall_at_3
value: 27.846
- type: recall_at_5
value: 33.638
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.365
- type: map_at_10
value: 36.591
- type: map_at_100
value: 37.730000000000004
- type: map_at_1000
value: 37.84
- type: map_at_3
value: 33.403
- type: map_at_5
value: 35.272999999999996
- type: mrr_at_1
value: 30.503999999999998
- type: mrr_at_10
value: 39.940999999999995
- type: mrr_at_100
value: 40.818
- type: mrr_at_1000
value: 40.876000000000005
- type: mrr_at_3
value: 37.065
- type: mrr_at_5
value: 38.814
- type: ndcg_at_1
value: 30.503999999999998
- type: ndcg_at_10
value: 42.185
- type: ndcg_at_100
value: 47.416000000000004
- type: ndcg_at_1000
value: 49.705
- type: ndcg_at_3
value: 36.568
- type: ndcg_at_5
value: 39.416000000000004
- type: precision_at_1
value: 30.503999999999998
- type: precision_at_10
value: 7.276000000000001
- type: precision_at_100
value: 1.118
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 16.729
- type: precision_at_5
value: 12.107999999999999
- type: recall_at_1
value: 26.365
- type: recall_at_10
value: 55.616
- type: recall_at_100
value: 78.129
- type: recall_at_1000
value: 93.95599999999999
- type: recall_at_3
value: 40.686
- type: recall_at_5
value: 47.668
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 22.750999999999998
- type: map_at_10
value: 33.446
- type: map_at_100
value: 35.235
- type: map_at_1000
value: 35.478
- type: map_at_3
value: 29.358
- type: map_at_5
value: 31.525
- type: mrr_at_1
value: 27.668
- type: mrr_at_10
value: 37.694
- type: mrr_at_100
value: 38.732
- type: mrr_at_1000
value: 38.779
- type: mrr_at_3
value: 34.223
- type: mrr_at_5
value: 36.08
- type: ndcg_at_1
value: 27.668
- type: ndcg_at_10
value: 40.557
- type: ndcg_at_100
value: 46.605999999999995
- type: ndcg_at_1000
value: 48.917
- type: ndcg_at_3
value: 33.677
- type: ndcg_at_5
value: 36.85
- type: precision_at_1
value: 27.668
- type: precision_at_10
value: 8.3
- type: precision_at_100
value: 1.6260000000000001
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 16.008
- type: precision_at_5
value: 12.292
- type: recall_at_1
value: 22.750999999999998
- type: recall_at_10
value: 55.643
- type: recall_at_100
value: 82.151
- type: recall_at_1000
value: 95.963
- type: recall_at_3
value: 36.623
- type: recall_at_5
value: 44.708
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.288999999999998
- type: map_at_10
value: 25.903
- type: map_at_100
value: 27.071
- type: map_at_1000
value: 27.173000000000002
- type: map_at_3
value: 22.935
- type: map_at_5
value: 24.573
- type: mrr_at_1
value: 18.669
- type: mrr_at_10
value: 27.682000000000002
- type: mrr_at_100
value: 28.691
- type: mrr_at_1000
value: 28.761
- type: mrr_at_3
value: 24.738
- type: mrr_at_5
value: 26.392
- type: ndcg_at_1
value: 18.669
- type: ndcg_at_10
value: 31.335
- type: ndcg_at_100
value: 36.913000000000004
- type: ndcg_at_1000
value: 39.300000000000004
- type: ndcg_at_3
value: 25.423000000000002
- type: ndcg_at_5
value: 28.262999999999998
- type: precision_at_1
value: 18.669
- type: precision_at_10
value: 5.379
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 11.214
- type: precision_at_5
value: 8.466
- type: recall_at_1
value: 17.288999999999998
- type: recall_at_10
value: 46.377
- type: recall_at_100
value: 71.53500000000001
- type: recall_at_1000
value: 88.947
- type: recall_at_3
value: 30.581999999999997
- type: recall_at_5
value: 37.354
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 21.795
- type: map_at_10
value: 37.614999999999995
- type: map_at_100
value: 40.037
- type: map_at_1000
value: 40.184999999999995
- type: map_at_3
value: 32.221
- type: map_at_5
value: 35.154999999999994
- type: mrr_at_1
value: 50.358000000000004
- type: mrr_at_10
value: 62.129
- type: mrr_at_100
value: 62.613
- type: mrr_at_1000
value: 62.62
- type: mrr_at_3
value: 59.272999999999996
- type: mrr_at_5
value: 61.138999999999996
- type: ndcg_at_1
value: 50.358000000000004
- type: ndcg_at_10
value: 48.362
- type: ndcg_at_100
value: 55.932
- type: ndcg_at_1000
value: 58.062999999999995
- type: ndcg_at_3
value: 42.111
- type: ndcg_at_5
value: 44.063
- type: precision_at_1
value: 50.358000000000004
- type: precision_at_10
value: 14.677999999999999
- type: precision_at_100
value: 2.2950000000000004
- type: precision_at_1000
value: 0.271
- type: precision_at_3
value: 31.77
- type: precision_at_5
value: 23.375
- type: recall_at_1
value: 21.795
- type: recall_at_10
value: 53.846000000000004
- type: recall_at_100
value: 78.952
- type: recall_at_1000
value: 90.41900000000001
- type: recall_at_3
value: 37.257
- type: recall_at_5
value: 44.661
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.728
- type: map_at_10
value: 22.691
- type: map_at_100
value: 31.734
- type: map_at_1000
value: 33.464
- type: map_at_3
value: 16.273
- type: map_at_5
value: 19.016
- type: mrr_at_1
value: 73.25
- type: mrr_at_10
value: 80.782
- type: mrr_at_100
value: 81.01899999999999
- type: mrr_at_1000
value: 81.021
- type: mrr_at_3
value: 79.583
- type: mrr_at_5
value: 80.146
- type: ndcg_at_1
value: 59.62499999999999
- type: ndcg_at_10
value: 46.304
- type: ndcg_at_100
value: 51.23
- type: ndcg_at_1000
value: 58.048
- type: ndcg_at_3
value: 51.541000000000004
- type: ndcg_at_5
value: 48.635
- type: precision_at_1
value: 73.25
- type: precision_at_10
value: 36.375
- type: precision_at_100
value: 11.53
- type: precision_at_1000
value: 2.23
- type: precision_at_3
value: 55.583000000000006
- type: precision_at_5
value: 47.15
- type: recall_at_1
value: 9.728
- type: recall_at_10
value: 28.793999999999997
- type: recall_at_100
value: 57.885
- type: recall_at_1000
value: 78.759
- type: recall_at_3
value: 17.79
- type: recall_at_5
value: 21.733
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.775
- type: f1
value: 41.89794273264891
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 85.378
- type: map_at_10
value: 91.51
- type: map_at_100
value: 91.666
- type: map_at_1000
value: 91.676
- type: map_at_3
value: 90.757
- type: map_at_5
value: 91.277
- type: mrr_at_1
value: 91.839
- type: mrr_at_10
value: 95.49
- type: mrr_at_100
value: 95.493
- type: mrr_at_1000
value: 95.493
- type: mrr_at_3
value: 95.345
- type: mrr_at_5
value: 95.47200000000001
- type: ndcg_at_1
value: 91.839
- type: ndcg_at_10
value: 93.806
- type: ndcg_at_100
value: 94.255
- type: ndcg_at_1000
value: 94.399
- type: ndcg_at_3
value: 93.027
- type: ndcg_at_5
value: 93.51
- type: precision_at_1
value: 91.839
- type: precision_at_10
value: 10.93
- type: precision_at_100
value: 1.1400000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 34.873
- type: precision_at_5
value: 21.44
- type: recall_at_1
value: 85.378
- type: recall_at_10
value: 96.814
- type: recall_at_100
value: 98.386
- type: recall_at_1000
value: 99.21600000000001
- type: recall_at_3
value: 94.643
- type: recall_at_5
value: 95.976
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.190000000000005
- type: map_at_10
value: 53.605000000000004
- type: map_at_100
value: 55.550999999999995
- type: map_at_1000
value: 55.665
- type: map_at_3
value: 46.62
- type: map_at_5
value: 50.517999999999994
- type: mrr_at_1
value: 60.34
- type: mrr_at_10
value: 70.775
- type: mrr_at_100
value: 71.238
- type: mrr_at_1000
value: 71.244
- type: mrr_at_3
value: 68.72399999999999
- type: mrr_at_5
value: 69.959
- type: ndcg_at_1
value: 60.34
- type: ndcg_at_10
value: 63.226000000000006
- type: ndcg_at_100
value: 68.60300000000001
- type: ndcg_at_1000
value: 69.901
- type: ndcg_at_3
value: 58.048
- type: ndcg_at_5
value: 59.789
- type: precision_at_1
value: 60.34
- type: precision_at_10
value: 17.130000000000003
- type: precision_at_100
value: 2.29
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 38.323
- type: precision_at_5
value: 27.87
- type: recall_at_1
value: 32.190000000000005
- type: recall_at_10
value: 73.041
- type: recall_at_100
value: 91.31
- type: recall_at_1000
value: 98.104
- type: recall_at_3
value: 53.70399999999999
- type: recall_at_5
value: 62.358999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.511
- type: map_at_10
value: 58.15
- type: map_at_100
value: 58.95399999999999
- type: map_at_1000
value: 59.018
- type: map_at_3
value: 55.31700000000001
- type: map_at_5
value: 57.04900000000001
- type: mrr_at_1
value: 87.022
- type: mrr_at_10
value: 91.32000000000001
- type: mrr_at_100
value: 91.401
- type: mrr_at_1000
value: 91.403
- type: mrr_at_3
value: 90.77
- type: mrr_at_5
value: 91.156
- type: ndcg_at_1
value: 87.022
- type: ndcg_at_10
value: 68.183
- type: ndcg_at_100
value: 70.781
- type: ndcg_at_1000
value: 72.009
- type: ndcg_at_3
value: 64.334
- type: ndcg_at_5
value: 66.449
- type: precision_at_1
value: 87.022
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 39.023
- type: precision_at_5
value: 25.080000000000002
- type: recall_at_1
value: 43.511
- type: recall_at_10
value: 67.02900000000001
- type: recall_at_100
value: 77.11
- type: recall_at_1000
value: 85.294
- type: recall_at_3
value: 58.535000000000004
- type: recall_at_5
value: 62.70099999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.0996
- type: ap
value: 87.86206089096373
- type: f1
value: 92.07554547510763
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 23.179
- type: map_at_10
value: 35.86
- type: map_at_100
value: 37.025999999999996
- type: map_at_1000
value: 37.068
- type: map_at_3
value: 31.921
- type: map_at_5
value: 34.172000000000004
- type: mrr_at_1
value: 23.926
- type: mrr_at_10
value: 36.525999999999996
- type: mrr_at_100
value: 37.627
- type: mrr_at_1000
value: 37.665
- type: mrr_at_3
value: 32.653
- type: mrr_at_5
value: 34.897
- type: ndcg_at_1
value: 23.910999999999998
- type: ndcg_at_10
value: 42.927
- type: ndcg_at_100
value: 48.464
- type: ndcg_at_1000
value: 49.533
- type: ndcg_at_3
value: 34.910000000000004
- type: ndcg_at_5
value: 38.937
- type: precision_at_1
value: 23.910999999999998
- type: precision_at_10
value: 6.758
- type: precision_at_100
value: 0.9520000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.838000000000001
- type: precision_at_5
value: 10.934000000000001
- type: recall_at_1
value: 23.179
- type: recall_at_10
value: 64.622
- type: recall_at_100
value: 90.135
- type: recall_at_1000
value: 98.301
- type: recall_at_3
value: 42.836999999999996
- type: recall_at_5
value: 52.512
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.59598723210215
- type: f1
value: 96.41913500001952
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.89557683538533
- type: f1
value: 63.379319722356264
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 78.93745796906524
- type: f1
value: 75.71616541785902
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.41223940820443
- type: f1
value: 81.2877893719078
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 35.03682528325662
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.942529406124
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.459949660460317
- type: mrr
value: 32.70509582031616
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.497
- type: map_at_10
value: 13.843
- type: map_at_100
value: 17.713
- type: map_at_1000
value: 19.241
- type: map_at_3
value: 10.096
- type: map_at_5
value: 11.85
- type: mrr_at_1
value: 48.916
- type: mrr_at_10
value: 57.764
- type: mrr_at_100
value: 58.251
- type: mrr_at_1000
value: 58.282999999999994
- type: mrr_at_3
value: 55.623999999999995
- type: mrr_at_5
value: 57.018
- type: ndcg_at_1
value: 46.594
- type: ndcg_at_10
value: 36.945
- type: ndcg_at_100
value: 34.06
- type: ndcg_at_1000
value: 43.05
- type: ndcg_at_3
value: 41.738
- type: ndcg_at_5
value: 39.330999999999996
- type: precision_at_1
value: 48.916
- type: precision_at_10
value: 27.43
- type: precision_at_100
value: 8.616
- type: precision_at_1000
value: 2.155
- type: precision_at_3
value: 39.112
- type: precision_at_5
value: 33.808
- type: recall_at_1
value: 6.497
- type: recall_at_10
value: 18.163
- type: recall_at_100
value: 34.566
- type: recall_at_1000
value: 67.15
- type: recall_at_3
value: 11.100999999999999
- type: recall_at_5
value: 14.205000000000002
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 31.916
- type: map_at_10
value: 48.123
- type: map_at_100
value: 49.103
- type: map_at_1000
value: 49.131
- type: map_at_3
value: 43.711
- type: map_at_5
value: 46.323
- type: mrr_at_1
value: 36.181999999999995
- type: mrr_at_10
value: 50.617999999999995
- type: mrr_at_100
value: 51.329
- type: mrr_at_1000
value: 51.348000000000006
- type: mrr_at_3
value: 47.010999999999996
- type: mrr_at_5
value: 49.175000000000004
- type: ndcg_at_1
value: 36.181999999999995
- type: ndcg_at_10
value: 56.077999999999996
- type: ndcg_at_100
value: 60.037
- type: ndcg_at_1000
value: 60.63499999999999
- type: ndcg_at_3
value: 47.859
- type: ndcg_at_5
value: 52.178999999999995
- type: precision_at_1
value: 36.181999999999995
- type: precision_at_10
value: 9.284
- type: precision_at_100
value: 1.149
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 22.006999999999998
- type: precision_at_5
value: 15.695
- type: recall_at_1
value: 31.916
- type: recall_at_10
value: 77.771
- type: recall_at_100
value: 94.602
- type: recall_at_1000
value: 98.967
- type: recall_at_3
value: 56.528
- type: recall_at_5
value: 66.527
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.486
- type: map_at_10
value: 85.978
- type: map_at_100
value: 86.587
- type: map_at_1000
value: 86.598
- type: map_at_3
value: 83.04899999999999
- type: map_at_5
value: 84.857
- type: mrr_at_1
value: 82.32000000000001
- type: mrr_at_10
value: 88.64
- type: mrr_at_100
value: 88.702
- type: mrr_at_1000
value: 88.702
- type: mrr_at_3
value: 87.735
- type: mrr_at_5
value: 88.36
- type: ndcg_at_1
value: 82.34
- type: ndcg_at_10
value: 89.67
- type: ndcg_at_100
value: 90.642
- type: ndcg_at_1000
value: 90.688
- type: ndcg_at_3
value: 86.932
- type: ndcg_at_5
value: 88.408
- type: precision_at_1
value: 82.34
- type: precision_at_10
value: 13.675999999999998
- type: precision_at_100
value: 1.544
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.24
- type: precision_at_5
value: 25.068
- type: recall_at_1
value: 71.486
- type: recall_at_10
value: 96.844
- type: recall_at_100
value: 99.843
- type: recall_at_1000
value: 99.996
- type: recall_at_3
value: 88.92099999999999
- type: recall_at_5
value: 93.215
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.75758437908334
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 68.03497914092789
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.808
- type: map_at_10
value: 16.059
- type: map_at_100
value: 19.048000000000002
- type: map_at_1000
value: 19.43
- type: map_at_3
value: 10.953
- type: map_at_5
value: 13.363
- type: mrr_at_1
value: 28.7
- type: mrr_at_10
value: 42.436
- type: mrr_at_100
value: 43.599
- type: mrr_at_1000
value: 43.62
- type: mrr_at_3
value: 38.45
- type: mrr_at_5
value: 40.89
- type: ndcg_at_1
value: 28.7
- type: ndcg_at_10
value: 26.346000000000004
- type: ndcg_at_100
value: 36.758
- type: ndcg_at_1000
value: 42.113
- type: ndcg_at_3
value: 24.254
- type: ndcg_at_5
value: 21.506
- type: precision_at_1
value: 28.7
- type: precision_at_10
value: 13.969999999999999
- type: precision_at_100
value: 2.881
- type: precision_at_1000
value: 0.414
- type: precision_at_3
value: 22.933
- type: precision_at_5
value: 19.220000000000002
- type: recall_at_1
value: 5.808
- type: recall_at_10
value: 28.310000000000002
- type: recall_at_100
value: 58.475
- type: recall_at_1000
value: 84.072
- type: recall_at_3
value: 13.957
- type: recall_at_5
value: 19.515
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.39274129958557
- type: cos_sim_spearman
value: 79.78021235170053
- type: euclidean_pearson
value: 79.35335401300166
- type: euclidean_spearman
value: 79.7271870968275
- type: manhattan_pearson
value: 79.35256263340601
- type: manhattan_spearman
value: 79.76036386976321
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.99130429246708
- type: cos_sim_spearman
value: 73.88322811171203
- type: euclidean_pearson
value: 80.7569419170376
- type: euclidean_spearman
value: 73.82542155409597
- type: manhattan_pearson
value: 80.79468183847625
- type: manhattan_spearman
value: 73.87027144047784
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.88548789489907
- type: cos_sim_spearman
value: 85.07535893847255
- type: euclidean_pearson
value: 84.6637222061494
- type: euclidean_spearman
value: 85.14200626702456
- type: manhattan_pearson
value: 84.75327892344734
- type: manhattan_spearman
value: 85.24406181838596
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.88140039325008
- type: cos_sim_spearman
value: 79.61211268112362
- type: euclidean_pearson
value: 81.29639728816458
- type: euclidean_spearman
value: 79.51284578041442
- type: manhattan_pearson
value: 81.3381797137111
- type: manhattan_spearman
value: 79.55683684039808
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.16716737270485
- type: cos_sim_spearman
value: 86.14823841857738
- type: euclidean_pearson
value: 85.36325733440725
- type: euclidean_spearman
value: 86.04919691402029
- type: manhattan_pearson
value: 85.3147511385052
- type: manhattan_spearman
value: 86.00676205857764
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.34266645861588
- type: cos_sim_spearman
value: 81.59914035005882
- type: euclidean_pearson
value: 81.15053076245988
- type: euclidean_spearman
value: 81.52776915798489
- type: manhattan_pearson
value: 81.1819647418673
- type: manhattan_spearman
value: 81.57479527353556
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.38263326821439
- type: cos_sim_spearman
value: 89.10946308202642
- type: euclidean_pearson
value: 88.87831312540068
- type: euclidean_spearman
value: 89.03615865973664
- type: manhattan_pearson
value: 88.79835539970384
- type: manhattan_spearman
value: 88.9766156339753
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 70.1574915581685
- type: cos_sim_spearman
value: 70.59144980004054
- type: euclidean_pearson
value: 71.43246306918755
- type: euclidean_spearman
value: 70.5544189562984
- type: manhattan_pearson
value: 71.4071414609503
- type: manhattan_spearman
value: 70.31799126163712
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.36215796635351
- type: cos_sim_spearman
value: 83.07276756467208
- type: euclidean_pearson
value: 83.06690453635584
- type: euclidean_spearman
value: 82.9635366303289
- type: manhattan_pearson
value: 83.04994049700815
- type: manhattan_spearman
value: 82.98120125356036
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.92530011616722
- type: mrr
value: 96.21826793395421
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 65.75
- type: map_at_10
value: 77.701
- type: map_at_100
value: 78.005
- type: map_at_1000
value: 78.006
- type: map_at_3
value: 75.48
- type: map_at_5
value: 76.927
- type: mrr_at_1
value: 68.333
- type: mrr_at_10
value: 78.511
- type: mrr_at_100
value: 78.704
- type: mrr_at_1000
value: 78.704
- type: mrr_at_3
value: 77
- type: mrr_at_5
value: 78.083
- type: ndcg_at_1
value: 68.333
- type: ndcg_at_10
value: 82.42699999999999
- type: ndcg_at_100
value: 83.486
- type: ndcg_at_1000
value: 83.511
- type: ndcg_at_3
value: 78.96300000000001
- type: ndcg_at_5
value: 81.028
- type: precision_at_1
value: 68.333
- type: precision_at_10
value: 10.667
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.333
- type: precision_at_5
value: 20.133000000000003
- type: recall_at_1
value: 65.75
- type: recall_at_10
value: 95.578
- type: recall_at_100
value: 99.833
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 91.75
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.75247524752476
- type: cos_sim_ap
value: 94.16065078045173
- type: cos_sim_f1
value: 87.22986247544205
- type: cos_sim_precision
value: 85.71428571428571
- type: cos_sim_recall
value: 88.8
- type: dot_accuracy
value: 99.74554455445545
- type: dot_ap
value: 93.90633887037264
- type: dot_f1
value: 86.9873417721519
- type: dot_precision
value: 88.1025641025641
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.75247524752476
- type: euclidean_ap
value: 94.17466319018055
- type: euclidean_f1
value: 87.3405299313052
- type: euclidean_precision
value: 85.74181117533719
- type: euclidean_recall
value: 89
- type: manhattan_accuracy
value: 99.75445544554455
- type: manhattan_ap
value: 94.27688371923577
- type: manhattan_f1
value: 87.74002954209749
- type: manhattan_precision
value: 86.42095053346266
- type: manhattan_recall
value: 89.1
- type: max_accuracy
value: 99.75445544554455
- type: max_ap
value: 94.27688371923577
- type: max_f1
value: 87.74002954209749
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 71.26500637517056
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 39.17507906280528
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.4848744828509
- type: mrr
value: 53.33678168236992
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.599864323827887
- type: cos_sim_spearman
value: 30.91116204665598
- type: dot_pearson
value: 30.82637894269936
- type: dot_spearman
value: 30.957573868416066
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.23600000000000002
- type: map_at_10
value: 1.892
- type: map_at_100
value: 11.586
- type: map_at_1000
value: 27.761999999999997
- type: map_at_3
value: 0.653
- type: map_at_5
value: 1.028
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 94
- type: mrr_at_100
value: 94
- type: mrr_at_1000
value: 94
- type: mrr_at_3
value: 94
- type: mrr_at_5
value: 94
- type: ndcg_at_1
value: 82
- type: ndcg_at_10
value: 77.48899999999999
- type: ndcg_at_100
value: 60.141
- type: ndcg_at_1000
value: 54.228
- type: ndcg_at_3
value: 82.358
- type: ndcg_at_5
value: 80.449
- type: precision_at_1
value: 88
- type: precision_at_10
value: 82.19999999999999
- type: precision_at_100
value: 61.760000000000005
- type: precision_at_1000
value: 23.684
- type: precision_at_3
value: 88
- type: precision_at_5
value: 85.6
- type: recall_at_1
value: 0.23600000000000002
- type: recall_at_10
value: 2.117
- type: recall_at_100
value: 14.985000000000001
- type: recall_at_1000
value: 51.107
- type: recall_at_3
value: 0.688
- type: recall_at_5
value: 1.1039999999999999
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.3040000000000003
- type: map_at_10
value: 9.025
- type: map_at_100
value: 15.312999999999999
- type: map_at_1000
value: 16.954
- type: map_at_3
value: 4.981
- type: map_at_5
value: 6.32
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 39.835
- type: mrr_at_100
value: 40.8
- type: mrr_at_1000
value: 40.8
- type: mrr_at_3
value: 35.034
- type: mrr_at_5
value: 37.687
- type: ndcg_at_1
value: 22.448999999999998
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 35.931999999999995
- type: ndcg_at_1000
value: 47.665
- type: ndcg_at_3
value: 23.311
- type: ndcg_at_5
value: 22.421
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 20.408
- type: precision_at_100
value: 7.815999999999999
- type: precision_at_1000
value: 1.553
- type: precision_at_3
value: 25.169999999999998
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.3040000000000003
- type: recall_at_10
value: 15.693999999999999
- type: recall_at_100
value: 48.917
- type: recall_at_1000
value: 84.964
- type: recall_at_3
value: 6.026
- type: recall_at_5
value: 9.066
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 82.6074
- type: ap
value: 23.187467098602013
- type: f1
value: 65.36829506379657
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.16355404640635
- type: f1
value: 63.534725639863346
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.91004094411276
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.55301901412649
- type: cos_sim_ap
value: 75.25312618556728
- type: cos_sim_f1
value: 68.76561719140429
- type: cos_sim_precision
value: 65.3061224489796
- type: cos_sim_recall
value: 72.61213720316623
- type: dot_accuracy
value: 86.29671574178936
- type: dot_ap
value: 75.11910195501207
- type: dot_f1
value: 68.44048376830045
- type: dot_precision
value: 66.12546125461255
- type: dot_recall
value: 70.92348284960423
- type: euclidean_accuracy
value: 86.5828217202122
- type: euclidean_ap
value: 75.22986344900924
- type: euclidean_f1
value: 68.81267797449549
- type: euclidean_precision
value: 64.8238861674831
- type: euclidean_recall
value: 73.3245382585752
- type: manhattan_accuracy
value: 86.61262442629791
- type: manhattan_ap
value: 75.24401608557328
- type: manhattan_f1
value: 68.80473982483257
- type: manhattan_precision
value: 67.21187720181177
- type: manhattan_recall
value: 70.47493403693932
- type: max_accuracy
value: 86.61262442629791
- type: max_ap
value: 75.25312618556728
- type: max_f1
value: 68.81267797449549
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.10688089416696
- type: cos_sim_ap
value: 84.17862178779863
- type: cos_sim_f1
value: 76.17305208781748
- type: cos_sim_precision
value: 71.31246641590543
- type: cos_sim_recall
value: 81.74468740375731
- type: dot_accuracy
value: 88.1844995536927
- type: dot_ap
value: 84.33816725235876
- type: dot_f1
value: 76.43554032918746
- type: dot_precision
value: 74.01557767200346
- type: dot_recall
value: 79.0190945488143
- type: euclidean_accuracy
value: 88.07001203089223
- type: euclidean_ap
value: 84.12267000814985
- type: euclidean_f1
value: 76.12232600180778
- type: euclidean_precision
value: 74.50604541433205
- type: euclidean_recall
value: 77.81028641823221
- type: manhattan_accuracy
value: 88.06419063142779
- type: manhattan_ap
value: 84.11648917164187
- type: manhattan_f1
value: 76.20579953925474
- type: manhattan_precision
value: 72.56772755762935
- type: manhattan_recall
value: 80.22790267939637
- type: max_accuracy
value: 88.1844995536927
- type: max_ap
value: 84.33816725235876
- type: max_f1
value: 76.43554032918746
---
<!-- **English** | [中文](./README_zh.md) -->
# gte-large-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
- **Model type:** Text Embeddings
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
<!-- - **Demo [optional]:** [More Information Needed] -->
### Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-large-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
Use with sentence-transformers:
```python
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
Use with `transformers.js`:
```js
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899]
```
## Training Details
### Training Data
- Masked language modeling (MLM): `c4-en`
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
- MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-large): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
- CPT: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000
- Fine-tuning: TODO
## Evaluation
### MTEB
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
### LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
## Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
journal={arXiv preprint arXiv:2407.19669},
year={2024}
}
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T05:20:55 | 2024-02-22T03:36:23 | 3,522,362 | 297 | ---
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-small-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 37.21923821573361
- type: f1
value: 68.0914945617093
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.75377499999999
- type: ap
value: 89.46766124546022
- type: f1
value: 92.73884001331487
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986
- type: f1
value: 46.55936786727896
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.846000000000004
- type: map_at_10
value: 51.388
- type: map_at_100
value: 52.132999999999996
- type: map_at_1000
value: 52.141000000000005
- type: map_at_3
value: 47.037
- type: map_at_5
value: 49.579
- type: mrr_at_1
value: 36.558
- type: mrr_at_10
value: 51.658
- type: mrr_at_100
value: 52.402
- type: mrr_at_1000
value: 52.410000000000004
- type: mrr_at_3
value: 47.345
- type: mrr_at_5
value: 49.797999999999995
- type: ndcg_at_1
value: 35.846000000000004
- type: ndcg_at_10
value: 59.550000000000004
- type: ndcg_at_100
value: 62.596
- type: ndcg_at_1000
value: 62.759
- type: ndcg_at_3
value: 50.666999999999994
- type: ndcg_at_5
value: 55.228
- type: precision_at_1
value: 35.846000000000004
- type: precision_at_10
value: 8.542
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.389
- type: precision_at_5
value: 14.438
- type: recall_at_1
value: 35.846000000000004
- type: recall_at_10
value: 85.42
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.166
- type: recall_at_5
value: 72.191
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.402770198163594
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.01545436974177
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.586465273207196
- type: mrr
value: 74.42169019038825
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.1891186537969
- type: cos_sim_spearman
value: 83.75492046087288
- type: euclidean_pearson
value: 84.11766204805357
- type: euclidean_spearman
value: 84.01456493126516
- type: manhattan_pearson
value: 84.2132950502772
- type: manhattan_spearman
value: 83.89227298813377
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.74025974025975
- type: f1
value: 85.71493566466381
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.467181385006434
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.719496037339056
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.587000000000003
- type: map_at_10
value: 41.114
- type: map_at_100
value: 42.532
- type: map_at_1000
value: 42.661
- type: map_at_3
value: 37.483
- type: map_at_5
value: 39.652
- type: mrr_at_1
value: 36.338
- type: mrr_at_10
value: 46.763
- type: mrr_at_100
value: 47.393
- type: mrr_at_1000
value: 47.445
- type: mrr_at_3
value: 43.538
- type: mrr_at_5
value: 45.556000000000004
- type: ndcg_at_1
value: 36.338
- type: ndcg_at_10
value: 47.658
- type: ndcg_at_100
value: 52.824000000000005
- type: ndcg_at_1000
value: 54.913999999999994
- type: ndcg_at_3
value: 41.989
- type: ndcg_at_5
value: 44.944
- type: precision_at_1
value: 36.338
- type: precision_at_10
value: 9.156
- type: precision_at_100
value: 1.4789999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.85
- type: recall_at_1
value: 29.587000000000003
- type: recall_at_10
value: 60.746
- type: recall_at_100
value: 82.157
- type: recall_at_1000
value: 95.645
- type: recall_at_3
value: 44.821
- type: recall_at_5
value: 52.819
- type: map_at_1
value: 30.239
- type: map_at_10
value: 39.989000000000004
- type: map_at_100
value: 41.196
- type: map_at_1000
value: 41.325
- type: map_at_3
value: 37.261
- type: map_at_5
value: 38.833
- type: mrr_at_1
value: 37.516
- type: mrr_at_10
value: 46.177
- type: mrr_at_100
value: 46.806
- type: mrr_at_1000
value: 46.849000000000004
- type: mrr_at_3
value: 44.002
- type: mrr_at_5
value: 45.34
- type: ndcg_at_1
value: 37.516
- type: ndcg_at_10
value: 45.586
- type: ndcg_at_100
value: 49.897000000000006
- type: ndcg_at_1000
value: 51.955
- type: ndcg_at_3
value: 41.684
- type: ndcg_at_5
value: 43.617
- type: precision_at_1
value: 37.516
- type: precision_at_10
value: 8.522
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 20.105999999999998
- type: precision_at_5
value: 14.152999999999999
- type: recall_at_1
value: 30.239
- type: recall_at_10
value: 55.03
- type: recall_at_100
value: 73.375
- type: recall_at_1000
value: 86.29599999999999
- type: recall_at_3
value: 43.269000000000005
- type: recall_at_5
value: 48.878
- type: map_at_1
value: 38.338
- type: map_at_10
value: 50.468999999999994
- type: map_at_100
value: 51.553000000000004
- type: map_at_1000
value: 51.608
- type: map_at_3
value: 47.107
- type: map_at_5
value: 49.101
- type: mrr_at_1
value: 44.201
- type: mrr_at_10
value: 54.057
- type: mrr_at_100
value: 54.764
- type: mrr_at_1000
value: 54.791000000000004
- type: mrr_at_3
value: 51.56699999999999
- type: mrr_at_5
value: 53.05
- type: ndcg_at_1
value: 44.201
- type: ndcg_at_10
value: 56.379000000000005
- type: ndcg_at_100
value: 60.645
- type: ndcg_at_1000
value: 61.73499999999999
- type: ndcg_at_3
value: 50.726000000000006
- type: ndcg_at_5
value: 53.58500000000001
- type: precision_at_1
value: 44.201
- type: precision_at_10
value: 9.141
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.654
- type: precision_at_5
value: 15.723999999999998
- type: recall_at_1
value: 38.338
- type: recall_at_10
value: 70.30499999999999
- type: recall_at_100
value: 88.77199999999999
- type: recall_at_1000
value: 96.49799999999999
- type: recall_at_3
value: 55.218
- type: recall_at_5
value: 62.104000000000006
- type: map_at_1
value: 25.682
- type: map_at_10
value: 33.498
- type: map_at_100
value: 34.461000000000006
- type: map_at_1000
value: 34.544000000000004
- type: map_at_3
value: 30.503999999999998
- type: map_at_5
value: 32.216
- type: mrr_at_1
value: 27.683999999999997
- type: mrr_at_10
value: 35.467999999999996
- type: mrr_at_100
value: 36.32
- type: mrr_at_1000
value: 36.386
- type: mrr_at_3
value: 32.618
- type: mrr_at_5
value: 34.262
- type: ndcg_at_1
value: 27.683999999999997
- type: ndcg_at_10
value: 38.378
- type: ndcg_at_100
value: 43.288
- type: ndcg_at_1000
value: 45.413
- type: ndcg_at_3
value: 32.586
- type: ndcg_at_5
value: 35.499
- type: precision_at_1
value: 27.683999999999997
- type: precision_at_10
value: 5.864
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.718
- type: recall_at_1
value: 25.682
- type: recall_at_10
value: 51.712
- type: recall_at_100
value: 74.446
- type: recall_at_1000
value: 90.472
- type: recall_at_3
value: 36.236000000000004
- type: recall_at_5
value: 43.234
- type: map_at_1
value: 16.073999999999998
- type: map_at_10
value: 24.352999999999998
- type: map_at_100
value: 25.438
- type: map_at_1000
value: 25.545
- type: map_at_3
value: 21.614
- type: map_at_5
value: 23.104
- type: mrr_at_1
value: 19.776
- type: mrr_at_10
value: 28.837000000000003
- type: mrr_at_100
value: 29.755
- type: mrr_at_1000
value: 29.817
- type: mrr_at_3
value: 26.201999999999998
- type: mrr_at_5
value: 27.714
- type: ndcg_at_1
value: 19.776
- type: ndcg_at_10
value: 29.701
- type: ndcg_at_100
value: 35.307
- type: ndcg_at_1000
value: 37.942
- type: ndcg_at_3
value: 24.764
- type: ndcg_at_5
value: 27.025
- type: precision_at_1
value: 19.776
- type: precision_at_10
value: 5.659
- type: precision_at_100
value: 0.971
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.905000000000001
- type: recall_at_1
value: 16.073999999999998
- type: recall_at_10
value: 41.647
- type: recall_at_100
value: 66.884
- type: recall_at_1000
value: 85.91499999999999
- type: recall_at_3
value: 27.916
- type: recall_at_5
value: 33.729
- type: map_at_1
value: 28.444999999999997
- type: map_at_10
value: 38.218999999999994
- type: map_at_100
value: 39.595
- type: map_at_1000
value: 39.709
- type: map_at_3
value: 35.586
- type: map_at_5
value: 36.895
- type: mrr_at_1
value: 34.841
- type: mrr_at_10
value: 44.106
- type: mrr_at_100
value: 44.98
- type: mrr_at_1000
value: 45.03
- type: mrr_at_3
value: 41.979
- type: mrr_at_5
value: 43.047999999999995
- type: ndcg_at_1
value: 34.841
- type: ndcg_at_10
value: 43.922
- type: ndcg_at_100
value: 49.504999999999995
- type: ndcg_at_1000
value: 51.675000000000004
- type: ndcg_at_3
value: 39.858
- type: ndcg_at_5
value: 41.408
- type: precision_at_1
value: 34.841
- type: precision_at_10
value: 7.872999999999999
- type: precision_at_100
value: 1.2449999999999999
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 18.993
- type: precision_at_5
value: 13.032
- type: recall_at_1
value: 28.444999999999997
- type: recall_at_10
value: 54.984
- type: recall_at_100
value: 78.342
- type: recall_at_1000
value: 92.77
- type: recall_at_3
value: 42.842999999999996
- type: recall_at_5
value: 47.247
- type: map_at_1
value: 23.072
- type: map_at_10
value: 32.354
- type: map_at_100
value: 33.800000000000004
- type: map_at_1000
value: 33.908
- type: map_at_3
value: 29.232000000000003
- type: map_at_5
value: 31.049
- type: mrr_at_1
value: 29.110000000000003
- type: mrr_at_10
value: 38.03
- type: mrr_at_100
value: 39.032
- type: mrr_at_1000
value: 39.086999999999996
- type: mrr_at_3
value: 35.407
- type: mrr_at_5
value: 36.76
- type: ndcg_at_1
value: 29.110000000000003
- type: ndcg_at_10
value: 38.231
- type: ndcg_at_100
value: 44.425
- type: ndcg_at_1000
value: 46.771
- type: ndcg_at_3
value: 33.095
- type: ndcg_at_5
value: 35.459
- type: precision_at_1
value: 29.110000000000003
- type: precision_at_10
value: 7.215000000000001
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 16.058
- type: precision_at_5
value: 11.644
- type: recall_at_1
value: 23.072
- type: recall_at_10
value: 50.285999999999994
- type: recall_at_100
value: 76.596
- type: recall_at_1000
value: 92.861
- type: recall_at_3
value: 35.702
- type: recall_at_5
value: 42.152
- type: map_at_1
value: 24.937916666666666
- type: map_at_10
value: 33.755250000000004
- type: map_at_100
value: 34.955999999999996
- type: map_at_1000
value: 35.070499999999996
- type: map_at_3
value: 30.98708333333333
- type: map_at_5
value: 32.51491666666666
- type: mrr_at_1
value: 29.48708333333333
- type: mrr_at_10
value: 37.92183333333334
- type: mrr_at_100
value: 38.76583333333333
- type: mrr_at_1000
value: 38.82466666666667
- type: mrr_at_3
value: 35.45125
- type: mrr_at_5
value: 36.827000000000005
- type: ndcg_at_1
value: 29.48708333333333
- type: ndcg_at_10
value: 39.05225
- type: ndcg_at_100
value: 44.25983333333334
- type: ndcg_at_1000
value: 46.568333333333335
- type: ndcg_at_3
value: 34.271583333333325
- type: ndcg_at_5
value: 36.483916666666666
- type: precision_at_1
value: 29.48708333333333
- type: precision_at_10
value: 6.865749999999999
- type: precision_at_100
value: 1.1195833333333332
- type: precision_at_1000
value: 0.15058333333333335
- type: precision_at_3
value: 15.742083333333333
- type: precision_at_5
value: 11.221916666666667
- type: recall_at_1
value: 24.937916666666666
- type: recall_at_10
value: 50.650416666666665
- type: recall_at_100
value: 73.55383333333334
- type: recall_at_1000
value: 89.61691666666667
- type: recall_at_3
value: 37.27808333333334
- type: recall_at_5
value: 42.99475
- type: map_at_1
value: 23.947
- type: map_at_10
value: 30.575000000000003
- type: map_at_100
value: 31.465
- type: map_at_1000
value: 31.558000000000003
- type: map_at_3
value: 28.814
- type: map_at_5
value: 29.738999999999997
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.415
- type: mrr_at_100
value: 34.18
- type: mrr_at_1000
value: 34.245
- type: mrr_at_3
value: 31.621
- type: mrr_at_5
value: 32.549
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.482
- type: ndcg_at_100
value: 38.915
- type: ndcg_at_1000
value: 41.355
- type: ndcg_at_3
value: 31.139
- type: ndcg_at_5
value: 32.589
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.322
- type: precision_at_100
value: 0.8160000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.344000000000001
- type: precision_at_5
value: 8.988
- type: recall_at_1
value: 23.947
- type: recall_at_10
value: 43.647999999999996
- type: recall_at_100
value: 63.851
- type: recall_at_1000
value: 82.0
- type: recall_at_3
value: 34.288000000000004
- type: recall_at_5
value: 38.117000000000004
- type: map_at_1
value: 16.197
- type: map_at_10
value: 22.968
- type: map_at_100
value: 24.095
- type: map_at_1000
value: 24.217
- type: map_at_3
value: 20.771
- type: map_at_5
value: 21.995
- type: mrr_at_1
value: 19.511
- type: mrr_at_10
value: 26.55
- type: mrr_at_100
value: 27.500999999999998
- type: mrr_at_1000
value: 27.578999999999997
- type: mrr_at_3
value: 24.421
- type: mrr_at_5
value: 25.604
- type: ndcg_at_1
value: 19.511
- type: ndcg_at_10
value: 27.386
- type: ndcg_at_100
value: 32.828
- type: ndcg_at_1000
value: 35.739
- type: ndcg_at_3
value: 23.405
- type: ndcg_at_5
value: 25.255
- type: precision_at_1
value: 19.511
- type: precision_at_10
value: 5.017
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.023
- type: precision_at_5
value: 8.025
- type: recall_at_1
value: 16.197
- type: recall_at_10
value: 37.09
- type: recall_at_100
value: 61.778
- type: recall_at_1000
value: 82.56599999999999
- type: recall_at_3
value: 26.034000000000002
- type: recall_at_5
value: 30.762
- type: map_at_1
value: 25.41
- type: map_at_10
value: 33.655
- type: map_at_100
value: 34.892
- type: map_at_1000
value: 34.995
- type: map_at_3
value: 30.94
- type: map_at_5
value: 32.303
- type: mrr_at_1
value: 29.477999999999998
- type: mrr_at_10
value: 37.443
- type: mrr_at_100
value: 38.383
- type: mrr_at_1000
value: 38.440000000000005
- type: mrr_at_3
value: 34.949999999999996
- type: mrr_at_5
value: 36.228
- type: ndcg_at_1
value: 29.477999999999998
- type: ndcg_at_10
value: 38.769
- type: ndcg_at_100
value: 44.245000000000005
- type: ndcg_at_1000
value: 46.593
- type: ndcg_at_3
value: 33.623
- type: ndcg_at_5
value: 35.766
- type: precision_at_1
value: 29.477999999999998
- type: precision_at_10
value: 6.455
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.893999999999998
- type: precision_at_5
value: 10.485
- type: recall_at_1
value: 25.41
- type: recall_at_10
value: 50.669
- type: recall_at_100
value: 74.084
- type: recall_at_1000
value: 90.435
- type: recall_at_3
value: 36.679
- type: recall_at_5
value: 41.94
- type: map_at_1
value: 23.339
- type: map_at_10
value: 31.852000000000004
- type: map_at_100
value: 33.411
- type: map_at_1000
value: 33.62
- type: map_at_3
value: 28.929
- type: map_at_5
value: 30.542
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.301
- type: mrr_at_100
value: 37.288
- type: mrr_at_1000
value: 37.349
- type: mrr_at_3
value: 33.663
- type: mrr_at_5
value: 35.165
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 37.462
- type: ndcg_at_100
value: 43.620999999999995
- type: ndcg_at_1000
value: 46.211
- type: ndcg_at_3
value: 32.68
- type: ndcg_at_5
value: 34.981
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.1739999999999995
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.265
- type: recall_at_1
value: 23.339
- type: recall_at_10
value: 48.376999999999995
- type: recall_at_100
value: 76.053
- type: recall_at_1000
value: 92.455
- type: recall_at_3
value: 34.735
- type: recall_at_5
value: 40.71
- type: map_at_1
value: 18.925
- type: map_at_10
value: 26.017000000000003
- type: map_at_100
value: 27.034000000000002
- type: map_at_1000
value: 27.156000000000002
- type: map_at_3
value: 23.604
- type: map_at_5
value: 24.75
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 27.915
- type: mrr_at_100
value: 28.788000000000004
- type: mrr_at_1000
value: 28.877999999999997
- type: mrr_at_3
value: 25.446999999999996
- type: mrr_at_5
value: 26.648
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 30.673000000000002
- type: ndcg_at_100
value: 35.618
- type: ndcg_at_1000
value: 38.517
- type: ndcg_at_3
value: 25.71
- type: ndcg_at_5
value: 27.679
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.9910000000000005
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.8740000000000006
- type: recall_at_1
value: 18.925
- type: recall_at_10
value: 43.311
- type: recall_at_100
value: 66.308
- type: recall_at_1000
value: 87.49
- type: recall_at_3
value: 29.596
- type: recall_at_5
value: 34.245
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.714
- type: map_at_10
value: 23.194
- type: map_at_100
value: 24.976000000000003
- type: map_at_1000
value: 25.166
- type: map_at_3
value: 19.709
- type: map_at_5
value: 21.523999999999997
- type: mrr_at_1
value: 30.619000000000003
- type: mrr_at_10
value: 42.563
- type: mrr_at_100
value: 43.386
- type: mrr_at_1000
value: 43.423
- type: mrr_at_3
value: 39.555
- type: mrr_at_5
value: 41.268
- type: ndcg_at_1
value: 30.619000000000003
- type: ndcg_at_10
value: 31.836
- type: ndcg_at_100
value: 38.652
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 26.733
- type: ndcg_at_5
value: 28.435
- type: precision_at_1
value: 30.619000000000003
- type: precision_at_10
value: 9.751999999999999
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 19.935
- type: precision_at_5
value: 14.984
- type: recall_at_1
value: 13.714
- type: recall_at_10
value: 37.26
- type: recall_at_100
value: 60.546
- type: recall_at_1000
value: 79.899
- type: recall_at_3
value: 24.325
- type: recall_at_5
value: 29.725
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.462
- type: map_at_10
value: 18.637
- type: map_at_100
value: 26.131999999999998
- type: map_at_1000
value: 27.607
- type: map_at_3
value: 13.333
- type: map_at_5
value: 15.654000000000002
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.32600000000001
- type: mrr_at_100
value: 74.60900000000001
- type: mrr_at_1000
value: 74.62
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 73.817
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.028999999999996
- type: ndcg_at_100
value: 44.199
- type: ndcg_at_1000
value: 51.629999999999995
- type: ndcg_at_3
value: 44.113
- type: ndcg_at_5
value: 41.731
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 31.900000000000002
- type: precision_at_100
value: 10.043000000000001
- type: precision_at_1000
value: 1.926
- type: precision_at_3
value: 47.417
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.462
- type: recall_at_10
value: 24.293
- type: recall_at_100
value: 50.146
- type: recall_at_1000
value: 74.034
- type: recall_at_3
value: 14.967
- type: recall_at_5
value: 18.682000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.84499999999999
- type: f1
value: 42.48106691979349
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.034
- type: map_at_10
value: 82.76
- type: map_at_100
value: 82.968
- type: map_at_1000
value: 82.98299999999999
- type: map_at_3
value: 81.768
- type: map_at_5
value: 82.418
- type: mrr_at_1
value: 80.048
- type: mrr_at_10
value: 87.64999999999999
- type: mrr_at_100
value: 87.712
- type: mrr_at_1000
value: 87.713
- type: mrr_at_3
value: 87.01100000000001
- type: mrr_at_5
value: 87.466
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 86.643
- type: ndcg_at_100
value: 87.361
- type: ndcg_at_1000
value: 87.606
- type: ndcg_at_3
value: 85.137
- type: ndcg_at_5
value: 86.016
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.372
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.638
- type: precision_at_5
value: 20.177
- type: recall_at_1
value: 74.034
- type: recall_at_10
value: 93.769
- type: recall_at_100
value: 96.569
- type: recall_at_1000
value: 98.039
- type: recall_at_3
value: 89.581
- type: recall_at_5
value: 91.906
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.5
- type: map_at_10
value: 32.857
- type: map_at_100
value: 34.589
- type: map_at_1000
value: 34.778
- type: map_at_3
value: 29.160999999999998
- type: map_at_5
value: 31.033
- type: mrr_at_1
value: 40.123
- type: mrr_at_10
value: 48.776
- type: mrr_at_100
value: 49.495
- type: mrr_at_1000
value: 49.539
- type: mrr_at_3
value: 46.605000000000004
- type: mrr_at_5
value: 47.654
- type: ndcg_at_1
value: 40.123
- type: ndcg_at_10
value: 40.343
- type: ndcg_at_100
value: 46.56
- type: ndcg_at_1000
value: 49.777
- type: ndcg_at_3
value: 37.322
- type: ndcg_at_5
value: 37.791000000000004
- type: precision_at_1
value: 40.123
- type: precision_at_10
value: 11.08
- type: precision_at_100
value: 1.752
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 24.897
- type: precision_at_5
value: 17.809
- type: recall_at_1
value: 20.5
- type: recall_at_10
value: 46.388
- type: recall_at_100
value: 69.552
- type: recall_at_1000
value: 89.011
- type: recall_at_3
value: 33.617999999999995
- type: recall_at_5
value: 38.211
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.135999999999996
- type: map_at_10
value: 61.673
- type: map_at_100
value: 62.562
- type: map_at_1000
value: 62.62
- type: map_at_3
value: 58.467999999999996
- type: map_at_5
value: 60.463
- type: mrr_at_1
value: 78.271
- type: mrr_at_10
value: 84.119
- type: mrr_at_100
value: 84.29299999999999
- type: mrr_at_1000
value: 84.299
- type: mrr_at_3
value: 83.18900000000001
- type: mrr_at_5
value: 83.786
- type: ndcg_at_1
value: 78.271
- type: ndcg_at_10
value: 69.935
- type: ndcg_at_100
value: 73.01299999999999
- type: ndcg_at_1000
value: 74.126
- type: ndcg_at_3
value: 65.388
- type: ndcg_at_5
value: 67.906
- type: precision_at_1
value: 78.271
- type: precision_at_10
value: 14.562
- type: precision_at_100
value: 1.6969999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 41.841
- type: precision_at_5
value: 27.087
- type: recall_at_1
value: 39.135999999999996
- type: recall_at_10
value: 72.809
- type: recall_at_100
value: 84.86200000000001
- type: recall_at_1000
value: 92.208
- type: recall_at_3
value: 62.76199999999999
- type: recall_at_5
value: 67.718
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.60600000000001
- type: ap
value: 86.6579587804335
- type: f1
value: 90.5938853929307
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.852
- type: map_at_10
value: 33.982
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.167
- type: map_at_3
value: 30.134
- type: map_at_5
value: 32.340999999999994
- type: mrr_at_1
value: 22.479
- type: mrr_at_10
value: 34.594
- type: mrr_at_100
value: 35.672
- type: mrr_at_1000
value: 35.716
- type: mrr_at_3
value: 30.84
- type: mrr_at_5
value: 32.998
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.833000000000006
- type: ndcg_at_100
value: 46.357
- type: ndcg_at_1000
value: 47.637
- type: ndcg_at_3
value: 32.995999999999995
- type: ndcg_at_5
value: 36.919000000000004
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.030999999999999
- type: precision_at_5
value: 10.413
- type: recall_at_1
value: 21.852
- type: recall_at_10
value: 61.934999999999995
- type: recall_at_100
value: 87.611
- type: recall_at_1000
value: 97.441
- type: recall_at_3
value: 40.583999999999996
- type: recall_at_5
value: 49.992999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.36069311445507
- type: f1
value: 93.16456330371453
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.74692202462381
- type: f1
value: 58.17903579421599
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.80833893745796
- type: f1
value: 72.70786592684664
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69872225958305
- type: f1
value: 78.61626934504731
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.058658628717694
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.85561739360599
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.290259910144385
- type: mrr
value: 32.44223046102856
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.288
- type: map_at_10
value: 12.267999999999999
- type: map_at_100
value: 15.557000000000002
- type: map_at_1000
value: 16.98
- type: map_at_3
value: 8.866
- type: map_at_5
value: 10.418
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 52.681
- type: mrr_at_100
value: 53.315999999999995
- type: mrr_at_1000
value: 53.357
- type: mrr_at_3
value: 51.393
- type: mrr_at_5
value: 51.903999999999996
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.305
- type: ndcg_at_100
value: 30.825999999999997
- type: ndcg_at_1000
value: 39.393
- type: ndcg_at_3
value: 39.931
- type: ndcg_at_5
value: 37.519999999999996
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.932
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.288
- type: recall_at_10
value: 16.195
- type: recall_at_100
value: 31.135
- type: recall_at_1000
value: 61.531000000000006
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 12.754999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.216
- type: map_at_10
value: 42.588
- type: map_at_100
value: 43.702999999999996
- type: map_at_1000
value: 43.739
- type: map_at_3
value: 38.177
- type: map_at_5
value: 40.754000000000005
- type: mrr_at_1
value: 31.866
- type: mrr_at_10
value: 45.189
- type: mrr_at_100
value: 46.056000000000004
- type: mrr_at_1000
value: 46.081
- type: mrr_at_3
value: 41.526999999999994
- type: mrr_at_5
value: 43.704
- type: ndcg_at_1
value: 31.837
- type: ndcg_at_10
value: 50.178
- type: ndcg_at_100
value: 54.98800000000001
- type: ndcg_at_1000
value: 55.812
- type: ndcg_at_3
value: 41.853
- type: ndcg_at_5
value: 46.153
- type: precision_at_1
value: 31.837
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.911000000000001
- type: recall_at_1
value: 28.216
- type: recall_at_10
value: 70.8
- type: recall_at_100
value: 91.857
- type: recall_at_1000
value: 97.941
- type: recall_at_3
value: 49.196
- type: recall_at_5
value: 59.072
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.22800000000001
- type: map_at_10
value: 85.115
- type: map_at_100
value: 85.72
- type: map_at_1000
value: 85.737
- type: map_at_3
value: 82.149
- type: map_at_5
value: 84.029
- type: mrr_at_1
value: 81.96
- type: mrr_at_10
value: 88.00200000000001
- type: mrr_at_100
value: 88.088
- type: mrr_at_1000
value: 88.089
- type: mrr_at_3
value: 87.055
- type: mrr_at_5
value: 87.715
- type: ndcg_at_1
value: 82.01
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.91
- type: ndcg_at_1000
value: 90.013
- type: ndcg_at_3
value: 85.957
- type: ndcg_at_5
value: 87.56
- type: precision_at_1
value: 82.01
- type: precision_at_10
value: 13.462
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.732000000000003
- type: recall_at_1
value: 71.22800000000001
- type: recall_at_10
value: 95.69
- type: recall_at_100
value: 99.531
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 87.632
- type: recall_at_5
value: 92.117
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 52.31768034366916
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.640266772723606
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7780000000000005
- type: map_at_10
value: 12.299
- type: map_at_100
value: 14.363000000000001
- type: map_at_1000
value: 14.71
- type: map_at_3
value: 8.738999999999999
- type: map_at_5
value: 10.397
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 34.845
- type: mrr_at_100
value: 35.916
- type: mrr_at_1000
value: 35.973
- type: mrr_at_3
value: 31.7
- type: mrr_at_5
value: 33.535
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 20.522000000000002
- type: ndcg_at_100
value: 28.737000000000002
- type: ndcg_at_1000
value: 34.596
- type: ndcg_at_3
value: 19.542
- type: ndcg_at_5
value: 16.958000000000002
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 10.67
- type: precision_at_100
value: 2.259
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 18.333
- type: precision_at_5
value: 14.879999999999999
- type: recall_at_1
value: 4.7780000000000005
- type: recall_at_10
value: 21.617
- type: recall_at_100
value: 45.905
- type: recall_at_1000
value: 74.42
- type: recall_at_3
value: 11.148
- type: recall_at_5
value: 15.082999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.22372750297885
- type: cos_sim_spearman
value: 79.40972617119405
- type: euclidean_pearson
value: 80.6101072020434
- type: euclidean_spearman
value: 79.53844217225202
- type: manhattan_pearson
value: 80.57265975286111
- type: manhattan_spearman
value: 79.46335611792958
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.43713315520749
- type: cos_sim_spearman
value: 77.44128693329532
- type: euclidean_pearson
value: 81.63869928101123
- type: euclidean_spearman
value: 77.29512977961515
- type: manhattan_pearson
value: 81.63704185566183
- type: manhattan_spearman
value: 77.29909412738657
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.59451537860527
- type: cos_sim_spearman
value: 82.97994638856723
- type: euclidean_pearson
value: 82.89478688288412
- type: euclidean_spearman
value: 83.58740751053104
- type: manhattan_pearson
value: 82.69140840941608
- type: manhattan_spearman
value: 83.33665956040555
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.00756527711764
- type: cos_sim_spearman
value: 81.83560996841379
- type: euclidean_pearson
value: 82.07684151976518
- type: euclidean_spearman
value: 82.00913052060511
- type: manhattan_pearson
value: 82.05690778488794
- type: manhattan_spearman
value: 82.02260252019525
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.13710262895447
- type: cos_sim_spearman
value: 87.26412811156248
- type: euclidean_pearson
value: 86.94151453230228
- type: euclidean_spearman
value: 87.5363796699571
- type: manhattan_pearson
value: 86.86989424083748
- type: manhattan_spearman
value: 87.47315940781353
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.0230597603627
- type: cos_sim_spearman
value: 84.93344499318864
- type: euclidean_pearson
value: 84.23754743431141
- type: euclidean_spearman
value: 85.09707376597099
- type: manhattan_pearson
value: 84.04325160987763
- type: manhattan_spearman
value: 84.89353071339909
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.75620824563921
- type: cos_sim_spearman
value: 87.15065513706398
- type: euclidean_pearson
value: 88.26281533633521
- type: euclidean_spearman
value: 87.51963738643983
- type: manhattan_pearson
value: 88.25599267618065
- type: manhattan_spearman
value: 87.58048736047483
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.74645319195137
- type: cos_sim_spearman
value: 65.29996325037214
- type: euclidean_pearson
value: 67.04297794086443
- type: euclidean_spearman
value: 65.43841726694343
- type: manhattan_pearson
value: 67.39459955690904
- type: manhattan_spearman
value: 65.92864704413651
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.31291020270801
- type: cos_sim_spearman
value: 85.86473738688068
- type: euclidean_pearson
value: 85.65537275064152
- type: euclidean_spearman
value: 86.13087454209642
- type: manhattan_pearson
value: 85.43946955047609
- type: manhattan_spearman
value: 85.91568175344916
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.93798118350695
- type: mrr
value: 95.93536274908824
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.594
- type: map_at_10
value: 66.81899999999999
- type: map_at_100
value: 67.368
- type: map_at_1000
value: 67.4
- type: map_at_3
value: 64.061
- type: map_at_5
value: 65.47
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.219
- type: mrr_at_100
value: 68.655
- type: mrr_at_1000
value: 68.684
- type: mrr_at_3
value: 66.22200000000001
- type: mrr_at_5
value: 67.289
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.275
- type: ndcg_at_100
value: 73.642
- type: ndcg_at_1000
value: 74.373
- type: ndcg_at_3
value: 66.521
- type: ndcg_at_5
value: 68.581
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.433
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.556
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 57.594
- type: recall_at_10
value: 83.622
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.64399999999999
- type: recall_at_5
value: 75.983
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85841584158416
- type: cos_sim_ap
value: 96.66996142314342
- type: cos_sim_f1
value: 92.83208020050125
- type: cos_sim_precision
value: 93.06532663316584
- type: cos_sim_recall
value: 92.60000000000001
- type: dot_accuracy
value: 99.85841584158416
- type: dot_ap
value: 96.6775307676576
- type: dot_f1
value: 92.69289729177312
- type: dot_precision
value: 94.77533960292581
- type: dot_recall
value: 90.7
- type: euclidean_accuracy
value: 99.86138613861387
- type: euclidean_ap
value: 96.6338454403108
- type: euclidean_f1
value: 92.92214357937311
- type: euclidean_precision
value: 93.96728016359918
- type: euclidean_recall
value: 91.9
- type: manhattan_accuracy
value: 99.86237623762376
- type: manhattan_ap
value: 96.60370449645053
- type: manhattan_f1
value: 92.91177970423253
- type: manhattan_precision
value: 94.7970863683663
- type: manhattan_recall
value: 91.10000000000001
- type: max_accuracy
value: 99.86237623762376
- type: max_ap
value: 96.6775307676576
- type: max_f1
value: 92.92214357937311
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.77977058695198
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.2725272535638
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.64052466362125
- type: mrr
value: 54.533067014684654
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.677624219206578
- type: cos_sim_spearman
value: 30.121368518123447
- type: dot_pearson
value: 30.69870088041608
- type: dot_spearman
value: 29.61284927093751
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.855
- type: map_at_100
value: 9.885
- type: map_at_1000
value: 23.416999999999998
- type: map_at_3
value: 0.637
- type: map_at_5
value: 1.024
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.067
- type: mrr_at_100
value: 93.067
- type: mrr_at_1000
value: 93.067
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 93.067
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 75.899
- type: ndcg_at_100
value: 55.115
- type: ndcg_at_1000
value: 48.368
- type: ndcg_at_3
value: 79.704
- type: ndcg_at_5
value: 78.39699999999999
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 79.60000000000001
- type: precision_at_100
value: 56.06
- type: precision_at_1000
value: 21.206
- type: precision_at_3
value: 84.667
- type: precision_at_5
value: 83.2
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.078
- type: recall_at_100
value: 13.297
- type: recall_at_1000
value: 44.979
- type: recall_at_3
value: 0.6689999999999999
- type: recall_at_5
value: 1.106
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.258
- type: map_at_10
value: 10.439
- type: map_at_100
value: 16.89
- type: map_at_1000
value: 18.407999999999998
- type: map_at_3
value: 5.668
- type: map_at_5
value: 7.718
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 51.159
- type: mrr_at_100
value: 51.714000000000006
- type: mrr_at_1000
value: 51.714000000000006
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 50.407999999999994
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 26.037
- type: ndcg_at_100
value: 37.924
- type: ndcg_at_1000
value: 49.126999999999995
- type: ndcg_at_3
value: 30.631999999999998
- type: ndcg_at_5
value: 28.571
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.529
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.258
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 48.439
- type: recall_at_1000
value: 82.80499999999999
- type: recall_at_3
value: 7.283
- type: recall_at_5
value: 10.732
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.8858
- type: ap
value: 13.835684144362109
- type: f1
value: 53.803351693244586
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.50650820599886
- type: f1
value: 60.84357825979259
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.52131044852134
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59337187816654
- type: cos_sim_ap
value: 73.23925826533437
- type: cos_sim_f1
value: 67.34693877551021
- type: cos_sim_precision
value: 62.40432237730752
- type: cos_sim_recall
value: 73.13984168865434
- type: dot_accuracy
value: 85.31322644096085
- type: dot_ap
value: 72.30723963807422
- type: dot_f1
value: 66.47051612112296
- type: dot_precision
value: 62.0792305930845
- type: dot_recall
value: 71.53034300791556
- type: euclidean_accuracy
value: 85.61125350181797
- type: euclidean_ap
value: 73.32843720487845
- type: euclidean_f1
value: 67.36549633745895
- type: euclidean_precision
value: 64.60755813953489
- type: euclidean_recall
value: 70.36939313984169
- type: manhattan_accuracy
value: 85.63509566668654
- type: manhattan_ap
value: 73.16658488311325
- type: manhattan_f1
value: 67.20597386434349
- type: manhattan_precision
value: 63.60424028268551
- type: manhattan_recall
value: 71.2401055408971
- type: max_accuracy
value: 85.63509566668654
- type: max_ap
value: 73.32843720487845
- type: max_f1
value: 67.36549633745895
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.33779640625606
- type: cos_sim_ap
value: 84.83868375898157
- type: cos_sim_f1
value: 77.16506154017773
- type: cos_sim_precision
value: 74.62064005753327
- type: cos_sim_recall
value: 79.88912842623961
- type: dot_accuracy
value: 88.02732176815307
- type: dot_ap
value: 83.95089283763002
- type: dot_f1
value: 76.29635101196631
- type: dot_precision
value: 73.31771720613288
- type: dot_recall
value: 79.52725592854944
- type: euclidean_accuracy
value: 88.44452206310397
- type: euclidean_ap
value: 84.98384576824827
- type: euclidean_f1
value: 77.29311047696697
- type: euclidean_precision
value: 74.51232583065381
- type: euclidean_recall
value: 80.28949799815214
- type: manhattan_accuracy
value: 88.47362906042613
- type: manhattan_ap
value: 84.91421462218432
- type: manhattan_f1
value: 77.05107637204792
- type: manhattan_precision
value: 74.74484256243214
- type: manhattan_recall
value: 79.50415768401602
- type: max_accuracy
value: 88.47362906042613
- type: max_ap
value: 84.98384576824827
- type: max_f1
value: 77.29311047696697
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
#### Usage of the ONNX files
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5')
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
model_output_ort = model_ort(**encoded_input)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# model_output and model_output_ort are identical
```
#### Usage via infinity
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
avsolatorio/GIST-small-Embedding-v0 | avsolatorio | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2402.16829",
"arxiv:2212.09741",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-03T06:14:01 | 2024-02-28T00:36:01 | 3,475,309 | 25 | ---
language:
- en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
model-index:
- name: GIST-small-Embedding-v0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.26865671641791
- type: ap
value: 38.25623793370476
- type: f1
value: 69.26434651320257
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.232225
- type: ap
value: 89.97936072879344
- type: f1
value: 93.22122653806187
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.715999999999994
- type: f1
value: 49.169789920136076
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.922
- type: map_at_10
value: 50.524
- type: map_at_100
value: 51.247
- type: map_at_1000
value: 51.249
- type: map_at_3
value: 45.887
- type: map_at_5
value: 48.592999999999996
- type: mrr_at_1
value: 34.922
- type: mrr_at_10
value: 50.382000000000005
- type: mrr_at_100
value: 51.104000000000006
- type: mrr_at_1000
value: 51.105999999999995
- type: mrr_at_3
value: 45.733000000000004
- type: mrr_at_5
value: 48.428
- type: ndcg_at_1
value: 34.922
- type: ndcg_at_10
value: 59.12
- type: ndcg_at_100
value: 62.083999999999996
- type: ndcg_at_1000
value: 62.137
- type: ndcg_at_3
value: 49.616
- type: ndcg_at_5
value: 54.501
- type: precision_at_1
value: 34.922
- type: precision_at_10
value: 8.649
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.152
- type: precision_at_5
value: 14.466999999999999
- type: recall_at_1
value: 34.922
- type: recall_at_10
value: 86.48599999999999
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 60.455000000000005
- type: recall_at_5
value: 72.333
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.623282347623714
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.86487843524932
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.3290291318171
- type: mrr
value: 75.2379853141626
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.52002953574285
- type: cos_sim_spearman
value: 86.98752423842483
- type: euclidean_pearson
value: 86.89442688314197
- type: euclidean_spearman
value: 86.88631711307471
- type: manhattan_pearson
value: 87.03723618507175
- type: manhattan_spearman
value: 86.76041062975224
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.64935064935065
- type: f1
value: 86.61903824934998
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.21904455377494
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.43342755570654
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.843
- type: map_at_10
value: 43.379
- type: map_at_100
value: 44.946999999999996
- type: map_at_1000
value: 45.078
- type: map_at_3
value: 39.598
- type: map_at_5
value: 41.746
- type: mrr_at_1
value: 39.199
- type: mrr_at_10
value: 49.672
- type: mrr_at_100
value: 50.321000000000005
- type: mrr_at_1000
value: 50.365
- type: mrr_at_3
value: 46.805
- type: mrr_at_5
value: 48.579
- type: ndcg_at_1
value: 39.199
- type: ndcg_at_10
value: 50.163999999999994
- type: ndcg_at_100
value: 55.418
- type: ndcg_at_1000
value: 57.353
- type: ndcg_at_3
value: 44.716
- type: ndcg_at_5
value: 47.268
- type: precision_at_1
value: 39.199
- type: precision_at_10
value: 9.757
- type: precision_at_100
value: 1.552
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 21.602
- type: precision_at_5
value: 15.479000000000001
- type: recall_at_1
value: 31.843
- type: recall_at_10
value: 62.743
- type: recall_at_100
value: 84.78099999999999
- type: recall_at_1000
value: 96.86099999999999
- type: recall_at_3
value: 46.927
- type: recall_at_5
value: 54.355
- type: map_at_1
value: 29.321
- type: map_at_10
value: 39.062999999999995
- type: map_at_100
value: 40.403
- type: map_at_1000
value: 40.534
- type: map_at_3
value: 36.367
- type: map_at_5
value: 37.756
- type: mrr_at_1
value: 35.987
- type: mrr_at_10
value: 44.708999999999996
- type: mrr_at_100
value: 45.394
- type: mrr_at_1000
value: 45.436
- type: mrr_at_3
value: 42.463
- type: mrr_at_5
value: 43.663000000000004
- type: ndcg_at_1
value: 35.987
- type: ndcg_at_10
value: 44.585
- type: ndcg_at_100
value: 49.297999999999995
- type: ndcg_at_1000
value: 51.315
- type: ndcg_at_3
value: 40.569
- type: ndcg_at_5
value: 42.197
- type: precision_at_1
value: 35.987
- type: precision_at_10
value: 8.369
- type: precision_at_100
value: 1.366
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 19.427
- type: precision_at_5
value: 13.58
- type: recall_at_1
value: 29.321
- type: recall_at_10
value: 54.333
- type: recall_at_100
value: 74.178
- type: recall_at_1000
value: 86.732
- type: recall_at_3
value: 42.46
- type: recall_at_5
value: 47.089999999999996
- type: map_at_1
value: 38.811
- type: map_at_10
value: 51.114000000000004
- type: map_at_100
value: 52.22
- type: map_at_1000
value: 52.275000000000006
- type: map_at_3
value: 47.644999999999996
- type: map_at_5
value: 49.675000000000004
- type: mrr_at_1
value: 44.389
- type: mrr_at_10
value: 54.459
- type: mrr_at_100
value: 55.208999999999996
- type: mrr_at_1000
value: 55.239000000000004
- type: mrr_at_3
value: 51.954
- type: mrr_at_5
value: 53.571999999999996
- type: ndcg_at_1
value: 44.389
- type: ndcg_at_10
value: 56.979
- type: ndcg_at_100
value: 61.266
- type: ndcg_at_1000
value: 62.315
- type: ndcg_at_3
value: 51.342
- type: ndcg_at_5
value: 54.33
- type: precision_at_1
value: 44.389
- type: precision_at_10
value: 9.26
- type: precision_at_100
value: 1.226
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 22.926
- type: precision_at_5
value: 15.987000000000002
- type: recall_at_1
value: 38.811
- type: recall_at_10
value: 70.841
- type: recall_at_100
value: 89.218
- type: recall_at_1000
value: 96.482
- type: recall_at_3
value: 56.123999999999995
- type: recall_at_5
value: 63.322
- type: map_at_1
value: 25.378
- type: map_at_10
value: 34.311
- type: map_at_100
value: 35.399
- type: map_at_1000
value: 35.482
- type: map_at_3
value: 31.917
- type: map_at_5
value: 33.275
- type: mrr_at_1
value: 27.683999999999997
- type: mrr_at_10
value: 36.575
- type: mrr_at_100
value: 37.492
- type: mrr_at_1000
value: 37.556
- type: mrr_at_3
value: 34.35
- type: mrr_at_5
value: 35.525
- type: ndcg_at_1
value: 27.683999999999997
- type: ndcg_at_10
value: 39.247
- type: ndcg_at_100
value: 44.424
- type: ndcg_at_1000
value: 46.478
- type: ndcg_at_3
value: 34.684
- type: ndcg_at_5
value: 36.886
- type: precision_at_1
value: 27.683999999999997
- type: precision_at_10
value: 5.989
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.84
- type: precision_at_5
value: 10.215
- type: recall_at_1
value: 25.378
- type: recall_at_10
value: 52.195
- type: recall_at_100
value: 75.764
- type: recall_at_1000
value: 91.012
- type: recall_at_3
value: 39.885999999999996
- type: recall_at_5
value: 45.279
- type: map_at_1
value: 17.326
- type: map_at_10
value: 25.247000000000003
- type: map_at_100
value: 26.473000000000003
- type: map_at_1000
value: 26.579000000000004
- type: map_at_3
value: 22.466
- type: map_at_5
value: 24.113
- type: mrr_at_1
value: 21.393
- type: mrr_at_10
value: 30.187
- type: mrr_at_100
value: 31.089
- type: mrr_at_1000
value: 31.15
- type: mrr_at_3
value: 27.279999999999998
- type: mrr_at_5
value: 29.127
- type: ndcg_at_1
value: 21.393
- type: ndcg_at_10
value: 30.668
- type: ndcg_at_100
value: 36.543
- type: ndcg_at_1000
value: 39.181
- type: ndcg_at_3
value: 25.552000000000003
- type: ndcg_at_5
value: 28.176000000000002
- type: precision_at_1
value: 21.393
- type: precision_at_10
value: 5.784000000000001
- type: precision_at_100
value: 1.001
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 12.231
- type: precision_at_5
value: 9.179
- type: recall_at_1
value: 17.326
- type: recall_at_10
value: 42.415000000000006
- type: recall_at_100
value: 68.605
- type: recall_at_1000
value: 87.694
- type: recall_at_3
value: 28.343
- type: recall_at_5
value: 35.086
- type: map_at_1
value: 29.069
- type: map_at_10
value: 40.027
- type: map_at_100
value: 41.308
- type: map_at_1000
value: 41.412
- type: map_at_3
value: 36.864000000000004
- type: map_at_5
value: 38.641999999999996
- type: mrr_at_1
value: 35.707
- type: mrr_at_10
value: 45.527
- type: mrr_at_100
value: 46.348
- type: mrr_at_1000
value: 46.392
- type: mrr_at_3
value: 43.086
- type: mrr_at_5
value: 44.645
- type: ndcg_at_1
value: 35.707
- type: ndcg_at_10
value: 46.117000000000004
- type: ndcg_at_100
value: 51.468
- type: ndcg_at_1000
value: 53.412000000000006
- type: ndcg_at_3
value: 41.224
- type: ndcg_at_5
value: 43.637
- type: precision_at_1
value: 35.707
- type: precision_at_10
value: 8.459999999999999
- type: precision_at_100
value: 1.2970000000000002
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 19.731
- type: precision_at_5
value: 14.013
- type: recall_at_1
value: 29.069
- type: recall_at_10
value: 58.343999999999994
- type: recall_at_100
value: 81.296
- type: recall_at_1000
value: 93.974
- type: recall_at_3
value: 44.7
- type: recall_at_5
value: 50.88700000000001
- type: map_at_1
value: 23.905
- type: map_at_10
value: 33.983000000000004
- type: map_at_100
value: 35.372
- type: map_at_1000
value: 35.487
- type: map_at_3
value: 30.902
- type: map_at_5
value: 32.505
- type: mrr_at_1
value: 29.794999999999998
- type: mrr_at_10
value: 39.28
- type: mrr_at_100
value: 40.215
- type: mrr_at_1000
value: 40.276
- type: mrr_at_3
value: 36.701
- type: mrr_at_5
value: 38.105
- type: ndcg_at_1
value: 29.794999999999998
- type: ndcg_at_10
value: 40.041
- type: ndcg_at_100
value: 45.884
- type: ndcg_at_1000
value: 48.271
- type: ndcg_at_3
value: 34.931
- type: ndcg_at_5
value: 37.044
- type: precision_at_1
value: 29.794999999999998
- type: precision_at_10
value: 7.546
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.933
- type: precision_at_5
value: 12.1
- type: recall_at_1
value: 23.905
- type: recall_at_10
value: 52.945
- type: recall_at_100
value: 77.551
- type: recall_at_1000
value: 93.793
- type: recall_at_3
value: 38.364
- type: recall_at_5
value: 44.044
- type: map_at_1
value: 25.24441666666667
- type: map_at_10
value: 34.4595
- type: map_at_100
value: 35.699999999999996
- type: map_at_1000
value: 35.8155
- type: map_at_3
value: 31.608333333333338
- type: map_at_5
value: 33.189416666666666
- type: mrr_at_1
value: 29.825250000000004
- type: mrr_at_10
value: 38.60875
- type: mrr_at_100
value: 39.46575
- type: mrr_at_1000
value: 39.52458333333333
- type: mrr_at_3
value: 36.145166666666675
- type: mrr_at_5
value: 37.57625
- type: ndcg_at_1
value: 29.825250000000004
- type: ndcg_at_10
value: 39.88741666666667
- type: ndcg_at_100
value: 45.17966666666667
- type: ndcg_at_1000
value: 47.440583333333336
- type: ndcg_at_3
value: 35.04591666666666
- type: ndcg_at_5
value: 37.32025
- type: precision_at_1
value: 29.825250000000004
- type: precision_at_10
value: 7.07225
- type: precision_at_100
value: 1.1462499999999998
- type: precision_at_1000
value: 0.15325
- type: precision_at_3
value: 16.18375
- type: precision_at_5
value: 11.526833333333334
- type: recall_at_1
value: 25.24441666666667
- type: recall_at_10
value: 51.744916666666676
- type: recall_at_100
value: 75.04574999999998
- type: recall_at_1000
value: 90.65558333333334
- type: recall_at_3
value: 38.28349999999999
- type: recall_at_5
value: 44.16591666666667
- type: map_at_1
value: 24.237000000000002
- type: map_at_10
value: 30.667
- type: map_at_100
value: 31.592
- type: map_at_1000
value: 31.688
- type: map_at_3
value: 28.810999999999996
- type: map_at_5
value: 29.788999999999998
- type: mrr_at_1
value: 26.840000000000003
- type: mrr_at_10
value: 33.305
- type: mrr_at_100
value: 34.089000000000006
- type: mrr_at_1000
value: 34.159
- type: mrr_at_3
value: 31.518
- type: mrr_at_5
value: 32.469
- type: ndcg_at_1
value: 26.840000000000003
- type: ndcg_at_10
value: 34.541
- type: ndcg_at_100
value: 39.206
- type: ndcg_at_1000
value: 41.592
- type: ndcg_at_3
value: 31.005
- type: ndcg_at_5
value: 32.554
- type: precision_at_1
value: 26.840000000000003
- type: precision_at_10
value: 5.3069999999999995
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 13.292000000000002
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 24.237000000000002
- type: recall_at_10
value: 43.862
- type: recall_at_100
value: 65.352
- type: recall_at_1000
value: 82.704
- type: recall_at_3
value: 34.009
- type: recall_at_5
value: 37.878
- type: map_at_1
value: 16.482
- type: map_at_10
value: 23.249
- type: map_at_100
value: 24.388
- type: map_at_1000
value: 24.519
- type: map_at_3
value: 20.971
- type: map_at_5
value: 22.192
- type: mrr_at_1
value: 19.993
- type: mrr_at_10
value: 26.985
- type: mrr_at_100
value: 27.975
- type: mrr_at_1000
value: 28.052
- type: mrr_at_3
value: 24.954
- type: mrr_at_5
value: 26.070999999999998
- type: ndcg_at_1
value: 19.993
- type: ndcg_at_10
value: 27.656
- type: ndcg_at_100
value: 33.256
- type: ndcg_at_1000
value: 36.275
- type: ndcg_at_3
value: 23.644000000000002
- type: ndcg_at_5
value: 25.466
- type: precision_at_1
value: 19.993
- type: precision_at_10
value: 5.093
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.149000000000001
- type: precision_at_5
value: 8.149000000000001
- type: recall_at_1
value: 16.482
- type: recall_at_10
value: 37.141999999999996
- type: recall_at_100
value: 62.696
- type: recall_at_1000
value: 84.333
- type: recall_at_3
value: 26.031
- type: recall_at_5
value: 30.660999999999998
- type: map_at_1
value: 24.887999999999998
- type: map_at_10
value: 34.101
- type: map_at_100
value: 35.27
- type: map_at_1000
value: 35.370000000000005
- type: map_at_3
value: 31.283
- type: map_at_5
value: 32.72
- type: mrr_at_1
value: 29.011
- type: mrr_at_10
value: 38.004
- type: mrr_at_100
value: 38.879000000000005
- type: mrr_at_1000
value: 38.938
- type: mrr_at_3
value: 35.571999999999996
- type: mrr_at_5
value: 36.789
- type: ndcg_at_1
value: 29.011
- type: ndcg_at_10
value: 39.586
- type: ndcg_at_100
value: 44.939
- type: ndcg_at_1000
value: 47.236
- type: ndcg_at_3
value: 34.4
- type: ndcg_at_5
value: 36.519
- type: precision_at_1
value: 29.011
- type: precision_at_10
value: 6.763
- type: precision_at_100
value: 1.059
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.609
- type: precision_at_5
value: 10.896
- type: recall_at_1
value: 24.887999999999998
- type: recall_at_10
value: 52.42
- type: recall_at_100
value: 75.803
- type: recall_at_1000
value: 91.725
- type: recall_at_3
value: 38.080999999999996
- type: recall_at_5
value: 43.47
- type: map_at_1
value: 23.953
- type: map_at_10
value: 32.649
- type: map_at_100
value: 34.181
- type: map_at_1000
value: 34.398
- type: map_at_3
value: 29.567
- type: map_at_5
value: 31.263
- type: mrr_at_1
value: 29.051
- type: mrr_at_10
value: 37.419999999999995
- type: mrr_at_100
value: 38.396
- type: mrr_at_1000
value: 38.458
- type: mrr_at_3
value: 34.782999999999994
- type: mrr_at_5
value: 36.254999999999995
- type: ndcg_at_1
value: 29.051
- type: ndcg_at_10
value: 38.595
- type: ndcg_at_100
value: 44.6
- type: ndcg_at_1000
value: 47.158
- type: ndcg_at_3
value: 33.56
- type: ndcg_at_5
value: 35.870000000000005
- type: precision_at_1
value: 29.051
- type: precision_at_10
value: 7.53
- type: precision_at_100
value: 1.538
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 11.542
- type: recall_at_1
value: 23.953
- type: recall_at_10
value: 50.08200000000001
- type: recall_at_100
value: 77.364
- type: recall_at_1000
value: 93.57799999999999
- type: recall_at_3
value: 35.432
- type: recall_at_5
value: 41.875
- type: map_at_1
value: 17.72
- type: map_at_10
value: 25.724000000000004
- type: map_at_100
value: 26.846999999999998
- type: map_at_1000
value: 26.964
- type: map_at_3
value: 22.909
- type: map_at_5
value: 24.596999999999998
- type: mrr_at_1
value: 18.854000000000003
- type: mrr_at_10
value: 27.182000000000002
- type: mrr_at_100
value: 28.182000000000002
- type: mrr_at_1000
value: 28.274
- type: mrr_at_3
value: 24.276
- type: mrr_at_5
value: 26.115
- type: ndcg_at_1
value: 18.854000000000003
- type: ndcg_at_10
value: 30.470000000000002
- type: ndcg_at_100
value: 35.854
- type: ndcg_at_1000
value: 38.701
- type: ndcg_at_3
value: 24.924
- type: ndcg_at_5
value: 27.895999999999997
- type: precision_at_1
value: 18.854000000000003
- type: precision_at_10
value: 5.009
- type: precision_at_100
value: 0.835
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 10.721
- type: precision_at_5
value: 8.133
- type: recall_at_1
value: 17.72
- type: recall_at_10
value: 43.617
- type: recall_at_100
value: 67.941
- type: recall_at_1000
value: 88.979
- type: recall_at_3
value: 29.044999999999998
- type: recall_at_5
value: 36.044
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.427
- type: map_at_10
value: 22.935
- type: map_at_100
value: 24.808
- type: map_at_1000
value: 24.994
- type: map_at_3
value: 19.533
- type: map_at_5
value: 21.261
- type: mrr_at_1
value: 30.945
- type: mrr_at_10
value: 43.242000000000004
- type: mrr_at_100
value: 44.013999999999996
- type: mrr_at_1000
value: 44.048
- type: mrr_at_3
value: 40.109
- type: mrr_at_5
value: 42.059999999999995
- type: ndcg_at_1
value: 30.945
- type: ndcg_at_10
value: 31.828
- type: ndcg_at_100
value: 38.801
- type: ndcg_at_1000
value: 42.126999999999995
- type: ndcg_at_3
value: 26.922
- type: ndcg_at_5
value: 28.483999999999998
- type: precision_at_1
value: 30.945
- type: precision_at_10
value: 9.844
- type: precision_at_100
value: 1.7309999999999999
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 20.477999999999998
- type: precision_at_5
value: 15.27
- type: recall_at_1
value: 13.427
- type: recall_at_10
value: 37.141000000000005
- type: recall_at_100
value: 61.007
- type: recall_at_1000
value: 79.742
- type: recall_at_3
value: 24.431
- type: recall_at_5
value: 29.725
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.122
- type: map_at_10
value: 18.799
- type: map_at_100
value: 25.724999999999998
- type: map_at_1000
value: 27.205000000000002
- type: map_at_3
value: 14.194999999999999
- type: map_at_5
value: 16.225
- type: mrr_at_1
value: 68.0
- type: mrr_at_10
value: 76.035
- type: mrr_at_100
value: 76.292
- type: mrr_at_1000
value: 76.297
- type: mrr_at_3
value: 74.458
- type: mrr_at_5
value: 75.558
- type: ndcg_at_1
value: 56.00000000000001
- type: ndcg_at_10
value: 39.761
- type: ndcg_at_100
value: 43.736999999999995
- type: ndcg_at_1000
value: 51.146
- type: ndcg_at_3
value: 45.921
- type: ndcg_at_5
value: 42.756
- type: precision_at_1
value: 68.0
- type: precision_at_10
value: 30.275000000000002
- type: precision_at_100
value: 9.343
- type: precision_at_1000
value: 1.8270000000000002
- type: precision_at_3
value: 49.167
- type: precision_at_5
value: 40.699999999999996
- type: recall_at_1
value: 9.122
- type: recall_at_10
value: 23.669999999999998
- type: recall_at_100
value: 48.719
- type: recall_at_1000
value: 72.033
- type: recall_at_3
value: 15.498999999999999
- type: recall_at_5
value: 18.657
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 55.885000000000005
- type: f1
value: 50.70726446938571
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 75.709
- type: map_at_10
value: 83.345
- type: map_at_100
value: 83.557
- type: map_at_1000
value: 83.572
- type: map_at_3
value: 82.425
- type: map_at_5
value: 83.013
- type: mrr_at_1
value: 81.593
- type: mrr_at_10
value: 88.331
- type: mrr_at_100
value: 88.408
- type: mrr_at_1000
value: 88.41
- type: mrr_at_3
value: 87.714
- type: mrr_at_5
value: 88.122
- type: ndcg_at_1
value: 81.593
- type: ndcg_at_10
value: 86.925
- type: ndcg_at_100
value: 87.67
- type: ndcg_at_1000
value: 87.924
- type: ndcg_at_3
value: 85.5
- type: ndcg_at_5
value: 86.283
- type: precision_at_1
value: 81.593
- type: precision_at_10
value: 10.264
- type: precision_at_100
value: 1.084
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 32.388
- type: precision_at_5
value: 19.991
- type: recall_at_1
value: 75.709
- type: recall_at_10
value: 93.107
- type: recall_at_100
value: 96.024
- type: recall_at_1000
value: 97.603
- type: recall_at_3
value: 89.08500000000001
- type: recall_at_5
value: 91.15299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.121
- type: map_at_10
value: 31.78
- type: map_at_100
value: 33.497
- type: map_at_1000
value: 33.696
- type: map_at_3
value: 27.893
- type: map_at_5
value: 30.087000000000003
- type: mrr_at_1
value: 38.272
- type: mrr_at_10
value: 47.176
- type: mrr_at_100
value: 48.002
- type: mrr_at_1000
value: 48.044
- type: mrr_at_3
value: 45.086999999999996
- type: mrr_at_5
value: 46.337
- type: ndcg_at_1
value: 38.272
- type: ndcg_at_10
value: 39.145
- type: ndcg_at_100
value: 45.696999999999996
- type: ndcg_at_1000
value: 49.0
- type: ndcg_at_3
value: 36.148
- type: ndcg_at_5
value: 37.023
- type: precision_at_1
value: 38.272
- type: precision_at_10
value: 11.065
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 24.587999999999997
- type: precision_at_5
value: 18.056
- type: recall_at_1
value: 19.121
- type: recall_at_10
value: 44.857
- type: recall_at_100
value: 69.774
- type: recall_at_1000
value: 89.645
- type: recall_at_3
value: 32.588
- type: recall_at_5
value: 37.939
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.428
- type: map_at_10
value: 56.891999999999996
- type: map_at_100
value: 57.82899999999999
- type: map_at_1000
value: 57.896
- type: map_at_3
value: 53.762
- type: map_at_5
value: 55.718
- type: mrr_at_1
value: 72.856
- type: mrr_at_10
value: 79.245
- type: mrr_at_100
value: 79.515
- type: mrr_at_1000
value: 79.525
- type: mrr_at_3
value: 78.143
- type: mrr_at_5
value: 78.822
- type: ndcg_at_1
value: 72.856
- type: ndcg_at_10
value: 65.204
- type: ndcg_at_100
value: 68.552
- type: ndcg_at_1000
value: 69.902
- type: ndcg_at_3
value: 60.632
- type: ndcg_at_5
value: 63.161
- type: precision_at_1
value: 72.856
- type: precision_at_10
value: 13.65
- type: precision_at_100
value: 1.6260000000000001
- type: precision_at_1000
value: 0.181
- type: precision_at_3
value: 38.753
- type: precision_at_5
value: 25.251
- type: recall_at_1
value: 36.428
- type: recall_at_10
value: 68.25099999999999
- type: recall_at_100
value: 81.317
- type: recall_at_1000
value: 90.27
- type: recall_at_3
value: 58.13
- type: recall_at_5
value: 63.126000000000005
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.4868
- type: ap
value: 84.88319192880247
- type: f1
value: 89.46144458052846
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.282999999999998
- type: map_at_10
value: 33.045
- type: map_at_100
value: 34.238
- type: map_at_1000
value: 34.29
- type: map_at_3
value: 29.305999999999997
- type: map_at_5
value: 31.391000000000002
- type: mrr_at_1
value: 21.92
- type: mrr_at_10
value: 33.649
- type: mrr_at_100
value: 34.791
- type: mrr_at_1000
value: 34.837
- type: mrr_at_3
value: 30.0
- type: mrr_at_5
value: 32.039
- type: ndcg_at_1
value: 21.92
- type: ndcg_at_10
value: 39.729
- type: ndcg_at_100
value: 45.484
- type: ndcg_at_1000
value: 46.817
- type: ndcg_at_3
value: 32.084
- type: ndcg_at_5
value: 35.789
- type: precision_at_1
value: 21.92
- type: precision_at_10
value: 6.297
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.639000000000001
- type: precision_at_5
value: 10.054
- type: recall_at_1
value: 21.282999999999998
- type: recall_at_10
value: 60.343999999999994
- type: recall_at_100
value: 86.981
- type: recall_at_1000
value: 97.205
- type: recall_at_3
value: 39.452999999999996
- type: recall_at_5
value: 48.333
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.47879616963064
- type: f1
value: 95.21800589958251
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.09256725946192
- type: f1
value: 60.554043889452515
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.53463349024882
- type: f1
value: 73.14418495756476
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.22663080026899
- type: f1
value: 79.331456217501
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.50316010430136
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.15612040042282
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.36227552557184
- type: mrr
value: 33.57901344209811
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.6610000000000005
- type: map_at_10
value: 12.992
- type: map_at_100
value: 16.756999999999998
- type: map_at_1000
value: 18.25
- type: map_at_3
value: 9.471
- type: map_at_5
value: 11.116
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 53.388999999999996
- type: mrr_at_100
value: 53.982
- type: mrr_at_1000
value: 54.033
- type: mrr_at_3
value: 51.858000000000004
- type: mrr_at_5
value: 53.019000000000005
- type: ndcg_at_1
value: 41.641
- type: ndcg_at_10
value: 34.691
- type: ndcg_at_100
value: 32.305
- type: ndcg_at_1000
value: 41.132999999999996
- type: ndcg_at_3
value: 40.614
- type: ndcg_at_5
value: 38.456
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 25.881999999999998
- type: precision_at_100
value: 8.483
- type: precision_at_1000
value: 2.131
- type: precision_at_3
value: 38.803
- type: precision_at_5
value: 33.87
- type: recall_at_1
value: 5.6610000000000005
- type: recall_at_10
value: 16.826
- type: recall_at_100
value: 32.939
- type: recall_at_1000
value: 65.161
- type: recall_at_3
value: 10.756
- type: recall_at_5
value: 13.331000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.692
- type: map_at_10
value: 41.065000000000005
- type: map_at_100
value: 42.235
- type: map_at_1000
value: 42.27
- type: map_at_3
value: 36.635
- type: map_at_5
value: 39.219
- type: mrr_at_1
value: 30.214000000000002
- type: mrr_at_10
value: 43.443
- type: mrr_at_100
value: 44.326
- type: mrr_at_1000
value: 44.352000000000004
- type: mrr_at_3
value: 39.623999999999995
- type: mrr_at_5
value: 41.898
- type: ndcg_at_1
value: 30.214000000000002
- type: ndcg_at_10
value: 48.692
- type: ndcg_at_100
value: 53.671
- type: ndcg_at_1000
value: 54.522000000000006
- type: ndcg_at_3
value: 40.245
- type: ndcg_at_5
value: 44.580999999999996
- type: precision_at_1
value: 30.214000000000002
- type: precision_at_10
value: 8.3
- type: precision_at_100
value: 1.1079999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 18.521
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 26.692
- type: recall_at_10
value: 69.699
- type: recall_at_100
value: 91.425
- type: recall_at_1000
value: 97.78099999999999
- type: recall_at_3
value: 47.711
- type: recall_at_5
value: 57.643
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.962
- type: map_at_10
value: 84.772
- type: map_at_100
value: 85.402
- type: map_at_1000
value: 85.418
- type: map_at_3
value: 81.89
- type: map_at_5
value: 83.685
- type: mrr_at_1
value: 81.67
- type: mrr_at_10
value: 87.681
- type: mrr_at_100
value: 87.792
- type: mrr_at_1000
value: 87.79299999999999
- type: mrr_at_3
value: 86.803
- type: mrr_at_5
value: 87.392
- type: ndcg_at_1
value: 81.69
- type: ndcg_at_10
value: 88.429
- type: ndcg_at_100
value: 89.66
- type: ndcg_at_1000
value: 89.762
- type: ndcg_at_3
value: 85.75
- type: ndcg_at_5
value: 87.20700000000001
- type: precision_at_1
value: 81.69
- type: precision_at_10
value: 13.395000000000001
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.507000000000005
- type: precision_at_5
value: 24.614
- type: recall_at_1
value: 70.962
- type: recall_at_10
value: 95.339
- type: recall_at_100
value: 99.543
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.54899999999999
- type: recall_at_5
value: 91.726
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.506631779239555
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.63731341848479
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.852
- type: map_at_10
value: 13.175
- type: map_at_100
value: 15.623999999999999
- type: map_at_1000
value: 16.002
- type: map_at_3
value: 9.103
- type: map_at_5
value: 11.068999999999999
- type: mrr_at_1
value: 23.9
- type: mrr_at_10
value: 35.847
- type: mrr_at_100
value: 36.968
- type: mrr_at_1000
value: 37.018
- type: mrr_at_3
value: 32.300000000000004
- type: mrr_at_5
value: 34.14
- type: ndcg_at_1
value: 23.9
- type: ndcg_at_10
value: 21.889
- type: ndcg_at_100
value: 30.903000000000002
- type: ndcg_at_1000
value: 36.992000000000004
- type: ndcg_at_3
value: 20.274
- type: ndcg_at_5
value: 17.773
- type: precision_at_1
value: 23.9
- type: precision_at_10
value: 11.61
- type: precision_at_100
value: 2.4539999999999997
- type: precision_at_1000
value: 0.391
- type: precision_at_3
value: 19.133
- type: precision_at_5
value: 15.740000000000002
- type: recall_at_1
value: 4.852
- type: recall_at_10
value: 23.507
- type: recall_at_100
value: 49.775000000000006
- type: recall_at_1000
value: 79.308
- type: recall_at_3
value: 11.637
- type: recall_at_5
value: 15.947
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.03345827446948
- type: cos_sim_spearman
value: 80.53174518259549
- type: euclidean_pearson
value: 83.44538971660883
- type: euclidean_spearman
value: 80.57344324098692
- type: manhattan_pearson
value: 83.36528808195459
- type: manhattan_spearman
value: 80.48931287157902
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.21363088257881
- type: cos_sim_spearman
value: 75.56589127055523
- type: euclidean_pearson
value: 82.32868324521908
- type: euclidean_spearman
value: 75.31928550664554
- type: manhattan_pearson
value: 82.31332875713211
- type: manhattan_spearman
value: 75.35376322099196
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.09085593258487
- type: cos_sim_spearman
value: 86.26355088415221
- type: euclidean_pearson
value: 85.49646115361156
- type: euclidean_spearman
value: 86.20652472228703
- type: manhattan_pearson
value: 85.44084081123815
- type: manhattan_spearman
value: 86.1162623448951
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.68250248349368
- type: cos_sim_spearman
value: 82.29883673695083
- type: euclidean_pearson
value: 84.17633035446019
- type: euclidean_spearman
value: 82.19990511264791
- type: manhattan_pearson
value: 84.17408410692279
- type: manhattan_spearman
value: 82.249873895981
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.31878760045024
- type: cos_sim_spearman
value: 88.7364409031183
- type: euclidean_pearson
value: 88.230537618603
- type: euclidean_spearman
value: 88.76484309646318
- type: manhattan_pearson
value: 88.17689071136469
- type: manhattan_spearman
value: 88.72809249037928
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.41078559110638
- type: cos_sim_spearman
value: 85.27439135411049
- type: euclidean_pearson
value: 84.5333571592088
- type: euclidean_spearman
value: 85.25645460575957
- type: manhattan_pearson
value: 84.38428921610226
- type: manhattan_spearman
value: 85.07796040798796
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.82374132382576
- type: cos_sim_spearman
value: 89.02101343562433
- type: euclidean_pearson
value: 89.50729765458932
- type: euclidean_spearman
value: 89.04184772869253
- type: manhattan_pearson
value: 89.51737904059856
- type: manhattan_spearman
value: 89.12925950440676
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.56051823873482
- type: cos_sim_spearman
value: 68.50988748185463
- type: euclidean_pearson
value: 69.16524346147456
- type: euclidean_spearman
value: 68.61859952449579
- type: manhattan_pearson
value: 69.10618915706995
- type: manhattan_spearman
value: 68.36401769459522
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.4159693872625
- type: cos_sim_spearman
value: 87.07819121764247
- type: euclidean_pearson
value: 87.03013260863153
- type: euclidean_spearman
value: 87.06547293631309
- type: manhattan_pearson
value: 86.8129744446062
- type: manhattan_spearman
value: 86.88494096335627
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.47758088996575
- type: mrr
value: 96.17891458577733
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.538999999999994
- type: map_at_10
value: 66.562
- type: map_at_100
value: 67.254
- type: map_at_1000
value: 67.284
- type: map_at_3
value: 63.722
- type: map_at_5
value: 65.422
- type: mrr_at_1
value: 60.0
- type: mrr_at_10
value: 67.354
- type: mrr_at_100
value: 67.908
- type: mrr_at_1000
value: 67.93299999999999
- type: mrr_at_3
value: 65.056
- type: mrr_at_5
value: 66.43900000000001
- type: ndcg_at_1
value: 60.0
- type: ndcg_at_10
value: 70.858
- type: ndcg_at_100
value: 73.67099999999999
- type: ndcg_at_1000
value: 74.26700000000001
- type: ndcg_at_3
value: 65.911
- type: ndcg_at_5
value: 68.42200000000001
- type: precision_at_1
value: 60.0
- type: precision_at_10
value: 9.4
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 17.0
- type: recall_at_1
value: 57.538999999999994
- type: recall_at_10
value: 83.233
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 69.883
- type: recall_at_5
value: 76.19399999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82574257425742
- type: cos_sim_ap
value: 95.78722833053911
- type: cos_sim_f1
value: 90.94650205761316
- type: cos_sim_precision
value: 93.64406779661016
- type: cos_sim_recall
value: 88.4
- type: dot_accuracy
value: 99.83366336633664
- type: dot_ap
value: 95.89733601612964
- type: dot_f1
value: 91.41981613891727
- type: dot_precision
value: 93.42379958246346
- type: dot_recall
value: 89.5
- type: euclidean_accuracy
value: 99.82574257425742
- type: euclidean_ap
value: 95.75227035138846
- type: euclidean_f1
value: 90.96509240246407
- type: euclidean_precision
value: 93.45991561181435
- type: euclidean_recall
value: 88.6
- type: manhattan_accuracy
value: 99.82574257425742
- type: manhattan_ap
value: 95.76278266220176
- type: manhattan_f1
value: 91.08409321175279
- type: manhattan_precision
value: 92.29979466119097
- type: manhattan_recall
value: 89.9
- type: max_accuracy
value: 99.83366336633664
- type: max_ap
value: 95.89733601612964
- type: max_f1
value: 91.41981613891727
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 61.905425988638605
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.159589881679736
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.0605499476397
- type: mrr
value: 53.91594516594517
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.202718009067
- type: cos_sim_spearman
value: 31.136199912366987
- type: dot_pearson
value: 30.66329011927951
- type: dot_spearman
value: 30.107664909625107
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.209
- type: map_at_10
value: 1.712
- type: map_at_100
value: 9.464
- type: map_at_1000
value: 23.437
- type: map_at_3
value: 0.609
- type: map_at_5
value: 0.9440000000000001
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 86.833
- type: mrr_at_100
value: 86.833
- type: mrr_at_1000
value: 86.833
- type: mrr_at_3
value: 85.333
- type: mrr_at_5
value: 86.833
- type: ndcg_at_1
value: 74.0
- type: ndcg_at_10
value: 69.14
- type: ndcg_at_100
value: 53.047999999999995
- type: ndcg_at_1000
value: 48.577
- type: ndcg_at_3
value: 75.592
- type: ndcg_at_5
value: 72.509
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 73.0
- type: precision_at_100
value: 54.44
- type: precision_at_1000
value: 21.326
- type: precision_at_3
value: 80.667
- type: precision_at_5
value: 77.2
- type: recall_at_1
value: 0.209
- type: recall_at_10
value: 1.932
- type: recall_at_100
value: 13.211999999999998
- type: recall_at_1000
value: 45.774
- type: recall_at_3
value: 0.644
- type: recall_at_5
value: 1.0290000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.609
- type: map_at_10
value: 8.334999999999999
- type: map_at_100
value: 14.604000000000001
- type: map_at_1000
value: 16.177
- type: map_at_3
value: 4.87
- type: map_at_5
value: 6.3149999999999995
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 45.047
- type: mrr_at_100
value: 45.808
- type: mrr_at_1000
value: 45.808
- type: mrr_at_3
value: 41.497
- type: mrr_at_5
value: 43.231
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 21.193
- type: ndcg_at_100
value: 34.97
- type: ndcg_at_1000
value: 46.69
- type: ndcg_at_3
value: 24.823
- type: ndcg_at_5
value: 22.872999999999998
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 7.4079999999999995
- type: precision_at_1000
value: 1.537
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 2.609
- type: recall_at_10
value: 13.63
- type: recall_at_100
value: 47.014
- type: recall_at_1000
value: 83.176
- type: recall_at_3
value: 5.925
- type: recall_at_5
value: 8.574
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.80239999999999
- type: ap
value: 15.497911013214791
- type: f1
value: 56.258411577947285
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.00452744765139
- type: f1
value: 61.42228624410908
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.00516915962345
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.62317458425225
- type: cos_sim_ap
value: 72.95115658063823
- type: cos_sim_f1
value: 66.78976523344764
- type: cos_sim_precision
value: 66.77215189873418
- type: cos_sim_recall
value: 66.80738786279683
- type: dot_accuracy
value: 85.62317458425225
- type: dot_ap
value: 73.10385271517778
- type: dot_f1
value: 66.94853829427399
- type: dot_precision
value: 61.74242424242424
- type: dot_recall
value: 73.11345646437995
- type: euclidean_accuracy
value: 85.65893783155511
- type: euclidean_ap
value: 72.87428208473992
- type: euclidean_f1
value: 66.70919994896005
- type: euclidean_precision
value: 64.5910551025451
- type: euclidean_recall
value: 68.97097625329816
- type: manhattan_accuracy
value: 85.59933241938367
- type: manhattan_ap
value: 72.67282695064966
- type: manhattan_f1
value: 66.67537215983286
- type: manhattan_precision
value: 66.00310237849017
- type: manhattan_recall
value: 67.36147757255937
- type: max_accuracy
value: 85.65893783155511
- type: max_ap
value: 73.10385271517778
- type: max_f1
value: 66.94853829427399
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.69096130709822
- type: cos_sim_ap
value: 85.30326978668063
- type: cos_sim_f1
value: 77.747088683189
- type: cos_sim_precision
value: 75.4491451753115
- type: cos_sim_recall
value: 80.189405605174
- type: dot_accuracy
value: 88.43870066363954
- type: dot_ap
value: 84.62999949222983
- type: dot_f1
value: 77.3074661963551
- type: dot_precision
value: 73.93871239808828
- type: dot_recall
value: 80.99784416384355
- type: euclidean_accuracy
value: 88.70066363953894
- type: euclidean_ap
value: 85.34184508966621
- type: euclidean_f1
value: 77.76871756856931
- type: euclidean_precision
value: 74.97855917667239
- type: euclidean_recall
value: 80.77456113335386
- type: manhattan_accuracy
value: 88.68319944114566
- type: manhattan_ap
value: 85.3026464242333
- type: manhattan_f1
value: 77.66561049296294
- type: manhattan_precision
value: 74.4665818849795
- type: manhattan_recall
value: 81.15183246073299
- type: max_accuracy
value: 88.70066363953894
- type: max_ap
value: 85.34184508966621
- type: max_f1
value: 77.76871756856931
---
<h1 align="center">GIST small Embedding v0</h1>
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
# Data
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
# Usage
The model can be easily loaded using the Sentence Transformers library.
```Python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
```
# Training Parameters
Below are the training parameters used to fine-tune the model:
```
Epochs = 40
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 16
Checkpoint step = 102000
Contrastive loss temperature = 0.01
```
# Evaluation
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
# Citation
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
```
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gte-base-en-v1.5 | Alibaba-NLP | sentence-similarity | [
"transformers",
"onnx",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"arxiv:2407.19669",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-20T02:53:42 | 2024-11-15T14:10:57 | 2,607,332 | 63 | ---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- sentence-transformers
- gte
- mteb
- transformers.js
- sentence-similarity
model-index:
- name: gte-base-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.7910447761194
- type: ap
value: 37.053785713650626
- type: f1
value: 68.51101510998551
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.016875
- type: ap
value: 89.17750268426342
- type: f1
value: 92.9970977240524
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.312000000000005
- type: f1
value: 52.98175784163017
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 38.193
- type: map_at_10
value: 54.848
- type: map_at_100
value: 55.388000000000005
- type: map_at_1000
value: 55.388999999999996
- type: map_at_3
value: 50.427
- type: map_at_5
value: 53.105000000000004
- type: mrr_at_1
value: 39.047
- type: mrr_at_10
value: 55.153
- type: mrr_at_100
value: 55.686
- type: mrr_at_1000
value: 55.688
- type: mrr_at_3
value: 50.676
- type: mrr_at_5
value: 53.417
- type: ndcg_at_1
value: 38.193
- type: ndcg_at_10
value: 63.486
- type: ndcg_at_100
value: 65.58
- type: ndcg_at_1000
value: 65.61
- type: ndcg_at_3
value: 54.494
- type: ndcg_at_5
value: 59.339
- type: precision_at_1
value: 38.193
- type: precision_at_10
value: 9.075
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.096
- type: precision_at_5
value: 15.619
- type: recall_at_1
value: 38.193
- type: recall_at_10
value: 90.754
- type: recall_at_100
value: 99.431
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.28699999999999
- type: recall_at_5
value: 78.094
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.508221208908964
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.04668382560096
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.828759903716815
- type: mrr
value: 74.37343358395991
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.03673698773017
- type: cos_sim_spearman
value: 83.6470866785058
- type: euclidean_pearson
value: 82.64048673096565
- type: euclidean_spearman
value: 83.63142367101115
- type: manhattan_pearson
value: 82.71493099760228
- type: manhattan_spearman
value: 83.60491704294326
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.73376623376623
- type: f1
value: 86.70294049278262
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.31923804167062
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.552547125348454
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 30.567
- type: map_at_10
value: 41.269
- type: map_at_100
value: 42.689
- type: map_at_1000
value: 42.84
- type: map_at_3
value: 37.567
- type: map_at_5
value: 39.706
- type: mrr_at_1
value: 37.053000000000004
- type: mrr_at_10
value: 46.900999999999996
- type: mrr_at_100
value: 47.662
- type: mrr_at_1000
value: 47.713
- type: mrr_at_3
value: 43.801
- type: mrr_at_5
value: 45.689
- type: ndcg_at_1
value: 37.053000000000004
- type: ndcg_at_10
value: 47.73
- type: ndcg_at_100
value: 53.128
- type: ndcg_at_1000
value: 55.300000000000004
- type: ndcg_at_3
value: 42.046
- type: ndcg_at_5
value: 44.782
- type: precision_at_1
value: 37.053000000000004
- type: precision_at_10
value: 9.142
- type: precision_at_100
value: 1.485
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.535
- type: recall_at_1
value: 30.567
- type: recall_at_10
value: 60.602999999999994
- type: recall_at_100
value: 83.22800000000001
- type: recall_at_1000
value: 96.696
- type: recall_at_3
value: 44.336999999999996
- type: recall_at_5
value: 51.949
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 28.538000000000004
- type: map_at_10
value: 38.757999999999996
- type: map_at_100
value: 40.129
- type: map_at_1000
value: 40.262
- type: map_at_3
value: 35.866
- type: map_at_5
value: 37.417
- type: mrr_at_1
value: 36.051
- type: mrr_at_10
value: 44.868
- type: mrr_at_100
value: 45.568999999999996
- type: mrr_at_1000
value: 45.615
- type: mrr_at_3
value: 42.558
- type: mrr_at_5
value: 43.883
- type: ndcg_at_1
value: 36.051
- type: ndcg_at_10
value: 44.584
- type: ndcg_at_100
value: 49.356
- type: ndcg_at_1000
value: 51.39
- type: ndcg_at_3
value: 40.389
- type: ndcg_at_5
value: 42.14
- type: precision_at_1
value: 36.051
- type: precision_at_10
value: 8.446
- type: precision_at_100
value: 1.411
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 19.639
- type: precision_at_5
value: 13.796
- type: recall_at_1
value: 28.538000000000004
- type: recall_at_10
value: 54.99000000000001
- type: recall_at_100
value: 75.098
- type: recall_at_1000
value: 87.848
- type: recall_at_3
value: 42.236000000000004
- type: recall_at_5
value: 47.377
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 37.188
- type: map_at_10
value: 50.861000000000004
- type: map_at_100
value: 51.917
- type: map_at_1000
value: 51.964999999999996
- type: map_at_3
value: 47.144000000000005
- type: map_at_5
value: 49.417
- type: mrr_at_1
value: 42.571
- type: mrr_at_10
value: 54.086999999999996
- type: mrr_at_100
value: 54.739000000000004
- type: mrr_at_1000
value: 54.762
- type: mrr_at_3
value: 51.285000000000004
- type: mrr_at_5
value: 53.0
- type: ndcg_at_1
value: 42.571
- type: ndcg_at_10
value: 57.282
- type: ndcg_at_100
value: 61.477000000000004
- type: ndcg_at_1000
value: 62.426
- type: ndcg_at_3
value: 51.0
- type: ndcg_at_5
value: 54.346000000000004
- type: precision_at_1
value: 42.571
- type: precision_at_10
value: 9.467
- type: precision_at_100
value: 1.2550000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 23.114
- type: precision_at_5
value: 16.250999999999998
- type: recall_at_1
value: 37.188
- type: recall_at_10
value: 73.068
- type: recall_at_100
value: 91.203
- type: recall_at_1000
value: 97.916
- type: recall_at_3
value: 56.552
- type: recall_at_5
value: 64.567
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 25.041000000000004
- type: map_at_10
value: 33.86
- type: map_at_100
value: 34.988
- type: map_at_1000
value: 35.064
- type: map_at_3
value: 31.049
- type: map_at_5
value: 32.845
- type: mrr_at_1
value: 26.893
- type: mrr_at_10
value: 35.594
- type: mrr_at_100
value: 36.617
- type: mrr_at_1000
value: 36.671
- type: mrr_at_3
value: 33.051
- type: mrr_at_5
value: 34.61
- type: ndcg_at_1
value: 26.893
- type: ndcg_at_10
value: 38.674
- type: ndcg_at_100
value: 44.178
- type: ndcg_at_1000
value: 46.089999999999996
- type: ndcg_at_3
value: 33.485
- type: ndcg_at_5
value: 36.402
- type: precision_at_1
value: 26.893
- type: precision_at_10
value: 5.989
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 14.2
- type: precision_at_5
value: 10.26
- type: recall_at_1
value: 25.041000000000004
- type: recall_at_10
value: 51.666000000000004
- type: recall_at_100
value: 76.896
- type: recall_at_1000
value: 91.243
- type: recall_at_3
value: 38.035999999999994
- type: recall_at_5
value: 44.999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 15.909999999999998
- type: map_at_10
value: 23.901
- type: map_at_100
value: 25.165
- type: map_at_1000
value: 25.291000000000004
- type: map_at_3
value: 21.356
- type: map_at_5
value: 22.816
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 28.382
- type: mrr_at_100
value: 29.465000000000003
- type: mrr_at_1000
value: 29.535
- type: mrr_at_3
value: 25.933
- type: mrr_at_5
value: 27.332
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 29.099000000000004
- type: ndcg_at_100
value: 35.127
- type: ndcg_at_1000
value: 38.096000000000004
- type: ndcg_at_3
value: 24.464
- type: ndcg_at_5
value: 26.709
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 5.398
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.774
- type: precision_at_5
value: 8.632
- type: recall_at_1
value: 15.909999999999998
- type: recall_at_10
value: 40.672000000000004
- type: recall_at_100
value: 66.855
- type: recall_at_1000
value: 87.922
- type: recall_at_3
value: 28.069
- type: recall_at_5
value: 33.812
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 30.175
- type: map_at_10
value: 41.36
- type: map_at_100
value: 42.701
- type: map_at_1000
value: 42.817
- type: map_at_3
value: 37.931
- type: map_at_5
value: 39.943
- type: mrr_at_1
value: 35.611
- type: mrr_at_10
value: 46.346
- type: mrr_at_100
value: 47.160000000000004
- type: mrr_at_1000
value: 47.203
- type: mrr_at_3
value: 43.712
- type: mrr_at_5
value: 45.367000000000004
- type: ndcg_at_1
value: 35.611
- type: ndcg_at_10
value: 47.532000000000004
- type: ndcg_at_100
value: 53.003
- type: ndcg_at_1000
value: 55.007
- type: ndcg_at_3
value: 42.043
- type: ndcg_at_5
value: 44.86
- type: precision_at_1
value: 35.611
- type: precision_at_10
value: 8.624
- type: precision_at_100
value: 1.332
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 20.083000000000002
- type: precision_at_5
value: 14.437
- type: recall_at_1
value: 30.175
- type: recall_at_10
value: 60.5
- type: recall_at_100
value: 83.399
- type: recall_at_1000
value: 96.255
- type: recall_at_3
value: 45.448
- type: recall_at_5
value: 52.432
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 22.467000000000002
- type: map_at_10
value: 33.812999999999995
- type: map_at_100
value: 35.248000000000005
- type: map_at_1000
value: 35.359
- type: map_at_3
value: 30.316
- type: map_at_5
value: 32.233000000000004
- type: mrr_at_1
value: 28.310999999999996
- type: mrr_at_10
value: 38.979
- type: mrr_at_100
value: 39.937
- type: mrr_at_1000
value: 39.989999999999995
- type: mrr_at_3
value: 36.244
- type: mrr_at_5
value: 37.871
- type: ndcg_at_1
value: 28.310999999999996
- type: ndcg_at_10
value: 40.282000000000004
- type: ndcg_at_100
value: 46.22
- type: ndcg_at_1000
value: 48.507
- type: ndcg_at_3
value: 34.596
- type: ndcg_at_5
value: 37.267
- type: precision_at_1
value: 28.310999999999996
- type: precision_at_10
value: 7.831
- type: precision_at_100
value: 1.257
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 17.275
- type: precision_at_5
value: 12.556999999999999
- type: recall_at_1
value: 22.467000000000002
- type: recall_at_10
value: 54.14099999999999
- type: recall_at_100
value: 79.593
- type: recall_at_1000
value: 95.063
- type: recall_at_3
value: 38.539
- type: recall_at_5
value: 45.403
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 24.18591666666667
- type: map_at_10
value: 33.84258333333333
- type: map_at_100
value: 35.11391666666666
- type: map_at_1000
value: 35.23258333333333
- type: map_at_3
value: 30.764249999999997
- type: map_at_5
value: 32.52333333333334
- type: mrr_at_1
value: 28.54733333333333
- type: mrr_at_10
value: 37.81725
- type: mrr_at_100
value: 38.716499999999996
- type: mrr_at_1000
value: 38.77458333333333
- type: mrr_at_3
value: 35.157833333333336
- type: mrr_at_5
value: 36.69816666666667
- type: ndcg_at_1
value: 28.54733333333333
- type: ndcg_at_10
value: 39.51508333333334
- type: ndcg_at_100
value: 44.95316666666666
- type: ndcg_at_1000
value: 47.257083333333334
- type: ndcg_at_3
value: 34.205833333333324
- type: ndcg_at_5
value: 36.78266666666667
- type: precision_at_1
value: 28.54733333333333
- type: precision_at_10
value: 7.082583333333334
- type: precision_at_100
value: 1.1590833333333332
- type: precision_at_1000
value: 0.15516666666666662
- type: precision_at_3
value: 15.908750000000001
- type: precision_at_5
value: 11.505416666666669
- type: recall_at_1
value: 24.18591666666667
- type: recall_at_10
value: 52.38758333333333
- type: recall_at_100
value: 76.13666666666667
- type: recall_at_1000
value: 91.99066666666667
- type: recall_at_3
value: 37.78333333333334
- type: recall_at_5
value: 44.30141666666666
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 21.975
- type: map_at_10
value: 29.781000000000002
- type: map_at_100
value: 30.847
- type: map_at_1000
value: 30.94
- type: map_at_3
value: 27.167
- type: map_at_5
value: 28.633999999999997
- type: mrr_at_1
value: 24.387
- type: mrr_at_10
value: 32.476
- type: mrr_at_100
value: 33.337
- type: mrr_at_1000
value: 33.403
- type: mrr_at_3
value: 29.881999999999998
- type: mrr_at_5
value: 31.339
- type: ndcg_at_1
value: 24.387
- type: ndcg_at_10
value: 34.596
- type: ndcg_at_100
value: 39.635
- type: ndcg_at_1000
value: 42.079
- type: ndcg_at_3
value: 29.516
- type: ndcg_at_5
value: 31.959
- type: precision_at_1
value: 24.387
- type: precision_at_10
value: 5.6129999999999995
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.73
- type: precision_at_5
value: 9.171999999999999
- type: recall_at_1
value: 21.975
- type: recall_at_10
value: 46.826
- type: recall_at_100
value: 69.554
- type: recall_at_1000
value: 87.749
- type: recall_at_3
value: 33.016
- type: recall_at_5
value: 38.97
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 15.614
- type: map_at_10
value: 22.927
- type: map_at_100
value: 24.185000000000002
- type: map_at_1000
value: 24.319
- type: map_at_3
value: 20.596
- type: map_at_5
value: 21.854000000000003
- type: mrr_at_1
value: 18.858
- type: mrr_at_10
value: 26.535999999999998
- type: mrr_at_100
value: 27.582
- type: mrr_at_1000
value: 27.665
- type: mrr_at_3
value: 24.295
- type: mrr_at_5
value: 25.532
- type: ndcg_at_1
value: 18.858
- type: ndcg_at_10
value: 27.583000000000002
- type: ndcg_at_100
value: 33.635
- type: ndcg_at_1000
value: 36.647
- type: ndcg_at_3
value: 23.348
- type: ndcg_at_5
value: 25.257
- type: precision_at_1
value: 18.858
- type: precision_at_10
value: 5.158
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 11.092
- type: precision_at_5
value: 8.1
- type: recall_at_1
value: 15.614
- type: recall_at_10
value: 37.916
- type: recall_at_100
value: 65.205
- type: recall_at_1000
value: 86.453
- type: recall_at_3
value: 26.137
- type: recall_at_5
value: 31.087999999999997
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 23.078000000000003
- type: map_at_10
value: 31.941999999999997
- type: map_at_100
value: 33.196999999999996
- type: map_at_1000
value: 33.303
- type: map_at_3
value: 28.927000000000003
- type: map_at_5
value: 30.707
- type: mrr_at_1
value: 26.866
- type: mrr_at_10
value: 35.557
- type: mrr_at_100
value: 36.569
- type: mrr_at_1000
value: 36.632
- type: mrr_at_3
value: 32.897999999999996
- type: mrr_at_5
value: 34.437
- type: ndcg_at_1
value: 26.866
- type: ndcg_at_10
value: 37.372
- type: ndcg_at_100
value: 43.248
- type: ndcg_at_1000
value: 45.632
- type: ndcg_at_3
value: 31.852999999999998
- type: ndcg_at_5
value: 34.582
- type: precision_at_1
value: 26.866
- type: precision_at_10
value: 6.511
- type: precision_at_100
value: 1.078
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 14.582999999999998
- type: precision_at_5
value: 10.634
- type: recall_at_1
value: 23.078000000000003
- type: recall_at_10
value: 50.334
- type: recall_at_100
value: 75.787
- type: recall_at_1000
value: 92.485
- type: recall_at_3
value: 35.386
- type: recall_at_5
value: 42.225
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 22.203999999999997
- type: map_at_10
value: 31.276
- type: map_at_100
value: 32.844
- type: map_at_1000
value: 33.062999999999995
- type: map_at_3
value: 27.733999999999998
- type: map_at_5
value: 29.64
- type: mrr_at_1
value: 27.272999999999996
- type: mrr_at_10
value: 36.083
- type: mrr_at_100
value: 37.008
- type: mrr_at_1000
value: 37.076
- type: mrr_at_3
value: 33.004
- type: mrr_at_5
value: 34.664
- type: ndcg_at_1
value: 27.272999999999996
- type: ndcg_at_10
value: 37.763000000000005
- type: ndcg_at_100
value: 43.566
- type: ndcg_at_1000
value: 46.356
- type: ndcg_at_3
value: 31.673000000000002
- type: ndcg_at_5
value: 34.501
- type: precision_at_1
value: 27.272999999999996
- type: precision_at_10
value: 7.470000000000001
- type: precision_at_100
value: 1.502
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 14.756
- type: precision_at_5
value: 11.225
- type: recall_at_1
value: 22.203999999999997
- type: recall_at_10
value: 51.437999999999995
- type: recall_at_100
value: 76.845
- type: recall_at_1000
value: 94.38600000000001
- type: recall_at_3
value: 34.258
- type: recall_at_5
value: 41.512
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.474
- type: map_at_10
value: 26.362999999999996
- type: map_at_100
value: 27.456999999999997
- type: map_at_1000
value: 27.567999999999998
- type: map_at_3
value: 23.518
- type: map_at_5
value: 25.068
- type: mrr_at_1
value: 18.669
- type: mrr_at_10
value: 27.998
- type: mrr_at_100
value: 28.953
- type: mrr_at_1000
value: 29.03
- type: mrr_at_3
value: 25.230999999999998
- type: mrr_at_5
value: 26.654
- type: ndcg_at_1
value: 18.669
- type: ndcg_at_10
value: 31.684
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.555
- type: ndcg_at_3
value: 26.057000000000002
- type: ndcg_at_5
value: 28.587
- type: precision_at_1
value: 18.669
- type: precision_at_10
value: 5.3420000000000005
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 11.583
- type: precision_at_5
value: 8.466
- type: recall_at_1
value: 17.474
- type: recall_at_10
value: 46.497
- type: recall_at_100
value: 69.977
- type: recall_at_1000
value: 89.872
- type: recall_at_3
value: 31.385999999999996
- type: recall_at_5
value: 37.283
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 17.173
- type: map_at_10
value: 30.407
- type: map_at_100
value: 32.528
- type: map_at_1000
value: 32.698
- type: map_at_3
value: 25.523
- type: map_at_5
value: 28.038
- type: mrr_at_1
value: 38.958
- type: mrr_at_10
value: 51.515
- type: mrr_at_100
value: 52.214000000000006
- type: mrr_at_1000
value: 52.237
- type: mrr_at_3
value: 48.502
- type: mrr_at_5
value: 50.251000000000005
- type: ndcg_at_1
value: 38.958
- type: ndcg_at_10
value: 40.355000000000004
- type: ndcg_at_100
value: 47.68
- type: ndcg_at_1000
value: 50.370000000000005
- type: ndcg_at_3
value: 33.946
- type: ndcg_at_5
value: 36.057
- type: precision_at_1
value: 38.958
- type: precision_at_10
value: 12.508
- type: precision_at_100
value: 2.054
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 25.581
- type: precision_at_5
value: 19.256999999999998
- type: recall_at_1
value: 17.173
- type: recall_at_10
value: 46.967
- type: recall_at_100
value: 71.47200000000001
- type: recall_at_1000
value: 86.238
- type: recall_at_3
value: 30.961
- type: recall_at_5
value: 37.539
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.999
- type: map_at_10
value: 18.989
- type: map_at_100
value: 26.133
- type: map_at_1000
value: 27.666
- type: map_at_3
value: 13.918
- type: map_at_5
value: 16.473
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.161
- type: mrr_at_100
value: 74.516
- type: mrr_at_1000
value: 74.524
- type: mrr_at_3
value: 72.875
- type: mrr_at_5
value: 73.613
- type: ndcg_at_1
value: 54.37499999999999
- type: ndcg_at_10
value: 39.902
- type: ndcg_at_100
value: 44.212
- type: ndcg_at_1000
value: 51.62
- type: ndcg_at_3
value: 45.193
- type: ndcg_at_5
value: 42.541000000000004
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 30.425
- type: precision_at_100
value: 9.754999999999999
- type: precision_at_1000
value: 2.043
- type: precision_at_3
value: 48.25
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.999
- type: recall_at_10
value: 24.133
- type: recall_at_100
value: 49.138999999999996
- type: recall_at_1000
value: 72.639
- type: recall_at_3
value: 15.287999999999998
- type: recall_at_5
value: 19.415
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.38999999999999
- type: f1
value: 41.444205512055234
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 87.35000000000001
- type: map_at_10
value: 92.837
- type: map_at_100
value: 92.996
- type: map_at_1000
value: 93.006
- type: map_at_3
value: 92.187
- type: map_at_5
value: 92.595
- type: mrr_at_1
value: 93.864
- type: mrr_at_10
value: 96.723
- type: mrr_at_100
value: 96.72500000000001
- type: mrr_at_1000
value: 96.72500000000001
- type: mrr_at_3
value: 96.64
- type: mrr_at_5
value: 96.71499999999999
- type: ndcg_at_1
value: 93.864
- type: ndcg_at_10
value: 94.813
- type: ndcg_at_100
value: 95.243
- type: ndcg_at_1000
value: 95.38600000000001
- type: ndcg_at_3
value: 94.196
- type: ndcg_at_5
value: 94.521
- type: precision_at_1
value: 93.864
- type: precision_at_10
value: 10.951
- type: precision_at_100
value: 1.1400000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 35.114000000000004
- type: precision_at_5
value: 21.476
- type: recall_at_1
value: 87.35000000000001
- type: recall_at_10
value: 96.941
- type: recall_at_100
value: 98.397
- type: recall_at_1000
value: 99.21600000000001
- type: recall_at_3
value: 95.149
- type: recall_at_5
value: 96.131
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 24.476
- type: map_at_10
value: 40.11
- type: map_at_100
value: 42.229
- type: map_at_1000
value: 42.378
- type: map_at_3
value: 34.512
- type: map_at_5
value: 38.037
- type: mrr_at_1
value: 47.839999999999996
- type: mrr_at_10
value: 57.053
- type: mrr_at_100
value: 57.772
- type: mrr_at_1000
value: 57.799
- type: mrr_at_3
value: 54.552
- type: mrr_at_5
value: 56.011
- type: ndcg_at_1
value: 47.839999999999996
- type: ndcg_at_10
value: 48.650999999999996
- type: ndcg_at_100
value: 55.681000000000004
- type: ndcg_at_1000
value: 57.979
- type: ndcg_at_3
value: 43.923
- type: ndcg_at_5
value: 46.037
- type: precision_at_1
value: 47.839999999999996
- type: precision_at_10
value: 13.395000000000001
- type: precision_at_100
value: 2.0660000000000003
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 29.064
- type: precision_at_5
value: 22.006
- type: recall_at_1
value: 24.476
- type: recall_at_10
value: 56.216
- type: recall_at_100
value: 81.798
- type: recall_at_1000
value: 95.48299999999999
- type: recall_at_3
value: 39.357
- type: recall_at_5
value: 47.802
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.728
- type: map_at_10
value: 57.737
- type: map_at_100
value: 58.531
- type: map_at_1000
value: 58.594
- type: map_at_3
value: 54.869
- type: map_at_5
value: 56.55
- type: mrr_at_1
value: 85.456
- type: mrr_at_10
value: 90.062
- type: mrr_at_100
value: 90.159
- type: mrr_at_1000
value: 90.16
- type: mrr_at_3
value: 89.37899999999999
- type: mrr_at_5
value: 89.81
- type: ndcg_at_1
value: 85.456
- type: ndcg_at_10
value: 67.755
- type: ndcg_at_100
value: 70.341
- type: ndcg_at_1000
value: 71.538
- type: ndcg_at_3
value: 63.735
- type: ndcg_at_5
value: 65.823
- type: precision_at_1
value: 85.456
- type: precision_at_10
value: 13.450000000000001
- type: precision_at_100
value: 1.545
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 38.861000000000004
- type: precision_at_5
value: 24.964
- type: recall_at_1
value: 42.728
- type: recall_at_10
value: 67.252
- type: recall_at_100
value: 77.265
- type: recall_at_1000
value: 85.246
- type: recall_at_3
value: 58.292
- type: recall_at_5
value: 62.41100000000001
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 87.4836
- type: ap
value: 82.29552224030336
- type: f1
value: 87.42791432227448
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 23.015
- type: map_at_10
value: 35.621
- type: map_at_100
value: 36.809
- type: map_at_1000
value: 36.853
- type: map_at_3
value: 31.832
- type: map_at_5
value: 34.006
- type: mrr_at_1
value: 23.738999999999997
- type: mrr_at_10
value: 36.309999999999995
- type: mrr_at_100
value: 37.422
- type: mrr_at_1000
value: 37.461
- type: mrr_at_3
value: 32.592999999999996
- type: mrr_at_5
value: 34.736
- type: ndcg_at_1
value: 23.724999999999998
- type: ndcg_at_10
value: 42.617
- type: ndcg_at_100
value: 48.217999999999996
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 34.905
- type: ndcg_at_5
value: 38.769
- type: precision_at_1
value: 23.724999999999998
- type: precision_at_10
value: 6.689
- type: precision_at_100
value: 0.9480000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.89
- type: precision_at_5
value: 10.897
- type: recall_at_1
value: 23.015
- type: recall_at_10
value: 64.041
- type: recall_at_100
value: 89.724
- type: recall_at_1000
value: 98.00999999999999
- type: recall_at_3
value: 43.064
- type: recall_at_5
value: 52.31099999999999
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.49794801641588
- type: f1
value: 96.28931114498003
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.81121751025992
- type: f1
value: 63.18740125901853
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.66644250168123
- type: f1
value: 74.93211186867839
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.77202420981843
- type: f1
value: 81.63681969283554
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.596687684870645
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.26965660101405
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.33619694846802
- type: mrr
value: 32.53719657720334
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.0729999999999995
- type: map_at_10
value: 13.245999999999999
- type: map_at_100
value: 16.747999999999998
- type: map_at_1000
value: 18.163
- type: map_at_3
value: 10.064
- type: map_at_5
value: 11.513
- type: mrr_at_1
value: 49.536
- type: mrr_at_10
value: 58.092
- type: mrr_at_100
value: 58.752
- type: mrr_at_1000
value: 58.78
- type: mrr_at_3
value: 56.398
- type: mrr_at_5
value: 57.389
- type: ndcg_at_1
value: 47.059
- type: ndcg_at_10
value: 35.881
- type: ndcg_at_100
value: 32.751999999999995
- type: ndcg_at_1000
value: 41.498000000000005
- type: ndcg_at_3
value: 42.518
- type: ndcg_at_5
value: 39.550999999999995
- type: precision_at_1
value: 49.536
- type: precision_at_10
value: 26.316
- type: precision_at_100
value: 8.084
- type: precision_at_1000
value: 2.081
- type: precision_at_3
value: 39.938
- type: precision_at_5
value: 34.056
- type: recall_at_1
value: 6.0729999999999995
- type: recall_at_10
value: 16.593
- type: recall_at_100
value: 32.883
- type: recall_at_1000
value: 64.654
- type: recall_at_3
value: 11.174000000000001
- type: recall_at_5
value: 13.528
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 30.043
- type: map_at_10
value: 45.318999999999996
- type: map_at_100
value: 46.381
- type: map_at_1000
value: 46.412
- type: map_at_3
value: 40.941
- type: map_at_5
value: 43.662
- type: mrr_at_1
value: 33.98
- type: mrr_at_10
value: 47.870000000000005
- type: mrr_at_100
value: 48.681999999999995
- type: mrr_at_1000
value: 48.703
- type: mrr_at_3
value: 44.341
- type: mrr_at_5
value: 46.547
- type: ndcg_at_1
value: 33.98
- type: ndcg_at_10
value: 52.957
- type: ndcg_at_100
value: 57.434
- type: ndcg_at_1000
value: 58.103
- type: ndcg_at_3
value: 44.896
- type: ndcg_at_5
value: 49.353
- type: precision_at_1
value: 33.98
- type: precision_at_10
value: 8.786
- type: precision_at_100
value: 1.1280000000000001
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 20.577
- type: precision_at_5
value: 14.942
- type: recall_at_1
value: 30.043
- type: recall_at_10
value: 73.593
- type: recall_at_100
value: 93.026
- type: recall_at_1000
value: 97.943
- type: recall_at_3
value: 52.955
- type: recall_at_5
value: 63.132
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.808
- type: map_at_10
value: 84.675
- type: map_at_100
value: 85.322
- type: map_at_1000
value: 85.33800000000001
- type: map_at_3
value: 81.68900000000001
- type: map_at_5
value: 83.543
- type: mrr_at_1
value: 81.5
- type: mrr_at_10
value: 87.59700000000001
- type: mrr_at_100
value: 87.705
- type: mrr_at_1000
value: 87.70599999999999
- type: mrr_at_3
value: 86.607
- type: mrr_at_5
value: 87.289
- type: ndcg_at_1
value: 81.51
- type: ndcg_at_10
value: 88.41799999999999
- type: ndcg_at_100
value: 89.644
- type: ndcg_at_1000
value: 89.725
- type: ndcg_at_3
value: 85.49900000000001
- type: ndcg_at_5
value: 87.078
- type: precision_at_1
value: 81.51
- type: precision_at_10
value: 13.438
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.363
- type: precision_at_5
value: 24.57
- type: recall_at_1
value: 70.808
- type: recall_at_10
value: 95.575
- type: recall_at_100
value: 99.667
- type: recall_at_1000
value: 99.98899999999999
- type: recall_at_3
value: 87.223
- type: recall_at_5
value: 91.682
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 58.614831329137715
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 66.86580408560826
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.093
- type: map_at_10
value: 13.014000000000001
- type: map_at_100
value: 15.412999999999998
- type: map_at_1000
value: 15.756999999999998
- type: map_at_3
value: 9.216000000000001
- type: map_at_5
value: 11.036999999999999
- type: mrr_at_1
value: 25.1
- type: mrr_at_10
value: 37.133
- type: mrr_at_100
value: 38.165
- type: mrr_at_1000
value: 38.198
- type: mrr_at_3
value: 33.217
- type: mrr_at_5
value: 35.732
- type: ndcg_at_1
value: 25.1
- type: ndcg_at_10
value: 21.918000000000003
- type: ndcg_at_100
value: 30.983
- type: ndcg_at_1000
value: 36.629
- type: ndcg_at_3
value: 20.544999999999998
- type: ndcg_at_5
value: 18.192
- type: precision_at_1
value: 25.1
- type: precision_at_10
value: 11.44
- type: precision_at_100
value: 2.459
- type: precision_at_1000
value: 0.381
- type: precision_at_3
value: 19.267
- type: precision_at_5
value: 16.16
- type: recall_at_1
value: 5.093
- type: recall_at_10
value: 23.215
- type: recall_at_100
value: 49.902
- type: recall_at_1000
value: 77.403
- type: recall_at_3
value: 11.733
- type: recall_at_5
value: 16.372999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.9365442977452
- type: cos_sim_spearman
value: 79.36960687383745
- type: euclidean_pearson
value: 79.6045204840714
- type: euclidean_spearman
value: 79.26382712751337
- type: manhattan_pearson
value: 79.4805084789529
- type: manhattan_spearman
value: 79.21847863209523
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.27906192961453
- type: cos_sim_spearman
value: 74.38364712099211
- type: euclidean_pearson
value: 78.54358927241223
- type: euclidean_spearman
value: 74.22185560806376
- type: manhattan_pearson
value: 78.50904327377751
- type: manhattan_spearman
value: 74.2627500781748
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.66863742649639
- type: cos_sim_spearman
value: 84.70630905216271
- type: euclidean_pearson
value: 84.64498334705334
- type: euclidean_spearman
value: 84.87204770690148
- type: manhattan_pearson
value: 84.65774227976077
- type: manhattan_spearman
value: 84.91251851797985
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.1577763924467
- type: cos_sim_spearman
value: 80.10314039230198
- type: euclidean_pearson
value: 81.51346991046043
- type: euclidean_spearman
value: 80.08678485109435
- type: manhattan_pearson
value: 81.57058914661894
- type: manhattan_spearman
value: 80.1516230725106
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.40310839662533
- type: cos_sim_spearman
value: 87.16293477217867
- type: euclidean_pearson
value: 86.50688711184775
- type: euclidean_spearman
value: 87.08651444923031
- type: manhattan_pearson
value: 86.54674677557857
- type: manhattan_spearman
value: 87.15079017870971
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.32886275207817
- type: cos_sim_spearman
value: 85.0190460590732
- type: euclidean_pearson
value: 84.42553652784679
- type: euclidean_spearman
value: 85.20027364279328
- type: manhattan_pearson
value: 84.42926246281078
- type: manhattan_spearman
value: 85.20187419804306
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.76732216967812
- type: cos_sim_spearman
value: 90.63701653633909
- type: euclidean_pearson
value: 90.26678186114682
- type: euclidean_spearman
value: 90.67288073455427
- type: manhattan_pearson
value: 90.20772020584582
- type: manhattan_spearman
value: 90.60764863983702
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 69.09280387698125
- type: cos_sim_spearman
value: 68.62743151172162
- type: euclidean_pearson
value: 69.89386398104689
- type: euclidean_spearman
value: 68.71191066733556
- type: manhattan_pearson
value: 69.92516500604872
- type: manhattan_spearman
value: 68.80452846992576
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.13178592019887
- type: cos_sim_spearman
value: 86.03947178806887
- type: euclidean_pearson
value: 85.87029414285313
- type: euclidean_spearman
value: 86.04960843306998
- type: manhattan_pearson
value: 85.92946858580146
- type: manhattan_spearman
value: 86.12575341860442
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.16657063002837
- type: mrr
value: 95.73671063867141
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 63.510999999999996
- type: map_at_10
value: 72.76899999999999
- type: map_at_100
value: 73.303
- type: map_at_1000
value: 73.32499999999999
- type: map_at_3
value: 70.514
- type: map_at_5
value: 71.929
- type: mrr_at_1
value: 66.333
- type: mrr_at_10
value: 73.75
- type: mrr_at_100
value: 74.119
- type: mrr_at_1000
value: 74.138
- type: mrr_at_3
value: 72.222
- type: mrr_at_5
value: 73.122
- type: ndcg_at_1
value: 66.333
- type: ndcg_at_10
value: 76.774
- type: ndcg_at_100
value: 78.78500000000001
- type: ndcg_at_1000
value: 79.254
- type: ndcg_at_3
value: 73.088
- type: ndcg_at_5
value: 75.002
- type: precision_at_1
value: 66.333
- type: precision_at_10
value: 9.833
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.222
- type: precision_at_5
value: 18.333
- type: recall_at_1
value: 63.510999999999996
- type: recall_at_10
value: 87.98899999999999
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 77.86699999999999
- type: recall_at_5
value: 82.73899999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.78514851485149
- type: cos_sim_ap
value: 94.94214383862038
- type: cos_sim_f1
value: 89.02255639097744
- type: cos_sim_precision
value: 89.2462311557789
- type: cos_sim_recall
value: 88.8
- type: dot_accuracy
value: 99.78217821782178
- type: dot_ap
value: 94.69965247836805
- type: dot_f1
value: 88.78695208970439
- type: dot_precision
value: 90.54054054054053
- type: dot_recall
value: 87.1
- type: euclidean_accuracy
value: 99.78118811881188
- type: euclidean_ap
value: 94.9865187695411
- type: euclidean_f1
value: 88.99950223992036
- type: euclidean_precision
value: 88.60257680872151
- type: euclidean_recall
value: 89.4
- type: manhattan_accuracy
value: 99.78811881188119
- type: manhattan_ap
value: 95.0021236766459
- type: manhattan_f1
value: 89.12071535022356
- type: manhattan_precision
value: 88.54886475814413
- type: manhattan_recall
value: 89.7
- type: max_accuracy
value: 99.78811881188119
- type: max_ap
value: 95.0021236766459
- type: max_f1
value: 89.12071535022356
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.93190546593995
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 37.602808534760655
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.29214480978073
- type: mrr
value: 53.123169722434426
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.967800769650022
- type: cos_sim_spearman
value: 31.168490040206926
- type: dot_pearson
value: 30.888603021128553
- type: dot_spearman
value: 31.028241262520385
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22300000000000003
- type: map_at_10
value: 1.781
- type: map_at_100
value: 9.905999999999999
- type: map_at_1000
value: 23.455000000000002
- type: map_at_3
value: 0.569
- type: map_at_5
value: 0.918
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 91.067
- type: mrr_at_100
value: 91.067
- type: mrr_at_1000
value: 91.067
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 91.067
- type: ndcg_at_1
value: 78.0
- type: ndcg_at_10
value: 73.13499999999999
- type: ndcg_at_100
value: 55.32
- type: ndcg_at_1000
value: 49.532
- type: ndcg_at_3
value: 73.715
- type: ndcg_at_5
value: 72.74199999999999
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 78.8
- type: precision_at_100
value: 56.32
- type: precision_at_1000
value: 21.504
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 78.0
- type: recall_at_1
value: 0.22300000000000003
- type: recall_at_10
value: 2.049
- type: recall_at_100
value: 13.553
- type: recall_at_1000
value: 46.367999999999995
- type: recall_at_3
value: 0.604
- type: recall_at_5
value: 1.015
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.0380000000000003
- type: map_at_10
value: 10.188
- type: map_at_100
value: 16.395
- type: map_at_1000
value: 18.024
- type: map_at_3
value: 6.236
- type: map_at_5
value: 7.276000000000001
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 46.292
- type: mrr_at_100
value: 47.446
- type: mrr_at_1000
value: 47.446
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 44.32
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 25.219
- type: ndcg_at_100
value: 37.802
- type: ndcg_at_1000
value: 49.274
- type: ndcg_at_3
value: 28.605999999999998
- type: ndcg_at_5
value: 26.21
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 21.837
- type: precision_at_100
value: 7.776
- type: precision_at_1000
value: 1.522
- type: precision_at_3
value: 28.571
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 3.0380000000000003
- type: recall_at_10
value: 16.298000000000002
- type: recall_at_100
value: 48.712
- type: recall_at_1000
value: 83.16799999999999
- type: recall_at_3
value: 7.265000000000001
- type: recall_at_5
value: 9.551
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 83.978
- type: ap
value: 24.751887949330015
- type: f1
value: 66.8685134049279
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.573288058856825
- type: f1
value: 61.973261751726604
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.75483298792469
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.36824223639506
- type: cos_sim_ap
value: 75.53126388573047
- type: cos_sim_f1
value: 67.9912831688245
- type: cos_sim_precision
value: 66.11817501869858
- type: cos_sim_recall
value: 69.9736147757256
- type: dot_accuracy
value: 86.39804494248078
- type: dot_ap
value: 75.27598891718046
- type: dot_f1
value: 67.91146284159763
- type: dot_precision
value: 63.90505003490807
- type: dot_recall
value: 72.45382585751979
- type: euclidean_accuracy
value: 86.36228169517793
- type: euclidean_ap
value: 75.51438087434647
- type: euclidean_f1
value: 68.02370523061066
- type: euclidean_precision
value: 66.46525679758308
- type: euclidean_recall
value: 69.65699208443272
- type: manhattan_accuracy
value: 86.46361089586935
- type: manhattan_ap
value: 75.50800785730111
- type: manhattan_f1
value: 67.9220437187253
- type: manhattan_precision
value: 67.79705573080967
- type: manhattan_recall
value: 68.04749340369392
- type: max_accuracy
value: 86.46361089586935
- type: max_ap
value: 75.53126388573047
- type: max_f1
value: 68.02370523061066
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.80350836341057
- type: cos_sim_ap
value: 85.51101933260743
- type: cos_sim_f1
value: 77.9152271629704
- type: cos_sim_precision
value: 75.27815662910056
- type: cos_sim_recall
value: 80.74376347397599
- type: dot_accuracy
value: 88.84425815966158
- type: dot_ap
value: 85.49726945962519
- type: dot_f1
value: 77.94445269567801
- type: dot_precision
value: 75.27251864601261
- type: dot_recall
value: 80.81305820757623
- type: euclidean_accuracy
value: 88.80350836341057
- type: euclidean_ap
value: 85.4882880790211
- type: euclidean_f1
value: 77.87063284615103
- type: euclidean_precision
value: 74.61022927689595
- type: euclidean_recall
value: 81.42901139513397
- type: manhattan_accuracy
value: 88.7161873714441
- type: manhattan_ap
value: 85.45753871906821
- type: manhattan_f1
value: 77.8686401480111
- type: manhattan_precision
value: 74.95903683123174
- type: manhattan_recall
value: 81.01324299353249
- type: max_accuracy
value: 88.84425815966158
- type: max_ap
value: 85.51101933260743
- type: max_f1
value: 77.94445269567801
---
<!-- **English** | [中文](./README_zh.md) -->
# gte-base-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
- **Model type:** Text Embeddings
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
<!-- - **Demo [optional]:** [More Information Needed] -->
### Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-base-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
Use with `sentence-transformers`:
```python
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
Use with `transformers.js`:
```js
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]
```
Use with infinity:
[Infinity](https://github.com/michaelfeil/infinity) is a MIT licensed server for OpenAI-compatible deployment.
```
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id Alibaba-NLP/gte-base-en-v1.5 --revision "4c742dc2b781e4ab062a4a77f4f7cbad4bdee970" --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997
```
## Training Details
### Training Data
- Masked language modeling (MLM): `c4-en`
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
- CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
- Fine-tuning: TODO
## Evaluation
### MTEB
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
### LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
## Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@misc{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
year={2024},
eprint={2407.19669},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.19669},
}
@misc{li2023gte,
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
year={2023},
eprint={2308.03281},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.03281},
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
mixedbread-ai/mxbai-embed-large-v1 | mixedbread-ai | feature-extraction | [
"sentence-transformers",
"onnx",
"safetensors",
"openvino",
"gguf",
"bert",
"feature-extraction",
"mteb",
"transformers.js",
"transformers",
"en",
"arxiv:2309.12871",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-07T15:45:34 | 2025-03-13T04:15:03 | 2,390,539 | 639 | ---
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- mteb
- transformers.js
- transformers
model-index:
- name: mxbai-angle-large-v1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.044776119403
- type: ap
value: 37.7362433623053
- type: f1
value: 68.92736573359774
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.84025000000001
- type: ap
value: 90.93190875404055
- type: f1
value: 93.8297833897293
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.184
- type: f1
value: 48.74163227751588
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 41.252
- type: map_at_10
value: 57.778
- type: map_at_100
value: 58.233000000000004
- type: map_at_1000
value: 58.23700000000001
- type: map_at_3
value: 53.449999999999996
- type: map_at_5
value: 56.376000000000005
- type: mrr_at_1
value: 41.679
- type: mrr_at_10
value: 57.92699999999999
- type: mrr_at_100
value: 58.389
- type: mrr_at_1000
value: 58.391999999999996
- type: mrr_at_3
value: 53.651
- type: mrr_at_5
value: 56.521
- type: ndcg_at_1
value: 41.252
- type: ndcg_at_10
value: 66.018
- type: ndcg_at_100
value: 67.774
- type: ndcg_at_1000
value: 67.84400000000001
- type: ndcg_at_3
value: 57.372
- type: ndcg_at_5
value: 62.646
- type: precision_at_1
value: 41.252
- type: precision_at_10
value: 9.189
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.902
- type: precision_at_5
value: 16.302
- type: recall_at_1
value: 41.252
- type: recall_at_10
value: 91.892
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 68.706
- type: recall_at_5
value: 81.50800000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.97294504317859
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.98071077674629
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.16477858490782
- type: mrr
value: 78.23583080508287
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.6277629421789
- type: cos_sim_spearman
value: 88.4056288400568
- type: euclidean_pearson
value: 87.94871847578163
- type: euclidean_spearman
value: 88.4056288400568
- type: manhattan_pearson
value: 87.73271254229648
- type: manhattan_spearman
value: 87.91826833762677
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.81818181818181
- type: f1
value: 87.79879337316918
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.91773608582761
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.73059477462478
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.745999999999995
- type: map_at_10
value: 43.632
- type: map_at_100
value: 45.206
- type: map_at_1000
value: 45.341
- type: map_at_3
value: 39.956
- type: map_at_5
value: 42.031
- type: mrr_at_1
value: 39.485
- type: mrr_at_10
value: 49.537
- type: mrr_at_100
value: 50.249
- type: mrr_at_1000
value: 50.294000000000004
- type: mrr_at_3
value: 46.757
- type: mrr_at_5
value: 48.481
- type: ndcg_at_1
value: 39.485
- type: ndcg_at_10
value: 50.058
- type: ndcg_at_100
value: 55.586
- type: ndcg_at_1000
value: 57.511
- type: ndcg_at_3
value: 44.786
- type: ndcg_at_5
value: 47.339999999999996
- type: precision_at_1
value: 39.485
- type: precision_at_10
value: 9.557
- type: precision_at_100
value: 1.552
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.412
- type: precision_at_5
value: 15.479000000000001
- type: recall_at_1
value: 32.745999999999995
- type: recall_at_10
value: 62.056
- type: recall_at_100
value: 85.088
- type: recall_at_1000
value: 96.952
- type: recall_at_3
value: 46.959
- type: recall_at_5
value: 54.06999999999999
- type: map_at_1
value: 31.898
- type: map_at_10
value: 42.142
- type: map_at_100
value: 43.349
- type: map_at_1000
value: 43.483
- type: map_at_3
value: 39.18
- type: map_at_5
value: 40.733000000000004
- type: mrr_at_1
value: 39.617999999999995
- type: mrr_at_10
value: 47.922
- type: mrr_at_100
value: 48.547000000000004
- type: mrr_at_1000
value: 48.597
- type: mrr_at_3
value: 45.86
- type: mrr_at_5
value: 46.949000000000005
- type: ndcg_at_1
value: 39.617999999999995
- type: ndcg_at_10
value: 47.739
- type: ndcg_at_100
value: 51.934999999999995
- type: ndcg_at_1000
value: 54.007000000000005
- type: ndcg_at_3
value: 43.748
- type: ndcg_at_5
value: 45.345
- type: precision_at_1
value: 39.617999999999995
- type: precision_at_10
value: 8.962
- type: precision_at_100
value: 1.436
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 21.083
- type: precision_at_5
value: 14.752
- type: recall_at_1
value: 31.898
- type: recall_at_10
value: 57.587999999999994
- type: recall_at_100
value: 75.323
- type: recall_at_1000
value: 88.304
- type: recall_at_3
value: 45.275
- type: recall_at_5
value: 49.99
- type: map_at_1
value: 40.458
- type: map_at_10
value: 52.942
- type: map_at_100
value: 53.974
- type: map_at_1000
value: 54.031
- type: map_at_3
value: 49.559999999999995
- type: map_at_5
value: 51.408
- type: mrr_at_1
value: 46.27
- type: mrr_at_10
value: 56.31699999999999
- type: mrr_at_100
value: 56.95099999999999
- type: mrr_at_1000
value: 56.98
- type: mrr_at_3
value: 53.835
- type: mrr_at_5
value: 55.252
- type: ndcg_at_1
value: 46.27
- type: ndcg_at_10
value: 58.964000000000006
- type: ndcg_at_100
value: 62.875
- type: ndcg_at_1000
value: 63.969
- type: ndcg_at_3
value: 53.297000000000004
- type: ndcg_at_5
value: 55.938
- type: precision_at_1
value: 46.27
- type: precision_at_10
value: 9.549000000000001
- type: precision_at_100
value: 1.2409999999999999
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 23.762
- type: precision_at_5
value: 16.262999999999998
- type: recall_at_1
value: 40.458
- type: recall_at_10
value: 73.446
- type: recall_at_100
value: 90.12400000000001
- type: recall_at_1000
value: 97.795
- type: recall_at_3
value: 58.123000000000005
- type: recall_at_5
value: 64.68
- type: map_at_1
value: 27.443
- type: map_at_10
value: 36.081
- type: map_at_100
value: 37.163000000000004
- type: map_at_1000
value: 37.232
- type: map_at_3
value: 33.308
- type: map_at_5
value: 34.724
- type: mrr_at_1
value: 29.492
- type: mrr_at_10
value: 38.138
- type: mrr_at_100
value: 39.065
- type: mrr_at_1000
value: 39.119
- type: mrr_at_3
value: 35.593
- type: mrr_at_5
value: 36.785000000000004
- type: ndcg_at_1
value: 29.492
- type: ndcg_at_10
value: 41.134
- type: ndcg_at_100
value: 46.300999999999995
- type: ndcg_at_1000
value: 48.106
- type: ndcg_at_3
value: 35.77
- type: ndcg_at_5
value: 38.032
- type: precision_at_1
value: 29.492
- type: precision_at_10
value: 6.249
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 15.065999999999999
- type: precision_at_5
value: 10.373000000000001
- type: recall_at_1
value: 27.443
- type: recall_at_10
value: 54.80199999999999
- type: recall_at_100
value: 78.21900000000001
- type: recall_at_1000
value: 91.751
- type: recall_at_3
value: 40.211000000000006
- type: recall_at_5
value: 45.599000000000004
- type: map_at_1
value: 18.731
- type: map_at_10
value: 26.717999999999996
- type: map_at_100
value: 27.897
- type: map_at_1000
value: 28.029
- type: map_at_3
value: 23.91
- type: map_at_5
value: 25.455
- type: mrr_at_1
value: 23.134
- type: mrr_at_10
value: 31.769
- type: mrr_at_100
value: 32.634
- type: mrr_at_1000
value: 32.707
- type: mrr_at_3
value: 28.938999999999997
- type: mrr_at_5
value: 30.531000000000002
- type: ndcg_at_1
value: 23.134
- type: ndcg_at_10
value: 32.249
- type: ndcg_at_100
value: 37.678
- type: ndcg_at_1000
value: 40.589999999999996
- type: ndcg_at_3
value: 26.985999999999997
- type: ndcg_at_5
value: 29.457
- type: precision_at_1
value: 23.134
- type: precision_at_10
value: 5.8709999999999996
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.852
- type: precision_at_5
value: 9.428
- type: recall_at_1
value: 18.731
- type: recall_at_10
value: 44.419
- type: recall_at_100
value: 67.851
- type: recall_at_1000
value: 88.103
- type: recall_at_3
value: 29.919
- type: recall_at_5
value: 36.230000000000004
- type: map_at_1
value: 30.324
- type: map_at_10
value: 41.265
- type: map_at_100
value: 42.559000000000005
- type: map_at_1000
value: 42.669000000000004
- type: map_at_3
value: 38.138
- type: map_at_5
value: 39.881
- type: mrr_at_1
value: 36.67
- type: mrr_at_10
value: 46.774
- type: mrr_at_100
value: 47.554
- type: mrr_at_1000
value: 47.593
- type: mrr_at_3
value: 44.338
- type: mrr_at_5
value: 45.723
- type: ndcg_at_1
value: 36.67
- type: ndcg_at_10
value: 47.367
- type: ndcg_at_100
value: 52.623
- type: ndcg_at_1000
value: 54.59
- type: ndcg_at_3
value: 42.323
- type: ndcg_at_5
value: 44.727
- type: precision_at_1
value: 36.67
- type: precision_at_10
value: 8.518
- type: precision_at_100
value: 1.2890000000000001
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 19.955000000000002
- type: precision_at_5
value: 14.11
- type: recall_at_1
value: 30.324
- type: recall_at_10
value: 59.845000000000006
- type: recall_at_100
value: 81.77499999999999
- type: recall_at_1000
value: 94.463
- type: recall_at_3
value: 46.019
- type: recall_at_5
value: 52.163000000000004
- type: map_at_1
value: 24.229
- type: map_at_10
value: 35.004000000000005
- type: map_at_100
value: 36.409000000000006
- type: map_at_1000
value: 36.521
- type: map_at_3
value: 31.793
- type: map_at_5
value: 33.432
- type: mrr_at_1
value: 30.365
- type: mrr_at_10
value: 40.502
- type: mrr_at_100
value: 41.372
- type: mrr_at_1000
value: 41.435
- type: mrr_at_3
value: 37.804
- type: mrr_at_5
value: 39.226
- type: ndcg_at_1
value: 30.365
- type: ndcg_at_10
value: 41.305
- type: ndcg_at_100
value: 47.028999999999996
- type: ndcg_at_1000
value: 49.375
- type: ndcg_at_3
value: 35.85
- type: ndcg_at_5
value: 38.12
- type: precision_at_1
value: 30.365
- type: precision_at_10
value: 7.808
- type: precision_at_100
value: 1.228
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 17.352
- type: precision_at_5
value: 12.42
- type: recall_at_1
value: 24.229
- type: recall_at_10
value: 54.673
- type: recall_at_100
value: 78.766
- type: recall_at_1000
value: 94.625
- type: recall_at_3
value: 39.602
- type: recall_at_5
value: 45.558
- type: map_at_1
value: 26.695
- type: map_at_10
value: 36.0895
- type: map_at_100
value: 37.309416666666664
- type: map_at_1000
value: 37.42558333333334
- type: map_at_3
value: 33.19616666666666
- type: map_at_5
value: 34.78641666666667
- type: mrr_at_1
value: 31.486083333333337
- type: mrr_at_10
value: 40.34774999999999
- type: mrr_at_100
value: 41.17533333333333
- type: mrr_at_1000
value: 41.231583333333326
- type: mrr_at_3
value: 37.90075
- type: mrr_at_5
value: 39.266999999999996
- type: ndcg_at_1
value: 31.486083333333337
- type: ndcg_at_10
value: 41.60433333333334
- type: ndcg_at_100
value: 46.74525
- type: ndcg_at_1000
value: 48.96166666666667
- type: ndcg_at_3
value: 36.68825
- type: ndcg_at_5
value: 38.966499999999996
- type: precision_at_1
value: 31.486083333333337
- type: precision_at_10
value: 7.29675
- type: precision_at_100
value: 1.1621666666666666
- type: precision_at_1000
value: 0.1545
- type: precision_at_3
value: 16.8815
- type: precision_at_5
value: 11.974583333333333
- type: recall_at_1
value: 26.695
- type: recall_at_10
value: 53.651916666666665
- type: recall_at_100
value: 76.12083333333332
- type: recall_at_1000
value: 91.31191666666668
- type: recall_at_3
value: 40.03575
- type: recall_at_5
value: 45.876666666666665
- type: map_at_1
value: 25.668000000000003
- type: map_at_10
value: 32.486
- type: map_at_100
value: 33.371
- type: map_at_1000
value: 33.458
- type: map_at_3
value: 30.261
- type: map_at_5
value: 31.418000000000003
- type: mrr_at_1
value: 28.988000000000003
- type: mrr_at_10
value: 35.414
- type: mrr_at_100
value: 36.149
- type: mrr_at_1000
value: 36.215
- type: mrr_at_3
value: 33.333
- type: mrr_at_5
value: 34.43
- type: ndcg_at_1
value: 28.988000000000003
- type: ndcg_at_10
value: 36.732
- type: ndcg_at_100
value: 41.331
- type: ndcg_at_1000
value: 43.575
- type: ndcg_at_3
value: 32.413
- type: ndcg_at_5
value: 34.316
- type: precision_at_1
value: 28.988000000000003
- type: precision_at_10
value: 5.7059999999999995
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 13.65
- type: precision_at_5
value: 9.417
- type: recall_at_1
value: 25.668000000000003
- type: recall_at_10
value: 47.147
- type: recall_at_100
value: 68.504
- type: recall_at_1000
value: 85.272
- type: recall_at_3
value: 35.19
- type: recall_at_5
value: 39.925
- type: map_at_1
value: 17.256
- type: map_at_10
value: 24.58
- type: map_at_100
value: 25.773000000000003
- type: map_at_1000
value: 25.899
- type: map_at_3
value: 22.236
- type: map_at_5
value: 23.507
- type: mrr_at_1
value: 20.957
- type: mrr_at_10
value: 28.416000000000004
- type: mrr_at_100
value: 29.447000000000003
- type: mrr_at_1000
value: 29.524
- type: mrr_at_3
value: 26.245
- type: mrr_at_5
value: 27.451999999999998
- type: ndcg_at_1
value: 20.957
- type: ndcg_at_10
value: 29.285
- type: ndcg_at_100
value: 35.003
- type: ndcg_at_1000
value: 37.881
- type: ndcg_at_3
value: 25.063000000000002
- type: ndcg_at_5
value: 26.983
- type: precision_at_1
value: 20.957
- type: precision_at_10
value: 5.344
- type: precision_at_100
value: 0.958
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 11.918
- type: precision_at_5
value: 8.596
- type: recall_at_1
value: 17.256
- type: recall_at_10
value: 39.644
- type: recall_at_100
value: 65.279
- type: recall_at_1000
value: 85.693
- type: recall_at_3
value: 27.825
- type: recall_at_5
value: 32.792
- type: map_at_1
value: 26.700000000000003
- type: map_at_10
value: 36.205999999999996
- type: map_at_100
value: 37.316
- type: map_at_1000
value: 37.425000000000004
- type: map_at_3
value: 33.166000000000004
- type: map_at_5
value: 35.032999999999994
- type: mrr_at_1
value: 31.436999999999998
- type: mrr_at_10
value: 40.61
- type: mrr_at_100
value: 41.415
- type: mrr_at_1000
value: 41.48
- type: mrr_at_3
value: 37.966
- type: mrr_at_5
value: 39.599000000000004
- type: ndcg_at_1
value: 31.436999999999998
- type: ndcg_at_10
value: 41.771
- type: ndcg_at_100
value: 46.784
- type: ndcg_at_1000
value: 49.183
- type: ndcg_at_3
value: 36.437000000000005
- type: ndcg_at_5
value: 39.291
- type: precision_at_1
value: 31.436999999999998
- type: precision_at_10
value: 6.987
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 16.448999999999998
- type: precision_at_5
value: 11.866
- type: recall_at_1
value: 26.700000000000003
- type: recall_at_10
value: 54.301
- type: recall_at_100
value: 75.871
- type: recall_at_1000
value: 92.529
- type: recall_at_3
value: 40.201
- type: recall_at_5
value: 47.208
- type: map_at_1
value: 24.296
- type: map_at_10
value: 33.116
- type: map_at_100
value: 34.81
- type: map_at_1000
value: 35.032000000000004
- type: map_at_3
value: 30.105999999999998
- type: map_at_5
value: 31.839000000000002
- type: mrr_at_1
value: 29.051
- type: mrr_at_10
value: 37.803
- type: mrr_at_100
value: 38.856
- type: mrr_at_1000
value: 38.903999999999996
- type: mrr_at_3
value: 35.211
- type: mrr_at_5
value: 36.545
- type: ndcg_at_1
value: 29.051
- type: ndcg_at_10
value: 39.007
- type: ndcg_at_100
value: 45.321
- type: ndcg_at_1000
value: 47.665
- type: ndcg_at_3
value: 34.1
- type: ndcg_at_5
value: 36.437000000000005
- type: precision_at_1
value: 29.051
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 16.14
- type: precision_at_5
value: 11.897
- type: recall_at_1
value: 24.296
- type: recall_at_10
value: 49.85
- type: recall_at_100
value: 78.457
- type: recall_at_1000
value: 92.618
- type: recall_at_3
value: 36.138999999999996
- type: recall_at_5
value: 42.223
- type: map_at_1
value: 20.591
- type: map_at_10
value: 28.902
- type: map_at_100
value: 29.886000000000003
- type: map_at_1000
value: 29.987000000000002
- type: map_at_3
value: 26.740000000000002
- type: map_at_5
value: 27.976
- type: mrr_at_1
value: 22.366
- type: mrr_at_10
value: 30.971
- type: mrr_at_100
value: 31.865
- type: mrr_at_1000
value: 31.930999999999997
- type: mrr_at_3
value: 28.927999999999997
- type: mrr_at_5
value: 30.231
- type: ndcg_at_1
value: 22.366
- type: ndcg_at_10
value: 33.641
- type: ndcg_at_100
value: 38.477
- type: ndcg_at_1000
value: 41.088
- type: ndcg_at_3
value: 29.486
- type: ndcg_at_5
value: 31.612000000000002
- type: precision_at_1
value: 22.366
- type: precision_at_10
value: 5.3420000000000005
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 12.939
- type: precision_at_5
value: 9.094
- type: recall_at_1
value: 20.591
- type: recall_at_10
value: 46.052
- type: recall_at_100
value: 68.193
- type: recall_at_1000
value: 87.638
- type: recall_at_3
value: 34.966
- type: recall_at_5
value: 40.082
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.091
- type: map_at_10
value: 26.38
- type: map_at_100
value: 28.421999999999997
- type: map_at_1000
value: 28.621999999999996
- type: map_at_3
value: 21.597
- type: map_at_5
value: 24.12
- type: mrr_at_1
value: 34.266999999999996
- type: mrr_at_10
value: 46.864
- type: mrr_at_100
value: 47.617
- type: mrr_at_1000
value: 47.644
- type: mrr_at_3
value: 43.312
- type: mrr_at_5
value: 45.501000000000005
- type: ndcg_at_1
value: 34.266999999999996
- type: ndcg_at_10
value: 36.095
- type: ndcg_at_100
value: 43.447
- type: ndcg_at_1000
value: 46.661
- type: ndcg_at_3
value: 29.337999999999997
- type: ndcg_at_5
value: 31.824
- type: precision_at_1
value: 34.266999999999996
- type: precision_at_10
value: 11.472
- type: precision_at_100
value: 1.944
- type: precision_at_1000
value: 0.255
- type: precision_at_3
value: 21.933
- type: precision_at_5
value: 17.224999999999998
- type: recall_at_1
value: 15.091
- type: recall_at_10
value: 43.022
- type: recall_at_100
value: 68.075
- type: recall_at_1000
value: 85.76
- type: recall_at_3
value: 26.564
- type: recall_at_5
value: 33.594
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.252
- type: map_at_10
value: 20.923
- type: map_at_100
value: 30.741000000000003
- type: map_at_1000
value: 32.542
- type: map_at_3
value: 14.442
- type: map_at_5
value: 17.399
- type: mrr_at_1
value: 70.25
- type: mrr_at_10
value: 78.17
- type: mrr_at_100
value: 78.444
- type: mrr_at_1000
value: 78.45100000000001
- type: mrr_at_3
value: 76.958
- type: mrr_at_5
value: 77.571
- type: ndcg_at_1
value: 58.375
- type: ndcg_at_10
value: 44.509
- type: ndcg_at_100
value: 49.897999999999996
- type: ndcg_at_1000
value: 57.269999999999996
- type: ndcg_at_3
value: 48.64
- type: ndcg_at_5
value: 46.697
- type: precision_at_1
value: 70.25
- type: precision_at_10
value: 36.05
- type: precision_at_100
value: 11.848
- type: precision_at_1000
value: 2.213
- type: precision_at_3
value: 52.917
- type: precision_at_5
value: 45.7
- type: recall_at_1
value: 9.252
- type: recall_at_10
value: 27.006999999999998
- type: recall_at_100
value: 57.008
- type: recall_at_1000
value: 80.697
- type: recall_at_3
value: 15.798000000000002
- type: recall_at_5
value: 20.4
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 50.88
- type: f1
value: 45.545495028653384
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 75.424
- type: map_at_10
value: 83.435
- type: map_at_100
value: 83.66900000000001
- type: map_at_1000
value: 83.685
- type: map_at_3
value: 82.39800000000001
- type: map_at_5
value: 83.07
- type: mrr_at_1
value: 81.113
- type: mrr_at_10
value: 87.77199999999999
- type: mrr_at_100
value: 87.862
- type: mrr_at_1000
value: 87.86500000000001
- type: mrr_at_3
value: 87.17099999999999
- type: mrr_at_5
value: 87.616
- type: ndcg_at_1
value: 81.113
- type: ndcg_at_10
value: 86.909
- type: ndcg_at_100
value: 87.746
- type: ndcg_at_1000
value: 88.017
- type: ndcg_at_3
value: 85.368
- type: ndcg_at_5
value: 86.28099999999999
- type: precision_at_1
value: 81.113
- type: precision_at_10
value: 10.363
- type: precision_at_100
value: 1.102
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 32.507999999999996
- type: precision_at_5
value: 20.138
- type: recall_at_1
value: 75.424
- type: recall_at_10
value: 93.258
- type: recall_at_100
value: 96.545
- type: recall_at_1000
value: 98.284
- type: recall_at_3
value: 89.083
- type: recall_at_5
value: 91.445
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.532
- type: map_at_10
value: 37.141999999999996
- type: map_at_100
value: 39.162
- type: map_at_1000
value: 39.322
- type: map_at_3
value: 32.885
- type: map_at_5
value: 35.093999999999994
- type: mrr_at_1
value: 44.29
- type: mrr_at_10
value: 53.516
- type: mrr_at_100
value: 54.24
- type: mrr_at_1000
value: 54.273
- type: mrr_at_3
value: 51.286
- type: mrr_at_5
value: 52.413
- type: ndcg_at_1
value: 44.29
- type: ndcg_at_10
value: 45.268
- type: ndcg_at_100
value: 52.125
- type: ndcg_at_1000
value: 54.778000000000006
- type: ndcg_at_3
value: 41.829
- type: ndcg_at_5
value: 42.525
- type: precision_at_1
value: 44.29
- type: precision_at_10
value: 12.5
- type: precision_at_100
value: 1.9720000000000002
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 28.035
- type: precision_at_5
value: 20.093
- type: recall_at_1
value: 22.532
- type: recall_at_10
value: 52.419000000000004
- type: recall_at_100
value: 77.43299999999999
- type: recall_at_1000
value: 93.379
- type: recall_at_3
value: 38.629000000000005
- type: recall_at_5
value: 43.858000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.359
- type: map_at_10
value: 63.966
- type: map_at_100
value: 64.87
- type: map_at_1000
value: 64.92599999999999
- type: map_at_3
value: 60.409
- type: map_at_5
value: 62.627
- type: mrr_at_1
value: 78.717
- type: mrr_at_10
value: 84.468
- type: mrr_at_100
value: 84.655
- type: mrr_at_1000
value: 84.661
- type: mrr_at_3
value: 83.554
- type: mrr_at_5
value: 84.133
- type: ndcg_at_1
value: 78.717
- type: ndcg_at_10
value: 72.03399999999999
- type: ndcg_at_100
value: 75.158
- type: ndcg_at_1000
value: 76.197
- type: ndcg_at_3
value: 67.049
- type: ndcg_at_5
value: 69.808
- type: precision_at_1
value: 78.717
- type: precision_at_10
value: 15.201
- type: precision_at_100
value: 1.764
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 43.313
- type: precision_at_5
value: 28.165000000000003
- type: recall_at_1
value: 39.359
- type: recall_at_10
value: 76.003
- type: recall_at_100
value: 88.197
- type: recall_at_1000
value: 95.003
- type: recall_at_3
value: 64.97
- type: recall_at_5
value: 70.41199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.83200000000001
- type: ap
value: 89.33560571859861
- type: f1
value: 92.82322915005167
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.983
- type: map_at_10
value: 34.259
- type: map_at_100
value: 35.432
- type: map_at_1000
value: 35.482
- type: map_at_3
value: 30.275999999999996
- type: map_at_5
value: 32.566
- type: mrr_at_1
value: 22.579
- type: mrr_at_10
value: 34.882999999999996
- type: mrr_at_100
value: 35.984
- type: mrr_at_1000
value: 36.028
- type: mrr_at_3
value: 30.964999999999996
- type: mrr_at_5
value: 33.245000000000005
- type: ndcg_at_1
value: 22.564
- type: ndcg_at_10
value: 41.258
- type: ndcg_at_100
value: 46.824
- type: ndcg_at_1000
value: 48.037
- type: ndcg_at_3
value: 33.17
- type: ndcg_at_5
value: 37.263000000000005
- type: precision_at_1
value: 22.564
- type: precision_at_10
value: 6.572
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.130999999999998
- type: precision_at_5
value: 10.544
- type: recall_at_1
value: 21.983
- type: recall_at_10
value: 62.775000000000006
- type: recall_at_100
value: 88.389
- type: recall_at_1000
value: 97.603
- type: recall_at_3
value: 40.878
- type: recall_at_5
value: 50.690000000000005
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.95120839033288
- type: f1
value: 93.73824125055208
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.78978568171455
- type: f1
value: 57.50180552858304
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.24411566913248
- type: f1
value: 74.37851403532832
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.94620040349699
- type: f1
value: 80.21293397970435
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.44403096245675
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.659594631336812
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.53833075108798
- type: mrr
value: 33.78840823218308
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.185999999999999
- type: map_at_10
value: 15.193999999999999
- type: map_at_100
value: 19.538
- type: map_at_1000
value: 21.178
- type: map_at_3
value: 11.208
- type: map_at_5
value: 12.745999999999999
- type: mrr_at_1
value: 48.916
- type: mrr_at_10
value: 58.141
- type: mrr_at_100
value: 58.656
- type: mrr_at_1000
value: 58.684999999999995
- type: mrr_at_3
value: 55.521
- type: mrr_at_5
value: 57.239
- type: ndcg_at_1
value: 47.059
- type: ndcg_at_10
value: 38.644
- type: ndcg_at_100
value: 36.272999999999996
- type: ndcg_at_1000
value: 44.996
- type: ndcg_at_3
value: 43.293
- type: ndcg_at_5
value: 40.819
- type: precision_at_1
value: 48.916
- type: precision_at_10
value: 28.607
- type: precision_at_100
value: 9.195
- type: precision_at_1000
value: 2.225
- type: precision_at_3
value: 40.454
- type: precision_at_5
value: 34.985
- type: recall_at_1
value: 7.185999999999999
- type: recall_at_10
value: 19.654
- type: recall_at_100
value: 37.224000000000004
- type: recall_at_1000
value: 68.663
- type: recall_at_3
value: 12.158
- type: recall_at_5
value: 14.674999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.552000000000003
- type: map_at_10
value: 47.75
- type: map_at_100
value: 48.728
- type: map_at_1000
value: 48.754
- type: map_at_3
value: 43.156
- type: map_at_5
value: 45.883
- type: mrr_at_1
value: 35.66
- type: mrr_at_10
value: 50.269
- type: mrr_at_100
value: 50.974
- type: mrr_at_1000
value: 50.991
- type: mrr_at_3
value: 46.519
- type: mrr_at_5
value: 48.764
- type: ndcg_at_1
value: 35.632000000000005
- type: ndcg_at_10
value: 55.786
- type: ndcg_at_100
value: 59.748999999999995
- type: ndcg_at_1000
value: 60.339
- type: ndcg_at_3
value: 47.292
- type: ndcg_at_5
value: 51.766999999999996
- type: precision_at_1
value: 35.632000000000005
- type: precision_at_10
value: 9.267
- type: precision_at_100
value: 1.149
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 21.601
- type: precision_at_5
value: 15.539
- type: recall_at_1
value: 31.552000000000003
- type: recall_at_10
value: 77.62400000000001
- type: recall_at_100
value: 94.527
- type: recall_at_1000
value: 98.919
- type: recall_at_3
value: 55.898
- type: recall_at_5
value: 66.121
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.414
- type: map_at_10
value: 85.37400000000001
- type: map_at_100
value: 86.01100000000001
- type: map_at_1000
value: 86.027
- type: map_at_3
value: 82.562
- type: map_at_5
value: 84.284
- type: mrr_at_1
value: 82.24000000000001
- type: mrr_at_10
value: 88.225
- type: mrr_at_100
value: 88.324
- type: mrr_at_1000
value: 88.325
- type: mrr_at_3
value: 87.348
- type: mrr_at_5
value: 87.938
- type: ndcg_at_1
value: 82.24000000000001
- type: ndcg_at_10
value: 88.97699999999999
- type: ndcg_at_100
value: 90.16
- type: ndcg_at_1000
value: 90.236
- type: ndcg_at_3
value: 86.371
- type: ndcg_at_5
value: 87.746
- type: precision_at_1
value: 82.24000000000001
- type: precision_at_10
value: 13.481000000000002
- type: precision_at_100
value: 1.534
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.86
- type: precision_at_5
value: 24.738
- type: recall_at_1
value: 71.414
- type: recall_at_10
value: 95.735
- type: recall_at_100
value: 99.696
- type: recall_at_1000
value: 99.979
- type: recall_at_3
value: 88.105
- type: recall_at_5
value: 92.17999999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 60.22146692057259
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 65.29273320614578
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.023
- type: map_at_10
value: 14.161000000000001
- type: map_at_100
value: 16.68
- type: map_at_1000
value: 17.072000000000003
- type: map_at_3
value: 9.763
- type: map_at_5
value: 11.977
- type: mrr_at_1
value: 24.8
- type: mrr_at_10
value: 37.602999999999994
- type: mrr_at_100
value: 38.618
- type: mrr_at_1000
value: 38.659
- type: mrr_at_3
value: 34.117
- type: mrr_at_5
value: 36.082
- type: ndcg_at_1
value: 24.8
- type: ndcg_at_10
value: 23.316
- type: ndcg_at_100
value: 32.613
- type: ndcg_at_1000
value: 38.609
- type: ndcg_at_3
value: 21.697
- type: ndcg_at_5
value: 19.241
- type: precision_at_1
value: 24.8
- type: precision_at_10
value: 12.36
- type: precision_at_100
value: 2.593
- type: precision_at_1000
value: 0.402
- type: precision_at_3
value: 20.767
- type: precision_at_5
value: 17.34
- type: recall_at_1
value: 5.023
- type: recall_at_10
value: 25.069999999999997
- type: recall_at_100
value: 52.563
- type: recall_at_1000
value: 81.525
- type: recall_at_3
value: 12.613
- type: recall_at_5
value: 17.583
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 87.71506247604255
- type: cos_sim_spearman
value: 82.91813463738802
- type: euclidean_pearson
value: 85.5154616194479
- type: euclidean_spearman
value: 82.91815254466314
- type: manhattan_pearson
value: 85.5280917850374
- type: manhattan_spearman
value: 82.92276537286398
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.43772054228462
- type: cos_sim_spearman
value: 78.75750601716682
- type: euclidean_pearson
value: 85.76074482955764
- type: euclidean_spearman
value: 78.75651057223058
- type: manhattan_pearson
value: 85.73390291701668
- type: manhattan_spearman
value: 78.72699385957797
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 89.58144067172472
- type: cos_sim_spearman
value: 90.3524512966946
- type: euclidean_pearson
value: 89.71365391594237
- type: euclidean_spearman
value: 90.35239632843408
- type: manhattan_pearson
value: 89.66905421746478
- type: manhattan_spearman
value: 90.31508211683513
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 87.77692637102102
- type: cos_sim_spearman
value: 85.45710562643485
- type: euclidean_pearson
value: 87.42456979928723
- type: euclidean_spearman
value: 85.45709386240908
- type: manhattan_pearson
value: 87.40754529526272
- type: manhattan_spearman
value: 85.44834854173303
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.28491331695997
- type: cos_sim_spearman
value: 89.62037029566964
- type: euclidean_pearson
value: 89.02479391362826
- type: euclidean_spearman
value: 89.62036733618466
- type: manhattan_pearson
value: 89.00394756040342
- type: manhattan_spearman
value: 89.60867744215236
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.08911381280191
- type: cos_sim_spearman
value: 86.5791780765767
- type: euclidean_pearson
value: 86.16063473577861
- type: euclidean_spearman
value: 86.57917745378766
- type: manhattan_pearson
value: 86.13677924604175
- type: manhattan_spearman
value: 86.56115615768685
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.58029496205235
- type: cos_sim_spearman
value: 89.49551253826998
- type: euclidean_pearson
value: 90.13714840963748
- type: euclidean_spearman
value: 89.49551253826998
- type: manhattan_pearson
value: 90.13039633601363
- type: manhattan_spearman
value: 89.4513453745516
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.01546399666435
- type: cos_sim_spearman
value: 69.33824484595624
- type: euclidean_pearson
value: 70.76511642998874
- type: euclidean_spearman
value: 69.33824484595624
- type: manhattan_pearson
value: 70.84320785047453
- type: manhattan_spearman
value: 69.54233632223537
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.26389196390119
- type: cos_sim_spearman
value: 89.09721478341385
- type: euclidean_pearson
value: 88.97208685922517
- type: euclidean_spearman
value: 89.09720927308881
- type: manhattan_pearson
value: 88.97513670502573
- type: manhattan_spearman
value: 89.07647853984004
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.53075025771936
- type: mrr
value: 96.24327651288436
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.428000000000004
- type: map_at_10
value: 70.088
- type: map_at_100
value: 70.589
- type: map_at_1000
value: 70.614
- type: map_at_3
value: 67.191
- type: map_at_5
value: 68.515
- type: mrr_at_1
value: 63.333
- type: mrr_at_10
value: 71.13000000000001
- type: mrr_at_100
value: 71.545
- type: mrr_at_1000
value: 71.569
- type: mrr_at_3
value: 68.944
- type: mrr_at_5
value: 70.078
- type: ndcg_at_1
value: 63.333
- type: ndcg_at_10
value: 74.72800000000001
- type: ndcg_at_100
value: 76.64999999999999
- type: ndcg_at_1000
value: 77.176
- type: ndcg_at_3
value: 69.659
- type: ndcg_at_5
value: 71.626
- type: precision_at_1
value: 63.333
- type: precision_at_10
value: 10
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 60.428000000000004
- type: recall_at_10
value: 87.98899999999999
- type: recall_at_100
value: 96.167
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 74.006
- type: recall_at_5
value: 79.05
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87326732673267
- type: cos_sim_ap
value: 96.81770773701805
- type: cos_sim_f1
value: 93.6318407960199
- type: cos_sim_precision
value: 93.16831683168317
- type: cos_sim_recall
value: 94.1
- type: dot_accuracy
value: 99.87326732673267
- type: dot_ap
value: 96.8174218946665
- type: dot_f1
value: 93.6318407960199
- type: dot_precision
value: 93.16831683168317
- type: dot_recall
value: 94.1
- type: euclidean_accuracy
value: 99.87326732673267
- type: euclidean_ap
value: 96.81770773701807
- type: euclidean_f1
value: 93.6318407960199
- type: euclidean_precision
value: 93.16831683168317
- type: euclidean_recall
value: 94.1
- type: manhattan_accuracy
value: 99.87227722772278
- type: manhattan_ap
value: 96.83164126821747
- type: manhattan_f1
value: 93.54677338669335
- type: manhattan_precision
value: 93.5935935935936
- type: manhattan_recall
value: 93.5
- type: max_accuracy
value: 99.87326732673267
- type: max_ap
value: 96.83164126821747
- type: max_f1
value: 93.6318407960199
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.6212042420246
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.779230635982564
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.217701909036286
- type: mrr
value: 56.17658995416349
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.954206018888453
- type: cos_sim_spearman
value: 32.71062599450096
- type: dot_pearson
value: 30.95420929056943
- type: dot_spearman
value: 32.71062599450096
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22699999999999998
- type: map_at_10
value: 1.924
- type: map_at_100
value: 10.525
- type: map_at_1000
value: 24.973
- type: map_at_3
value: 0.638
- type: map_at_5
value: 1.0659999999999998
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 91.067
- type: mrr_at_100
value: 91.067
- type: mrr_at_1000
value: 91.067
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 91.067
- type: ndcg_at_1
value: 81
- type: ndcg_at_10
value: 75.566
- type: ndcg_at_100
value: 56.387
- type: ndcg_at_1000
value: 49.834
- type: ndcg_at_3
value: 80.899
- type: ndcg_at_5
value: 80.75099999999999
- type: precision_at_1
value: 84
- type: precision_at_10
value: 79
- type: precision_at_100
value: 57.56
- type: precision_at_1000
value: 21.8
- type: precision_at_3
value: 84.667
- type: precision_at_5
value: 85.2
- type: recall_at_1
value: 0.22699999999999998
- type: recall_at_10
value: 2.136
- type: recall_at_100
value: 13.861
- type: recall_at_1000
value: 46.299
- type: recall_at_3
value: 0.6649999999999999
- type: recall_at_5
value: 1.145
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.752
- type: map_at_10
value: 9.951
- type: map_at_100
value: 16.794999999999998
- type: map_at_1000
value: 18.251
- type: map_at_3
value: 5.288
- type: map_at_5
value: 6.954000000000001
- type: mrr_at_1
value: 38.775999999999996
- type: mrr_at_10
value: 50.458000000000006
- type: mrr_at_100
value: 51.324999999999996
- type: mrr_at_1000
value: 51.339999999999996
- type: mrr_at_3
value: 46.939
- type: mrr_at_5
value: 47.857
- type: ndcg_at_1
value: 36.735
- type: ndcg_at_10
value: 25.198999999999998
- type: ndcg_at_100
value: 37.938
- type: ndcg_at_1000
value: 49.145
- type: ndcg_at_3
value: 29.348000000000003
- type: ndcg_at_5
value: 25.804
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_10
value: 22.041
- type: precision_at_100
value: 7.939
- type: precision_at_1000
value: 1.555
- type: precision_at_3
value: 29.932
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.752
- type: recall_at_10
value: 16.197
- type: recall_at_100
value: 49.166
- type: recall_at_1000
value: 84.18900000000001
- type: recall_at_3
value: 6.438000000000001
- type: recall_at_5
value: 9.093
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.47980000000001
- type: ap
value: 14.605194452178754
- type: f1
value: 55.07362924988948
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.708545557441994
- type: f1
value: 60.04751270975683
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.21105960597211
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.58419264469214
- type: cos_sim_ap
value: 78.55300004517404
- type: cos_sim_f1
value: 71.49673530889001
- type: cos_sim_precision
value: 68.20795400095831
- type: cos_sim_recall
value: 75.11873350923483
- type: dot_accuracy
value: 87.58419264469214
- type: dot_ap
value: 78.55297659559511
- type: dot_f1
value: 71.49673530889001
- type: dot_precision
value: 68.20795400095831
- type: dot_recall
value: 75.11873350923483
- type: euclidean_accuracy
value: 87.58419264469214
- type: euclidean_ap
value: 78.55300477331477
- type: euclidean_f1
value: 71.49673530889001
- type: euclidean_precision
value: 68.20795400095831
- type: euclidean_recall
value: 75.11873350923483
- type: manhattan_accuracy
value: 87.5663110210407
- type: manhattan_ap
value: 78.49982050876562
- type: manhattan_f1
value: 71.35488740722104
- type: manhattan_precision
value: 68.18946862226497
- type: manhattan_recall
value: 74.82849604221636
- type: max_accuracy
value: 87.58419264469214
- type: max_ap
value: 78.55300477331477
- type: max_f1
value: 71.49673530889001
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.09069740365584
- type: cos_sim_ap
value: 86.22749303724757
- type: cos_sim_f1
value: 78.36863452005407
- type: cos_sim_precision
value: 76.49560117302053
- type: cos_sim_recall
value: 80.33569448721897
- type: dot_accuracy
value: 89.09069740365584
- type: dot_ap
value: 86.22750233655673
- type: dot_f1
value: 78.36863452005407
- type: dot_precision
value: 76.49560117302053
- type: dot_recall
value: 80.33569448721897
- type: euclidean_accuracy
value: 89.09069740365584
- type: euclidean_ap
value: 86.22749355597347
- type: euclidean_f1
value: 78.36863452005407
- type: euclidean_precision
value: 76.49560117302053
- type: euclidean_recall
value: 80.33569448721897
- type: manhattan_accuracy
value: 89.08293553770326
- type: manhattan_ap
value: 86.21913616084771
- type: manhattan_f1
value: 78.3907031479847
- type: manhattan_precision
value: 75.0352013517319
- type: manhattan_recall
value: 82.06036341238065
- type: max_accuracy
value: 89.09069740365584
- type: max_ap
value: 86.22750233655673
- type: max_f1
value: 78.3907031479847
---
<br><br>
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</p>
<p align="center">
<b>The crispy sentence embedding family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b>
</p>
<p align="center">
<sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup>
</p>
# mixedbread-ai/mxbai-embed-large-v1
Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages:` for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl).
## Quickstart
Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt `Represent this sentence for searching relevant passages: ` for query if you want to use it for retrieval. Besides that you don't need any prompt.
### sentence-transformers
```
python -m pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
from sentence_transformers.quantization import quantize_embeddings
# 1. Specify preffered dimensions
dimensions = 512
# 2. load model
model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1", truncate_dim=dimensions)
# The prompt used for query retrieval tasks:
# query_prompt = 'Represent this sentence for searching relevant passages: '
query = "A man is eating a piece of bread"
docs = [
"A man is eating food.",
"A man is eating pasta.",
"The girl is carrying a baby.",
"A man is riding a horse.",
]
# 2. Encode
query_embedding = model.encode(query, prompt_name="query")
# Equivalent Alternatives:
# query_embedding = model.encode(query_prompt + query)
# query_embedding = model.encode(query, prompt=query_prompt)
docs_embeddings = model.encode(docs)
# Optional: Quantize the embeddings
binary_query_embedding = quantize_embeddings(query_embedding, precision="ubinary")
binary_docs_embeddings = quantize_embeddings(docs_embeddings, precision="ubinary")
similarities = cos_sim(query_embedding, docs_embeddings)
print('similarities:', similarities)
```
### Transformers
```python
from typing import Dict
import torch
import numpy as np
from transformers import AutoModel, AutoTokenizer
from sentence_transformers.util import cos_sim
# For retrieval you need to pass this prompt. Please find our more in our blog post.
def transform_query(query: str) -> str:
""" For retrieval, add the prompt for query (not for documents).
"""
return f'Represent this sentence for searching relevant passages: {query}'
# The model works really well with cls pooling (default) but also with mean pooling.
def pooling(outputs: torch.Tensor, inputs: Dict, strategy: str = 'cls') -> np.ndarray:
if strategy == 'cls':
outputs = outputs[:, 0]
elif strategy == 'mean':
outputs = torch.sum(
outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"], dim=1, keepdim=True)
else:
raise NotImplementedError
return outputs.detach().cpu().numpy()
# 1. load model
model_id = 'mixedbread-ai/mxbai-embed-large-v1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id).cuda()
docs = [
transform_query('A man is eating a piece of bread'),
"A man is eating food.",
"A man is eating pasta.",
"The girl is carrying a baby.",
"A man is riding a horse.",
]
# 2. encode
inputs = tokenizer(docs, padding=True, return_tensors='pt')
for k, v in inputs.items():
inputs[k] = v.cuda()
outputs = model(**inputs).last_hidden_state
embeddings = pooling(outputs, inputs, 'cls')
similarities = cos_sim(embeddings[0], embeddings[1:])
print('similarities:', similarities)
```
### Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```
npm i @xenova/transformers
```
You can then use the model to compute embeddings like this:
```javascript
import { pipeline, cos_sim } from '@xenova/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'mixedbread-ai/mxbai-embed-large-v1', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const docs = [
'Represent this sentence for searching relevant passages: A man is eating a piece of bread',
'A man is eating food.',
'A man is eating pasta.',
'The girl is carrying a baby.',
'A man is riding a horse.',
]
const output = await extractor(docs, { pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => cos_sim(source_embeddings, x));
console.log(similarities); // [0.7919578577247139, 0.6369278664248345, 0.16512018371357193, 0.3620778366720027]
```
### Using API
You can use the model via our API as follows:
```python
from mixedbread_ai.client import MixedbreadAI, EncodingFormat
from sklearn.metrics.pairwise import cosine_similarity
import os
mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}")
english_sentences = [
'What is the capital of Australia?',
'Canberra is the capital of Australia.'
]
res = mxbai.embeddings(
input=english_sentences,
model="mixedbread-ai/mxbai-embed-large-v1",
normalized=True,
encoding_format=[EncodingFormat.FLOAT, EncodingFormat.UBINARY, EncodingFormat.INT_8],
dimensions=512
)
encoded_embeddings = res.data[0].embedding
print(res.dimensions, encoded_embeddings.ubinary, encoded_embeddings.float_, encoded_embeddings.int_8)
```
The API comes with native int8 and binary quantization support! Check out the [docs](https://mixedbread.ai/docs) for more information.
### Infinity
```bash
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id mixedbread-ai/mxbai-embed-large-v1 --revision "main" --dtype float16 --engine torch --port 7997
```
## Evaluation
As of March 2024, our model archives SOTA performance for Bert-large sized models on the [MTEB](https://huggingface.co/spaces/mteb/leaderboard). It ourperforms commercial models like OpenAIs text-embedding-3-large and matches the performance of model 20x it's size like the [echo-mistral-7b](https://huggingface.co/jspringer/echo-mistral-7b-instruct-lasttoken). Our model was trained with no overlap of the MTEB data, which indicates that our model generalizes well across several domains, tasks and text length. We know there are some limitations with this model, which will be fixed in v2.
| Model | Avg (56 datasets) | Classification (12 datasets) | Clustering (11 datasets) | PairClassification (3 datasets) | Reranking (4 datasets) | Retrieval (15 datasets) | STS (10 datasets) | Summarization (1 dataset) |
| --------------------------------------------------------------------------------------------- | ----------------- | ---------------------------- | ------------------------ | ------------------------------- | ---------------------- | ----------------------- | ----------------- | ------------------------- |
| **mxbai-embed-large-v1** | **64.68** | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85.00 | 32.71 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [mxbai-embed-2d-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-2d-large-v1) | 63.25 | 74.14 | 46.07 | 85.89 | 58.94 | 51.42 | 84.9 | 31.55 |
| [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) | 62.39 | 74.12 | 43.91 | 85.15 | 55.69 | 52.81 | 82.06 | 30.08 |
| [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) | 60.38 | 73.45 | 41.73 | 85.38 | 56.98 | 47.87 | 80.7 | 31.6 |
| *Proprietary Models* | | | | | | | | |
| [OpenAI text-embedding-3-large](https://openai.com/blog/new-embedding-models-and-api-updates) | 64.58 | 75.45 | 49.01 | 85.72 | 59.16 | 55.44 | 81.73 | 29.92 |
| [Cohere embed-english-v3.0](https://txt.cohere.com/introducing-embed-v3/) | 64.47 | 76.49 | 47.43 | 85.84 | 58.01 | 55.00 | 82.62 | 30.18 |
| [OpenAI text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) | 60.99 | 70.93 | 45.90 | 84.89 | 56.32 | 49.25 | 80.97 | 30.80 |
Please find more information in our [blog post](https://mixedbread.ai/blog/mxbai-embed-large-v1).
## Matryoshka and Binary Quantization
Embeddings in their commonly used form (float arrays) have a high memory footprint when used at scale. Two approaches to solve this problem are Matryoshka Representation Learning (MRL) and (Binary) Quantization. While MRL reduces the number of dimensions of an embedding, binary quantization transforms the value of each dimension from a float32 into a lower precision (int8 or even binary). <b> The model supports both approaches! </b>
You can also take it one step further, and combine both MRL and quantization. This combination of binary quantization and MRL allows you to reduce the memory usage of your embeddings significantly. This leads to much lower costs when using a vector database in particular. You can read more about the technology and its advantages in our [blog post](https://www.mixedbread.ai/blog/binary-mrl).
## Community
Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat.
## License
Apache 2.0
## Citation
```bibtex
@online{emb2024mxbai,
title={Open Source Strikes Bread - New Fluffy Embeddings Model},
author={Sean Lee and Aamir Shakir and Darius Koenig and Julius Lipp},
year={2024},
url={https://www.mixedbread.ai/blog/mxbai-embed-large-v1},
}
@article{li2023angle,
title={AnglE-optimized Text Embeddings},
author={Li, Xianming and Li, Jing},
journal={arXiv preprint arXiv:2309.12871},
year={2023}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/multilingual-e5-small | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-30T07:31:03 | 2025-02-17T03:22:45 | 2,371,021 | 183 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: intfloat/multilingual-e5-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.22266660528253
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.79980849482483
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.747820820329736
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.045143830596146
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
value: 88.124
- type: recall_at_1000
value: 97.08
- type: recall_at_3
value: 41.099999999999994
- type: recall_at_5
value: 50.381
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.06703146374831
- type: f1
value: 90.86867815863172
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.46970977740209
- type: f1
value: 86.36832872036588
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.26951300867245
- type: f1
value: 88.93561193959502
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 84.22799874725963
- type: f1
value: 84.30490069236556
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.02007888131948
- type: f1
value: 85.39376041027991
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.34900542495481
- type: f1
value: 85.39859673336713
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.078431372549
- type: f1
value: 53.45071102002276
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
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value: 74.4754539340955
- type: f1
value: 74.38427146553252
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (es)
type: mteb/amazon_massive_scenario
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.82515131136518
- type: f1
value: 69.63516462173847
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fa)
type: mteb/amazon_massive_scenario
config: fa
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.70880968392737
- type: f1
value: 67.45420662567926
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fi)
type: mteb/amazon_massive_scenario
config: fi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 65.95494283792871
- type: f1
value: 65.06191009049222
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.75924680564896
- type: f1
value: 68.30833379585945
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.806321452589096
- type: f1
value: 63.273048243765054
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.68997982515133
- type: f1
value: 66.54703855381324
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.46940147948891
- type: f1
value: 65.91017343463396
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.49899125756556
- type: f1
value: 57.90333469917769
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.9219905850706
- type: f1
value: 67.23169403762938
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.486213853396094
- type: f1
value: 54.85282355583758
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.04169468728985
- type: f1
value: 68.83833333320462
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.88702084734365
- type: f1
value: 74.04474735232299
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.63416274377943
- type: f1
value: 55.11332211687954
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.23604572965702
- type: f1
value: 50.86529813991055
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 46.62407531943511
- type: f1
value: 43.63485467164535
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.15601882985878
- type: f1
value: 57.522837510959924
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.84532616005382
- type: f1
value: 69.60021127179697
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.65770006724949
- type: f1
value: 55.84219135523227
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.53665097511768
- type: f1
value: 65.09087787792639
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.31405514458642
- type: f1
value: 58.06135303831491
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.88231338264964
- type: f1
value: 62.751099407787926
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.86012104909213
- type: f1
value: 56.29118323058282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.37390719569602
- type: f1
value: 66.27922244885102
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.8675184936113
- type: f1
value: 70.22146529932019
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.2212508406187
- type: f1
value: 67.77454802056282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.18090114324143
- type: f1
value: 68.03737625431621
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.65030262273034
- type: f1
value: 63.792945486912856
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.772749631087066
- type: f1
value: 63.4539101720024
- type: f1_weighted
value: 62.778603897469566
- type: main_score
value: 63.772749631087066
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.17821116341627
- type: f1
value: 59.3935969827171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.86146603900471
- type: f1
value: 60.133692735032376
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.89441829186282
- type: f1
value: 70.03064076194089
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.15063887020847
- type: f1
value: 56.23326278499678
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.43846671149966
- type: f1
value: 57.70440450281974
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8507061197041
- type: f1
value: 59.22916396061171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.65568258238063
- type: f1
value: 69.90736239440633
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8843308675185
- type: f1
value: 59.30332663713599
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.05312710154674
- type: f1
value: 67.44024062594775
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.111634162743776
- type: f1
value: 60.89083013084519
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.44115669132482
- type: f1
value: 67.92227541674552
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4687289845326
- type: f1
value: 74.16376793486025
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.31876260928043
- type: f1
value: 68.5246745215607
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.90431696479766
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.259158476693774
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.28445330838555
- type: mrr
value: 31.15758529581164
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.353
- type: map_at_10
value: 11.565
- type: map_at_100
value: 14.097000000000001
- type: map_at_1000
value: 15.354999999999999
- type: map_at_3
value: 8.749
- type: map_at_5
value: 9.974
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.589
- type: mrr_at_100
value: 51.187000000000005
- type: mrr_at_1000
value: 51.233
- type: mrr_at_3
value: 48.246
- type: mrr_at_5
value: 49.546
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 31.009999999999998
- type: ndcg_at_100
value: 28.026
- type: ndcg_at_1000
value: 36.905
- type: ndcg_at_3
value: 35.983
- type: ndcg_at_5
value: 33.764
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.786
- type: precision_at_100
value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
- type: recall_at_10
value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
- type: recall_at_3
value: 9.792
- type: recall_at_5
value: 11.882
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.852
- type: map_at_10
value: 48.924
- type: map_at_100
value: 49.854
- type: map_at_1000
value: 49.886
- type: map_at_3
value: 44.9
- type: map_at_5
value: 47.387
- type: mrr_at_1
value: 38.035999999999994
- type: mrr_at_10
value: 51.644
- type: mrr_at_100
value: 52.339
- type: mrr_at_1000
value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
- type: ndcg_at_1000
value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
- type: precision_at_1
value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 39.13064340585255
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 58.97884249325877
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.52464042600003
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.071631948736
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 62.156507936507936
- type: precision
value: 59.84964285714286
- type: recall
value: 68.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.52126366950182
- type: f1
value: 72.8496210148701
- type: precision
value: 70.92171498003819
- type: recall
value: 77.52126366950182
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.78260869565217
- type: f1
value: 65.32422360248447
- type: precision
value: 63.063067367415194
- type: recall
value: 70.78260869565217
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.43478260869566
- type: f1
value: 73.02608695652172
- type: precision
value: 70.63768115942028
- type: recall
value: 78.43478260869566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.9
- type: f1
value: 55.309753694581275
- type: precision
value: 53.130476190476195
- type: recall
value: 60.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.89999999999999
- type: f1
value: 67.92023809523809
- type: precision
value: 65.82595238095237
- type: recall
value: 72.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.80337756332931
- type: f1
value: 39.42174900558496
- type: precision
value: 36.97101116280851
- type: recall
value: 46.80337756332931
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
- type: recall
value: 89.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.199999999999996
- type: f1
value: 39.95484348984349
- type: precision
value: 37.561071428571424
- type: recall
value: 47.199999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.8
- type: f1
value: 84.68190476190475
- type: precision
value: 83.275
- type: recall
value: 87.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.76190476190476
- type: f1
value: 42.14965986394558
- type: precision
value: 39.96743626743626
- type: recall
value: 48.76190476190476
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.10000000000001
- type: f1
value: 59.58580086580086
- type: precision
value: 57.150238095238095
- type: recall
value: 66.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.3
- type: f1
value: 84.0
- type: precision
value: 82.48666666666666
- type: recall
value: 87.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 87.79523809523809
- type: precision
value: 86.6
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.0
- type: f1
value: 83.81
- type: precision
value: 82.36666666666666
- type: recall
value: 87.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.9
- type: f1
value: 57.76533189033189
- type: precision
value: 55.50595238095239
- type: recall
value: 63.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.1
- type: f1
value: 71.83690476190478
- type: precision
value: 70.04928571428573
- type: recall
value: 76.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.3
- type: f1
value: 59.32626984126984
- type: precision
value: 56.62535714285713
- type: recall
value: 66.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.76666666666667
- type: main_score
value: 89.76666666666667
- type: precision
value: 88.64999999999999
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.16666666666666
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.71428571428571
- type: f1
value: 82.29142600436403
- type: precision
value: 80.8076626877166
- type: recall
value: 85.71428571428571
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.88888888888889
- type: f1
value: 85.7834757834758
- type: precision
value: 84.43732193732193
- type: recall
value: 88.88888888888889
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 85.67190476190476
- type: precision
value: 84.43333333333332
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.72727272727273
- type: f1
value: 78.21969696969695
- type: precision
value: 76.18181818181819
- type: recall
value: 82.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.0062893081761
- type: f1
value: 55.13976240391334
- type: precision
value: 52.92112499659669
- type: recall
value: 61.0062893081761
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.86666666666666
- type: precision
value: 85.69166666666668
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.54085603112841
- type: f1
value: 68.56031128404669
- type: precision
value: 66.53047989623866
- type: recall
value: 73.54085603112841
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.58974358974359
- type: f1
value: 36.45299145299145
- type: precision
value: 33.81155881155882
- type: recall
value: 43.58974358974359
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.599999999999994
- type: f1
value: 53.264689754689755
- type: precision
value: 50.869166666666665
- type: recall
value: 59.599999999999994
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.2
- type: f1
value: 81.61666666666665
- type: precision
value: 80.02833333333335
- type: recall
value: 85.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.78504672897196
- type: f1
value: 58.00029669188548
- type: precision
value: 55.815809968847354
- type: recall
value: 63.78504672897196
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.5
- type: f1
value: 61.518333333333345
- type: precision
value: 59.622363699102834
- type: recall
value: 66.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 85.60222222222221
- type: precision
value: 84.27916666666665
- type: recall
value: 88.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.699999999999996
- type: f1
value: 52.732375957375965
- type: precision
value: 50.63214035964035
- type: recall
value: 58.699999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 89.99666666666667
- type: precision
value: 89.03333333333333
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.10000000000001
- type: f1
value: 87.55666666666667
- type: precision
value: 86.36166666666668
- type: recall
value: 90.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 88.89000000000001
- type: precision
value: 87.71166666666666
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.7
- type: f1
value: 60.67427750410509
- type: precision
value: 58.71785714285714
- type: recall
value: 65.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.39999999999999
- type: f1
value: 81.93190476190475
- type: precision
value: 80.37833333333333
- type: recall
value: 85.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.833333333333336
- type: f1
value: 42.006625781625786
- type: precision
value: 40.077380952380956
- type: recall
value: 47.833333333333336
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.4
- type: f1
value: 8.24465007215007
- type: precision
value: 7.664597069597071
- type: recall
value: 10.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.6
- type: f1
value: 77.76333333333334
- type: precision
value: 75.57833333333332
- type: recall
value: 82.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.67857142857143
- type: f1
value: 44.302721088435376
- type: precision
value: 41.49801587301587
- type: recall
value: 52.67857142857143
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.3205268935236
- type: f1
value: 22.426666605171157
- type: precision
value: 20.685900116470915
- type: recall
value: 28.3205268935236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22.7
- type: f1
value: 17.833970473970474
- type: precision
value: 16.407335164835164
- type: recall
value: 22.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 89.92999999999999
- type: precision
value: 88.87
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.25
- type: precision
value: 88.21666666666667
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.19999999999999
- type: f1
value: 63.38269841269841
- type: precision
value: 61.14773809523809
- type: recall
value: 69.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.8
- type: f1
value: 42.839915639915645
- type: precision
value: 40.770287114845935
- type: recall
value: 48.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.8
- type: f1
value: 85.90666666666668
- type: precision
value: 84.54166666666666
- type: recall
value: 88.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.6
- type: f1
value: 40.85892920804686
- type: precision
value: 38.838223114604695
- type: recall
value: 46.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.0
- type: f1
value: 80.14190476190475
- type: precision
value: 78.45333333333333
- type: recall
value: 84.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5
- type: f1
value: 87.78333333333333
- type: precision
value: 86.5
- type: recall
value: 90.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.5
- type: f1
value: 69.48397546897547
- type: precision
value: 67.51869047619049
- type: recall
value: 74.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 32.846715328467155
- type: f1
value: 27.828177499710343
- type: precision
value: 26.63451511991658
- type: recall
value: 32.846715328467155
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.0
- type: f1
value: 6.07664116764988
- type: precision
value: 5.544177607179943
- type: recall
value: 8.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.38555555555554
- type: precision
value: 82.91583333333334
- type: recall
value: 87.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 84.08333333333331
- type: precision
value: 82.47333333333333
- type: recall
value: 87.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.95238095238095
- type: f1
value: 76.13095238095238
- type: precision
value: 74.05753968253967
- type: recall
value: 80.95238095238095
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.799999999999999
- type: f1
value: 6.971422975172975
- type: precision
value: 6.557814916172301
- type: recall
value: 8.799999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hsb-eng)
type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.099378881987576
- type: f1
value: 37.01649742022413
- type: precision
value: 34.69420618488942
- type: recall
value: 44.099378881987576
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.3
- type: f1
value: 80.32666666666667
- type: precision
value: 78.60666666666665
- type: recall
value: 84.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.5
- type: f1
value: 90.49666666666666
- type: precision
value: 89.56666666666668
- type: recall
value: 92.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.0
- type: f1
value: 8.268423529875141
- type: precision
value: 7.878118605532398
- type: recall
value: 10.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.22077922077922
- type: f1
value: 74.27128427128426
- type: precision
value: 72.28715728715729
- type: recall
value: 79.22077922077922
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.64885496183206
- type: f1
value: 58.87495456197747
- type: precision
value: 55.992366412213734
- type: recall
value: 65.64885496183206
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.06986899563319
- type: f1
value: 94.78408539543909
- type: precision
value: 94.15332362930616
- type: recall
value: 96.06986899563319
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.2
- type: f1
value: 71.72571428571428
- type: precision
value: 69.41000000000001
- type: recall
value: 77.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.4406779661017
- type: f1
value: 83.2391713747646
- type: precision
value: 81.74199623352166
- type: recall
value: 86.4406779661017
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.4
- type: f1
value: 6.017828743398003
- type: precision
value: 5.4829865484756795
- type: recall
value: 8.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.5
- type: f1
value: 79.74833333333333
- type: precision
value: 78.04837662337664
- type: recall
value: 83.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.4
- type: f1
value: 54.467301587301584
- type: precision
value: 52.23242424242424
- type: recall
value: 60.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.9
- type: f1
value: 69.68699134199134
- type: precision
value: 67.59873015873016
- type: recall
value: 74.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.9652380952381
- type: precision
value: 83.66166666666666
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.1
- type: f1
value: 7.681244588744588
- type: precision
value: 7.370043290043291
- type: recall
value: 9.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.9651474530831
- type: f1
value: 76.84220605132133
- type: precision
value: 75.19606398962966
- type: recall
value: 80.9651474530831
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.705
- type: precision
value: 82.3120634920635
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 23.98763072676116
- type: precision
value: 22.506399397703746
- type: recall
value: 29.64426877470356
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.4225352112676
- type: f1
value: 62.84037558685445
- type: precision
value: 59.56572769953053
- type: recall
value: 70.4225352112676
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 19.64071856287425
- type: f1
value: 15.125271011207756
- type: precision
value: 13.865019261197494
- type: recall
value: 19.64071856287425
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.80666666666666
- type: precision
value: 86.70833333333331
- type: recall
value: 90.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 18.407224958949097
- type: precision
value: 16.982385430661292
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.98591549295775
- type: f1
value: 49.94718309859154
- type: precision
value: 47.77864154624717
- type: recall
value: 55.98591549295775
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.07692307692307
- type: f1
value: 66.74358974358974
- type: precision
value: 64.06837606837607
- type: recall
value: 73.07692307692307
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.25
- type: precision
value: 92.43333333333332
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.78705636743215
- type: f1
value: 31.63899658680452
- type: precision
value: 29.72264397629742
- type: recall
value: 37.78705636743215
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.6
- type: f1
value: 16.91697302697303
- type: precision
value: 15.71225147075147
- type: recall
value: 21.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.01628664495115
- type: f1
value: 81.38514037536838
- type: precision
value: 79.83170466883823
- type: recall
value: 85.01628664495115
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.39999999999999
- type: f1
value: 79.96380952380952
- type: precision
value: 78.48333333333333
- type: recall
value: 83.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
- type: precision
value: 77.58833333333334
- type: recall
value: 83.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.59055118110236
- type: f1
value: 71.66854143232096
- type: precision
value: 70.30183727034121
- type: recall
value: 75.59055118110236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.5
- type: f1
value: 59.26095238095238
- type: precision
value: 56.81909090909092
- type: recall
value: 65.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.26315789473685
- type: f1
value: 47.986523325858506
- type: precision
value: 45.33950006595436
- type: recall
value: 55.26315789473685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.89999999999999
- type: f1
value: 78.835
- type: precision
value: 77.04761904761905
- type: recall
value: 82.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
- type: precision
value: 33.57371794871795
- type: recall
value: 43.269230769230774
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
value: 14.133999999999999
- type: map_at_1000
value: 15.668000000000001
- type: map_at_3
value: 5.862
- type: map_at_5
value: 7.17
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 42.931000000000004
- type: mrr_at_100
value: 44.81
- type: mrr_at_1000
value: 44.81
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.701
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 21.163
- type: ndcg_at_100
value: 33.306000000000004
- type: ndcg_at_1000
value: 45.275999999999996
- type: ndcg_at_3
value: 25.685999999999996
- type: ndcg_at_5
value: 23.732
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_100
value: 6.938999999999999
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.896
- type: recall_at_10
value: 13.333999999999998
- type: recall_at_100
value: 43.517
- type: recall_at_1000
value: 79.836
- type: recall_at_3
value: 6.306000000000001
- type: recall_at_5
value: 8.825
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.3874
- type: ap
value: 13.829909072469423
- type: f1
value: 53.54534203543492
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 33.21527881409797
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.97943613280086
- type: cos_sim_ap
value: 70.75454316885921
- type: cos_sim_f1
value: 65.38274012676743
- type: cos_sim_precision
value: 60.761214318078835
- type: cos_sim_recall
value: 70.76517150395777
- type: dot_accuracy
value: 79.0546581629612
- type: dot_ap
value: 47.3197121792147
- type: dot_f1
value: 49.20106524633821
- type: dot_precision
value: 42.45499808502489
- type: dot_recall
value: 58.49604221635884
- type: euclidean_accuracy
value: 85.08076533349228
- type: euclidean_ap
value: 70.95016106374474
- type: euclidean_f1
value: 65.43987900176455
- type: euclidean_precision
value: 62.64478764478765
- type: euclidean_recall
value: 68.49604221635884
- type: manhattan_accuracy
value: 84.93771234428085
- type: manhattan_ap
value: 70.63668388755362
- type: manhattan_f1
value: 65.23895401262398
- type: manhattan_precision
value: 56.946084218811485
- type: manhattan_recall
value: 76.35883905013192
- type: max_accuracy
value: 85.08076533349228
- type: max_ap
value: 70.95016106374474
- type: max_f1
value: 65.43987900176455
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.69096130709822
- type: cos_sim_ap
value: 84.82526278228542
- type: cos_sim_f1
value: 77.65485060585536
- type: cos_sim_precision
value: 75.94582658619167
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 80.97954748321496
- type: dot_ap
value: 64.81642914145866
- type: dot_f1
value: 60.631996987229975
- type: dot_precision
value: 54.5897293631712
- type: dot_recall
value: 68.17831844779796
- type: euclidean_accuracy
value: 88.6987231730508
- type: euclidean_ap
value: 84.80003825477253
- type: euclidean_f1
value: 77.67194179854496
- type: euclidean_precision
value: 75.7128235122094
- type: euclidean_recall
value: 79.73514012935017
- type: manhattan_accuracy
value: 88.62692591298949
- type: manhattan_ap
value: 84.80451408255276
- type: manhattan_f1
value: 77.69888949572183
- type: manhattan_precision
value: 73.70311528631622
- type: manhattan_recall
value: 82.15275639051433
- type: max_accuracy
value: 88.6987231730508
- type: max_ap
value: 84.82526278228542
- type: max_f1
value: 77.69888949572183
- task:
type: BitextMining
dataset:
name: MTEB BUCC.v2 (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: 1739dc11ffe9b7bfccd7f3d585aeb4c544fc6677
metrics:
- type: accuracy
value: 95.72566678212678
- type: f1
value: 94.42443135896548
- type: main_score
value: 94.42443135896548
- type: precision
value: 93.80868260016165
- type: recall
value: 95.72566678212678
- task:
type: Retrieval
dataset:
name: MTEB BelebeleRetrieval (rus_Cyrl-rus_Cyrl)
type: facebook/belebele
config: rus_Cyrl-rus_Cyrl
split: test
revision: 75b399394a9803252cfec289d103de462763db7c
metrics:
- type: main_score
value: 92.23599999999999
- type: map_at_1
value: 87.111
- type: map_at_10
value: 90.717
- type: map_at_100
value: 90.879
- type: map_at_1000
value: 90.881
- type: map_at_20
value: 90.849
- type: map_at_3
value: 90.074
- type: map_at_5
value: 90.535
- type: mrr_at_1
value: 87.1111111111111
- type: mrr_at_10
value: 90.7173721340388
- type: mrr_at_100
value: 90.87859682638407
- type: mrr_at_1000
value: 90.88093553612326
- type: mrr_at_20
value: 90.84863516113515
- type: mrr_at_3
value: 90.07407407407409
- type: mrr_at_5
value: 90.53518518518521
- type: nauc_map_at_1000_diff1
value: 92.37373187280554
- type: nauc_map_at_1000_max
value: 79.90465445423249
- type: nauc_map_at_1000_std
value: -0.6220290556185463
- type: nauc_map_at_100_diff1
value: 92.37386697345335
- type: nauc_map_at_100_max
value: 79.90991577223959
- type: nauc_map_at_100_std
value: -0.602247514642845
- type: nauc_map_at_10_diff1
value: 92.30907447072467
- type: nauc_map_at_10_max
value: 79.86831935337598
- type: nauc_map_at_10_std
value: -0.7455191860719699
- type: nauc_map_at_1_diff1
value: 93.29828518358822
- type: nauc_map_at_1_max
value: 78.69539619887887
- type: nauc_map_at_1_std
value: -4.097150817605763
- type: nauc_map_at_20_diff1
value: 92.38414149703077
- type: nauc_map_at_20_max
value: 79.94789814504661
- type: nauc_map_at_20_std
value: -0.3928031130400773
- type: nauc_map_at_3_diff1
value: 92.21688899306734
- type: nauc_map_at_3_max
value: 80.34586671780885
- type: nauc_map_at_3_std
value: 0.24088319695435909
- type: nauc_map_at_5_diff1
value: 92.27931726042982
- type: nauc_map_at_5_max
value: 79.99198834003367
- type: nauc_map_at_5_std
value: -0.6296366922840796
- type: nauc_mrr_at_1000_diff1
value: 92.37373187280554
- type: nauc_mrr_at_1000_max
value: 79.90465445423249
- type: nauc_mrr_at_1000_std
value: -0.6220290556185463
- type: nauc_mrr_at_100_diff1
value: 92.37386697345335
- type: nauc_mrr_at_100_max
value: 79.90991577223959
- type: nauc_mrr_at_100_std
value: -0.602247514642845
- type: nauc_mrr_at_10_diff1
value: 92.30907447072467
- type: nauc_mrr_at_10_max
value: 79.86831935337598
- type: nauc_mrr_at_10_std
value: -0.7455191860719699
- type: nauc_mrr_at_1_diff1
value: 93.29828518358822
- type: nauc_mrr_at_1_max
value: 78.69539619887887
- type: nauc_mrr_at_1_std
value: -4.097150817605763
- type: nauc_mrr_at_20_diff1
value: 92.38414149703077
- type: nauc_mrr_at_20_max
value: 79.94789814504661
- type: nauc_mrr_at_20_std
value: -0.3928031130400773
- type: nauc_mrr_at_3_diff1
value: 92.21688899306734
- type: nauc_mrr_at_3_max
value: 80.34586671780885
- type: nauc_mrr_at_3_std
value: 0.24088319695435909
- type: nauc_mrr_at_5_diff1
value: 92.27931726042982
- type: nauc_mrr_at_5_max
value: 79.99198834003367
- type: nauc_mrr_at_5_std
value: -0.6296366922840796
- type: nauc_ndcg_at_1000_diff1
value: 92.30526497646306
- type: nauc_ndcg_at_1000_max
value: 80.12734537480418
- type: nauc_ndcg_at_1000_std
value: 0.22849408935578744
- type: nauc_ndcg_at_100_diff1
value: 92.31347123202318
- type: nauc_ndcg_at_100_max
value: 80.29207038703142
- type: nauc_ndcg_at_100_std
value: 0.816825944406239
- type: nauc_ndcg_at_10_diff1
value: 92.05430189845808
- type: nauc_ndcg_at_10_max
value: 80.16515667442968
- type: nauc_ndcg_at_10_std
value: 0.7486447532544893
- type: nauc_ndcg_at_1_diff1
value: 93.29828518358822
- type: nauc_ndcg_at_1_max
value: 78.69539619887887
- type: nauc_ndcg_at_1_std
value: -4.097150817605763
- type: nauc_ndcg_at_20_diff1
value: 92.40147868825079
- type: nauc_ndcg_at_20_max
value: 80.5117307181802
- type: nauc_ndcg_at_20_std
value: 2.0431351539517033
- type: nauc_ndcg_at_3_diff1
value: 91.88894444422789
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value: 60.93103908229921
- type: nauc_recall_at_100_max
value: 52.62104841936668
- type: nauc_recall_at_100_std
value: 85.60090702947748
- type: nauc_recall_at_10_diff1
value: 76.26517273576097
- type: nauc_recall_at_10_max
value: 65.20136943666347
- type: nauc_recall_at_10_std
value: 26.50357920946174
- type: nauc_recall_at_1_diff1
value: 86.37079221403225
- type: nauc_recall_at_1_max
value: 61.856861655370686
- type: nauc_recall_at_1_std
value: 4.708911881992707
- type: nauc_recall_at_20_diff1
value: 73.47946930710269
- type: nauc_recall_at_20_max
value: 70.19520986689254
- type: nauc_recall_at_20_std
value: 45.93186111653943
- type: nauc_recall_at_3_diff1
value: 79.02026879450173
- type: nauc_recall_at_3_max
value: 58.750746246923924
- type: nauc_recall_at_3_std
value: 16.740684654251076
- type: nauc_recall_at_5_diff1
value: 76.4758566228162
- type: nauc_recall_at_5_max
value: 61.862709220131386
- type: nauc_recall_at_5_std
value: 20.18336254550361
- type: ndcg_at_1
value: 73.444
- type: ndcg_at_10
value: 82.748
- type: ndcg_at_100
value: 84.416
- type: ndcg_at_1000
value: 84.52300000000001
- type: ndcg_at_20
value: 83.646
- type: ndcg_at_3
value: 80.267
- type: ndcg_at_5
value: 81.922
- type: precision_at_1
value: 73.444
- type: precision_at_10
value: 9.167
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.761
- type: precision_at_3
value: 28.37
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 73.444
- type: recall_at_10
value: 91.667
- type: recall_at_100
value: 99.222
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 95.222
- type: recall_at_3
value: 85.111
- type: recall_at_5
value: 89.11099999999999
- task:
type: BitextMining
dataset:
name: MTEB BibleNLPBitextMining (eng_Latn-rus_Cyrl)
type: davidstap/biblenlp-corpus-mmteb
config: eng_Latn-rus_Cyrl
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
- type: accuracy
value: 96.875
- type: f1
value: 95.83333333333333
- type: main_score
value: 95.83333333333333
- type: precision
value: 95.3125
- type: recall
value: 96.875
- task:
type: BitextMining
dataset:
name: MTEB BibleNLPBitextMining (rus_Cyrl-eng_Latn)
type: davidstap/biblenlp-corpus-mmteb
config: rus_Cyrl-eng_Latn
split: train
revision: 264a18480c529d9e922483839b4b9758e690b762
metrics:
- type: accuracy
value: 88.671875
- type: f1
value: 85.3515625
- type: main_score
value: 85.3515625
- type: precision
value: 83.85416666666667
- type: recall
value: 88.671875
- task:
type: MultilabelClassification
dataset:
name: MTEB CEDRClassification (default)
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
- type: accuracy
value: 40.06907545164719
- type: f1
value: 26.285000550712407
- type: lrap
value: 64.4280021253997
- type: main_score
value: 40.06907545164719
- task:
type: Classification
dataset:
name: MTEB CyrillicTurkicLangClassification (default)
type: tatiana-merz/cyrillic_turkic_langs
config: default
split: test
revision: e42d330f33d65b7b72dfd408883daf1661f06f18
metrics:
- type: accuracy
value: 43.3447265625
- type: f1
value: 40.08400146827895
- type: f1_weighted
value: 40.08499428040896
- type: main_score
value: 43.3447265625
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ace_Arab-rus_Cyrl)
type: mteb/flores
config: ace_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 6.225296442687747
- type: f1
value: 5.5190958860075
- type: main_score
value: 5.5190958860075
- type: precision
value: 5.3752643758000005
- type: recall
value: 6.225296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bam_Latn-rus_Cyrl)
type: mteb/flores
config: bam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.37944664031622
- type: f1
value: 64.54819836666252
- type: main_score
value: 64.54819836666252
- type: precision
value: 63.07479233454916
- type: recall
value: 68.37944664031622
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dzo_Tibt-rus_Cyrl)
type: mteb/flores
config: dzo_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 0.09881422924901186
- type: f1
value: 0.00019509225912934226
- type: main_score
value: 0.00019509225912934226
- type: precision
value: 9.76425190207627e-05
- type: recall
value: 0.09881422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hin_Deva-rus_Cyrl)
type: mteb/flores
config: hin_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.47299077733861
- type: main_score
value: 99.47299077733861
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (khm_Khmr-rus_Cyrl)
type: mteb/flores
config: khm_Khmr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 88.83399209486166
- type: f1
value: 87.71151056318254
- type: main_score
value: 87.71151056318254
- type: precision
value: 87.32012500709193
- type: recall
value: 88.83399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mag_Deva-rus_Cyrl)
type: mteb/flores
config: mag_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.02371541501977
- type: f1
value: 97.7239789196311
- type: main_score
value: 97.7239789196311
- type: precision
value: 97.61904761904762
- type: recall
value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pap_Latn-rus_Cyrl)
type: mteb/flores
config: pap_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.68187806922984
- type: main_score
value: 93.68187806922984
- type: precision
value: 93.58925452707051
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sot_Latn-rus_Cyrl)
type: mteb/flores
config: sot_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 90.9090909090909
- type: f1
value: 89.23171936758892
- type: main_score
value: 89.23171936758892
- type: precision
value: 88.51790014083866
- type: recall
value: 90.9090909090909
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tur_Latn-rus_Cyrl)
type: mteb/flores
config: tur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ace_Latn-rus_Cyrl)
type: mteb/flores
config: ace_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 66.10671936758892
- type: f1
value: 63.81888256297873
- type: main_score
value: 63.81888256297873
- type: precision
value: 63.01614067933451
- type: recall
value: 66.10671936758892
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ban_Latn-rus_Cyrl)
type: mteb/flores
config: ban_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 79.44664031620553
- type: f1
value: 77.6311962082713
- type: main_score
value: 77.6311962082713
- type: precision
value: 76.93977931929739
- type: recall
value: 79.44664031620553
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ell_Grek-rus_Cyrl)
type: mteb/flores
config: ell_Grek-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.40711462450594
- type: f1
value: 99.2094861660079
- type: main_score
value: 99.2094861660079
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hne_Deva-rus_Cyrl)
type: mteb/flores
config: hne_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 96.83794466403161
- type: f1
value: 96.25352907961603
- type: main_score
value: 96.25352907961603
- type: precision
value: 96.02155091285526
- type: recall
value: 96.83794466403161
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kik_Latn-rus_Cyrl)
type: mteb/flores
config: kik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 76.28458498023716
- type: f1
value: 73.5596919895859
- type: main_score
value: 73.5596919895859
- type: precision
value: 72.40900759055246
- type: recall
value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mai_Deva-rus_Cyrl)
type: mteb/flores
config: mai_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.72727272727273
- type: f1
value: 97.37812911725956
- type: main_score
value: 97.37812911725956
- type: precision
value: 97.26002258610953
- type: recall
value: 97.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pbt_Arab-rus_Cyrl)
type: mteb/flores
config: pbt_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.0711462450593
- type: f1
value: 93.34700387331966
- type: main_score
value: 93.34700387331966
- type: precision
value: 93.06920556920556
- type: recall
value: 94.0711462450593
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (spa_Latn-rus_Cyrl)
type: mteb/flores
config: spa_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.9459815546772
- type: main_score
value: 98.9459815546772
- type: precision
value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (twi_Latn-rus_Cyrl)
type: mteb/flores
config: twi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 80.73122529644269
- type: f1
value: 77.77434363246721
- type: main_score
value: 77.77434363246721
- type: precision
value: 76.54444287596462
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acm_Arab-rus_Cyrl)
type: mteb/flores
config: acm_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 94.56521739130434
- type: f1
value: 92.92490118577075
- type: main_score
value: 92.92490118577075
- type: precision
value: 92.16897233201581
- type: recall
value: 94.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/flores
config: bel_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 98.98550724637681
- type: main_score
value: 98.98550724637681
- type: precision
value: 98.88833992094862
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (eng_Latn-rus_Cyrl)
type: mteb/flores
config: eng_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.60474308300395
- type: f1
value: 99.4729907773386
- type: main_score
value: 99.4729907773386
- type: precision
value: 99.40711462450594
- type: recall
value: 99.60474308300395
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hrv_Latn-rus_Cyrl)
type: mteb/flores
config: hrv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.2094861660079
- type: f1
value: 99.05138339920948
- type: main_score
value: 99.05138339920948
- type: precision
value: 99.00691699604744
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kin_Latn-rus_Cyrl)
type: mteb/flores
config: kin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 88.2411067193676
- type: f1
value: 86.5485246227658
- type: main_score
value: 86.5485246227658
- type: precision
value: 85.90652101521667
- type: recall
value: 88.2411067193676
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mal_Mlym-rus_Cyrl)
type: mteb/flores
config: mal_Mlym-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.07971014492753
- type: main_score
value: 98.07971014492753
- type: precision
value: 97.88372859025033
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pes_Arab-rus_Cyrl)
type: mteb/flores
config: pes_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.51778656126481
- type: f1
value: 98.0566534914361
- type: main_score
value: 98.0566534914361
- type: precision
value: 97.82608695652173
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (srd_Latn-rus_Cyrl)
type: mteb/flores
config: srd_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 82.6086956521739
- type: f1
value: 80.9173470979821
- type: main_score
value: 80.9173470979821
- type: precision
value: 80.24468672882627
- type: recall
value: 82.6086956521739
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tzm_Tfng-rus_Cyrl)
type: mteb/flores
config: tzm_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 7.41106719367589
- type: f1
value: 6.363562740945329
- type: main_score
value: 6.363562740945329
- type: precision
value: 6.090373175353411
- type: recall
value: 7.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (acq_Arab-rus_Cyrl)
type: mteb/flores
config: acq_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.25691699604744
- type: f1
value: 93.81422924901187
- type: main_score
value: 93.81422924901187
- type: precision
value: 93.14064558629775
- type: recall
value: 95.25691699604744
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bem_Latn-rus_Cyrl)
type: mteb/flores
config: bem_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 68.08300395256917
- type: f1
value: 65.01368772860867
- type: main_score
value: 65.01368772860867
- type: precision
value: 63.91052337510628
- type: recall
value: 68.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (epo_Latn-rus_Cyrl)
type: mteb/flores
config: epo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.41897233201581
- type: f1
value: 98.17193675889328
- type: main_score
value: 98.17193675889328
- type: precision
value: 98.08210564139418
- type: recall
value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hun_Latn-rus_Cyrl)
type: mteb/flores
config: hun_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 99.30830039525692
- type: f1
value: 99.1106719367589
- type: main_score
value: 99.1106719367589
- type: precision
value: 99.01185770750988
- type: recall
value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kir_Cyrl-rus_Cyrl)
type: mteb/flores
config: kir_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.5296442687747
- type: f1
value: 97.07549806364035
- type: main_score
value: 97.07549806364035
- type: precision
value: 96.90958498023716
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mar_Deva-rus_Cyrl)
type: mteb/flores
config: mar_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (plt_Latn-rus_Cyrl)
type: mteb/flores
config: plt_Latn-rus_Cyrl
split: devtest
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/flores
config: srp_Cyrl-rus_Cyrl
split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (uig_Arab-rus_Cyrl)
type: mteb/flores
config: uig_Arab-rus_Cyrl
split: devtest
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metrics:
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ben_Beng-rus_Cyrl)
type: mteb/flores
config: ben_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (est_Latn-rus_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
config: hye_Armn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kmb_Latn-rus_Cyrl)
type: mteb/flores
config: kmb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
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metrics:
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dataset:
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type: mteb/flores
config: pol_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ssw_Latn-rus_Cyrl)
type: mteb/flores
config: ssw_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/flores
config: ukr_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (afr_Latn-rus_Cyrl)
type: mteb/flores
config: afr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (bho_Deva-rus_Cyrl)
type: mteb/flores
config: bho_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
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metrics:
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dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
name: MTEB FloresBitextMining (kmr_Latn-rus_Cyrl)
type: mteb/flores
config: kmr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (por_Latn-rus_Cyrl)
type: mteb/flores
config: por_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (sun_Latn-rus_Cyrl)
type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (umb_Latn-rus_Cyrl)
type: mteb/flores
config: umb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ajp_Arab-rus_Cyrl)
type: mteb/flores
config: ajp_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bjn_Arab-rus_Cyrl)
type: mteb/flores
config: bjn_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
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type: mteb/flores
config: ewe_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ilo_Latn-rus_Cyrl)
type: mteb/flores
config: ilo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Arab-rus_Cyrl)
type: mteb/flores
config: knc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 10.079051383399209
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mkd_Cyrl-rus_Cyrl)
type: mteb/flores
config: mkd_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (prs_Arab-rus_Cyrl)
type: mteb/flores
config: prs_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (swe_Latn-rus_Cyrl)
type: mteb/flores
config: swe_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (urd_Arab-rus_Cyrl)
type: mteb/flores
config: urd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (aka_Latn-rus_Cyrl)
type: mteb/flores
config: aka_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.22529644268775
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bjn_Latn-rus_Cyrl)
type: mteb/flores
config: bjn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.27667984189723
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (fao_Latn-rus_Cyrl)
type: mteb/flores
config: fao_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.9288537549407
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ind_Latn-rus_Cyrl)
type: mteb/flores
config: ind_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (knc_Latn-rus_Cyrl)
type: mteb/flores
config: knc_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 33.49802371541502
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (mlt_Latn-rus_Cyrl)
type: mteb/flores
config: mlt_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.94845939633488
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value: 91.40316205533597
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (quy_Latn-rus_Cyrl)
type: mteb/flores
config: quy_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.133716022178575
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value: 40.612648221343875
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (swh_Latn-rus_Cyrl)
type: mteb/flores
config: swh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.18741765480895
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value: 97.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (uzn_Latn-rus_Cyrl)
type: mteb/flores
config: uzn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.0501756697409
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value: 96.83794466403161
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (als_Latn-rus_Cyrl)
type: mteb/flores
config: als_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (bod_Tibt-rus_Cyrl)
type: mteb/flores
config: bod_Tibt-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 1.0869565217391304
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value: 0.9224649610442628
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value: 0.9224649610442628
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value: 0.8894275740459898
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value: 1.0869565217391304
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fij_Latn-rus_Cyrl)
type: mteb/flores
config: fij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.24110671936759
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value: 60.373189068189525
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value: 60.373189068189525
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value: 59.32326368115546
- type: recall
value: 63.24110671936759
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (isl_Latn-rus_Cyrl)
type: mteb/flores
config: isl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.03162055335969
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value: 87.3102634715907
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value: 87.3102634715907
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value: 86.65991814698712
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value: 89.03162055335969
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kon_Latn-rus_Cyrl)
type: mteb/flores
config: kon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.91304347826086
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value: 71.518235523573
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value: 70.58714102449801
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value: 73.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mni_Beng-rus_Cyrl)
type: mteb/flores
config: mni_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 29.545454545454547
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value: 27.59513619889114
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value: 27.59513619889114
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value: 26.983849851025344
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value: 29.545454545454547
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ron_Latn-rus_Cyrl)
type: mteb/flores
config: ron_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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value: 99.2094861660079
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value: 99.2094861660079
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value: 99.1106719367589
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value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (szl_Latn-rus_Cyrl)
type: mteb/flores
config: szl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.26482213438736
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value: 85.18912031587512
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value: 85.18912031587512
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value: 84.77199409959775
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value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vec_Latn-rus_Cyrl)
type: mteb/flores
config: vec_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.67193675889328
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value: 84.62529734716581
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value: 84.62529734716581
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value: 84.2611422440705
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value: 85.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (amh_Ethi-rus_Cyrl)
type: mteb/flores
config: amh_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.76284584980237
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value: 93.91735076517685
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value: 93.91735076517685
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value: 93.57553798858147
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value: 94.76284584980237
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bos_Latn-rus_Cyrl)
type: mteb/flores
config: bos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 99.05655938264634
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value: 99.05655938264634
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value: 99.01185770750988
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value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fin_Latn-rus_Cyrl)
type: mteb/flores
config: fin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.43741765480895
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value: 97.43741765480895
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value: 97.1590909090909
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ita_Latn-rus_Cyrl)
type: mteb/flores
config: ita_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.70355731225297
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value: 99.60474308300395
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value: 99.55533596837944
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value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kor_Hang-rus_Cyrl)
type: mteb/flores
config: kor_Hang-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.49868247694334
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value: 96.10507246376811
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mos_Latn-rus_Cyrl)
type: mteb/flores
config: mos_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.683794466403164
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value: 32.766819308009076
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value: 32.766819308009076
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value: 32.1637493670237
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value: 34.683794466403164
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (run_Latn-rus_Cyrl)
type: mteb/flores
config: run_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.399209486166
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value: 81.10578750604326
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value: 81.10578750604326
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value: 80.16763162673529
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value: 83.399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tam_Taml-rus_Cyrl)
type: mteb/flores
config: tam_Taml-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.41897233201581
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value: 98.01548089591567
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value: 98.01548089591567
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value: 97.84020327498588
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value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (vie_Latn-rus_Cyrl)
type: mteb/flores
config: vie_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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value: 98.81422924901186
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value: 98.66600790513834
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value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (apc_Arab-rus_Cyrl)
type: mteb/flores
config: apc_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.87351778656127
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value: 92.10803689064558
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value: 91.30434782608695
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value: 93.87351778656127
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bug_Latn-rus_Cyrl)
type: mteb/flores
config: bug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 57.608695652173914
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value: 54.95878654927162
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value: 54.95878654927162
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value: 54.067987427805654
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value: 57.608695652173914
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fon_Latn-rus_Cyrl)
type: mteb/flores
config: fon_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 61.95652173913043
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value: 58.06537275812945
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value: 58.06537275812945
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value: 56.554057596959204
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value: 61.95652173913043
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jav_Latn-rus_Cyrl)
type: mteb/flores
config: jav_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.47826086956522
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value: 92.4784405318002
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value: 92.4784405318002
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value: 92.09168143201127
- type: recall
value: 93.47826086956522
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lao_Laoo-rus_Cyrl)
type: mteb/flores
config: lao_Laoo-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.10671936758892
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value: 89.76104922745239
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value: 89.76104922745239
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value: 89.24754593232855
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value: 91.10671936758892
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mri_Latn-rus_Cyrl)
type: mteb/flores
config: mri_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.14624505928853
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value: 68.26947125119062
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value: 68.26947125119062
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value: 67.15942311051006
- type: recall
value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Arab)
type: mteb/flores
config: rus_Cyrl-ace_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 19.565217391304348
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value: 16.321465000323805
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value: 16.321465000323805
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value: 15.478527409347508
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value: 19.565217391304348
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bam_Latn)
type: mteb/flores
config: rus_Cyrl-bam_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.41897233201581
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value: 68.77366228182746
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value: 68.77366228182746
- type: precision
value: 66.96012924273795
- type: recall
value: 73.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dzo_Tibt)
type: mteb/flores
config: rus_Cyrl-dzo_Tibt
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 0.592885375494071
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value: 0.02458062426370458
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value: 0.02458062426370458
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value: 0.012824114724683876
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value: 0.592885375494071
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hin_Deva)
type: mteb/flores
config: rus_Cyrl-hin_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.90118577075098
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value: 99.86824769433464
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value: 99.86824769433464
- type: precision
value: 99.85177865612648
- type: recall
value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khm_Khmr)
type: mteb/flores
config: rus_Cyrl-khm_Khmr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.13438735177866
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value: 96.24505928853755
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value: 96.24505928853755
- type: precision
value: 95.81686429512516
- type: recall
value: 97.13438735177866
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mag_Deva)
type: mteb/flores
config: rus_Cyrl-mag_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.50592885375494
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value: 99.35770750988142
- type: main_score
value: 99.35770750988142
- type: precision
value: 99.29183135704875
- type: recall
value: 99.50592885375494
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pap_Latn)
type: mteb/flores
config: rus_Cyrl-pap_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.93675889328063
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value: 96.05072463768116
- type: main_score
value: 96.05072463768116
- type: precision
value: 95.66040843214758
- type: recall
value: 96.93675889328063
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sot_Latn)
type: mteb/flores
config: rus_Cyrl-sot_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.67588932806325
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value: 91.7786561264822
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value: 91.7786561264822
- type: precision
value: 90.91238471673255
- type: recall
value: 93.67588932806325
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tur_Latn)
type: mteb/flores
config: rus_Cyrl-tur_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.68247694334651
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value: 98.68247694334651
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value: 98.51778656126481
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ace_Latn)
type: mteb/flores
config: rus_Cyrl-ace_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 74.1106719367589
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value: 70.21737923911836
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value: 70.21737923911836
- type: precision
value: 68.7068791410511
- type: recall
value: 74.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ban_Latn)
type: mteb/flores
config: rus_Cyrl-ban_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.7193675889328
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value: 78.76470334510617
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value: 78.76470334510617
- type: precision
value: 77.76208475761422
- type: recall
value: 81.7193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ell_Grek)
type: mteb/flores
config: rus_Cyrl-ell_Grek
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
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value: 97.76021080368908
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value: 97.76021080368908
- type: precision
value: 97.48023715415019
- type: recall
value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hne_Deva)
type: mteb/flores
config: rus_Cyrl-hne_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.51778656126481
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value: 98.0566534914361
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value: 98.0566534914361
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value: 97.82608695652173
- type: recall
value: 98.51778656126481
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kik_Latn)
type: mteb/flores
config: rus_Cyrl-kik_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.73122529644269
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value: 76.42689244220864
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value: 76.42689244220864
- type: precision
value: 74.63877909530083
- type: recall
value: 80.73122529644269
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-mai_Deva)
type: mteb/flores
config: rus_Cyrl-mai_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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value: 98.56719367588933
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value: 98.56719367588933
- type: precision
value: 98.40250329380763
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pbt_Arab)
type: mteb/flores
config: rus_Cyrl-pbt_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.5296442687747
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value: 96.73913043478261
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value: 96.73913043478261
- type: precision
value: 96.36034255599473
- type: recall
value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-spa_Latn)
type: mteb/flores
config: rus_Cyrl-spa_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.40711462450594
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value: 99.20948616600789
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value: 99.20948616600789
- type: precision
value: 99.1106719367589
- type: recall
value: 99.40711462450594
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-twi_Latn)
type: mteb/flores
config: rus_Cyrl-twi_Latn
split: devtest
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type: BitextMining
dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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type: BitextMining
dataset:
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type: mteb/flores
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metrics:
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dataset:
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metrics:
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type: BitextMining
dataset:
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split: devtest
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dataset:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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type: BitextMining
dataset:
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dataset:
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revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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dataset:
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metrics:
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dataset:
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split: devtest
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metrics:
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value: 71.44268774703558
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type: BitextMining
dataset:
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type: mteb/flores
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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dataset:
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type: mteb/flores
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split: devtest
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type: BitextMining
dataset:
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dataset:
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type: mteb/flores
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dataset:
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type: mteb/flores
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metrics:
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dataset:
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dataset:
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revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
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dataset:
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dataset:
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dataset:
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split: devtest
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dataset:
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dataset:
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dataset:
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dataset:
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metrics:
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dataset:
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type: mteb/flores
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split: devtest
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metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lmo_Latn)
type: mteb/flores
config: rus_Cyrl-lmo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.56413514022209
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value: 77.15313068573938
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value: 82.41106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-npi_Deva)
type: mteb/flores
config: rus_Cyrl-npi_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
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value: 98.12252964426878
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-shn_Mymr)
type: mteb/flores
config: rus_Cyrl-shn_Mymr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 49.71860043649573
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value: 57.11462450592886
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tgl_Latn)
type: mteb/flores
config: rus_Cyrl-tgl_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.88735177865613
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value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-yue_Hant)
type: mteb/flores
config: rus_Cyrl-yue_Hant
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.09420289855072
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value: 98.9953886693017
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-asm_Beng)
type: mteb/flores
config: rus_Cyrl-asm_Beng
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.55335968379447
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ckb_Arab)
type: mteb/flores
config: rus_Cyrl-ckb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.88537549407114
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-gle_Latn)
type: mteb/flores
config: rus_Cyrl-gle_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.699604743083
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Arab)
type: mteb/flores
config: rus_Cyrl-kas_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 88.3399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltg_Latn)
type: mteb/flores
config: rus_Cyrl-ltg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.399209486166
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nso_Latn)
type: mteb/flores
config: rus_Cyrl-nso_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 88.01548089591567
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value: 91.20553359683794
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sin_Sinh)
type: mteb/flores
config: rus_Cyrl-sin_Sinh
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tha_Thai)
type: mteb/flores
config: rus_Cyrl-tha_Thai
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.94861660079052
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hans)
type: mteb/flores
config: rus_Cyrl-zho_Hans
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ast_Latn)
type: mteb/flores
config: rus_Cyrl-ast_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.46640316205533
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-crh_Latn)
type: mteb/flores
config: rus_Cyrl-crh_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.66403162055336
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-glg_Latn)
type: mteb/flores
config: rus_Cyrl-glg_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kas_Deva)
type: mteb/flores
config: rus_Cyrl-kas_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ltz_Latn)
type: mteb/flores
config: rus_Cyrl-ltz_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.40316205533597
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nus_Latn)
type: mteb/flores
config: rus_Cyrl-nus_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.93280632411067
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-slk_Latn)
type: mteb/flores
config: rus_Cyrl-slk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.14262187088273
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tir_Ethi)
type: mteb/flores
config: rus_Cyrl-tir_Ethi
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.30434782608695
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value: 87.61528326745717
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value: 91.30434782608695
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zho_Hant)
type: mteb/flores
config: rus_Cyrl-zho_Hant
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-awa_Deva)
type: mteb/flores
config: rus_Cyrl-awa_Deva
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-cym_Latn)
type: mteb/flores
config: rus_Cyrl-cym_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.94861660079052
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-grn_Latn)
type: mteb/flores
config: rus_Cyrl-grn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.99324893260821
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value: 77.96442687747036
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kat_Geor)
type: mteb/flores
config: rus_Cyrl-kat_Geor
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.91304347826086
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value: 98.40250329380764
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value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lua_Latn)
type: mteb/flores
config: rus_Cyrl-lua_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 72.03557312252964
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value: 65.30797101449275
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value: 72.03557312252964
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-nya_Latn)
type: mteb/flores
config: rus_Cyrl-nya_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.29249011857708
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value: 90.0494071146245
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value: 90.0494071146245
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value: 89.04808959156786
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value: 92.29249011857708
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-slv_Latn)
type: mteb/flores
config: rus_Cyrl-slv_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.10606060606061
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tpi_Latn)
type: mteb/flores
config: rus_Cyrl-tpi_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.53359683794467
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value: 76.59481822525301
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value: 75.12913223140497
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value: 80.53359683794467
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zsm_Latn)
type: mteb/flores
config: rus_Cyrl-zsm_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.58620365142104
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value: 96.26152832674572
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ayr_Latn)
type: mteb/flores
config: rus_Cyrl-ayr_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 45.55335968379446
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value: 40.13076578531388
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value: 40.13076578531388
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value: 38.398064362362355
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value: 45.55335968379446
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dan_Latn)
type: mteb/flores
config: rus_Cyrl-dan_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.68247694334651
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value: 98.51778656126481
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-guj_Gujr)
type: mteb/flores
config: rus_Cyrl-guj_Gujr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.51778656126481
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value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kaz_Cyrl)
type: mteb/flores
config: rus_Cyrl-kaz_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.81422924901186
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value: 98.25428194993412
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value: 98.81422924901186
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lug_Latn)
type: mteb/flores
config: rus_Cyrl-lug_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.21343873517787
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value: 77.97485726833554
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value: 77.97485726833554
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value: 76.22376717485415
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value: 82.21343873517787
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-oci_Latn)
type: mteb/flores
config: rus_Cyrl-oci_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.87351778656127
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value: 92.25319969885187
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value: 92.25319969885187
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value: 91.5638528138528
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value: 93.87351778656127
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-smo_Latn)
type: mteb/flores
config: rus_Cyrl-smo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.88142292490119
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value: 81.24364765669114
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value: 81.24364765669114
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value: 79.69991416137661
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value: 84.88142292490119
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tsn_Latn)
type: mteb/flores
config: rus_Cyrl-tsn_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.05533596837944
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value: 83.90645586297761
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value: 83.90645586297761
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value: 82.56752305665349
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value: 87.05533596837944
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-zul_Latn)
type: mteb/flores
config: rus_Cyrl-zul_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 93.77140974967062
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value: 93.16534914361002
- type: recall
value: 95.15810276679841
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-azb_Arab)
type: mteb/flores
config: rus_Cyrl-azb_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 75.21519543258673
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value: 81.91699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-deu_Latn)
type: mteb/flores
config: rus_Cyrl-deu_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2588932806324
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value: 99.50592885375494
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hat_Latn)
type: mteb/flores
config: rus_Cyrl-hat_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.75774044795784
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value: 91.00790513833992
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kbp_Latn)
type: mteb/flores
config: rus_Cyrl-kbp_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 41.739069205043556
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value: 47.233201581027664
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-luo_Latn)
type: mteb/flores
config: rus_Cyrl-luo_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 60.47430830039525
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-ory_Orya)
type: mteb/flores
config: rus_Cyrl-ory_Orya
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.1106719367589
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value: 98.69894598155467
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value: 99.1106719367589
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-sna_Latn)
type: mteb/flores
config: rus_Cyrl-sna_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.25329380764163
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value: 89.72332015810277
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tso_Latn)
type: mteb/flores
config: rus_Cyrl-tso_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.38735177865613
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-azj_Latn)
type: mteb/flores
config: rus_Cyrl-azj_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.23682476943345
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dik_Latn)
type: mteb/flores
config: rus_Cyrl-dik_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.072590197096126
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value: 46.047430830039524
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-hau_Latn)
type: mteb/flores
config: rus_Cyrl-hau_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.34486166007905
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value: 87.94466403162056
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-kea_Latn)
type: mteb/flores
config: rus_Cyrl-kea_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.42687747035573
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value: 85.78416149068323
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value: 89.42687747035573
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lus_Latn)
type: mteb/flores
config: rus_Cyrl-lus_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 68.97233201581028
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value: 62.42670749487858
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value: 68.97233201581028
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pag_Latn)
type: mteb/flores
config: rus_Cyrl-pag_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.75494071146245
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value: 73.05532028358115
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value: 78.75494071146245
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-snd_Arab)
type: mteb/flores
config: rus_Cyrl-snd_Arab
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.8498023715415
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value: 93.97233201581028
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value: 95.8498023715415
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tuk_Latn)
type: mteb/flores
config: rus_Cyrl-tuk_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 61.191590257043934
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value: 68.08300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-bak_Cyrl)
type: mteb/flores
config: rus_Cyrl-bak_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.04743083003953
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-dyu_Latn)
type: mteb/flores
config: rus_Cyrl-dyu_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 37.45059288537549
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value: 29.99928568357422
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value: 37.45059288537549
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-heb_Hebr)
type: mteb/flores
config: rus_Cyrl-heb_Hebr
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.23320158102767
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value: 96.38998682476942
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value: 95.99802371541502
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value: 97.23320158102767
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-khk_Cyrl)
type: mteb/flores
config: rus_Cyrl-khk_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.41897233201581
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value: 98.00724637681158
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value: 97.82938076416336
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value: 98.41897233201581
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-lvs_Latn)
type: mteb/flores
config: rus_Cyrl-lvs_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.4308300395257
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value: 96.61396574440053
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value: 96.2203557312253
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value: 97.4308300395257
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-pan_Guru)
type: mteb/flores
config: rus_Cyrl-pan_Guru
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
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value: 98.96245059288538
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-som_Latn)
type: mteb/flores
config: rus_Cyrl-som_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.74703557312253
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value: 84.52898550724638
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value: 83.09288537549409
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value: 87.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (rus_Cyrl-tum_Latn)
type: mteb/flores
config: rus_Cyrl-tum_Latn
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.15415019762845
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value: 83.85069640504425
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value: 82.43671183888576
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value: 87.15415019762845
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Latn-rus_Cyrl)
type: mteb/flores
config: taq_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 28.55731225296443
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value: 26.260342858265577
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value: 28.55731225296443
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (war_Latn-rus_Cyrl)
type: mteb/flores
config: war_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.86166007905138
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value: 94.03147083483051
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value: 94.03147083483051
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value: 93.70653606003322
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value: 94.86166007905138
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Arab-rus_Cyrl)
type: mteb/flores
config: arb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.34387351778656
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value: 95.23056653491436
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value: 95.23056653491436
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value: 94.70520421607378
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value: 96.34387351778656
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bul_Cyrl-rus_Cyrl)
type: mteb/flores
config: bul_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.90118577075098
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value: 99.86824769433464
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value: 99.86824769433464
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value: 99.85177865612648
- type: recall
value: 99.90118577075098
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fra_Latn-rus_Cyrl)
type: mteb/flores
config: fra_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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value: 98.9459815546772
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value: 98.9459815546772
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value: 98.81422924901186
- type: recall
value: 99.2094861660079
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (jpn_Jpan-rus_Cyrl)
type: mteb/flores
config: jpn_Jpan-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.3201581027668
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value: 97.76021080368905
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value: 97.76021080368905
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value: 97.48023715415019
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value: 98.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lij_Latn-rus_Cyrl)
type: mteb/flores
config: lij_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 83.49802371541502
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value: 81.64800059239636
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value: 81.64800059239636
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value: 80.9443055878478
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value: 83.49802371541502
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (mya_Mymr-rus_Cyrl)
type: mteb/flores
config: mya_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 90.21739130434783
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value: 88.76776366313682
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value: 88.76776366313682
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value: 88.18370446119435
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value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sag_Latn-rus_Cyrl)
type: mteb/flores
config: sag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 41.699604743083
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value: 39.53066322643847
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value: 39.53066322643847
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value: 38.822876239229274
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value: 41.699604743083
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (taq_Tfng-rus_Cyrl)
type: mteb/flores
config: taq_Tfng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 10.67193675889328
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value: 9.205744965817951
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value: 9.205744965817951
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value: 8.85195219073817
- type: recall
value: 10.67193675889328
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (wol_Latn-rus_Cyrl)
type: mteb/flores
config: wol_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.537549407114625
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value: 60.65190727391827
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value: 60.65190727391827
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value: 59.61144833427442
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value: 63.537549407114625
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arb_Latn-rus_Cyrl)
type: mteb/flores
config: arb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 13.142292490118576
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value: 12.372910318176764
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value: 12.372910318176764
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value: 12.197580895919188
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value: 13.142292490118576
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cat_Latn-rus_Cyrl)
type: mteb/flores
config: cat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.80599472990777
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value: 98.80599472990777
- type: precision
value: 98.72953133822698
- type: recall
value: 99.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fur_Latn-rus_Cyrl)
type: mteb/flores
config: fur_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.02766798418972
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value: 79.36184294084613
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value: 79.36184294084613
- type: precision
value: 78.69187826527705
- type: recall
value: 81.02766798418972
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kab_Latn-rus_Cyrl)
type: mteb/flores
config: kab_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.387351778656125
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value: 32.02306921576947
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value: 32.02306921576947
- type: precision
value: 31.246670347137467
- type: recall
value: 34.387351778656125
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lim_Latn-rus_Cyrl)
type: mteb/flores
config: lim_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 78.26086956521739
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (nld_Latn-rus_Cyrl)
type: mteb/flores
config: nld_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (san_Deva-rus_Cyrl)
type: mteb/flores
config: san_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.23822997079216
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value: 87.94466403162056
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tat_Cyrl-rus_Cyrl)
type: mteb/flores
config: tat_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.03557312252964
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (xho_Latn-rus_Cyrl)
type: mteb/flores
config: xho_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 84.95270479733395
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value: 86.95652173913044
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ars_Arab-rus_Cyrl)
type: mteb/flores
config: ars_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 96.6403162055336
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ceb_Latn-rus_Cyrl)
type: mteb/flores
config: ceb_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.7509881422925
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (fuv_Latn-rus_Cyrl)
type: mteb/flores
config: fuv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kac_Latn-rus_Cyrl)
type: mteb/flores
config: kac_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.683794466403164
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (lin_Latn-rus_Cyrl)
type: mteb/flores
config: lin_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.85770750988142
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (nno_Latn-rus_Cyrl)
type: mteb/flores
config: nno_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.4308300395257
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (sat_Olck-rus_Cyrl)
type: mteb/flores
config: sat_Olck-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 1.185770750988142
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tel_Telu-rus_Cyrl)
type: mteb/flores
config: tel_Telu-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.12252964426878
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ydd_Hebr-rus_Cyrl)
type: mteb/flores
config: ydd_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 75.8893280632411
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ary_Arab-rus_Cyrl)
type: mteb/flores
config: ary_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.65014273166447
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value: 86.56126482213439
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ces_Latn-rus_Cyrl)
type: mteb/flores
config: ces_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.60474308300395
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value: 99.40711462450594
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value: 99.60474308300395
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (gaz_Latn-rus_Cyrl)
type: mteb/flores
config: gaz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 40.14285046917723
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value: 42.58893280632411
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kam_Latn-rus_Cyrl)
type: mteb/flores
config: kam_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 45.25691699604743
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value: 41.87472710984596
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value: 45.25691699604743
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lit_Latn-rus_Cyrl)
type: mteb/flores
config: lit_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.33201581027669
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value: 96.62384716732542
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value: 96.3175230566535
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value: 97.33201581027669
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nob_Latn-rus_Cyrl)
type: mteb/flores
config: nob_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.30368906455863
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value: 98.10606060606061
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value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (scn_Latn-rus_Cyrl)
type: mteb/flores
config: scn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 70.45454545454545
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value: 67.95229103411222
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value: 70.45454545454545
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgk_Cyrl-rus_Cyrl)
type: mteb/flores
config: tgk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.4901185770751
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value: 91.24759298672342
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value: 92.4901185770751
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (yor_Latn-rus_Cyrl)
type: mteb/flores
config: yor_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 67.98418972332016
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value: 64.72874247330768
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value: 63.450823399938685
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value: 67.98418972332016
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (arz_Arab-rus_Cyrl)
type: mteb/flores
config: arz_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.56521739130434
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value: 93.07971014492755
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value: 92.42753623188406
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value: 94.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (cjk_Latn-rus_Cyrl)
type: mteb/flores
config: cjk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.63636363636363
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value: 36.25747140862938
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value: 35.49101355074723
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value: 38.63636363636363
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gla_Latn-rus_Cyrl)
type: mteb/flores
config: gla_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 69.26877470355731
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value: 66.11797423328613
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value: 64.89369649409694
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value: 69.26877470355731
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kan_Knda-rus_Cyrl)
type: mteb/flores
config: kan_Knda-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.51505740636176
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value: 97.51505740636176
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value: 97.30731225296442
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lmo_Latn-rus_Cyrl)
type: mteb/flores
config: lmo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 73.3201581027668
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value: 71.06371608677273
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value: 71.06371608677273
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value: 70.26320288266223
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value: 73.3201581027668
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (npi_Deva-rus_Cyrl)
type: mteb/flores
config: npi_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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value: 97.36645107198466
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value: 97.36645107198466
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value: 97.1772068511199
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value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (shn_Mymr-rus_Cyrl)
type: mteb/flores
config: shn_Mymr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 39.426877470355734
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value: 37.16728785513024
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value: 37.16728785513024
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value: 36.56918548278505
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value: 39.426877470355734
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tgl_Latn-rus_Cyrl)
type: mteb/flores
config: tgl_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
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value: 97.6378693769998
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value: 97.6378693769998
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value: 97.55371440154047
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value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (yue_Hant-rus_Cyrl)
type: mteb/flores
config: yue_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.92490118577075
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value: 97.3833051006964
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value: 97.3833051006964
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value: 97.1590909090909
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value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (asm_Beng-rus_Cyrl)
type: mteb/flores
config: asm_Beng-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.78656126482213
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value: 91.76917395296842
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value: 91.76917395296842
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value: 91.38292866553736
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value: 92.78656126482213
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ckb_Arab-rus_Cyrl)
type: mteb/flores
config: ckb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.8300395256917
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value: 79.17664345468799
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value: 79.17664345468799
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value: 78.5622171683459
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value: 80.8300395256917
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (gle_Latn-rus_Cyrl)
type: mteb/flores
config: gle_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.86956521739131
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value: 84.45408265372492
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value: 84.45408265372492
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value: 83.8774340026703
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value: 85.86956521739131
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Arab-rus_Cyrl)
type: mteb/flores
config: kas_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 76.28458498023716
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value: 74.11216313578267
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value: 74.11216313578267
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value: 73.2491277759584
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value: 76.28458498023716
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltg_Latn-rus_Cyrl)
type: mteb/flores
config: ltg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 71.14624505928853
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value: 68.69245357723618
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value: 68.69245357723618
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value: 67.8135329666459
- type: recall
value: 71.14624505928853
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (nso_Latn-rus_Cyrl)
type: mteb/flores
config: nso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.64822134387352
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value: 85.98419219986725
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value: 85.98419219986725
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value: 85.32513873917036
- type: recall
value: 87.64822134387352
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sin_Sinh-rus_Cyrl)
type: mteb/flores
config: sin_Sinh-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.62845849802372
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value: 97.10144927536231
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value: 97.10144927536231
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value: 96.87986585219788
- type: recall
value: 97.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tha_Thai-rus_Cyrl)
type: mteb/flores
config: tha_Thai-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.71541501976284
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value: 98.28722002635045
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value: 98.28722002635045
- type: precision
value: 98.07312252964427
- type: recall
value: 98.71541501976284
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hans-rus_Cyrl)
type: mteb/flores
config: zho_Hans-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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value: 98.68247694334651
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value: 98.68247694334651
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value: 98.51778656126481
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ast_Latn-rus_Cyrl)
type: mteb/flores
config: ast_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 94.61352657004831
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value: 95.65217391304348
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (crh_Latn-rus_Cyrl)
type: mteb/flores
config: crh_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 91.85631013694254
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value: 93.08300395256917
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (glg_Latn-rus_Cyrl)
type: mteb/flores
config: glg_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 95.55335968379447
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kas_Deva-rus_Cyrl)
type: mteb/flores
config: kas_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 55.03952569169961
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ltz_Latn-rus_Cyrl)
type: mteb/flores
config: ltz_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 87.64822134387352
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (nus_Latn-rus_Cyrl)
type: mteb/flores
config: nus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 27.4703557312253
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (slk_Latn-rus_Cyrl)
type: mteb/flores
config: slk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tir_Ethi-rus_Cyrl)
type: mteb/flores
config: tir_Ethi-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 80.73122529644269
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (zho_Hant-rus_Cyrl)
type: mteb/flores
config: zho_Hant-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.22134387351778
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (awa_Deva-rus_Cyrl)
type: mteb/flores
config: awa_Deva-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (cym_Latn-rus_Cyrl)
type: mteb/flores
config: cym_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 92.68774703557312
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (grn_Latn-rus_Cyrl)
type: mteb/flores
config: grn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.13043478260869
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (kat_Geor-rus_Cyrl)
type: mteb/flores
config: kat_Geor-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (lua_Latn-rus_Cyrl)
type: mteb/flores
config: lua_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 63.63636363636363
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (nya_Latn-rus_Cyrl)
type: mteb/flores
config: nya_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 89.2292490118577
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (slv_Latn-rus_Cyrl)
type: mteb/flores
config: slv_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.01185770750988
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tpi_Latn-rus_Cyrl)
type: mteb/flores
config: tpi_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 77.37154150197628
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zsm_Latn-rus_Cyrl)
type: mteb/flores
config: zsm_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.2094861660079
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (ayr_Latn-rus_Cyrl)
type: mteb/flores
config: ayr_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 27.766798418972332
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (dan_Latn-rus_Cyrl)
type: mteb/flores
config: dan_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.30830039525692
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (guj_Gujr-rus_Cyrl)
type: mteb/flores
config: guj_Gujr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kaz_Cyrl-rus_Cyrl)
type: mteb/flores
config: kaz_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.5296442687747
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lug_Latn-rus_Cyrl)
type: mteb/flores
config: lug_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 64.90348248593789
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value: 68.57707509881423
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (oci_Latn-rus_Cyrl)
type: mteb/flores
config: oci_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.00208833384637
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value: 86.26482213438736
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (smo_Latn-rus_Cyrl)
type: mteb/flores
config: smo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 74.8312632736777
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value: 77.96442687747036
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type: BitextMining
dataset:
name: MTEB FloresBitextMining (tsn_Latn-rus_Cyrl)
type: mteb/flores
config: tsn_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.16794543904518
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value: 84.38735177865613
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (zul_Latn-rus_Cyrl)
type: mteb/flores
config: zul_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 90.21739130434783
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value: 88.77570602050753
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value: 88.15978104021582
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value: 90.21739130434783
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azb_Arab-rus_Cyrl)
type: mteb/flores
config: azb_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 65.71146245059289
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value: 64.18825390221271
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value: 63.66811154793568
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value: 65.71146245059289
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (deu_Latn-rus_Cyrl)
type: mteb/flores
config: deu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 99.70355731225297
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value: 99.55533596837944
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value: 99.70355731225297
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hat_Latn-rus_Cyrl)
type: mteb/flores
config: hat_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 86.7588932806324
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value: 85.55235467420822
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value: 86.7588932806324
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kbp_Latn-rus_Cyrl)
type: mteb/flores
config: kbp_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 34.88142292490119
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value: 31.432098549546318
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value: 34.88142292490119
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (luo_Latn-rus_Cyrl)
type: mteb/flores
config: luo_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 49.60489626836975
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value: 48.69639631803339
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value: 52.27272727272727
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (ory_Orya-rus_Cyrl)
type: mteb/flores
config: ory_Orya-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 97.82608695652173
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value: 97.27437417654808
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value: 97.04968944099377
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value: 97.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (sna_Latn-rus_Cyrl)
type: mteb/flores
config: sna_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 85.37549407114624
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value: 83.09911316305177
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value: 82.1284950958864
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value: 85.37549407114624
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tso_Latn-rus_Cyrl)
type: mteb/flores
config: tso_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 82.90513833992095
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value: 80.28290385503824
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value: 80.28290385503824
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value: 79.23672543237761
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value: 82.90513833992095
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (azj_Latn-rus_Cyrl)
type: mteb/flores
config: azj_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 98.02371541501977
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value: 97.49200075287031
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value: 97.49200075287031
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value: 97.266139657444
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value: 98.02371541501977
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dik_Latn-rus_Cyrl)
type: mteb/flores
config: dik_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 38.43873517786561
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value: 35.78152442955223
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value: 35.78152442955223
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value: 34.82424325078237
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value: 38.43873517786561
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (hau_Latn-rus_Cyrl)
type: mteb/flores
config: hau_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.42292490118577
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value: 79.24612283124593
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value: 79.24612283124593
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value: 78.34736070751448
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value: 81.42292490118577
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (kea_Latn-rus_Cyrl)
type: mteb/flores
config: kea_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 81.62055335968378
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value: 80.47015182884748
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value: 80.47015182884748
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value: 80.02671028885862
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value: 81.62055335968378
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lus_Latn-rus_Cyrl)
type: mteb/flores
config: lus_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
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value: 62.74703557312253
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value: 60.53900079111122
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value: 60.53900079111122
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value: 59.80024202850289
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value: 62.74703557312253
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pag_Latn-rus_Cyrl)
type: mteb/flores
config: pag_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 74.01185770750988
- type: f1
value: 72.57280648279529
- type: main_score
value: 72.57280648279529
- type: precision
value: 71.99952968456789
- type: recall
value: 74.01185770750988
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (snd_Arab-rus_Cyrl)
type: mteb/flores
config: snd_Arab-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 91.30434782608695
- type: f1
value: 90.24653499445358
- type: main_score
value: 90.24653499445358
- type: precision
value: 89.83134068200232
- type: recall
value: 91.30434782608695
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tuk_Latn-rus_Cyrl)
type: mteb/flores
config: tuk_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 47.62845849802372
- type: f1
value: 45.812928836644254
- type: main_score
value: 45.812928836644254
- type: precision
value: 45.23713833170355
- type: recall
value: 47.62845849802372
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (bak_Cyrl-rus_Cyrl)
type: mteb/flores
config: bak_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.8498023715415
- type: f1
value: 95.18904459615922
- type: main_score
value: 95.18904459615922
- type: precision
value: 94.92812441182006
- type: recall
value: 95.8498023715415
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (dyu_Latn-rus_Cyrl)
type: mteb/flores
config: dyu_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 29.64426877470356
- type: f1
value: 27.287335193938166
- type: main_score
value: 27.287335193938166
- type: precision
value: 26.583996026587492
- type: recall
value: 29.64426877470356
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (heb_Hebr-rus_Cyrl)
type: mteb/flores
config: heb_Hebr-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 98.91304347826086
- type: f1
value: 98.55072463768116
- type: main_score
value: 98.55072463768116
- type: precision
value: 98.36956521739131
- type: recall
value: 98.91304347826086
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (khk_Cyrl-rus_Cyrl)
type: mteb/flores
config: khk_Cyrl-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 95.15810276679841
- type: f1
value: 94.44009547764487
- type: main_score
value: 94.44009547764487
- type: precision
value: 94.16579797014579
- type: recall
value: 95.15810276679841
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (lvs_Latn-rus_Cyrl)
type: mteb/flores
config: lvs_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.51467241585817
- type: main_score
value: 97.51467241585817
- type: precision
value: 97.36166007905138
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (pan_Guru-rus_Cyrl)
type: mteb/flores
config: pan_Guru-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 97.92490118577075
- type: f1
value: 97.42918313570486
- type: main_score
value: 97.42918313570486
- type: precision
value: 97.22261434217955
- type: recall
value: 97.92490118577075
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (som_Latn-rus_Cyrl)
type: mteb/flores
config: som_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 75.69169960474308
- type: f1
value: 73.7211667065916
- type: main_score
value: 73.7211667065916
- type: precision
value: 72.95842401892384
- type: recall
value: 75.69169960474308
- task:
type: BitextMining
dataset:
name: MTEB FloresBitextMining (tum_Latn-rus_Cyrl)
type: mteb/flores
config: tum_Latn-rus_Cyrl
split: devtest
revision: e6b647fcb6299a2f686f742f4d4c023e553ea67e
metrics:
- type: accuracy
value: 85.67193675889328
- type: f1
value: 82.9296066252588
- type: main_score
value: 82.9296066252588
- type: precision
value: 81.77330225447936
- type: recall
value: 85.67193675889328
- task:
type: Classification
dataset:
name: MTEB GeoreviewClassification (default)
type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
metrics:
- type: accuracy
value: 44.6630859375
- type: f1
value: 42.607425073610536
- type: f1_weighted
value: 42.60639474586065
- type: main_score
value: 44.6630859375
- task:
type: Clustering
dataset:
name: MTEB GeoreviewClusteringP2P (default)
type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
- type: main_score
value: 58.15951247070825
- type: v_measure
value: 58.15951247070825
- type: v_measure_std
value: 0.6739615788288809
- task:
type: Classification
dataset:
name: MTEB HeadlineClassification (default)
type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
- type: accuracy
value: 73.935546875
- type: f1
value: 73.8654872186846
- type: f1_weighted
value: 73.86733122685095
- type: main_score
value: 73.935546875
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 59.16015624999999
- type: ap
value: 55.52276605836938
- type: ap_weighted
value: 55.52276605836938
- type: f1
value: 58.614248199637956
- type: f1_weighted
value: 58.614248199637956
- type: main_score
value: 59.16015624999999
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 49.959999999999994
- type: f1
value: 48.4900332316098
- type: f1_weighted
value: 48.4900332316098
- type: main_score
value: 49.959999999999994
- task:
type: Classification
dataset:
name: MTEB LanguageClassification (default)
type: papluca/language-identification
config: default
split: test
revision: aa56583bf2bc52b0565770607d6fc3faebecf9e2
metrics:
- type: accuracy
value: 71.005859375
- type: f1
value: 69.63481100303348
- type: f1_weighted
value: 69.64640413409529
- type: main_score
value: 71.005859375
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (ru)
type: reciTAL/mlsum
config: ru
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: main_score
value: 42.11280087032343
- type: v_measure
value: 42.11280087032343
- type: v_measure_std
value: 6.7619971723605135
- type: main_score
value: 43.00112546945811
- type: v_measure
value: 43.00112546945811
- type: v_measure_std
value: 1.4740560414835675
- type: main_score
value: 39.81446080575161
- type: v_measure
value: 39.81446080575161
- type: v_measure_std
value: 7.125661320308298
- type: main_score
value: 39.29659668980239
- type: v_measure
value: 39.29659668980239
- type: v_measure_std
value: 2.6570502923023094
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ru)
type: Shitao/MLDR
config: ru
split: dev
revision: d67138e705d963e346253a80e59676ddb418810a
metrics:
- type: main_score
value: 38.671
- type: map_at_1
value: 30.0
- type: map_at_10
value: 36.123
- type: map_at_100
value: 36.754999999999995
- type: map_at_1000
value: 36.806
- type: map_at_20
value: 36.464
- type: map_at_3
value: 35.25
- type: map_at_5
value: 35.8
- type: mrr_at_1
value: 30.0
- type: mrr_at_10
value: 36.122817460317464
- type: mrr_at_100
value: 36.75467016625293
- type: mrr_at_1000
value: 36.80612724920882
- type: mrr_at_20
value: 36.46359681984682
- type: mrr_at_3
value: 35.25
- type: mrr_at_5
value: 35.800000000000004
- type: nauc_map_at_1000_diff1
value: 55.61987610843598
- type: nauc_map_at_1000_max
value: 52.506795017152186
- type: nauc_map_at_1000_std
value: 2.95487192066911
- type: nauc_map_at_100_diff1
value: 55.598419532054734
- type: nauc_map_at_100_max
value: 52.48192017040307
- type: nauc_map_at_100_std
value: 2.930120252521189
- type: nauc_map_at_10_diff1
value: 56.02309155375198
- type: nauc_map_at_10_max
value: 52.739573233234424
- type: nauc_map_at_10_std
value: 2.4073432421641545
- type: nauc_map_at_1_diff1
value: 52.57059856776112
- type: nauc_map_at_1_max
value: 50.55668152952304
- type: nauc_map_at_1_std
value: 1.6572084853398048
- type: nauc_map_at_20_diff1
value: 55.75769029917031
- type: nauc_map_at_20_max
value: 52.53663737242853
- type: nauc_map_at_20_std
value: 2.8489192879814
- type: nauc_map_at_3_diff1
value: 56.90294128342709
- type: nauc_map_at_3_max
value: 53.10608389782041
- type: nauc_map_at_3_std
value: 1.4909731657889491
- type: nauc_map_at_5_diff1
value: 56.1258315436073
- type: nauc_map_at_5_max
value: 52.398078357541564
- type: nauc_map_at_5_std
value: 1.8256862015101467
- type: nauc_mrr_at_1000_diff1
value: 55.61987610843598
- type: nauc_mrr_at_1000_max
value: 52.506795017152186
- type: nauc_mrr_at_1000_std
value: 2.95487192066911
- type: nauc_mrr_at_100_diff1
value: 55.598419532054734
- type: nauc_mrr_at_100_max
value: 52.48192017040307
- type: nauc_mrr_at_100_std
value: 2.930120252521189
- type: nauc_mrr_at_10_diff1
value: 56.02309155375198
- type: nauc_mrr_at_10_max
value: 52.739573233234424
- type: nauc_mrr_at_10_std
value: 2.4073432421641545
- type: nauc_mrr_at_1_diff1
value: 52.57059856776112
- type: nauc_mrr_at_1_max
value: 50.55668152952304
- type: nauc_mrr_at_1_std
value: 1.6572084853398048
- type: nauc_mrr_at_20_diff1
value: 55.75769029917031
- type: nauc_mrr_at_20_max
value: 52.53663737242853
- type: nauc_mrr_at_20_std
value: 2.8489192879814
- type: nauc_mrr_at_3_diff1
value: 56.90294128342709
- type: nauc_mrr_at_3_max
value: 53.10608389782041
- type: nauc_mrr_at_3_std
value: 1.4909731657889491
- type: nauc_mrr_at_5_diff1
value: 56.1258315436073
- type: nauc_mrr_at_5_max
value: 52.398078357541564
- type: nauc_mrr_at_5_std
value: 1.8256862015101467
- type: nauc_ndcg_at_1000_diff1
value: 55.30733548408918
- type: nauc_ndcg_at_1000_max
value: 53.51143366189318
- type: nauc_ndcg_at_1000_std
value: 7.133789405525702
- type: nauc_ndcg_at_100_diff1
value: 54.32209039488095
- type: nauc_ndcg_at_100_max
value: 52.67499334461009
- type: nauc_ndcg_at_100_std
value: 6.878823275077807
- type: nauc_ndcg_at_10_diff1
value: 56.266780806997716
- type: nauc_ndcg_at_10_max
value: 53.52837255793743
- type: nauc_ndcg_at_10_std
value: 3.756832592964262
- type: nauc_ndcg_at_1_diff1
value: 52.57059856776112
- type: nauc_ndcg_at_1_max
value: 50.55668152952304
- type: nauc_ndcg_at_1_std
value: 1.6572084853398048
- type: nauc_ndcg_at_20_diff1
value: 55.39255420432796
- type: nauc_ndcg_at_20_max
value: 52.946114684072235
- type: nauc_ndcg_at_20_std
value: 5.414933414031693
- type: nauc_ndcg_at_3_diff1
value: 57.92826624996289
- type: nauc_ndcg_at_3_max
value: 53.89907760306972
- type: nauc_ndcg_at_3_std
value: 1.6661401245309218
- type: nauc_ndcg_at_5_diff1
value: 56.47508936029308
- type: nauc_ndcg_at_5_max
value: 52.66800998045517
- type: nauc_ndcg_at_5_std
value: 2.4127296184140423
- type: nauc_precision_at_1000_diff1
value: 57.25924020238401
- type: nauc_precision_at_1000_max
value: 65.1132590931922
- type: nauc_precision_at_1000_std
value: 40.60788709618145
- type: nauc_precision_at_100_diff1
value: 46.49620002554606
- type: nauc_precision_at_100_max
value: 53.02960148167071
- type: nauc_precision_at_100_std
value: 28.206028867032863
- type: nauc_precision_at_10_diff1
value: 56.562744749606765
- type: nauc_precision_at_10_max
value: 56.00594967783547
- type: nauc_precision_at_10_std
value: 8.368379831645163
- type: nauc_precision_at_1_diff1
value: 52.57059856776112
- type: nauc_precision_at_1_max
value: 50.55668152952304
- type: nauc_precision_at_1_std
value: 1.6572084853398048
- type: nauc_precision_at_20_diff1
value: 53.25915754614111
- type: nauc_precision_at_20_max
value: 54.03255118937036
- type: nauc_precision_at_20_std
value: 15.161611674272718
- type: nauc_precision_at_3_diff1
value: 60.726785748943854
- type: nauc_precision_at_3_max
value: 56.139896875869354
- type: nauc_precision_at_3_std
value: 2.2306901035769893
- type: nauc_precision_at_5_diff1
value: 57.1201127525187
- type: nauc_precision_at_5_max
value: 53.28665761862506
- type: nauc_precision_at_5_std
value: 4.358720050112237
- type: nauc_recall_at_1000_diff1
value: 57.259240202383964
- type: nauc_recall_at_1000_max
value: 65.11325909319218
- type: nauc_recall_at_1000_std
value: 40.60788709618142
- type: nauc_recall_at_100_diff1
value: 46.49620002554603
- type: nauc_recall_at_100_max
value: 53.02960148167071
- type: nauc_recall_at_100_std
value: 28.206028867032835
- type: nauc_recall_at_10_diff1
value: 56.562744749606765
- type: nauc_recall_at_10_max
value: 56.00594967783549
- type: nauc_recall_at_10_std
value: 8.368379831645147
- type: nauc_recall_at_1_diff1
value: 52.57059856776112
- type: nauc_recall_at_1_max
value: 50.55668152952304
- type: nauc_recall_at_1_std
value: 1.6572084853398048
- type: nauc_recall_at_20_diff1
value: 53.259157546141154
- type: nauc_recall_at_20_max
value: 54.03255118937038
- type: nauc_recall_at_20_std
value: 15.16161167427274
- type: nauc_recall_at_3_diff1
value: 60.72678574894387
- type: nauc_recall_at_3_max
value: 56.13989687586933
- type: nauc_recall_at_3_std
value: 2.2306901035770066
- type: nauc_recall_at_5_diff1
value: 57.12011275251864
- type: nauc_recall_at_5_max
value: 53.28665761862502
- type: nauc_recall_at_5_std
value: 4.3587200501122245
- type: ndcg_at_1
value: 30.0
- type: ndcg_at_10
value: 38.671
- type: ndcg_at_100
value: 42.173
- type: ndcg_at_1000
value: 44.016
- type: ndcg_at_20
value: 39.845000000000006
- type: ndcg_at_3
value: 36.863
- type: ndcg_at_5
value: 37.874
- type: precision_at_1
value: 30.0
- type: precision_at_10
value: 4.65
- type: precision_at_100
value: 0.64
- type: precision_at_1000
value: 0.08
- type: precision_at_20
value: 2.55
- type: precision_at_3
value: 13.833
- type: precision_at_5
value: 8.799999999999999
- type: recall_at_1
value: 30.0
- type: recall_at_10
value: 46.5
- type: recall_at_100
value: 64.0
- type: recall_at_1000
value: 79.5
- type: recall_at_20
value: 51.0
- type: recall_at_3
value: 41.5
- type: recall_at_5
value: 44.0
- task:
type: Classification
dataset:
name: MTEB MultilingualSentimentClassification (rus)
type: mteb/multilingual-sentiment-classification
config: rus
split: test
revision: 2b9b4d10fc589af67794141fe8cbd3739de1eb33
metrics:
- type: accuracy
value: 79.52710495963092
- type: ap
value: 84.5713457178972
- type: ap_weighted
value: 84.5713457178972
- type: f1
value: 77.88661181524105
- type: f1_weighted
value: 79.87563079922718
- type: main_score
value: 79.52710495963092
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (arb_Arab-rus_Cyrl)
type: mteb/NTREX
config: arb_Arab-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 86.47971957936905
- type: f1
value: 82.79864240805654
- type: main_score
value: 82.79864240805654
- type: precision
value: 81.21485800128767
- type: recall
value: 86.47971957936905
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (bel_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: bel_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.84226339509264
- type: f1
value: 93.56399067465667
- type: main_score
value: 93.56399067465667
- type: precision
value: 93.01619095309631
- type: recall
value: 94.84226339509264
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ben_Beng-rus_Cyrl)
type: mteb/NTREX
config: ben_Beng-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.18828242363544
- type: f1
value: 90.42393889620612
- type: main_score
value: 90.42393889620612
- type: precision
value: 89.67904925153297
- type: recall
value: 92.18828242363544
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (bos_Latn-rus_Cyrl)
type: mteb/NTREX
config: bos_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
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value: 94.69203805708563
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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dataset:
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config: rus_Cyrl-lit_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 90.33550325488233
- type: f1
value: 87.68831819157307
- type: main_score
value: 87.68831819157307
- type: precision
value: 86.51524906407231
- type: recall
value: 90.33550325488233
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-mkd_Cyrl)
type: mteb/NTREX
config: rus_Cyrl-mkd_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.94391587381071
- type: f1
value: 94.90402270071775
- type: main_score
value: 94.90402270071775
- type: precision
value: 94.43915873810715
- type: recall
value: 95.94391587381071
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-nld_Latn)
type: mteb/NTREX
config: rus_Cyrl-nld_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.98948422633951
- type: f1
value: 91.04323151393756
- type: main_score
value: 91.04323151393756
- type: precision
value: 90.14688699716241
- type: recall
value: 92.98948422633951
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-pol_Latn)
type: mteb/NTREX
config: rus_Cyrl-pol_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.34151226840261
- type: f1
value: 92.8726422967785
- type: main_score
value: 92.8726422967785
- type: precision
value: 92.19829744616925
- type: recall
value: 94.34151226840261
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-por_Latn)
type: mteb/NTREX
config: rus_Cyrl-por_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 86.17926890335504
- type: f1
value: 82.7304882287356
- type: main_score
value: 82.7304882287356
- type: precision
value: 81.28162481817964
- type: recall
value: 86.17926890335504
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-slk_Latn)
type: mteb/NTREX
config: rus_Cyrl-slk_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.7391086629945
- type: f1
value: 90.75112669003506
- type: main_score
value: 90.75112669003506
- type: precision
value: 89.8564513436822
- type: recall
value: 92.7391086629945
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-slv_Latn)
type: mteb/NTREX
config: rus_Cyrl-slv_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.8893340010015
- type: f1
value: 91.05992321816058
- type: main_score
value: 91.05992321816058
- type: precision
value: 90.22589439715128
- type: recall
value: 92.8893340010015
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-spa_Latn)
type: mteb/NTREX
config: rus_Cyrl-spa_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.49474211316975
- type: f1
value: 95.4715406442998
- type: main_score
value: 95.4715406442998
- type: precision
value: 94.9799699549324
- type: recall
value: 96.49474211316975
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Cyrl)
type: mteb/NTREX
config: rus_Cyrl-srp_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 81.07160741111667
- type: f1
value: 76.55687285507015
- type: main_score
value: 76.55687285507015
- type: precision
value: 74.71886401030116
- type: recall
value: 81.07160741111667
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-srp_Latn)
type: mteb/NTREX
config: rus_Cyrl-srp_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.14271407110667
- type: f1
value: 93.73302377809138
- type: main_score
value: 93.73302377809138
- type: precision
value: 93.06960440660991
- type: recall
value: 95.14271407110667
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-swa_Latn)
type: mteb/NTREX
config: rus_Cyrl-swa_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.79218828242364
- type: f1
value: 93.25988983475212
- type: main_score
value: 93.25988983475212
- type: precision
value: 92.53463528626273
- type: recall
value: 94.79218828242364
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-swe_Latn)
type: mteb/NTREX
config: rus_Cyrl-swe_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.04256384576865
- type: f1
value: 93.58704723752295
- type: main_score
value: 93.58704723752295
- type: precision
value: 92.91437155733601
- type: recall
value: 95.04256384576865
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-tam_Taml)
type: mteb/NTREX
config: rus_Cyrl-tam_Taml
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.28993490235354
- type: f1
value: 91.63912535469872
- type: main_score
value: 91.63912535469872
- type: precision
value: 90.87738750983617
- type: recall
value: 93.28993490235354
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-tur_Latn)
type: mteb/NTREX
config: rus_Cyrl-tur_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.74061091637456
- type: f1
value: 91.96628275746953
- type: main_score
value: 91.96628275746953
- type: precision
value: 91.15923885828742
- type: recall
value: 93.74061091637456
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-ukr_Cyrl)
type: mteb/NTREX
config: rus_Cyrl-ukr_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.99399098647972
- type: f1
value: 94.89567684860624
- type: main_score
value: 94.89567684860624
- type: precision
value: 94.37072275079286
- type: recall
value: 95.99399098647972
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-vie_Latn)
type: mteb/NTREX
config: rus_Cyrl-vie_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 91.4371557336004
- type: f1
value: 88.98681355366382
- type: main_score
value: 88.98681355366382
- type: precision
value: 87.89183775663496
- type: recall
value: 91.4371557336004
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-zho_Hant)
type: mteb/NTREX
config: rus_Cyrl-zho_Hant
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.7891837756635
- type: f1
value: 90.79047142141783
- type: main_score
value: 90.79047142141783
- type: precision
value: 89.86980470706058
- type: recall
value: 92.7891837756635
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (rus_Cyrl-zul_Latn)
type: mteb/NTREX
config: rus_Cyrl-zul_Latn
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 87.43114672008012
- type: f1
value: 84.04618833011422
- type: main_score
value: 84.04618833011422
- type: precision
value: 82.52259341393041
- type: recall
value: 87.43114672008012
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (slk_Latn-rus_Cyrl)
type: mteb/NTREX
config: slk_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.34301452178268
- type: f1
value: 94.20392493502158
- type: main_score
value: 94.20392493502158
- type: precision
value: 93.67384409948257
- type: recall
value: 95.34301452178268
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (slv_Latn-rus_Cyrl)
type: mteb/NTREX
config: slv_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 92.23835753630446
- type: f1
value: 90.5061759305625
- type: main_score
value: 90.5061759305625
- type: precision
value: 89.74231188051918
- type: recall
value: 92.23835753630446
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (spa_Latn-rus_Cyrl)
type: mteb/NTREX
config: spa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.54481722583876
- type: f1
value: 95.54665331330328
- type: main_score
value: 95.54665331330328
- type: precision
value: 95.06342847604739
- type: recall
value: 96.54481722583876
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: srp_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 83.62543815723585
- type: f1
value: 80.77095672699816
- type: main_score
value: 80.77095672699816
- type: precision
value: 79.74674313056886
- type: recall
value: 83.62543815723585
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (srp_Latn-rus_Cyrl)
type: mteb/NTREX
config: srp_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 94.44166249374061
- type: f1
value: 93.00733206591994
- type: main_score
value: 93.00733206591994
- type: precision
value: 92.37203026762366
- type: recall
value: 94.44166249374061
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swa_Latn-rus_Cyrl)
type: mteb/NTREX
config: swa_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 90.23535302954431
- type: f1
value: 87.89596482636041
- type: main_score
value: 87.89596482636041
- type: precision
value: 86.87060227370694
- type: recall
value: 90.23535302954431
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (swe_Latn-rus_Cyrl)
type: mteb/NTREX
config: swe_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 95.44316474712068
- type: f1
value: 94.1896177599733
- type: main_score
value: 94.1896177599733
- type: precision
value: 93.61542313470206
- type: recall
value: 95.44316474712068
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tam_Taml-rus_Cyrl)
type: mteb/NTREX
config: tam_Taml-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 89.68452679018529
- type: f1
value: 87.37341160650037
- type: main_score
value: 87.37341160650037
- type: precision
value: 86.38389402285247
- type: recall
value: 89.68452679018529
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (tur_Latn-rus_Cyrl)
type: mteb/NTREX
config: tur_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.89083625438157
- type: f1
value: 92.33892505424804
- type: main_score
value: 92.33892505424804
- type: precision
value: 91.63125640842216
- type: recall
value: 93.89083625438157
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (ukr_Cyrl-rus_Cyrl)
type: mteb/NTREX
config: ukr_Cyrl-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 96.14421632448673
- type: f1
value: 95.11028447433054
- type: main_score
value: 95.11028447433054
- type: precision
value: 94.62944416624937
- type: recall
value: 96.14421632448673
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (vie_Latn-rus_Cyrl)
type: mteb/NTREX
config: vie_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 93.79068602904357
- type: f1
value: 92.14989150392256
- type: main_score
value: 92.14989150392256
- type: precision
value: 91.39292271740945
- type: recall
value: 93.79068602904357
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zho_Hant-rus_Cyrl)
type: mteb/NTREX
config: zho_Hant-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 89.13370055082625
- type: f1
value: 86.51514618639217
- type: main_score
value: 86.51514618639217
- type: precision
value: 85.383920035898
- type: recall
value: 89.13370055082625
- task:
type: BitextMining
dataset:
name: MTEB NTREXBitextMining (zul_Latn-rus_Cyrl)
type: mteb/NTREX
config: zul_Latn-rus_Cyrl
split: test
revision: ed9a4403ed4adbfaf4aab56d5b2709e9f6c3ba33
metrics:
- type: accuracy
value: 81.17175763645467
- type: f1
value: 77.72331766047338
- type: main_score
value: 77.72331766047338
- type: precision
value: 76.24629555848075
- type: recall
value: 81.17175763645467
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (ru)
type: GEM/opusparcus
config: ru
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 73.09136420525657
- type: cosine_accuracy_threshold
value: 87.70400881767273
- type: cosine_ap
value: 86.51938550599533
- type: cosine_f1
value: 80.84358523725834
- type: cosine_f1_threshold
value: 86.90648078918457
- type: cosine_precision
value: 73.24840764331209
- type: cosine_recall
value: 90.19607843137256
- type: dot_accuracy
value: 73.09136420525657
- type: dot_accuracy_threshold
value: 87.7040147781372
- type: dot_ap
value: 86.51934769946833
- type: dot_f1
value: 80.84358523725834
- type: dot_f1_threshold
value: 86.90648078918457
- type: dot_precision
value: 73.24840764331209
- type: dot_recall
value: 90.19607843137256
- type: euclidean_accuracy
value: 73.09136420525657
- type: euclidean_accuracy_threshold
value: 49.590304493904114
- type: euclidean_ap
value: 86.51934769946833
- type: euclidean_f1
value: 80.84358523725834
- type: euclidean_f1_threshold
value: 51.173269748687744
- type: euclidean_precision
value: 73.24840764331209
- type: euclidean_recall
value: 90.19607843137256
- type: main_score
value: 86.51976811057995
- type: manhattan_accuracy
value: 73.40425531914893
- type: manhattan_accuracy_threshold
value: 757.8278541564941
- type: manhattan_ap
value: 86.51976811057995
- type: manhattan_f1
value: 80.92898615453328
- type: manhattan_f1_threshold
value: 778.3821105957031
- type: manhattan_precision
value: 74.32321575061526
- type: manhattan_recall
value: 88.8235294117647
- type: max_ap
value: 86.51976811057995
- type: max_f1
value: 80.92898615453328
- type: max_precision
value: 74.32321575061526
- type: max_recall
value: 90.19607843137256
- type: similarity_accuracy
value: 73.09136420525657
- type: similarity_accuracy_threshold
value: 87.70400881767273
- type: similarity_ap
value: 86.51938550599533
- type: similarity_f1
value: 80.84358523725834
- type: similarity_f1_threshold
value: 86.90648078918457
- type: similarity_precision
value: 73.24840764331209
- type: similarity_recall
value: 90.19607843137256
- task:
type: Retrieval
dataset:
name: MTEB PublicHealthQA (russian)
type: xhluca/publichealth-qa
config: russian
split: test
revision: main
metrics:
- type: main_score
value: 79.303
- type: map_at_1
value: 61.538000000000004
- type: map_at_10
value: 74.449
- type: map_at_100
value: 74.687
- type: map_at_1000
value: 74.687
- type: map_at_20
value: 74.589
- type: map_at_3
value: 73.333
- type: map_at_5
value: 74.256
- type: mrr_at_1
value: 61.53846153846154
- type: mrr_at_10
value: 74.44871794871794
- type: mrr_at_100
value: 74.68730304304074
- type: mrr_at_1000
value: 74.68730304304074
- type: mrr_at_20
value: 74.58857808857809
- type: mrr_at_3
value: 73.33333333333333
- type: mrr_at_5
value: 74.25641025641025
- type: nauc_map_at_1000_diff1
value: 61.375798048778506
- type: nauc_map_at_1000_max
value: 51.37093181241067
- type: nauc_map_at_1000_std
value: 41.735794471409015
- type: nauc_map_at_100_diff1
value: 61.375798048778506
- type: nauc_map_at_100_max
value: 51.37093181241067
- type: nauc_map_at_100_std
value: 41.735794471409015
- type: nauc_map_at_10_diff1
value: 61.12796039757213
- type: nauc_map_at_10_max
value: 51.843445267118014
- type: nauc_map_at_10_std
value: 42.243121474939365
- type: nauc_map_at_1_diff1
value: 66.39100974909151
- type: nauc_map_at_1_max
value: 44.77165601342703
- type: nauc_map_at_1_std
value: 32.38542979413408
- type: nauc_map_at_20_diff1
value: 61.16611123434347
- type: nauc_map_at_20_max
value: 51.52605092407306
- type: nauc_map_at_20_std
value: 41.94787773313971
- type: nauc_map_at_3_diff1
value: 61.40157474408937
- type: nauc_map_at_3_max
value: 51.47230077853947
- type: nauc_map_at_3_std
value: 42.63540269440141
- type: nauc_map_at_5_diff1
value: 61.07631147583098
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- type: nauc_mrr_at_1_diff1
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- type: nauc_mrr_at_1_max
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- type: nauc_mrr_at_1_std
value: -9.187785187137633
- type: nauc_mrr_at_20_diff1
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- type: nauc_mrr_at_20_std
value: -7.283132775160146
- type: nauc_mrr_at_3_diff1
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- type: nauc_mrr_at_5_max
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- type: nauc_mrr_at_5_std
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- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_max
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- type: nauc_ndcg_at_1000_std
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- type: nauc_ndcg_at_100_max
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value: -4.092442251697269
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value: 40.300065598615205
- type: nauc_ndcg_at_10_max
value: 34.87866296788365
- type: nauc_ndcg_at_10_std
value: -5.866529277842453
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value: 51.74612915209495
- type: nauc_ndcg_at_1_max
value: 37.71907067970078
- type: nauc_ndcg_at_1_std
value: -9.064124266098696
- type: nauc_ndcg_at_20_diff1
value: 40.493949850214584
- type: nauc_ndcg_at_20_max
value: 35.69331503650286
- type: nauc_ndcg_at_20_std
value: -4.995310342975443
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value: 41.269443212112364
- type: nauc_ndcg_at_3_max
value: 32.572844460953334
- type: nauc_ndcg_at_3_std
value: -9.063015396458791
- type: nauc_ndcg_at_5_diff1
value: 41.37039652522888
- type: nauc_ndcg_at_5_max
value: 34.67416011393571
- type: nauc_ndcg_at_5_std
value: -7.106845569862319
- type: nauc_precision_at_1000_diff1
value: -9.571769961090155
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- type: nauc_precision_at_1000_std
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- type: nauc_precision_at_100_max
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value: 9.327890294080992
- type: nauc_precision_at_10_diff1
value: -1.006879647532931
- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_diff1
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- type: nauc_precision_at_5_max
value: 22.839056540604822
- type: nauc_precision_at_5_std
value: 1.5126905486524331
- type: nauc_recall_at_1000_diff1
value: -0.8869709920433502
- type: nauc_recall_at_1000_max
value: 45.092324433377264
- type: nauc_recall_at_1000_std
value: 62.21264093315108
- type: nauc_recall_at_100_diff1
value: 16.036715011075714
- type: nauc_recall_at_100_max
value: 39.79963411771158
- type: nauc_recall_at_100_std
value: 28.41850069503361
- type: nauc_recall_at_10_diff1
value: 25.189622794479998
- type: nauc_recall_at_10_max
value: 30.82355277039427
- type: nauc_recall_at_10_std
value: 0.0964544736531047
- type: nauc_recall_at_1_diff1
value: 44.36306892906905
- type: nauc_recall_at_1_max
value: 25.61348630699028
- type: nauc_recall_at_1_std
value: -8.713074613333902
- type: nauc_recall_at_20_diff1
value: 20.43424504746087
- type: nauc_recall_at_20_max
value: 33.96010554649377
- type: nauc_recall_at_20_std
value: 6.900984030301936
- type: nauc_recall_at_3_diff1
value: 33.86531858793492
- type: nauc_recall_at_3_max
value: 27.725692256711188
- type: nauc_recall_at_3_std
value: -8.533124289305709
- type: nauc_recall_at_5_diff1
value: 32.006964557701686
- type: nauc_recall_at_5_max
value: 31.493370659289806
- type: nauc_recall_at_5_std
value: -4.8639793547793255
- type: ndcg_at_1
value: 60.461
- type: ndcg_at_10
value: 68.529
- type: ndcg_at_100
value: 71.664
- type: ndcg_at_1000
value: 72.396
- type: ndcg_at_20
value: 70.344
- type: ndcg_at_3
value: 61.550000000000004
- type: ndcg_at_5
value: 64.948
- type: precision_at_1
value: 60.461
- type: precision_at_10
value: 13.28
- type: precision_at_100
value: 1.555
- type: precision_at_1000
value: 0.164
- type: precision_at_20
value: 7.216
- type: precision_at_3
value: 33.077
- type: precision_at_5
value: 23.014000000000003
- type: recall_at_1
value: 42.529
- type: recall_at_10
value: 81.169
- type: recall_at_100
value: 93.154
- type: recall_at_1000
value: 98.18299999999999
- type: recall_at_20
value: 87.132
- type: recall_at_3
value: 63.905
- type: recall_at_5
value: 71.967
- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
- type: accuracy
value: 61.17675781250001
- type: f1
value: 60.354535346041374
- type: f1_weighted
value: 60.35437313166116
- type: main_score
value: 61.17675781250001
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS (default)
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 78.1301041727274
- type: cosine_spearman
value: 78.08238025421747
- type: euclidean_pearson
value: 77.35224254583635
- type: euclidean_spearman
value: 78.08235336582496
- type: main_score
value: 78.08238025421747
- type: manhattan_pearson
value: 77.24138550052075
- type: manhattan_spearman
value: 77.98199107904142
- type: pearson
value: 78.1301041727274
- type: spearman
value: 78.08238025421747
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 54.990234375
- type: f1
value: 53.537019057131374
- type: f1_weighted
value: 53.552745354520766
- type: main_score
value: 54.990234375
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: main_score
value: 50.775228895355106
- type: v_measure
value: 50.775228895355106
- type: v_measure_std
value: 0.9533571150165796
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 41.71875
- type: f1
value: 39.289100975858304
- type: f1_weighted
value: 39.29257829217775
- type: main_score
value: 41.71875
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: main_score
value: 45.10904808834516
- type: v_measure
value: 45.10904808834516
- type: v_measure_std
value: 1.0572643410157534
- task:
type: Classification
dataset:
name: MTEB SIB200Classification (rus_Cyrl)
type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
- type: accuracy
value: 66.36363636363637
- type: f1
value: 64.6940336621617
- type: f1_weighted
value: 66.43317771876966
- type: main_score
value: 66.36363636363637
- task:
type: Clustering
dataset:
name: MTEB SIB200ClusteringS2S (rus_Cyrl)
type: mteb/sib200
config: rus_Cyrl
split: test
revision: a74d7350ea12af010cfb1c21e34f1f81fd2e615b
metrics:
- type: main_score
value: 33.99178497314711
- type: v_measure
value: 33.99178497314711
- type: v_measure_std
value: 4.036337464043786
- task:
type: STS
dataset:
name: MTEB STS22.v2 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
metrics:
- type: cosine_pearson
value: 50.724322379215934
- type: cosine_spearman
value: 59.90449732164651
- type: euclidean_pearson
value: 50.227545226784024
- type: euclidean_spearman
value: 59.898906527601085
- type: main_score
value: 59.90449732164651
- type: manhattan_pearson
value: 50.21762139819405
- type: manhattan_spearman
value: 59.761039813759
- type: pearson
value: 50.724322379215934
- type: spearman
value: 59.90449732164651
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (ru)
type: mteb/stsb_multi_mt
config: ru
split: dev
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 78.43928769569945
- type: cosine_spearman
value: 78.23961768018884
- type: euclidean_pearson
value: 77.4718694027985
- type: euclidean_spearman
value: 78.23887044760475
- type: main_score
value: 78.23961768018884
- type: manhattan_pearson
value: 77.34517128089547
- type: manhattan_spearman
value: 78.1146477340426
- type: pearson
value: 78.43928769569945
- type: spearman
value: 78.23961768018884
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 22.8125
- type: f1
value: 17.31969589593409
- type: lrap
value: 33.82412380642287
- type: main_score
value: 22.8125
- task:
type: PairClassification
dataset:
name: MTEB TERRa (default)
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
- type: cosine_accuracy
value: 57.32899022801303
- type: cosine_accuracy_threshold
value: 85.32201051712036
- type: cosine_ap
value: 55.14264553720072
- type: cosine_f1
value: 66.83544303797468
- type: cosine_f1_threshold
value: 85.32201051712036
- type: cosine_precision
value: 54.54545454545454
- type: cosine_recall
value: 86.27450980392157
- type: dot_accuracy
value: 57.32899022801303
- type: dot_accuracy_threshold
value: 85.32201051712036
- type: dot_ap
value: 55.14264553720072
- type: dot_f1
value: 66.83544303797468
- type: dot_f1_threshold
value: 85.32201051712036
- type: dot_precision
value: 54.54545454545454
- type: dot_recall
value: 86.27450980392157
- type: euclidean_accuracy
value: 57.32899022801303
- type: euclidean_accuracy_threshold
value: 54.18117046356201
- type: euclidean_ap
value: 55.14264553720072
- type: euclidean_f1
value: 66.83544303797468
- type: euclidean_f1_threshold
value: 54.18117046356201
- type: euclidean_precision
value: 54.54545454545454
- type: euclidean_recall
value: 86.27450980392157
- type: main_score
value: 55.14264553720072
- type: manhattan_accuracy
value: 57.32899022801303
- type: manhattan_accuracy_threshold
value: 828.8480758666992
- type: manhattan_ap
value: 55.077974053622555
- type: manhattan_f1
value: 66.82352941176471
- type: manhattan_f1_threshold
value: 885.6784820556641
- type: manhattan_precision
value: 52.20588235294118
- type: manhattan_recall
value: 92.81045751633987
- type: max_ap
value: 55.14264553720072
- type: max_f1
value: 66.83544303797468
- type: max_precision
value: 54.54545454545454
- type: max_recall
value: 92.81045751633987
- type: similarity_accuracy
value: 57.32899022801303
- type: similarity_accuracy_threshold
value: 85.32201051712036
- type: similarity_ap
value: 55.14264553720072
- type: similarity_f1
value: 66.83544303797468
- type: similarity_f1_threshold
value: 85.32201051712036
- type: similarity_precision
value: 54.54545454545454
- type: similarity_recall
value: 86.27450980392157
- task:
type: PairClassification
dataset:
name: MTEB XNLI (ru)
type: mteb/xnli
config: ru
split: test
revision: 09698e0180d87dc247ca447d3a1248b931ac0cdb
metrics:
- type: cosine_accuracy
value: 67.6923076923077
- type: cosine_accuracy_threshold
value: 87.6681923866272
- type: cosine_ap
value: 73.18693800863593
- type: cosine_f1
value: 70.40641099026904
- type: cosine_f1_threshold
value: 85.09706258773804
- type: cosine_precision
value: 57.74647887323944
- type: cosine_recall
value: 90.17595307917888
- type: dot_accuracy
value: 67.6923076923077
- type: dot_accuracy_threshold
value: 87.66818642616272
- type: dot_ap
value: 73.18693800863593
- type: dot_f1
value: 70.40641099026904
- type: dot_f1_threshold
value: 85.09706258773804
- type: dot_precision
value: 57.74647887323944
- type: dot_recall
value: 90.17595307917888
- type: euclidean_accuracy
value: 67.6923076923077
- type: euclidean_accuracy_threshold
value: 49.662476778030396
- type: euclidean_ap
value: 73.18693800863593
- type: euclidean_f1
value: 70.40641099026904
- type: euclidean_f1_threshold
value: 54.59475517272949
- type: euclidean_precision
value: 57.74647887323944
- type: euclidean_recall
value: 90.17595307917888
- type: main_score
value: 73.18693800863593
- type: manhattan_accuracy
value: 67.54578754578755
- type: manhattan_accuracy_threshold
value: 777.1001815795898
- type: manhattan_ap
value: 72.98861474758783
- type: manhattan_f1
value: 70.6842435655995
- type: manhattan_f1_threshold
value: 810.3782653808594
- type: manhattan_precision
value: 61.80021953896817
- type: manhattan_recall
value: 82.55131964809385
- type: max_ap
value: 73.18693800863593
- type: max_f1
value: 70.6842435655995
- type: max_precision
value: 61.80021953896817
- type: max_recall
value: 90.17595307917888
- type: similarity_accuracy
value: 67.6923076923077
- type: similarity_accuracy_threshold
value: 87.6681923866272
- type: similarity_ap
value: 73.18693800863593
- type: similarity_f1
value: 70.40641099026904
- type: similarity_f1_threshold
value: 85.09706258773804
- type: similarity_precision
value: 57.74647887323944
- type: similarity_recall
value: 90.17595307917888
- task:
type: PairClassification
dataset:
name: MTEB XNLIV2 (russian)
type: mteb/xnli2.0-multi-pair
config: russian
split: test
revision: 5b7d477a8c62cdd18e2fed7e015497c20b4371ad
metrics:
- type: cosine_accuracy
value: 68.35164835164835
- type: cosine_accuracy_threshold
value: 88.48621845245361
- type: cosine_ap
value: 73.10205506215699
- type: cosine_f1
value: 71.28712871287128
- type: cosine_f1_threshold
value: 87.00399398803711
- type: cosine_precision
value: 61.67023554603854
- type: cosine_recall
value: 84.4574780058651
- type: dot_accuracy
value: 68.35164835164835
- type: dot_accuracy_threshold
value: 88.48622441291809
- type: dot_ap
value: 73.10191110714706
- type: dot_f1
value: 71.28712871287128
- type: dot_f1_threshold
value: 87.00399398803711
- type: dot_precision
value: 61.67023554603854
- type: dot_recall
value: 84.4574780058651
- type: euclidean_accuracy
value: 68.35164835164835
- type: euclidean_accuracy_threshold
value: 47.98704385757446
- type: euclidean_ap
value: 73.10205506215699
- type: euclidean_f1
value: 71.28712871287128
- type: euclidean_f1_threshold
value: 50.982362031936646
- type: euclidean_precision
value: 61.67023554603854
- type: euclidean_recall
value: 84.4574780058651
- type: main_score
value: 73.10205506215699
- type: manhattan_accuracy
value: 67.91208791208791
- type: manhattan_accuracy_threshold
value: 746.1360931396484
- type: manhattan_ap
value: 72.8954736175069
- type: manhattan_f1
value: 71.1297071129707
- type: manhattan_f1_threshold
value: 808.0789566040039
- type: manhattan_precision
value: 60.04036326942482
- type: manhattan_recall
value: 87.2434017595308
- type: max_ap
value: 73.10205506215699
- type: max_f1
value: 71.28712871287128
- type: max_precision
value: 61.67023554603854
- type: max_recall
value: 87.2434017595308
- type: similarity_accuracy
value: 68.35164835164835
- type: similarity_accuracy_threshold
value: 88.48621845245361
- type: similarity_ap
value: 73.10205506215699
- type: similarity_f1
value: 71.28712871287128
- type: similarity_f1_threshold
value: 87.00399398803711
- type: similarity_precision
value: 61.67023554603854
- type: similarity_recall
value: 84.4574780058651
- task:
type: Retrieval
dataset:
name: MTEB XQuADRetrieval (ru)
type: google/xquad
config: ru
split: validation
revision: 51adfef1c1287aab1d2d91b5bead9bcfb9c68583
metrics:
- type: main_score
value: 95.705
- type: map_at_1
value: 90.802
- type: map_at_10
value: 94.427
- type: map_at_100
value: 94.451
- type: map_at_1000
value: 94.451
- type: map_at_20
value: 94.446
- type: map_at_3
value: 94.121
- type: map_at_5
value: 94.34
- type: mrr_at_1
value: 90.80168776371308
- type: mrr_at_10
value: 94.42659567343111
- type: mrr_at_100
value: 94.45099347521871
- type: mrr_at_1000
value: 94.45099347521871
- type: mrr_at_20
value: 94.44574530017569
- type: mrr_at_3
value: 94.12095639943743
- type: mrr_at_5
value: 94.34036568213786
- type: nauc_map_at_1000_diff1
value: 87.40573202946949
- type: nauc_map_at_1000_max
value: 65.56220344468791
- type: nauc_map_at_1000_std
value: 8.865583291735863
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value: 87.40573202946949
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value: 65.56220344468791
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value: 8.865583291735863
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value: 87.43657080570291
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value: 65.71295628534446
- type: nauc_map_at_10_std
value: 9.055399339099655
- type: nauc_map_at_1_diff1
value: 88.08395824560428
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value: 62.92813192908893
- type: nauc_map_at_1_std
value: 6.738987385482432
- type: nauc_map_at_20_diff1
value: 87.40979818966589
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value: 65.59474346926105
- type: nauc_map_at_20_std
value: 8.944420599300914
- type: nauc_map_at_3_diff1
value: 86.97771892161035
- type: nauc_map_at_3_max
value: 66.14330030122467
- type: nauc_map_at_3_std
value: 8.62516327793521
- type: nauc_map_at_5_diff1
value: 87.30273362211798
- type: nauc_map_at_5_max
value: 66.1522476584607
- type: nauc_map_at_5_std
value: 9.780940862679724
- type: nauc_mrr_at_1000_diff1
value: 87.40573202946949
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value: 65.56220344468791
- type: nauc_mrr_at_1000_std
value: 8.865583291735863
- type: nauc_mrr_at_100_diff1
value: 87.40573202946949
- type: nauc_mrr_at_100_max
value: 65.56220344468791
- type: nauc_mrr_at_100_std
value: 8.865583291735863
- type: nauc_mrr_at_10_diff1
value: 87.43657080570291
- type: nauc_mrr_at_10_max
value: 65.71295628534446
- type: nauc_mrr_at_10_std
value: 9.055399339099655
- type: nauc_mrr_at_1_diff1
value: 88.08395824560428
- type: nauc_mrr_at_1_max
value: 62.92813192908893
- type: nauc_mrr_at_1_std
value: 6.738987385482432
- type: nauc_mrr_at_20_diff1
value: 87.40979818966589
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value: 65.59474346926105
- type: nauc_mrr_at_20_std
value: 8.944420599300914
- type: nauc_mrr_at_3_diff1
value: 86.97771892161035
- type: nauc_mrr_at_3_max
value: 66.14330030122467
- type: nauc_mrr_at_3_std
value: 8.62516327793521
- type: nauc_mrr_at_5_diff1
value: 87.30273362211798
- type: nauc_mrr_at_5_max
value: 66.1522476584607
- type: nauc_mrr_at_5_std
value: 9.780940862679724
- type: nauc_ndcg_at_1000_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_1000_max
value: 66.00874244792789
- type: nauc_ndcg_at_1000_std
value: 9.479929342875067
- type: nauc_ndcg_at_100_diff1
value: 87.37823158814116
- type: nauc_ndcg_at_100_max
value: 66.00874244792789
- type: nauc_ndcg_at_100_std
value: 9.479929342875067
- type: nauc_ndcg_at_10_diff1
value: 87.54508467181488
- type: nauc_ndcg_at_10_max
value: 66.88756470312894
- type: nauc_ndcg_at_10_std
value: 10.812624405397022
- type: nauc_ndcg_at_1_diff1
value: 88.08395824560428
- type: nauc_ndcg_at_1_max
value: 62.92813192908893
- type: nauc_ndcg_at_1_std
value: 6.738987385482432
- type: nauc_ndcg_at_20_diff1
value: 87.42097894104597
- type: nauc_ndcg_at_20_max
value: 66.37031898778943
- type: nauc_ndcg_at_20_std
value: 10.34862538094813
- type: nauc_ndcg_at_3_diff1
value: 86.50039907157999
- type: nauc_ndcg_at_3_max
value: 67.97798288917929
- type: nauc_ndcg_at_3_std
value: 10.162410286746852
- type: nauc_ndcg_at_5_diff1
value: 87.13322094568531
- type: nauc_ndcg_at_5_max
value: 68.08576118683821
- type: nauc_ndcg_at_5_std
value: 12.639637379592855
- type: nauc_precision_at_1000_diff1
value: 100.0
- type: nauc_precision_at_1000_max
value: 100.0
- type: nauc_precision_at_1000_std
value: 100.0
- type: nauc_precision_at_100_diff1
value: 100.0
- type: nauc_precision_at_100_max
value: 100.0
- type: nauc_precision_at_100_std
value: 100.0
- type: nauc_precision_at_10_diff1
value: 93.46711505595813
- type: nauc_precision_at_10_max
value: 100.0
- type: nauc_precision_at_10_std
value: 65.42573557179935
- type: nauc_precision_at_1_diff1
value: 88.08395824560428
- type: nauc_precision_at_1_max
value: 62.92813192908893
- type: nauc_precision_at_1_std
value: 6.738987385482432
- type: nauc_precision_at_20_diff1
value: 91.28948674127133
- type: nauc_precision_at_20_max
value: 100.0
- type: nauc_precision_at_20_std
value: 90.74278258632364
- type: nauc_precision_at_3_diff1
value: 82.64606115071832
- type: nauc_precision_at_3_max
value: 83.26201582412921
- type: nauc_precision_at_3_std
value: 23.334013491433762
- type: nauc_precision_at_5_diff1
value: 85.0867539350284
- type: nauc_precision_at_5_max
value: 96.57011448655484
- type: nauc_precision_at_5_std
value: 56.46869543426768
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: .nan
- type: nauc_recall_at_100_max
value: .nan
- type: nauc_recall_at_100_std
value: .nan
- type: nauc_recall_at_10_diff1
value: 93.46711505595623
- type: nauc_recall_at_10_max
value: 100.0
- type: nauc_recall_at_10_std
value: 65.42573557180279
- type: nauc_recall_at_1_diff1
value: 88.08395824560428
- type: nauc_recall_at_1_max
value: 62.92813192908893
- type: nauc_recall_at_1_std
value: 6.738987385482432
- type: nauc_recall_at_20_diff1
value: 91.28948674127474
- type: nauc_recall_at_20_max
value: 100.0
- type: nauc_recall_at_20_std
value: 90.74278258632704
- type: nauc_recall_at_3_diff1
value: 82.64606115071967
- type: nauc_recall_at_3_max
value: 83.26201582413023
- type: nauc_recall_at_3_std
value: 23.334013491434007
- type: nauc_recall_at_5_diff1
value: 85.08675393502854
- type: nauc_recall_at_5_max
value: 96.57011448655487
- type: nauc_recall_at_5_std
value: 56.46869543426658
- type: ndcg_at_1
value: 90.802
- type: ndcg_at_10
value: 95.705
- type: ndcg_at_100
value: 95.816
- type: ndcg_at_1000
value: 95.816
- type: ndcg_at_20
value: 95.771
- type: ndcg_at_3
value: 95.11699999999999
- type: ndcg_at_5
value: 95.506
- type: precision_at_1
value: 90.802
- type: precision_at_10
value: 9.949
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.987
- type: precision_at_3
value: 32.658
- type: precision_at_5
value: 19.781000000000002
- type: recall_at_1
value: 90.802
- type: recall_at_10
value: 99.494
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.747
- type: recall_at_3
value: 97.975
- type: recall_at_5
value: 98.90299999999999
---
## Multilingual-E5-small
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"TRANSLATION",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/multilingual-e5-large | intfloat | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"feature-extraction",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-30T07:38:19 | 2025-02-17T03:50:15 | 2,239,718 | 894 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
model-index:
- name: multilingual-e5-large
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.05970149253731
- type: ap
value: 43.486574390835635
- type: f1
value: 73.32700092140148
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.22055674518201
- type: ap
value: 81.55756710830498
- type: f1
value: 69.28271787752661
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 80.41979010494754
- type: ap
value: 29.34879922376344
- type: f1
value: 67.62475449011278
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.8372591006424
- type: ap
value: 26.557560591210738
- type: f1
value: 64.96619417368707
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.489875
- type: ap
value: 90.98758636917603
- type: f1
value: 93.48554819717332
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.564
- type: f1
value: 46.75122173518047
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.400000000000006
- type: f1
value: 44.17195682400632
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.068
- type: f1
value: 42.38155696855596
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.89
- type: f1
value: 40.84407321682663
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.120000000000005
- type: f1
value: 39.522976223819114
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.832
- type: f1
value: 38.0392533394713
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.055
- type: map_at_100
value: 46.900999999999996
- type: map_at_1000
value: 46.911
- type: map_at_3
value: 41.548
- type: map_at_5
value: 44.297
- type: mrr_at_1
value: 31.152
- type: mrr_at_10
value: 46.231
- type: mrr_at_100
value: 47.07
- type: mrr_at_1000
value: 47.08
- type: mrr_at_3
value: 41.738
- type: mrr_at_5
value: 44.468999999999994
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 54.379999999999995
- type: ndcg_at_100
value: 58.138
- type: ndcg_at_1000
value: 58.389
- type: ndcg_at_3
value: 45.156
- type: ndcg_at_5
value: 50.123
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.54
- type: precision_at_5
value: 13.542000000000002
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 55.619
- type: recall_at_5
value: 67.71000000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.30960650674069
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 38.427074197498996
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.28270056031872
- type: mrr
value: 74.38332673789738
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.05942144105269
- type: cos_sim_spearman
value: 82.51212105850809
- type: euclidean_pearson
value: 81.95639829909122
- type: euclidean_spearman
value: 82.3717564144213
- type: manhattan_pearson
value: 81.79273425468256
- type: manhattan_spearman
value: 82.20066817871039
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.46764091858039
- type: f1
value: 99.37717466945023
- type: precision
value: 99.33194154488518
- type: recall
value: 99.46764091858039
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.29407880255337
- type: f1
value: 98.11248073959938
- type: precision
value: 98.02443319392472
- type: recall
value: 98.29407880255337
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.79009352268791
- type: f1
value: 97.5176076665512
- type: precision
value: 97.38136473848286
- type: recall
value: 97.79009352268791
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.20133403545726
- type: precision
value: 99.17500438827453
- type: recall
value: 99.26276987888363
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.72727272727273
- type: f1
value: 84.67672206031433
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.34220182511161
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.4987096128766
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.558249999999997
- type: map_at_10
value: 34.44425000000001
- type: map_at_100
value: 35.59833333333333
- type: map_at_1000
value: 35.706916666666665
- type: map_at_3
value: 31.691749999999995
- type: map_at_5
value: 33.252916666666664
- type: mrr_at_1
value: 30.252666666666666
- type: mrr_at_10
value: 38.60675
- type: mrr_at_100
value: 39.42666666666666
- type: mrr_at_1000
value: 39.48408333333334
- type: mrr_at_3
value: 36.17441666666665
- type: mrr_at_5
value: 37.56275
- type: ndcg_at_1
value: 30.252666666666666
- type: ndcg_at_10
value: 39.683
- type: ndcg_at_100
value: 44.68541666666667
- type: ndcg_at_1000
value: 46.94316666666668
- type: ndcg_at_3
value: 34.961749999999995
- type: ndcg_at_5
value: 37.215666666666664
- type: precision_at_1
value: 30.252666666666666
- type: precision_at_10
value: 6.904166666666667
- type: precision_at_100
value: 1.0989999999999995
- type: precision_at_1000
value: 0.14733333333333334
- type: precision_at_3
value: 16.037666666666667
- type: precision_at_5
value: 11.413583333333333
- type: recall_at_1
value: 25.558249999999997
- type: recall_at_10
value: 51.13341666666666
- type: recall_at_100
value: 73.08366666666667
- type: recall_at_1000
value: 88.79483333333334
- type: recall_at_3
value: 37.989083333333326
- type: recall_at_5
value: 43.787833333333325
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.338
- type: map_at_10
value: 18.360000000000003
- type: map_at_100
value: 19.942
- type: map_at_1000
value: 20.134
- type: map_at_3
value: 15.174000000000001
- type: map_at_5
value: 16.830000000000002
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 33.768
- type: mrr_at_100
value: 34.707
- type: mrr_at_1000
value: 34.766000000000005
- type: mrr_at_3
value: 30.977
- type: mrr_at_5
value: 32.528
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 25.733
- type: ndcg_at_100
value: 32.288
- type: ndcg_at_1000
value: 35.992000000000004
- type: ndcg_at_3
value: 20.866
- type: ndcg_at_5
value: 22.612
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 8.124
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 15.679000000000002
- type: precision_at_5
value: 12.117
- type: recall_at_1
value: 10.338
- type: recall_at_10
value: 31.154
- type: recall_at_100
value: 54.161
- type: recall_at_1000
value: 75.21900000000001
- type: recall_at_3
value: 19.427
- type: recall_at_5
value: 24.214
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.498
- type: map_at_10
value: 19.103
- type: map_at_100
value: 27.375
- type: map_at_1000
value: 28.981
- type: map_at_3
value: 13.764999999999999
- type: map_at_5
value: 15.950000000000001
- type: mrr_at_1
value: 65.5
- type: mrr_at_10
value: 74.53800000000001
- type: mrr_at_100
value: 74.71799999999999
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.792
- type: mrr_at_5
value: 73.554
- type: ndcg_at_1
value: 53.37499999999999
- type: ndcg_at_10
value: 41.286
- type: ndcg_at_100
value: 45.972
- type: ndcg_at_1000
value: 53.123
- type: ndcg_at_3
value: 46.172999999999995
- type: ndcg_at_5
value: 43.033
- type: precision_at_1
value: 65.5
- type: precision_at_10
value: 32.725
- type: precision_at_100
value: 10.683
- type: precision_at_1000
value: 1.978
- type: precision_at_3
value: 50
- type: precision_at_5
value: 41.349999999999994
- type: recall_at_1
value: 8.498
- type: recall_at_10
value: 25.070999999999998
- type: recall_at_100
value: 52.383
- type: recall_at_1000
value: 74.91499999999999
- type: recall_at_3
value: 15.207999999999998
- type: recall_at_5
value: 18.563
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.5
- type: f1
value: 41.93833713984145
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.914
- type: map_at_10
value: 78.10000000000001
- type: map_at_100
value: 78.333
- type: map_at_1000
value: 78.346
- type: map_at_3
value: 76.626
- type: map_at_5
value: 77.627
- type: mrr_at_1
value: 72.74199999999999
- type: mrr_at_10
value: 82.414
- type: mrr_at_100
value: 82.511
- type: mrr_at_1000
value: 82.513
- type: mrr_at_3
value: 81.231
- type: mrr_at_5
value: 82.065
- type: ndcg_at_1
value: 72.74199999999999
- type: ndcg_at_10
value: 82.806
- type: ndcg_at_100
value: 83.677
- type: ndcg_at_1000
value: 83.917
- type: ndcg_at_3
value: 80.305
- type: ndcg_at_5
value: 81.843
- type: precision_at_1
value: 72.74199999999999
- type: precision_at_10
value: 10.24
- type: precision_at_100
value: 1.089
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.268
- type: precision_at_5
value: 19.706000000000003
- type: recall_at_1
value: 67.914
- type: recall_at_10
value: 92.889
- type: recall_at_100
value: 96.42699999999999
- type: recall_at_1000
value: 97.92
- type: recall_at_3
value: 86.21
- type: recall_at_5
value: 90.036
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.166
- type: map_at_10
value: 35.57
- type: map_at_100
value: 37.405
- type: map_at_1000
value: 37.564
- type: map_at_3
value: 30.379
- type: map_at_5
value: 33.324
- type: mrr_at_1
value: 43.519000000000005
- type: mrr_at_10
value: 51.556000000000004
- type: mrr_at_100
value: 52.344
- type: mrr_at_1000
value: 52.373999999999995
- type: mrr_at_3
value: 48.868
- type: mrr_at_5
value: 50.319
- type: ndcg_at_1
value: 43.519000000000005
- type: ndcg_at_10
value: 43.803
- type: ndcg_at_100
value: 50.468999999999994
- type: ndcg_at_1000
value: 53.111
- type: ndcg_at_3
value: 38.893
- type: ndcg_at_5
value: 40.653
- type: precision_at_1
value: 43.519000000000005
- type: precision_at_10
value: 12.253
- type: precision_at_100
value: 1.931
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 25.617
- type: precision_at_5
value: 19.383
- type: recall_at_1
value: 22.166
- type: recall_at_10
value: 51.6
- type: recall_at_100
value: 76.574
- type: recall_at_1000
value: 92.192
- type: recall_at_3
value: 34.477999999999994
- type: recall_at_5
value: 41.835
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.041
- type: map_at_10
value: 62.961999999999996
- type: map_at_100
value: 63.79899999999999
- type: map_at_1000
value: 63.854
- type: map_at_3
value: 59.399
- type: map_at_5
value: 61.669
- type: mrr_at_1
value: 78.082
- type: mrr_at_10
value: 84.321
- type: mrr_at_100
value: 84.49600000000001
- type: mrr_at_1000
value: 84.502
- type: mrr_at_3
value: 83.421
- type: mrr_at_5
value: 83.977
- type: ndcg_at_1
value: 78.082
- type: ndcg_at_10
value: 71.229
- type: ndcg_at_100
value: 74.10900000000001
- type: ndcg_at_1000
value: 75.169
- type: ndcg_at_3
value: 66.28699999999999
- type: ndcg_at_5
value: 69.084
- type: precision_at_1
value: 78.082
- type: precision_at_10
value: 14.993
- type: precision_at_100
value: 1.7239999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 42.737
- type: precision_at_5
value: 27.843
- type: recall_at_1
value: 39.041
- type: recall_at_10
value: 74.96300000000001
- type: recall_at_100
value: 86.199
- type: recall_at_1000
value: 93.228
- type: recall_at_3
value: 64.105
- type: recall_at_5
value: 69.608
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.23160000000001
- type: ap
value: 85.5674856808308
- type: f1
value: 90.18033354786317
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.091
- type: map_at_10
value: 36.753
- type: map_at_100
value: 37.913000000000004
- type: map_at_1000
value: 37.958999999999996
- type: map_at_3
value: 32.818999999999996
- type: map_at_5
value: 35.171
- type: mrr_at_1
value: 24.742
- type: mrr_at_10
value: 37.285000000000004
- type: mrr_at_100
value: 38.391999999999996
- type: mrr_at_1000
value: 38.431
- type: mrr_at_3
value: 33.440999999999995
- type: mrr_at_5
value: 35.75
- type: ndcg_at_1
value: 24.742
- type: ndcg_at_10
value: 43.698
- type: ndcg_at_100
value: 49.145
- type: ndcg_at_1000
value: 50.23800000000001
- type: ndcg_at_3
value: 35.769
- type: ndcg_at_5
value: 39.961999999999996
- type: precision_at_1
value: 24.742
- type: precision_at_10
value: 6.7989999999999995
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.183
- type: recall_at_1
value: 24.091
- type: recall_at_10
value: 65.068
- type: recall_at_100
value: 89.899
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 43.68
- type: recall_at_5
value: 53.754999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.66621067031465
- type: f1
value: 93.49622853272142
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
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value: 67.74714189643578
- type: f1
value: 66.8212192745412
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (cy)
type: mteb/amazon_massive_scenario
config: cy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.09213180901143
- type: f1
value: 56.70735546356339
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (da)
type: mteb/amazon_massive_scenario
config: da
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.05716207128448
- type: f1
value: 74.8413712365364
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (de)
type: mteb/amazon_massive_scenario
config: de
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.69737726967047
- type: f1
value: 74.7664341963
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (el)
type: mteb/amazon_massive_scenario
config: el
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.90383322125084
- type: f1
value: 73.59201554448323
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.51176866173503
- type: f1
value: 77.46104434577758
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (es)
type: mteb/amazon_massive_scenario
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.31069266980496
- type: f1
value: 74.61048660675635
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fa)
type: mteb/amazon_massive_scenario
config: fa
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.95225285810356
- type: f1
value: 72.33160006574627
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fi)
type: mteb/amazon_massive_scenario
config: fi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.12373907195696
- type: f1
value: 73.20921012557481
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.86684599865501
- type: f1
value: 73.82348774610831
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.40215198386012
- type: f1
value: 71.11945183971858
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.12844653665098
- type: f1
value: 71.34450495911766
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.52252858103566
- type: f1
value: 73.98878711342999
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.93611297915265
- type: f1
value: 63.723200467653385
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.11903160726295
- type: f1
value: 73.82138439467096
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.15198386012105
- type: f1
value: 66.02172193802167
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.32414256893072
- type: f1
value: 74.30943421170574
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.46805648957633
- type: f1
value: 77.62808409298209
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.318762609280434
- type: f1
value: 62.094284066075076
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.34902488231338
- type: f1
value: 57.12893860987984
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 50.88433086751849
- type: f1
value: 48.2272350802058
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.4425016812374
- type: f1
value: 64.61463095996173
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.04707464694015
- type: f1
value: 75.05099199098998
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.50437121721586
- type: f1
value: 69.83397721096314
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.94283792871553
- type: f1
value: 68.8704663703913
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.79488903833222
- type: f1
value: 63.615424063345436
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.88231338264963
- type: f1
value: 68.57892302593237
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.248150638870214
- type: f1
value: 61.06680605338809
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.84196368527236
- type: f1
value: 74.52566464968763
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.8285137861466
- type: f1
value: 74.8853197608802
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.13248150638869
- type: f1
value: 74.3982040999179
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.49024882313383
- type: f1
value: 73.82153848368573
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.72158708809684
- type: f1
value: 71.85049433180541
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.137861466039
- type: f1
value: 75.37628348188467
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.86953597848016
- type: f1
value: 71.87537624521661
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.27572293207801
- type: f1
value: 68.80017302344231
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.09952925353059
- type: f1
value: 76.07992707688408
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.140551445864155
- type: f1
value: 61.73855010331415
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.27774041694687
- type: f1
value: 64.83664868894539
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.69468728984533
- type: f1
value: 64.76239666920868
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.44653665097512
- type: f1
value: 73.14646052013873
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.71351714862139
- type: f1
value: 66.67212180163382
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.9946200403497
- type: f1
value: 73.87348793725525
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.15400134498992
- type: f1
value: 67.09433241421094
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.11365164761264
- type: f1
value: 73.59502539433753
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.82582380632145
- type: f1
value: 76.89992945316313
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.81237390719569
- type: f1
value: 72.36499770986265
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.480506569594695
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.71252128004552
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.421396787056548
- type: mrr
value: 32.48155274872267
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.595
- type: map_at_10
value: 12.642000000000001
- type: map_at_100
value: 15.726
- type: map_at_1000
value: 17.061999999999998
- type: map_at_3
value: 9.125
- type: map_at_5
value: 10.866000000000001
- type: mrr_at_1
value: 43.344
- type: mrr_at_10
value: 52.227999999999994
- type: mrr_at_100
value: 52.898999999999994
- type: mrr_at_1000
value: 52.944
- type: mrr_at_3
value: 49.845
- type: mrr_at_5
value: 51.115
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 33.995
- type: ndcg_at_100
value: 30.869999999999997
- type: ndcg_at_1000
value: 39.487
- type: ndcg_at_3
value: 38.903999999999996
- type: ndcg_at_5
value: 37.236999999999995
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 25.480000000000004
- type: precision_at_100
value: 7.672
- type: precision_at_1000
value: 2.028
- type: precision_at_3
value: 36.636
- type: precision_at_5
value: 32.632
- type: recall_at_1
value: 5.595
- type: recall_at_10
value: 16.466
- type: recall_at_100
value: 31.226
- type: recall_at_1000
value: 62.778999999999996
- type: recall_at_3
value: 9.931
- type: recall_at_5
value: 12.884
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.414
- type: map_at_10
value: 56.754000000000005
- type: map_at_100
value: 57.457
- type: map_at_1000
value: 57.477999999999994
- type: map_at_3
value: 52.873999999999995
- type: map_at_5
value: 55.175
- type: mrr_at_1
value: 45.278
- type: mrr_at_10
value: 59.192
- type: mrr_at_100
value: 59.650000000000006
- type: mrr_at_1000
value: 59.665
- type: mrr_at_3
value: 56.141
- type: mrr_at_5
value: 57.998000000000005
- type: ndcg_at_1
value: 45.278
- type: ndcg_at_10
value: 64.056
- type: ndcg_at_100
value: 66.89
- type: ndcg_at_1000
value: 67.364
- type: ndcg_at_3
value: 56.97
- type: ndcg_at_5
value: 60.719
- type: precision_at_1
value: 45.278
- type: precision_at_10
value: 9.994
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.512
- type: precision_at_5
value: 17.509
- type: recall_at_1
value: 40.414
- type: recall_at_10
value: 83.596
- type: recall_at_100
value: 95.72
- type: recall_at_1000
value: 99.24
- type: recall_at_3
value: 65.472
- type: recall_at_5
value: 74.039
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.352
- type: map_at_10
value: 84.369
- type: map_at_100
value: 85.02499999999999
- type: map_at_1000
value: 85.04
- type: map_at_3
value: 81.42399999999999
- type: map_at_5
value: 83.279
- type: mrr_at_1
value: 81.05
- type: mrr_at_10
value: 87.401
- type: mrr_at_100
value: 87.504
- type: mrr_at_1000
value: 87.505
- type: mrr_at_3
value: 86.443
- type: mrr_at_5
value: 87.10799999999999
- type: ndcg_at_1
value: 81.04
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.411
- type: ndcg_at_1000
value: 89.507
- type: ndcg_at_3
value: 85.28099999999999
- type: ndcg_at_5
value: 86.888
- type: precision_at_1
value: 81.04
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.5350000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 70.352
- type: recall_at_10
value: 95.358
- type: recall_at_100
value: 99.541
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.111
- type: recall_at_5
value: 91.643
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 46.54068723291946
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.216287629895994
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.023000000000001
- type: map_at_10
value: 10.071
- type: map_at_100
value: 11.892
- type: map_at_1000
value: 12.196
- type: map_at_3
value: 7.234
- type: map_at_5
value: 8.613999999999999
- type: mrr_at_1
value: 19.900000000000002
- type: mrr_at_10
value: 30.516
- type: mrr_at_100
value: 31.656000000000002
- type: mrr_at_1000
value: 31.723000000000003
- type: mrr_at_3
value: 27.400000000000002
- type: mrr_at_5
value: 29.270000000000003
- type: ndcg_at_1
value: 19.900000000000002
- type: ndcg_at_10
value: 17.474
- type: ndcg_at_100
value: 25.020999999999997
- type: ndcg_at_1000
value: 30.728
- type: ndcg_at_3
value: 16.588
- type: ndcg_at_5
value: 14.498
- type: precision_at_1
value: 19.900000000000002
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 2.011
- type: precision_at_1000
value: 0.33899999999999997
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.023000000000001
- type: recall_at_10
value: 18.497
- type: recall_at_100
value: 40.8
- type: recall_at_1000
value: 68.812
- type: recall_at_3
value: 9.508
- type: recall_at_5
value: 12.983
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.967008785134
- type: cos_sim_spearman
value: 80.23142141101837
- type: euclidean_pearson
value: 81.20166064704539
- type: euclidean_spearman
value: 80.18961335654585
- type: manhattan_pearson
value: 81.13925443187625
- type: manhattan_spearman
value: 80.07948723044424
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.94262461316023
- type: cos_sim_spearman
value: 80.01596278563865
- type: euclidean_pearson
value: 83.80799622922581
- type: euclidean_spearman
value: 79.94984954947103
- type: manhattan_pearson
value: 83.68473841756281
- type: manhattan_spearman
value: 79.84990707951822
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.57346443146068
- type: cos_sim_spearman
value: 81.54689837570866
- type: euclidean_pearson
value: 81.10909881516007
- type: euclidean_spearman
value: 81.56746243261762
- type: manhattan_pearson
value: 80.87076036186582
- type: manhattan_spearman
value: 81.33074987964402
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.54733787179849
- type: cos_sim_spearman
value: 77.72202105610411
- type: euclidean_pearson
value: 78.9043595478849
- type: euclidean_spearman
value: 77.93422804309435
- type: manhattan_pearson
value: 78.58115121621368
- type: manhattan_spearman
value: 77.62508135122033
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.59880017237558
- type: cos_sim_spearman
value: 89.31088630824758
- type: euclidean_pearson
value: 88.47069261564656
- type: euclidean_spearman
value: 89.33581971465233
- type: manhattan_pearson
value: 88.40774264100956
- type: manhattan_spearman
value: 89.28657485627835
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.08055117917084
- type: cos_sim_spearman
value: 85.78491813080304
- type: euclidean_pearson
value: 84.99329155500392
- type: euclidean_spearman
value: 85.76728064677287
- type: manhattan_pearson
value: 84.87947428989587
- type: manhattan_spearman
value: 85.62429454917464
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 82.14190939287384
- type: cos_sim_spearman
value: 82.27331573306041
- type: euclidean_pearson
value: 81.891896953716
- type: euclidean_spearman
value: 82.37695542955998
- type: manhattan_pearson
value: 81.73123869460504
- type: manhattan_spearman
value: 82.19989168441421
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.84695301843362
- type: cos_sim_spearman
value: 77.87790986014461
- type: euclidean_pearson
value: 76.91981583106315
- type: euclidean_spearman
value: 77.88154772749589
- type: manhattan_pearson
value: 76.94953277451093
- type: manhattan_spearman
value: 77.80499230728604
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.44657840482016
- type: cos_sim_spearman
value: 75.05531095119674
- type: euclidean_pearson
value: 75.88161755829299
- type: euclidean_spearman
value: 74.73176238219332
- type: manhattan_pearson
value: 75.63984765635362
- type: manhattan_spearman
value: 74.86476440770737
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.64700140524133
- type: cos_sim_spearman
value: 86.16014210425672
- type: euclidean_pearson
value: 86.49086860843221
- type: euclidean_spearman
value: 86.09729326815614
- type: manhattan_pearson
value: 86.43406265125513
- type: manhattan_spearman
value: 86.17740150939994
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.91170098764921
- type: cos_sim_spearman
value: 88.12437004058931
- type: euclidean_pearson
value: 88.81828254494437
- type: euclidean_spearman
value: 88.14831794572122
- type: manhattan_pearson
value: 88.93442183448961
- type: manhattan_spearman
value: 88.15254630778304
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.91390577997292
- type: cos_sim_spearman
value: 71.22979457536074
- type: euclidean_pearson
value: 74.40314008106749
- type: euclidean_spearman
value: 72.54972136083246
- type: manhattan_pearson
value: 73.85687539530218
- type: manhattan_spearman
value: 72.09500771742637
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.9301067983089
- type: cos_sim_spearman
value: 80.74989828346473
- type: euclidean_pearson
value: 81.36781301814257
- type: euclidean_spearman
value: 80.9448819964426
- type: manhattan_pearson
value: 81.0351322685609
- type: manhattan_spearman
value: 80.70192121844177
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.13820465980005
- type: cos_sim_spearman
value: 86.73532498758757
- type: euclidean_pearson
value: 87.21329451846637
- type: euclidean_spearman
value: 86.57863198601002
- type: manhattan_pearson
value: 87.06973713818554
- type: manhattan_spearman
value: 86.47534918791499
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.48720108904415
- type: cos_sim_spearman
value: 85.62221757068387
- type: euclidean_pearson
value: 86.1010129512749
- type: euclidean_spearman
value: 85.86580966509942
- type: manhattan_pearson
value: 86.26800938808971
- type: manhattan_spearman
value: 85.88902721678429
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.98021347333516
- type: cos_sim_spearman
value: 84.53806553803501
- type: euclidean_pearson
value: 84.61483347248364
- type: euclidean_spearman
value: 85.14191408011702
- type: manhattan_pearson
value: 84.75297588825967
- type: manhattan_spearman
value: 85.33176753669242
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.51856644893233
- type: cos_sim_spearman
value: 85.27510748506413
- type: euclidean_pearson
value: 85.09886861540977
- type: euclidean_spearman
value: 85.62579245860887
- type: manhattan_pearson
value: 84.93017860464607
- type: manhattan_spearman
value: 85.5063988898453
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.581573200584195
- type: cos_sim_spearman
value: 63.05503590247928
- type: euclidean_pearson
value: 63.652564812602094
- type: euclidean_spearman
value: 62.64811520876156
- type: manhattan_pearson
value: 63.506842893061076
- type: manhattan_spearman
value: 62.51289573046917
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.2248801729127
- type: cos_sim_spearman
value: 56.5936604678561
- type: euclidean_pearson
value: 43.98149464089
- type: euclidean_spearman
value: 56.108561882423615
- type: manhattan_pearson
value: 43.86880305903564
- type: manhattan_spearman
value: 56.04671150510166
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.17564527009831
- type: cos_sim_spearman
value: 64.57978560979488
- type: euclidean_pearson
value: 58.8818330154583
- type: euclidean_spearman
value: 64.99214839071281
- type: manhattan_pearson
value: 58.72671436121381
- type: manhattan_spearman
value: 65.10713416616109
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 26.772131864023297
- type: cos_sim_spearman
value: 34.68200792408681
- type: euclidean_pearson
value: 16.68082419005441
- type: euclidean_spearman
value: 34.83099932652166
- type: manhattan_pearson
value: 16.52605949659529
- type: manhattan_spearman
value: 34.82075801399475
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.42415189043831
- type: cos_sim_spearman
value: 63.54594264576758
- type: euclidean_pearson
value: 57.36577498297745
- type: euclidean_spearman
value: 63.111466379158074
- type: manhattan_pearson
value: 57.584543715873885
- type: manhattan_spearman
value: 63.22361054139183
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.55216762405518
- type: cos_sim_spearman
value: 56.98670142896412
- type: euclidean_pearson
value: 50.15318757562699
- type: euclidean_spearman
value: 56.524941926541906
- type: manhattan_pearson
value: 49.955618528674904
- type: manhattan_spearman
value: 56.37102209240117
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 49.20540980338571
- type: cos_sim_spearman
value: 59.9009453504406
- type: euclidean_pearson
value: 49.557749853620535
- type: euclidean_spearman
value: 59.76631621172456
- type: manhattan_pearson
value: 49.62340591181147
- type: manhattan_spearman
value: 59.94224880322436
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.508169956576985
- type: cos_sim_spearman
value: 66.82461565306046
- type: euclidean_pearson
value: 56.2274426480083
- type: euclidean_spearman
value: 66.6775323848333
- type: manhattan_pearson
value: 55.98277796300661
- type: manhattan_spearman
value: 66.63669848497175
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.86478788045507
- type: cos_sim_spearman
value: 76.7946552053193
- type: euclidean_pearson
value: 75.01598530490269
- type: euclidean_spearman
value: 76.83618917858281
- type: manhattan_pearson
value: 74.68337628304332
- type: manhattan_spearman
value: 76.57480204017773
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.922619099401984
- type: cos_sim_spearman
value: 56.599362477240774
- type: euclidean_pearson
value: 56.68307052369783
- type: euclidean_spearman
value: 54.28760436777401
- type: manhattan_pearson
value: 56.67763566500681
- type: manhattan_spearman
value: 53.94619541711359
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.74357206710913
- type: cos_sim_spearman
value: 72.5208244925311
- type: euclidean_pearson
value: 67.49254562186032
- type: euclidean_spearman
value: 72.02469076238683
- type: manhattan_pearson
value: 67.45251772238085
- type: manhattan_spearman
value: 72.05538819984538
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.25734330033191
- type: cos_sim_spearman
value: 76.98349083946823
- type: euclidean_pearson
value: 73.71642838667736
- type: euclidean_spearman
value: 77.01715504651384
- type: manhattan_pearson
value: 73.61712711868105
- type: manhattan_spearman
value: 77.01392571153896
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.18215462781212
- type: cos_sim_spearman
value: 65.54373266117607
- type: euclidean_pearson
value: 64.54126095439005
- type: euclidean_spearman
value: 65.30410369102711
- type: manhattan_pearson
value: 63.50332221148234
- type: manhattan_spearman
value: 64.3455878104313
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.30509221440029
- type: cos_sim_spearman
value: 65.99582704642478
- type: euclidean_pearson
value: 63.43818859884195
- type: euclidean_spearman
value: 66.83172582815764
- type: manhattan_pearson
value: 63.055779168508764
- type: manhattan_spearman
value: 65.49585020501449
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.587830825340404
- type: cos_sim_spearman
value: 68.93467614588089
- type: euclidean_pearson
value: 62.3073527367404
- type: euclidean_spearman
value: 69.69758171553175
- type: manhattan_pearson
value: 61.9074580815789
- type: manhattan_spearman
value: 69.57696375597865
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.143220125577066
- type: cos_sim_spearman
value: 67.78857859159226
- type: euclidean_pearson
value: 55.58225107923733
- type: euclidean_spearman
value: 67.80662907184563
- type: manhattan_pearson
value: 56.24953502726514
- type: manhattan_spearman
value: 67.98262125431616
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 21.826928900322066
- type: cos_sim_spearman
value: 49.578506634400405
- type: euclidean_pearson
value: 27.939890138843214
- type: euclidean_spearman
value: 52.71950519136242
- type: manhattan_pearson
value: 26.39878683847546
- type: manhattan_spearman
value: 47.54609580342499
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.27603854632001
- type: cos_sim_spearman
value: 50.709255283710995
- type: euclidean_pearson
value: 59.5419024445929
- type: euclidean_spearman
value: 50.709255283710995
- type: manhattan_pearson
value: 59.03256832438492
- type: manhattan_spearman
value: 61.97797868009122
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.00757054859712
- type: cos_sim_spearman
value: 87.29283629622222
- type: euclidean_pearson
value: 86.54824171775536
- type: euclidean_spearman
value: 87.24364730491402
- type: manhattan_pearson
value: 86.5062156915074
- type: manhattan_spearman
value: 87.15052170378574
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.03549357197389
- type: mrr
value: 95.05437645143527
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.260999999999996
- type: map_at_10
value: 66.259
- type: map_at_100
value: 66.884
- type: map_at_1000
value: 66.912
- type: map_at_3
value: 63.685
- type: map_at_5
value: 65.35499999999999
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 67.5
- type: mrr_at_100
value: 68.013
- type: mrr_at_1000
value: 68.038
- type: mrr_at_3
value: 65.61099999999999
- type: mrr_at_5
value: 66.861
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 70.41
- type: ndcg_at_100
value: 73.10600000000001
- type: ndcg_at_1000
value: 73.846
- type: ndcg_at_3
value: 66.133
- type: ndcg_at_5
value: 68.499
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.667
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 57.260999999999996
- type: recall_at_10
value: 81.94399999999999
- type: recall_at_100
value: 93.867
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.339
- type: recall_at_5
value: 76.25
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74356435643564
- type: cos_sim_ap
value: 93.13411948212683
- type: cos_sim_f1
value: 86.80521991300147
- type: cos_sim_precision
value: 84.00374181478017
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.67920792079208
- type: dot_ap
value: 89.27277565444479
- type: dot_f1
value: 83.9276990718124
- type: dot_precision
value: 82.04393505253104
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.74257425742574
- type: euclidean_ap
value: 93.17993008259062
- type: euclidean_f1
value: 86.69396110542476
- type: euclidean_precision
value: 88.78406708595388
- type: euclidean_recall
value: 84.7
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 93.14413755550099
- type: manhattan_f1
value: 86.82483594144371
- type: manhattan_precision
value: 87.66564729867483
- type: manhattan_recall
value: 86
- type: max_accuracy
value: 99.74356435643564
- type: max_ap
value: 93.17993008259062
- type: max_f1
value: 86.82483594144371
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.525863806168566
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.68850574423839
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.71580650644033
- type: mrr
value: 50.50971903913081
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.152190498799484
- type: cos_sim_spearman
value: 29.686180371952727
- type: dot_pearson
value: 27.248664793816342
- type: dot_spearman
value: 28.37748983721745
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.6209999999999998
- type: map_at_100
value: 9.690999999999999
- type: map_at_1000
value: 23.733
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.885
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.56700000000001
- type: mrr_at_100
value: 86.56700000000001
- type: mrr_at_1000
value: 86.56700000000001
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 86.56700000000001
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 71.326
- type: ndcg_at_100
value: 54.208999999999996
- type: ndcg_at_1000
value: 49.252
- type: ndcg_at_3
value: 74.235
- type: ndcg_at_5
value: 73.833
- type: precision_at_1
value: 78
- type: precision_at_10
value: 74.8
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 78
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.894
- type: recall_at_100
value: 13.245999999999999
- type: recall_at_1000
value: 46.373
- type: recall_at_3
value: 0.613
- type: recall_at_5
value: 0.991
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.69999999999999
- type: precision
value: 94.11666666666667
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.20809248554913
- type: f1
value: 63.431048720066066
- type: precision
value: 61.69143958161298
- type: recall
value: 68.20809248554913
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.21951219512195
- type: f1
value: 66.82926829268293
- type: precision
value: 65.1260162601626
- type: recall
value: 71.21951219512195
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.26666666666667
- type: precision
value: 95.8
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.63333333333333
- type: precision
value: 96.26666666666668
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.86666666666666
- type: precision
value: 94.31666666666668
- type: recall
value: 96
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.01492537313433
- type: f1
value: 40.178867566927266
- type: precision
value: 38.179295828549556
- type: recall
value: 47.01492537313433
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.62537480063796
- type: precision
value: 82.44555555555554
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.48780487804879
- type: f1
value: 75.45644599303138
- type: precision
value: 73.37398373983739
- type: recall
value: 80.48780487804879
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.95666666666666
- type: precision
value: 91.125
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.73754556500607
- type: f1
value: 89.65168084244632
- type: precision
value: 88.73025516403402
- type: recall
value: 91.73754556500607
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.04347826086956
- type: f1
value: 76.2128364389234
- type: precision
value: 74.2
- type: recall
value: 81.04347826086956
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.65217391304348
- type: f1
value: 79.4376811594203
- type: precision
value: 77.65797101449274
- type: recall
value: 83.65217391304348
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 85.02690476190476
- type: precision
value: 83.96261904761904
- type: recall
value: 87.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.3
- type: f1
value: 86.52333333333333
- type: precision
value: 85.22833333333332
- type: recall
value: 89.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.01809408926418
- type: f1
value: 59.00594446432805
- type: precision
value: 56.827215807915444
- type: recall
value: 65.01809408926418
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.2
- type: f1
value: 88.58
- type: precision
value: 87.33333333333334
- type: recall
value: 91.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 53.299166276284915
- type: precision
value: 51.3383908045977
- type: recall
value: 59.199999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.2
- type: precision
value: 90.25
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.76190476190476
- type: f1
value: 59.867110667110666
- type: precision
value: 58.07390192653351
- type: recall
value: 64.76190476190476
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.2
- type: f1
value: 71.48147546897547
- type: precision
value: 69.65409090909091
- type: recall
value: 76.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.8
- type: f1
value: 92.14
- type: precision
value: 91.35833333333333
- type: recall
value: 93.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 92.93333333333334
- type: precision
value: 92.13333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.1
- type: f1
value: 69.14817460317461
- type: precision
value: 67.2515873015873
- type: recall
value: 74.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 94.01333333333335
- type: precision
value: 93.46666666666667
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.9
- type: f1
value: 72.07523809523809
- type: precision
value: 70.19777777777779
- type: recall
value: 76.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.31666666666666
- type: precision
value: 91.43333333333332
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.1
- type: precision
value: 96.76666666666668
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.85714285714286
- type: f1
value: 90.92093441150045
- type: precision
value: 90.00449236298293
- type: recall
value: 92.85714285714286
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.16239316239316
- type: f1
value: 91.33903133903132
- type: precision
value: 90.56267806267806
- type: recall
value: 93.16239316239316
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.25666666666666
- type: precision
value: 89.25833333333334
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.22727272727272
- type: f1
value: 87.53030303030303
- type: precision
value: 86.37121212121211
- type: recall
value: 90.22727272727272
- task:
type: BitextMining
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.03563941299791
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97
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value: 97
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type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 76.26459143968872
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dataset:
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type: mteb/tatoeba-bitext-mining
config: gsw-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.119658119658126
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 74.3
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value: 74.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.8
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 70.60358255451713
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value: 76.63551401869158
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95
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value: 95
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type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.7
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ita-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.6
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type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.39999999999999
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: lvs-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.9
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: glg-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 12.9
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type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.7
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.39285714285714
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: arq-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.89999999999999
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type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.9
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value: 77.9
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type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 59.4
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value: 59.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.7
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 32.11678832116788
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.5
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.1
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type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: amh-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.56107660455487
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.8
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value: 96.8
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type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.2
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value: 7.631246556216846
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value: 9.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.48917748917748
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type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.09923664122137
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value: 70.8998380754106
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value: 77.09923664122137
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type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 98.2532751091703
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value: 98.2532751091703
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type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.8
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value: 82.8
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type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.35028248587571
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value: 94.35028248587571
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type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.5
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value: 8.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.7
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value: 91.025
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value: 90.30428571428571
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value: 92.7
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type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 81
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value: 77.8232380952381
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value: 76.60194444444444
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value: 81
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91
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value: 88.70857142857142
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value: 87.7
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value: 91
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.39999999999999
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value: 95.3
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value: 94.76666666666667
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value: 96.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.1
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value: 7.001008218834307
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value: 6.708329562594269
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value: 8.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.1313672922252
- type: f1
value: 84.09070598748882
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value: 82.79171454104429
- type: recall
value: 87.1313672922252
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333333
- type: precision
value: 94.73333333333332
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.29249011857708
- type: f1
value: 36.981018542283365
- type: precision
value: 35.415877813576024
- type: recall
value: 42.29249011857708
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.80281690140845
- type: f1
value: 80.86854460093896
- type: precision
value: 79.60093896713614
- type: recall
value: 83.80281690140845
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.26946107784431
- type: f1
value: 39.80235464678088
- type: precision
value: 38.14342660001342
- type: recall
value: 45.26946107784431
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.9
- type: precision
value: 92.26666666666668
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.93103448275862
- type: f1
value: 33.15192743764172
- type: precision
value: 31.57456528146183
- type: recall
value: 37.93103448275862
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.01408450704226
- type: f1
value: 63.41549295774648
- type: precision
value: 61.342778895595806
- type: recall
value: 69.01408450704226
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.66666666666667
- type: f1
value: 71.60705960705961
- type: precision
value: 69.60683760683762
- type: recall
value: 76.66666666666667
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.48333333333333
- type: precision
value: 93.83333333333333
- type: recall
value: 95.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 52.81837160751566
- type: f1
value: 48.435977731384824
- type: precision
value: 47.11291973845539
- type: recall
value: 52.81837160751566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.9
- type: f1
value: 38.88962621607783
- type: precision
value: 36.95936507936508
- type: recall
value: 44.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.55374592833876
- type: f1
value: 88.22553125484721
- type: precision
value: 87.26927252985884
- type: recall
value: 90.55374592833876
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.13333333333333
- type: precision
value: 92.45333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.99666666666667
- type: precision
value: 91.26666666666668
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.03937007874016
- type: f1
value: 81.75853018372703
- type: precision
value: 80.34120734908137
- type: recall
value: 85.03937007874016
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.3
- type: f1
value: 85.5
- type: precision
value: 84.25833333333334
- type: recall
value: 88.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.51246537396122
- type: f1
value: 60.02297410192148
- type: precision
value: 58.133467727289236
- type: recall
value: 65.51246537396122
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.89
- type: precision
value: 94.39166666666667
- type: recall
value: 96
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.692307692307686
- type: f1
value: 53.162393162393165
- type: precision
value: 51.70673076923077
- type: recall
value: 57.692307692307686
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.60000000000001
- type: f1
value: 89.21190476190475
- type: precision
value: 88.08666666666667
- type: recall
value: 91.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88
- type: f1
value: 85.47
- type: precision
value: 84.43266233766234
- type: recall
value: 88
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 90.64999999999999
- type: precision
value: 89.68333333333332
- type: recall
value: 92.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.30660377358491
- type: f1
value: 76.33044137466307
- type: precision
value: 74.78970125786164
- type: recall
value: 80.30660377358491
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.44
- type: precision
value: 94.99166666666666
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.53284671532847
- type: f1
value: 95.37712895377129
- type: precision
value: 94.7992700729927
- type: recall
value: 96.53284671532847
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89
- type: f1
value: 86.23190476190476
- type: precision
value: 85.035
- type: recall
value: 89
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.585
- type: map_at_10
value: 9.012
- type: map_at_100
value: 14.027000000000001
- type: map_at_1000
value: 15.565000000000001
- type: map_at_3
value: 5.032
- type: map_at_5
value: 6.657
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 45.377
- type: mrr_at_100
value: 46.119
- type: mrr_at_1000
value: 46.127
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 42.585
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 23.395
- type: ndcg_at_100
value: 33.342
- type: ndcg_at_1000
value: 45.523
- type: ndcg_at_3
value: 25.158
- type: ndcg_at_5
value: 23.427
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 6.714
- type: precision_at_1000
value: 1.473
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.585
- type: recall_at_10
value: 15.418999999999999
- type: recall_at_100
value: 42.485
- type: recall_at_1000
value: 79.536
- type: recall_at_3
value: 6.239999999999999
- type: recall_at_5
value: 8.996
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.3234
- type: ap
value: 14.361688653847423
- type: f1
value: 54.819068624319044
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.97792869269949
- type: f1
value: 62.28965628513728
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.90540145385218
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.53513739047506
- type: cos_sim_ap
value: 75.27741586677557
- type: cos_sim_f1
value: 69.18792902473774
- type: cos_sim_precision
value: 67.94708725515136
- type: cos_sim_recall
value: 70.47493403693932
- type: dot_accuracy
value: 84.7052512368123
- type: dot_ap
value: 69.36075482849378
- type: dot_f1
value: 64.44688376631296
- type: dot_precision
value: 59.92288500793831
- type: dot_recall
value: 69.70976253298153
- type: euclidean_accuracy
value: 86.60666388508076
- type: euclidean_ap
value: 75.47512772621097
- type: euclidean_f1
value: 69.413872536473
- type: euclidean_precision
value: 67.39562624254472
- type: euclidean_recall
value: 71.55672823218997
- type: manhattan_accuracy
value: 86.52917684925792
- type: manhattan_ap
value: 75.34000110496703
- type: manhattan_f1
value: 69.28489190226429
- type: manhattan_precision
value: 67.24608889992551
- type: manhattan_recall
value: 71.45118733509234
- type: max_accuracy
value: 86.60666388508076
- type: max_ap
value: 75.47512772621097
- type: max_f1
value: 69.413872536473
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.01695967710637
- type: cos_sim_ap
value: 85.8298270742901
- type: cos_sim_f1
value: 78.46988128389272
- type: cos_sim_precision
value: 74.86017897091722
- type: cos_sim_recall
value: 82.44533415460425
- type: dot_accuracy
value: 88.19420188613343
- type: dot_ap
value: 83.82679165901324
- type: dot_f1
value: 76.55833777304208
- type: dot_precision
value: 75.6884875846501
- type: dot_recall
value: 77.44841392054204
- type: euclidean_accuracy
value: 89.03054294252338
- type: euclidean_ap
value: 85.89089555185325
- type: euclidean_f1
value: 78.62997658079624
- type: euclidean_precision
value: 74.92329149232914
- type: euclidean_recall
value: 82.72251308900523
- type: manhattan_accuracy
value: 89.0266620095471
- type: manhattan_ap
value: 85.86458997929147
- type: manhattan_f1
value: 78.50685331000291
- type: manhattan_precision
value: 74.5499861534201
- type: manhattan_recall
value: 82.90729904527257
- type: max_accuracy
value: 89.03054294252338
- type: max_ap
value: 85.89089555185325
- type: max_f1
value: 78.62997658079624
---
## Multilingual-E5-large
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-large')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"TRANSLATION",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T05:20:08 | 2024-02-21T02:51:44 | 2,014,014 | 495 | ---
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-large-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8507462686567
- type: ap
value: 38.566457320228245
- type: f1
value: 69.69386648043475
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.416675
- type: ap
value: 89.1928861155922
- type: f1
value: 92.39477019574215
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.175999999999995
- type: f1
value: 47.80712792870253
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.184999999999995
- type: map_at_10
value: 55.654
- type: map_at_100
value: 56.25
- type: map_at_1000
value: 56.255
- type: map_at_3
value: 51.742999999999995
- type: map_at_5
value: 54.129000000000005
- type: mrr_at_1
value: 40.967
- type: mrr_at_10
value: 55.96
- type: mrr_at_100
value: 56.54900000000001
- type: mrr_at_1000
value: 56.554
- type: mrr_at_3
value: 51.980000000000004
- type: mrr_at_5
value: 54.44
- type: ndcg_at_1
value: 40.184999999999995
- type: ndcg_at_10
value: 63.542
- type: ndcg_at_100
value: 65.96499999999999
- type: ndcg_at_1000
value: 66.08699999999999
- type: ndcg_at_3
value: 55.582
- type: ndcg_at_5
value: 59.855000000000004
- type: precision_at_1
value: 40.184999999999995
- type: precision_at_10
value: 8.841000000000001
- type: precision_at_100
value: 0.987
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.238
- type: precision_at_5
value: 15.405
- type: recall_at_1
value: 40.184999999999995
- type: recall_at_10
value: 88.407
- type: recall_at_100
value: 98.72
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.714
- type: recall_at_5
value: 77.027
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.567077926750066
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.19453389182364
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.46555939623092
- type: mrr
value: 77.82361605768807
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.9554128814735
- type: cos_sim_spearman
value: 84.65373612172036
- type: euclidean_pearson
value: 83.2905059954138
- type: euclidean_spearman
value: 84.52240782811128
- type: manhattan_pearson
value: 82.99533802997436
- type: manhattan_spearman
value: 84.20673798475734
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.78896103896103
- type: f1
value: 87.77189310964883
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.714538337650495
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.90108349284447
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.795
- type: map_at_10
value: 43.669000000000004
- type: map_at_100
value: 45.151
- type: map_at_1000
value: 45.278
- type: map_at_3
value: 40.006
- type: map_at_5
value: 42.059999999999995
- type: mrr_at_1
value: 39.771
- type: mrr_at_10
value: 49.826
- type: mrr_at_100
value: 50.504000000000005
- type: mrr_at_1000
value: 50.549
- type: mrr_at_3
value: 47.115
- type: mrr_at_5
value: 48.832
- type: ndcg_at_1
value: 39.771
- type: ndcg_at_10
value: 50.217999999999996
- type: ndcg_at_100
value: 55.454
- type: ndcg_at_1000
value: 57.37
- type: ndcg_at_3
value: 44.885000000000005
- type: ndcg_at_5
value: 47.419
- type: precision_at_1
value: 39.771
- type: precision_at_10
value: 9.642000000000001
- type: precision_at_100
value: 1.538
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 21.268
- type: precision_at_5
value: 15.536
- type: recall_at_1
value: 32.795
- type: recall_at_10
value: 62.580999999999996
- type: recall_at_100
value: 84.438
- type: recall_at_1000
value: 96.492
- type: recall_at_3
value: 47.071000000000005
- type: recall_at_5
value: 54.079
- type: map_at_1
value: 32.671
- type: map_at_10
value: 43.334
- type: map_at_100
value: 44.566
- type: map_at_1000
value: 44.702999999999996
- type: map_at_3
value: 40.343
- type: map_at_5
value: 41.983
- type: mrr_at_1
value: 40.764
- type: mrr_at_10
value: 49.382
- type: mrr_at_100
value: 49.988
- type: mrr_at_1000
value: 50.03300000000001
- type: mrr_at_3
value: 47.293
- type: mrr_at_5
value: 48.51
- type: ndcg_at_1
value: 40.764
- type: ndcg_at_10
value: 49.039
- type: ndcg_at_100
value: 53.259
- type: ndcg_at_1000
value: 55.253
- type: ndcg_at_3
value: 45.091
- type: ndcg_at_5
value: 46.839999999999996
- type: precision_at_1
value: 40.764
- type: precision_at_10
value: 9.191
- type: precision_at_100
value: 1.476
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 21.72
- type: precision_at_5
value: 15.299
- type: recall_at_1
value: 32.671
- type: recall_at_10
value: 58.816
- type: recall_at_100
value: 76.654
- type: recall_at_1000
value: 89.05999999999999
- type: recall_at_3
value: 46.743
- type: recall_at_5
value: 51.783
- type: map_at_1
value: 40.328
- type: map_at_10
value: 53.32599999999999
- type: map_at_100
value: 54.37499999999999
- type: map_at_1000
value: 54.429
- type: map_at_3
value: 49.902
- type: map_at_5
value: 52.002
- type: mrr_at_1
value: 46.332
- type: mrr_at_10
value: 56.858
- type: mrr_at_100
value: 57.522
- type: mrr_at_1000
value: 57.54899999999999
- type: mrr_at_3
value: 54.472
- type: mrr_at_5
value: 55.996
- type: ndcg_at_1
value: 46.332
- type: ndcg_at_10
value: 59.313
- type: ndcg_at_100
value: 63.266999999999996
- type: ndcg_at_1000
value: 64.36
- type: ndcg_at_3
value: 53.815000000000005
- type: ndcg_at_5
value: 56.814
- type: precision_at_1
value: 46.332
- type: precision_at_10
value: 9.53
- type: precision_at_100
value: 1.238
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 24.054000000000002
- type: precision_at_5
value: 16.589000000000002
- type: recall_at_1
value: 40.328
- type: recall_at_10
value: 73.421
- type: recall_at_100
value: 90.059
- type: recall_at_1000
value: 97.81
- type: recall_at_3
value: 59.009
- type: recall_at_5
value: 66.352
- type: map_at_1
value: 27.424
- type: map_at_10
value: 36.332
- type: map_at_100
value: 37.347
- type: map_at_1000
value: 37.422
- type: map_at_3
value: 33.743
- type: map_at_5
value: 35.176
- type: mrr_at_1
value: 29.153000000000002
- type: mrr_at_10
value: 38.233
- type: mrr_at_100
value: 39.109
- type: mrr_at_1000
value: 39.164
- type: mrr_at_3
value: 35.876000000000005
- type: mrr_at_5
value: 37.169000000000004
- type: ndcg_at_1
value: 29.153000000000002
- type: ndcg_at_10
value: 41.439
- type: ndcg_at_100
value: 46.42
- type: ndcg_at_1000
value: 48.242000000000004
- type: ndcg_at_3
value: 36.362
- type: ndcg_at_5
value: 38.743
- type: precision_at_1
value: 29.153000000000002
- type: precision_at_10
value: 6.315999999999999
- type: precision_at_100
value: 0.927
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 15.443000000000001
- type: precision_at_5
value: 10.644
- type: recall_at_1
value: 27.424
- type: recall_at_10
value: 55.364000000000004
- type: recall_at_100
value: 78.211
- type: recall_at_1000
value: 91.74600000000001
- type: recall_at_3
value: 41.379
- type: recall_at_5
value: 47.14
- type: map_at_1
value: 19.601
- type: map_at_10
value: 27.826
- type: map_at_100
value: 29.017
- type: map_at_1000
value: 29.137
- type: map_at_3
value: 25.125999999999998
- type: map_at_5
value: 26.765
- type: mrr_at_1
value: 24.005000000000003
- type: mrr_at_10
value: 32.716
- type: mrr_at_100
value: 33.631
- type: mrr_at_1000
value: 33.694
- type: mrr_at_3
value: 29.934
- type: mrr_at_5
value: 31.630999999999997
- type: ndcg_at_1
value: 24.005000000000003
- type: ndcg_at_10
value: 33.158
- type: ndcg_at_100
value: 38.739000000000004
- type: ndcg_at_1000
value: 41.495
- type: ndcg_at_3
value: 28.185
- type: ndcg_at_5
value: 30.796
- type: precision_at_1
value: 24.005000000000003
- type: precision_at_10
value: 5.908
- type: precision_at_100
value: 1.005
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 13.391
- type: precision_at_5
value: 9.876
- type: recall_at_1
value: 19.601
- type: recall_at_10
value: 44.746
- type: recall_at_100
value: 68.82300000000001
- type: recall_at_1000
value: 88.215
- type: recall_at_3
value: 31.239
- type: recall_at_5
value: 37.695
- type: map_at_1
value: 30.130000000000003
- type: map_at_10
value: 40.96
- type: map_at_100
value: 42.282
- type: map_at_1000
value: 42.392
- type: map_at_3
value: 37.889
- type: map_at_5
value: 39.661
- type: mrr_at_1
value: 36.958999999999996
- type: mrr_at_10
value: 46.835
- type: mrr_at_100
value: 47.644
- type: mrr_at_1000
value: 47.688
- type: mrr_at_3
value: 44.562000000000005
- type: mrr_at_5
value: 45.938
- type: ndcg_at_1
value: 36.958999999999996
- type: ndcg_at_10
value: 47.06
- type: ndcg_at_100
value: 52.345
- type: ndcg_at_1000
value: 54.35
- type: ndcg_at_3
value: 42.301
- type: ndcg_at_5
value: 44.635999999999996
- type: precision_at_1
value: 36.958999999999996
- type: precision_at_10
value: 8.479000000000001
- type: precision_at_100
value: 1.284
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 20.244
- type: precision_at_5
value: 14.224999999999998
- type: recall_at_1
value: 30.130000000000003
- type: recall_at_10
value: 59.27
- type: recall_at_100
value: 81.195
- type: recall_at_1000
value: 94.21199999999999
- type: recall_at_3
value: 45.885
- type: recall_at_5
value: 52.016
- type: map_at_1
value: 26.169999999999998
- type: map_at_10
value: 36.451
- type: map_at_100
value: 37.791000000000004
- type: map_at_1000
value: 37.897
- type: map_at_3
value: 33.109
- type: map_at_5
value: 34.937000000000005
- type: mrr_at_1
value: 32.877
- type: mrr_at_10
value: 42.368
- type: mrr_at_100
value: 43.201
- type: mrr_at_1000
value: 43.259
- type: mrr_at_3
value: 39.763999999999996
- type: mrr_at_5
value: 41.260000000000005
- type: ndcg_at_1
value: 32.877
- type: ndcg_at_10
value: 42.659000000000006
- type: ndcg_at_100
value: 48.161
- type: ndcg_at_1000
value: 50.345
- type: ndcg_at_3
value: 37.302
- type: ndcg_at_5
value: 39.722
- type: precision_at_1
value: 32.877
- type: precision_at_10
value: 7.9
- type: precision_at_100
value: 1.236
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.846
- type: precision_at_5
value: 12.9
- type: recall_at_1
value: 26.169999999999998
- type: recall_at_10
value: 55.35
- type: recall_at_100
value: 78.755
- type: recall_at_1000
value: 93.518
- type: recall_at_3
value: 40.176
- type: recall_at_5
value: 46.589000000000006
- type: map_at_1
value: 27.15516666666667
- type: map_at_10
value: 36.65741666666667
- type: map_at_100
value: 37.84991666666666
- type: map_at_1000
value: 37.96316666666667
- type: map_at_3
value: 33.74974999999999
- type: map_at_5
value: 35.3765
- type: mrr_at_1
value: 32.08233333333334
- type: mrr_at_10
value: 41.033833333333334
- type: mrr_at_100
value: 41.84524999999999
- type: mrr_at_1000
value: 41.89983333333333
- type: mrr_at_3
value: 38.62008333333333
- type: mrr_at_5
value: 40.03441666666666
- type: ndcg_at_1
value: 32.08233333333334
- type: ndcg_at_10
value: 42.229
- type: ndcg_at_100
value: 47.26716666666667
- type: ndcg_at_1000
value: 49.43466666666667
- type: ndcg_at_3
value: 37.36408333333333
- type: ndcg_at_5
value: 39.6715
- type: precision_at_1
value: 32.08233333333334
- type: precision_at_10
value: 7.382583333333334
- type: precision_at_100
value: 1.16625
- type: precision_at_1000
value: 0.15408333333333332
- type: precision_at_3
value: 17.218
- type: precision_at_5
value: 12.21875
- type: recall_at_1
value: 27.15516666666667
- type: recall_at_10
value: 54.36683333333333
- type: recall_at_100
value: 76.37183333333333
- type: recall_at_1000
value: 91.26183333333333
- type: recall_at_3
value: 40.769916666666674
- type: recall_at_5
value: 46.702333333333335
- type: map_at_1
value: 25.749
- type: map_at_10
value: 33.001999999999995
- type: map_at_100
value: 33.891
- type: map_at_1000
value: 33.993
- type: map_at_3
value: 30.703999999999997
- type: map_at_5
value: 31.959
- type: mrr_at_1
value: 28.834
- type: mrr_at_10
value: 35.955
- type: mrr_at_100
value: 36.709
- type: mrr_at_1000
value: 36.779
- type: mrr_at_3
value: 33.947
- type: mrr_at_5
value: 35.089
- type: ndcg_at_1
value: 28.834
- type: ndcg_at_10
value: 37.329
- type: ndcg_at_100
value: 41.79
- type: ndcg_at_1000
value: 44.169000000000004
- type: ndcg_at_3
value: 33.184999999999995
- type: ndcg_at_5
value: 35.107
- type: precision_at_1
value: 28.834
- type: precision_at_10
value: 5.7669999999999995
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.213000000000001
- type: precision_at_5
value: 9.754999999999999
- type: recall_at_1
value: 25.749
- type: recall_at_10
value: 47.791
- type: recall_at_100
value: 68.255
- type: recall_at_1000
value: 85.749
- type: recall_at_3
value: 36.199
- type: recall_at_5
value: 41.071999999999996
- type: map_at_1
value: 17.777
- type: map_at_10
value: 25.201
- type: map_at_100
value: 26.423999999999996
- type: map_at_1000
value: 26.544
- type: map_at_3
value: 22.869
- type: map_at_5
value: 24.023
- type: mrr_at_1
value: 21.473
- type: mrr_at_10
value: 29.12
- type: mrr_at_100
value: 30.144
- type: mrr_at_1000
value: 30.215999999999998
- type: mrr_at_3
value: 26.933
- type: mrr_at_5
value: 28.051
- type: ndcg_at_1
value: 21.473
- type: ndcg_at_10
value: 30.003
- type: ndcg_at_100
value: 35.766
- type: ndcg_at_1000
value: 38.501000000000005
- type: ndcg_at_3
value: 25.773000000000003
- type: ndcg_at_5
value: 27.462999999999997
- type: precision_at_1
value: 21.473
- type: precision_at_10
value: 5.482
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.205
- type: precision_at_5
value: 8.692
- type: recall_at_1
value: 17.777
- type: recall_at_10
value: 40.582
- type: recall_at_100
value: 66.305
- type: recall_at_1000
value: 85.636
- type: recall_at_3
value: 28.687
- type: recall_at_5
value: 33.089
- type: map_at_1
value: 26.677
- type: map_at_10
value: 36.309000000000005
- type: map_at_100
value: 37.403999999999996
- type: map_at_1000
value: 37.496
- type: map_at_3
value: 33.382
- type: map_at_5
value: 34.98
- type: mrr_at_1
value: 31.343
- type: mrr_at_10
value: 40.549
- type: mrr_at_100
value: 41.342
- type: mrr_at_1000
value: 41.397
- type: mrr_at_3
value: 38.029
- type: mrr_at_5
value: 39.451
- type: ndcg_at_1
value: 31.343
- type: ndcg_at_10
value: 42.1
- type: ndcg_at_100
value: 47.089999999999996
- type: ndcg_at_1000
value: 49.222
- type: ndcg_at_3
value: 36.836999999999996
- type: ndcg_at_5
value: 39.21
- type: precision_at_1
value: 31.343
- type: precision_at_10
value: 7.164
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 16.915
- type: precision_at_5
value: 11.940000000000001
- type: recall_at_1
value: 26.677
- type: recall_at_10
value: 55.54599999999999
- type: recall_at_100
value: 77.094
- type: recall_at_1000
value: 92.01
- type: recall_at_3
value: 41.191
- type: recall_at_5
value: 47.006
- type: map_at_1
value: 24.501
- type: map_at_10
value: 33.102
- type: map_at_100
value: 34.676
- type: map_at_1000
value: 34.888000000000005
- type: map_at_3
value: 29.944
- type: map_at_5
value: 31.613999999999997
- type: mrr_at_1
value: 29.447000000000003
- type: mrr_at_10
value: 37.996
- type: mrr_at_100
value: 38.946
- type: mrr_at_1000
value: 38.995000000000005
- type: mrr_at_3
value: 35.079
- type: mrr_at_5
value: 36.69
- type: ndcg_at_1
value: 29.447000000000003
- type: ndcg_at_10
value: 39.232
- type: ndcg_at_100
value: 45.247
- type: ndcg_at_1000
value: 47.613
- type: ndcg_at_3
value: 33.922999999999995
- type: ndcg_at_5
value: 36.284
- type: precision_at_1
value: 29.447000000000003
- type: precision_at_10
value: 7.648000000000001
- type: precision_at_100
value: 1.516
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 16.008
- type: precision_at_5
value: 11.779
- type: recall_at_1
value: 24.501
- type: recall_at_10
value: 51.18899999999999
- type: recall_at_100
value: 78.437
- type: recall_at_1000
value: 92.842
- type: recall_at_3
value: 35.808
- type: recall_at_5
value: 42.197
- type: map_at_1
value: 22.039
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.275
- type: map_at_1000
value: 31.379
- type: map_at_3
value: 27.98
- type: map_at_5
value: 29.358
- type: mrr_at_1
value: 24.03
- type: mrr_at_10
value: 32.568000000000005
- type: mrr_at_100
value: 33.403
- type: mrr_at_1000
value: 33.475
- type: mrr_at_3
value: 30.436999999999998
- type: mrr_at_5
value: 31.796000000000003
- type: ndcg_at_1
value: 24.03
- type: ndcg_at_10
value: 35.198
- type: ndcg_at_100
value: 39.668
- type: ndcg_at_1000
value: 42.296
- type: ndcg_at_3
value: 30.709999999999997
- type: ndcg_at_5
value: 33.024
- type: precision_at_1
value: 24.03
- type: precision_at_10
value: 5.564
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 13.309000000000001
- type: precision_at_5
value: 9.39
- type: recall_at_1
value: 22.039
- type: recall_at_10
value: 47.746
- type: recall_at_100
value: 68.23599999999999
- type: recall_at_1000
value: 87.852
- type: recall_at_3
value: 35.852000000000004
- type: recall_at_5
value: 41.410000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.692999999999998
- type: map_at_10
value: 26.903
- type: map_at_100
value: 28.987000000000002
- type: map_at_1000
value: 29.176999999999996
- type: map_at_3
value: 22.137
- type: map_at_5
value: 24.758
- type: mrr_at_1
value: 35.57
- type: mrr_at_10
value: 47.821999999999996
- type: mrr_at_100
value: 48.608000000000004
- type: mrr_at_1000
value: 48.638999999999996
- type: mrr_at_3
value: 44.452000000000005
- type: mrr_at_5
value: 46.546
- type: ndcg_at_1
value: 35.57
- type: ndcg_at_10
value: 36.567
- type: ndcg_at_100
value: 44.085
- type: ndcg_at_1000
value: 47.24
- type: ndcg_at_3
value: 29.964000000000002
- type: ndcg_at_5
value: 32.511
- type: precision_at_1
value: 35.57
- type: precision_at_10
value: 11.485
- type: precision_at_100
value: 1.9619999999999997
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 22.237000000000002
- type: precision_at_5
value: 17.471999999999998
- type: recall_at_1
value: 15.692999999999998
- type: recall_at_10
value: 43.056
- type: recall_at_100
value: 68.628
- type: recall_at_1000
value: 86.075
- type: recall_at_3
value: 26.918999999999997
- type: recall_at_5
value: 34.14
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.53
- type: map_at_10
value: 20.951
- type: map_at_100
value: 30.136000000000003
- type: map_at_1000
value: 31.801000000000002
- type: map_at_3
value: 15.021
- type: map_at_5
value: 17.471999999999998
- type: mrr_at_1
value: 71.0
- type: mrr_at_10
value: 79.176
- type: mrr_at_100
value: 79.418
- type: mrr_at_1000
value: 79.426
- type: mrr_at_3
value: 78.125
- type: mrr_at_5
value: 78.61200000000001
- type: ndcg_at_1
value: 58.5
- type: ndcg_at_10
value: 44.106
- type: ndcg_at_100
value: 49.268
- type: ndcg_at_1000
value: 56.711999999999996
- type: ndcg_at_3
value: 48.934
- type: ndcg_at_5
value: 45.826
- type: precision_at_1
value: 71.0
- type: precision_at_10
value: 35.0
- type: precision_at_100
value: 11.360000000000001
- type: precision_at_1000
value: 2.046
- type: precision_at_3
value: 52.833
- type: precision_at_5
value: 44.15
- type: recall_at_1
value: 9.53
- type: recall_at_10
value: 26.811
- type: recall_at_100
value: 55.916999999999994
- type: recall_at_1000
value: 79.973
- type: recall_at_3
value: 16.413
- type: recall_at_5
value: 19.980999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.519999999999996
- type: f1
value: 46.36601294761231
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.413
- type: map_at_10
value: 83.414
- type: map_at_100
value: 83.621
- type: map_at_1000
value: 83.635
- type: map_at_3
value: 82.337
- type: map_at_5
value: 83.039
- type: mrr_at_1
value: 80.19800000000001
- type: mrr_at_10
value: 87.715
- type: mrr_at_100
value: 87.778
- type: mrr_at_1000
value: 87.779
- type: mrr_at_3
value: 87.106
- type: mrr_at_5
value: 87.555
- type: ndcg_at_1
value: 80.19800000000001
- type: ndcg_at_10
value: 87.182
- type: ndcg_at_100
value: 87.90299999999999
- type: ndcg_at_1000
value: 88.143
- type: ndcg_at_3
value: 85.60600000000001
- type: ndcg_at_5
value: 86.541
- type: precision_at_1
value: 80.19800000000001
- type: precision_at_10
value: 10.531
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.933
- type: precision_at_5
value: 20.429
- type: recall_at_1
value: 74.413
- type: recall_at_10
value: 94.363
- type: recall_at_100
value: 97.165
- type: recall_at_1000
value: 98.668
- type: recall_at_3
value: 90.108
- type: recall_at_5
value: 92.52
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.701
- type: map_at_10
value: 37.122
- type: map_at_100
value: 39.178000000000004
- type: map_at_1000
value: 39.326
- type: map_at_3
value: 32.971000000000004
- type: map_at_5
value: 35.332
- type: mrr_at_1
value: 44.753
- type: mrr_at_10
value: 53.452
- type: mrr_at_100
value: 54.198
- type: mrr_at_1000
value: 54.225
- type: mrr_at_3
value: 50.952
- type: mrr_at_5
value: 52.464
- type: ndcg_at_1
value: 44.753
- type: ndcg_at_10
value: 45.021
- type: ndcg_at_100
value: 52.028
- type: ndcg_at_1000
value: 54.596000000000004
- type: ndcg_at_3
value: 41.622
- type: ndcg_at_5
value: 42.736000000000004
- type: precision_at_1
value: 44.753
- type: precision_at_10
value: 12.284
- type: precision_at_100
value: 1.955
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 27.828999999999997
- type: precision_at_5
value: 20.061999999999998
- type: recall_at_1
value: 22.701
- type: recall_at_10
value: 51.432
- type: recall_at_100
value: 77.009
- type: recall_at_1000
value: 92.511
- type: recall_at_3
value: 37.919000000000004
- type: recall_at_5
value: 44.131
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.189
- type: map_at_10
value: 66.24600000000001
- type: map_at_100
value: 67.098
- type: map_at_1000
value: 67.149
- type: map_at_3
value: 62.684
- type: map_at_5
value: 64.974
- type: mrr_at_1
value: 80.378
- type: mrr_at_10
value: 86.127
- type: mrr_at_100
value: 86.29299999999999
- type: mrr_at_1000
value: 86.297
- type: mrr_at_3
value: 85.31400000000001
- type: mrr_at_5
value: 85.858
- type: ndcg_at_1
value: 80.378
- type: ndcg_at_10
value: 74.101
- type: ndcg_at_100
value: 76.993
- type: ndcg_at_1000
value: 77.948
- type: ndcg_at_3
value: 69.232
- type: ndcg_at_5
value: 72.04599999999999
- type: precision_at_1
value: 80.378
- type: precision_at_10
value: 15.595999999999998
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 44.884
- type: precision_at_5
value: 29.145
- type: recall_at_1
value: 40.189
- type: recall_at_10
value: 77.981
- type: recall_at_100
value: 89.21
- type: recall_at_1000
value: 95.48299999999999
- type: recall_at_3
value: 67.326
- type: recall_at_5
value: 72.863
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.84599999999999
- type: ap
value: 89.4710787567357
- type: f1
value: 92.83752676932258
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.132
- type: map_at_10
value: 35.543
- type: map_at_100
value: 36.702
- type: map_at_1000
value: 36.748999999999995
- type: map_at_3
value: 31.737
- type: map_at_5
value: 33.927
- type: mrr_at_1
value: 23.782
- type: mrr_at_10
value: 36.204
- type: mrr_at_100
value: 37.29
- type: mrr_at_1000
value: 37.330999999999996
- type: mrr_at_3
value: 32.458999999999996
- type: mrr_at_5
value: 34.631
- type: ndcg_at_1
value: 23.782
- type: ndcg_at_10
value: 42.492999999999995
- type: ndcg_at_100
value: 47.985
- type: ndcg_at_1000
value: 49.141
- type: ndcg_at_3
value: 34.748000000000005
- type: ndcg_at_5
value: 38.651
- type: precision_at_1
value: 23.782
- type: precision_at_10
value: 6.665
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.776
- type: precision_at_5
value: 10.84
- type: recall_at_1
value: 23.132
- type: recall_at_10
value: 63.794
- type: recall_at_100
value: 89.027
- type: recall_at_1000
value: 97.807
- type: recall_at_3
value: 42.765
- type: recall_at_5
value: 52.11
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.59188326493388
- type: f1
value: 94.3842594786827
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.49384404924761
- type: f1
value: 59.7580539534629
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.56220578345663
- type: f1
value: 75.27228165561478
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.53463349024884
- type: f1
value: 80.4893958236536
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.56100273484962
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.470380028839607
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.06102792457849
- type: mrr
value: 33.30709199672238
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.776999999999999
- type: map_at_10
value: 14.924000000000001
- type: map_at_100
value: 18.955
- type: map_at_1000
value: 20.538999999999998
- type: map_at_3
value: 10.982
- type: map_at_5
value: 12.679000000000002
- type: mrr_at_1
value: 47.988
- type: mrr_at_10
value: 57.232000000000006
- type: mrr_at_100
value: 57.818999999999996
- type: mrr_at_1000
value: 57.847
- type: mrr_at_3
value: 54.901999999999994
- type: mrr_at_5
value: 56.481
- type: ndcg_at_1
value: 46.594
- type: ndcg_at_10
value: 38.129000000000005
- type: ndcg_at_100
value: 35.54
- type: ndcg_at_1000
value: 44.172
- type: ndcg_at_3
value: 43.025999999999996
- type: ndcg_at_5
value: 41.052
- type: precision_at_1
value: 47.988
- type: precision_at_10
value: 28.111000000000004
- type: precision_at_100
value: 8.929
- type: precision_at_1000
value: 2.185
- type: precision_at_3
value: 40.144000000000005
- type: precision_at_5
value: 35.232
- type: recall_at_1
value: 6.776999999999999
- type: recall_at_10
value: 19.289
- type: recall_at_100
value: 36.359
- type: recall_at_1000
value: 67.54
- type: recall_at_3
value: 11.869
- type: recall_at_5
value: 14.999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.108000000000004
- type: map_at_10
value: 47.126000000000005
- type: map_at_100
value: 48.171
- type: map_at_1000
value: 48.199
- type: map_at_3
value: 42.734
- type: map_at_5
value: 45.362
- type: mrr_at_1
value: 34.936
- type: mrr_at_10
value: 49.571
- type: mrr_at_100
value: 50.345
- type: mrr_at_1000
value: 50.363
- type: mrr_at_3
value: 45.959
- type: mrr_at_5
value: 48.165
- type: ndcg_at_1
value: 34.936
- type: ndcg_at_10
value: 55.028999999999996
- type: ndcg_at_100
value: 59.244
- type: ndcg_at_1000
value: 59.861
- type: ndcg_at_3
value: 46.872
- type: ndcg_at_5
value: 51.217999999999996
- type: precision_at_1
value: 34.936
- type: precision_at_10
value: 9.099
- type: precision_at_100
value: 1.145
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 21.456
- type: precision_at_5
value: 15.411
- type: recall_at_1
value: 31.108000000000004
- type: recall_at_10
value: 76.53999999999999
- type: recall_at_100
value: 94.39
- type: recall_at_1000
value: 98.947
- type: recall_at_3
value: 55.572
- type: recall_at_5
value: 65.525
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.56400000000001
- type: map_at_10
value: 85.482
- type: map_at_100
value: 86.114
- type: map_at_1000
value: 86.13
- type: map_at_3
value: 82.607
- type: map_at_5
value: 84.405
- type: mrr_at_1
value: 82.42
- type: mrr_at_10
value: 88.304
- type: mrr_at_100
value: 88.399
- type: mrr_at_1000
value: 88.399
- type: mrr_at_3
value: 87.37
- type: mrr_at_5
value: 88.024
- type: ndcg_at_1
value: 82.45
- type: ndcg_at_10
value: 89.06500000000001
- type: ndcg_at_100
value: 90.232
- type: ndcg_at_1000
value: 90.305
- type: ndcg_at_3
value: 86.375
- type: ndcg_at_5
value: 87.85300000000001
- type: precision_at_1
value: 82.45
- type: precision_at_10
value: 13.486999999999998
- type: precision_at_100
value: 1.534
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.813
- type: precision_at_5
value: 24.773999999999997
- type: recall_at_1
value: 71.56400000000001
- type: recall_at_10
value: 95.812
- type: recall_at_100
value: 99.7
- type: recall_at_1000
value: 99.979
- type: recall_at_3
value: 87.966
- type: recall_at_5
value: 92.268
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 57.241876648614145
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.66212576446223
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.308
- type: map_at_10
value: 13.803
- type: map_at_100
value: 16.176
- type: map_at_1000
value: 16.561
- type: map_at_3
value: 9.761000000000001
- type: map_at_5
value: 11.802
- type: mrr_at_1
value: 26.200000000000003
- type: mrr_at_10
value: 37.621
- type: mrr_at_100
value: 38.767
- type: mrr_at_1000
value: 38.815
- type: mrr_at_3
value: 34.117
- type: mrr_at_5
value: 36.107
- type: ndcg_at_1
value: 26.200000000000003
- type: ndcg_at_10
value: 22.64
- type: ndcg_at_100
value: 31.567
- type: ndcg_at_1000
value: 37.623
- type: ndcg_at_3
value: 21.435000000000002
- type: ndcg_at_5
value: 18.87
- type: precision_at_1
value: 26.200000000000003
- type: precision_at_10
value: 11.74
- type: precision_at_100
value: 2.465
- type: precision_at_1000
value: 0.391
- type: precision_at_3
value: 20.033
- type: precision_at_5
value: 16.64
- type: recall_at_1
value: 5.308
- type: recall_at_10
value: 23.794999999999998
- type: recall_at_100
value: 50.015
- type: recall_at_1000
value: 79.283
- type: recall_at_3
value: 12.178
- type: recall_at_5
value: 16.882
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.93231134675553
- type: cos_sim_spearman
value: 81.68319292603205
- type: euclidean_pearson
value: 81.8396814380367
- type: euclidean_spearman
value: 81.24641903349945
- type: manhattan_pearson
value: 81.84698799204274
- type: manhattan_spearman
value: 81.24269997904105
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.73241671587446
- type: cos_sim_spearman
value: 79.05091082971826
- type: euclidean_pearson
value: 83.91146869578044
- type: euclidean_spearman
value: 79.87978465370936
- type: manhattan_pearson
value: 83.90888338917678
- type: manhattan_spearman
value: 79.87482848584241
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.14970731146177
- type: cos_sim_spearman
value: 86.37363490084627
- type: euclidean_pearson
value: 83.02154218530433
- type: euclidean_spearman
value: 83.80258761957367
- type: manhattan_pearson
value: 83.01664495119347
- type: manhattan_spearman
value: 83.77567458007952
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.40474139886784
- type: cos_sim_spearman
value: 82.77768789165984
- type: euclidean_pearson
value: 80.7065877443695
- type: euclidean_spearman
value: 81.375940662505
- type: manhattan_pearson
value: 80.6507552270278
- type: manhattan_spearman
value: 81.32782179098741
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.08585968722274
- type: cos_sim_spearman
value: 88.03110031451399
- type: euclidean_pearson
value: 85.74012019602384
- type: euclidean_spearman
value: 86.13592849438209
- type: manhattan_pearson
value: 85.74404842369206
- type: manhattan_spearman
value: 86.14492318960154
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.95069052788875
- type: cos_sim_spearman
value: 86.4867991595147
- type: euclidean_pearson
value: 84.31013325754635
- type: euclidean_spearman
value: 85.01529258006482
- type: manhattan_pearson
value: 84.26995570085374
- type: manhattan_spearman
value: 84.96982104986162
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.54617647971897
- type: cos_sim_spearman
value: 87.49834181751034
- type: euclidean_pearson
value: 86.01015322577122
- type: euclidean_spearman
value: 84.63362652063199
- type: manhattan_pearson
value: 86.13807574475706
- type: manhattan_spearman
value: 84.7772370721132
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.20047755786615
- type: cos_sim_spearman
value: 67.05324077987636
- type: euclidean_pearson
value: 66.91930642976601
- type: euclidean_spearman
value: 65.21491856099105
- type: manhattan_pearson
value: 66.78756851976624
- type: manhattan_spearman
value: 65.12356257740728
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.19852871539686
- type: cos_sim_spearman
value: 87.5161895296395
- type: euclidean_pearson
value: 84.59848645207485
- type: euclidean_spearman
value: 85.26427328757919
- type: manhattan_pearson
value: 84.59747366996524
- type: manhattan_spearman
value: 85.24045855146915
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.63320317811032
- type: mrr
value: 96.26242947321379
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.928000000000004
- type: map_at_10
value: 70.112
- type: map_at_100
value: 70.59299999999999
- type: map_at_1000
value: 70.623
- type: map_at_3
value: 66.846
- type: map_at_5
value: 68.447
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 71.212
- type: mrr_at_100
value: 71.616
- type: mrr_at_1000
value: 71.64500000000001
- type: mrr_at_3
value: 68.77799999999999
- type: mrr_at_5
value: 70.094
- type: ndcg_at_1
value: 64.0
- type: ndcg_at_10
value: 74.607
- type: ndcg_at_100
value: 76.416
- type: ndcg_at_1000
value: 77.102
- type: ndcg_at_3
value: 69.126
- type: ndcg_at_5
value: 71.41300000000001
- type: precision_at_1
value: 64.0
- type: precision_at_10
value: 9.933
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.556
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 60.928000000000004
- type: recall_at_10
value: 87.322
- type: recall_at_100
value: 94.833
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 72.628
- type: recall_at_5
value: 78.428
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.86237623762376
- type: cos_sim_ap
value: 96.72586477206649
- type: cos_sim_f1
value: 93.01858362631845
- type: cos_sim_precision
value: 93.4409687184662
- type: cos_sim_recall
value: 92.60000000000001
- type: dot_accuracy
value: 99.78019801980199
- type: dot_ap
value: 93.72748205246228
- type: dot_f1
value: 89.04109589041096
- type: dot_precision
value: 87.16475095785441
- type: dot_recall
value: 91.0
- type: euclidean_accuracy
value: 99.85445544554456
- type: euclidean_ap
value: 96.6661459876145
- type: euclidean_f1
value: 92.58337481333997
- type: euclidean_precision
value: 92.17046580773042
- type: euclidean_recall
value: 93.0
- type: manhattan_accuracy
value: 99.85445544554456
- type: manhattan_ap
value: 96.6883549244056
- type: manhattan_f1
value: 92.57598405580468
- type: manhattan_precision
value: 92.25422045680239
- type: manhattan_recall
value: 92.9
- type: max_accuracy
value: 99.86237623762376
- type: max_ap
value: 96.72586477206649
- type: max_f1
value: 93.01858362631845
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.39930057069995
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.96398659903402
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.946944700355395
- type: mrr
value: 56.97151398438164
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.541657650692905
- type: cos_sim_spearman
value: 31.605804192286303
- type: dot_pearson
value: 28.26905996736398
- type: dot_spearman
value: 27.864801765851187
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22599999999999998
- type: map_at_10
value: 1.8870000000000002
- type: map_at_100
value: 9.78
- type: map_at_1000
value: 22.514
- type: map_at_3
value: 0.6669999999999999
- type: map_at_5
value: 1.077
- type: mrr_at_1
value: 82.0
- type: mrr_at_10
value: 89.86699999999999
- type: mrr_at_100
value: 89.86699999999999
- type: mrr_at_1000
value: 89.86699999999999
- type: mrr_at_3
value: 89.667
- type: mrr_at_5
value: 89.667
- type: ndcg_at_1
value: 79.0
- type: ndcg_at_10
value: 74.818
- type: ndcg_at_100
value: 53.715999999999994
- type: ndcg_at_1000
value: 47.082
- type: ndcg_at_3
value: 82.134
- type: ndcg_at_5
value: 79.81899999999999
- type: precision_at_1
value: 82.0
- type: precision_at_10
value: 78.0
- type: precision_at_100
value: 54.48
- type: precision_at_1000
value: 20.518
- type: precision_at_3
value: 87.333
- type: precision_at_5
value: 85.2
- type: recall_at_1
value: 0.22599999999999998
- type: recall_at_10
value: 2.072
- type: recall_at_100
value: 13.013
- type: recall_at_1000
value: 43.462
- type: recall_at_3
value: 0.695
- type: recall_at_5
value: 1.139
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.328
- type: map_at_10
value: 9.795
- type: map_at_100
value: 15.801000000000002
- type: map_at_1000
value: 17.23
- type: map_at_3
value: 4.734
- type: map_at_5
value: 6.644
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 46.902
- type: mrr_at_100
value: 47.495
- type: mrr_at_1000
value: 47.495
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 44.218
- type: ndcg_at_1
value: 28.571
- type: ndcg_at_10
value: 24.806
- type: ndcg_at_100
value: 36.419000000000004
- type: ndcg_at_1000
value: 47.272999999999996
- type: ndcg_at_3
value: 25.666
- type: ndcg_at_5
value: 25.448999999999998
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 23.061
- type: precision_at_100
value: 7.714
- type: precision_at_1000
value: 1.484
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 2.328
- type: recall_at_10
value: 16.524
- type: recall_at_100
value: 47.179
- type: recall_at_1000
value: 81.22200000000001
- type: recall_at_3
value: 5.745
- type: recall_at_5
value: 9.339
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.9142
- type: ap
value: 14.335574772555415
- type: f1
value: 54.62839595194111
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.94340690435768
- type: f1
value: 60.286487936731916
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.26597708987974
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.48882398521786
- type: cos_sim_ap
value: 79.04326607602204
- type: cos_sim_f1
value: 71.64566826860633
- type: cos_sim_precision
value: 70.55512918905092
- type: cos_sim_recall
value: 72.77044854881267
- type: dot_accuracy
value: 84.19264469213805
- type: dot_ap
value: 67.96360043562528
- type: dot_f1
value: 64.06418393006827
- type: dot_precision
value: 58.64941898706424
- type: dot_recall
value: 70.58047493403694
- type: euclidean_accuracy
value: 87.45902127913214
- type: euclidean_ap
value: 78.9742237648272
- type: euclidean_f1
value: 71.5553235908142
- type: euclidean_precision
value: 70.77955601445535
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.41729749061214
- type: manhattan_ap
value: 78.90073137580596
- type: manhattan_f1
value: 71.3942611553533
- type: manhattan_precision
value: 68.52705653967483
- type: manhattan_recall
value: 74.51187335092348
- type: max_accuracy
value: 87.48882398521786
- type: max_ap
value: 79.04326607602204
- type: max_f1
value: 71.64566826860633
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.68125897465751
- type: cos_sim_ap
value: 85.6003454431979
- type: cos_sim_f1
value: 77.6957163958641
- type: cos_sim_precision
value: 73.0110366307807
- type: cos_sim_recall
value: 83.02279026793964
- type: dot_accuracy
value: 87.7672992587418
- type: dot_ap
value: 82.4971301112899
- type: dot_f1
value: 75.90528233151184
- type: dot_precision
value: 72.0370626469368
- type: dot_recall
value: 80.21250384970742
- type: euclidean_accuracy
value: 88.4503434625684
- type: euclidean_ap
value: 84.91949884748384
- type: euclidean_f1
value: 76.92365018444684
- type: euclidean_precision
value: 74.53245721712759
- type: euclidean_recall
value: 79.47336002463813
- type: manhattan_accuracy
value: 88.47556952691427
- type: manhattan_ap
value: 84.8963689101517
- type: manhattan_f1
value: 76.85901249256395
- type: manhattan_precision
value: 74.31693989071039
- type: manhattan_recall
value: 79.58115183246073
- type: max_accuracy
value: 88.68125897465751
- type: max_ap
value: 85.6003454431979
- type: max_f1
value: 77.6957163958641
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
#### Usage of the ONNX files
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
model_output_ort = model_ort(**encoded_input)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# model_output and model_output_ort are identical
```
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
intfloat/e5-large-v2 | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-19T07:23:33 | 2025-02-17T03:26:51 | 1,896,878 | 241 | ---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-large-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.22388059701493
- type: ap
value: 43.20816505595132
- type: f1
value: 73.27811303522058
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.748325
- type: ap
value: 90.72534979701297
- type: f1
value: 93.73895874282185
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.612
- type: f1
value: 47.61157345898393
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.541999999999998
- type: map_at_10
value: 38.208
- type: map_at_100
value: 39.417
- type: map_at_1000
value: 39.428999999999995
- type: map_at_3
value: 33.95
- type: map_at_5
value: 36.329
- type: mrr_at_1
value: 23.755000000000003
- type: mrr_at_10
value: 38.288
- type: mrr_at_100
value: 39.511
- type: mrr_at_1000
value: 39.523
- type: mrr_at_3
value: 34.009
- type: mrr_at_5
value: 36.434
- type: ndcg_at_1
value: 23.541999999999998
- type: ndcg_at_10
value: 46.417
- type: ndcg_at_100
value: 51.812000000000005
- type: ndcg_at_1000
value: 52.137
- type: ndcg_at_3
value: 37.528
- type: ndcg_at_5
value: 41.81
- type: precision_at_1
value: 23.541999999999998
- type: precision_at_10
value: 7.269
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 15.979
- type: precision_at_5
value: 11.664
- type: recall_at_1
value: 23.541999999999998
- type: recall_at_10
value: 72.688
- type: recall_at_100
value: 96.871
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 47.937000000000005
- type: recall_at_5
value: 58.321
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.546499570522094
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.01607489943561
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.616107510107774
- type: mrr
value: 72.75106626214661
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.33018094733868
- type: cos_sim_spearman
value: 83.60190492611737
- type: euclidean_pearson
value: 82.1492450218961
- type: euclidean_spearman
value: 82.70308926526991
- type: manhattan_pearson
value: 81.93959600076842
- type: manhattan_spearman
value: 82.73260801016369
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.54545454545455
- type: f1
value: 84.49582530928923
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.362725540120096
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.849509608178145
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.502999999999997
- type: map_at_10
value: 43.323
- type: map_at_100
value: 44.708999999999996
- type: map_at_1000
value: 44.838
- type: map_at_3
value: 38.987
- type: map_at_5
value: 41.516999999999996
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 49.13
- type: mrr_at_100
value: 49.697
- type: mrr_at_1000
value: 49.741
- type: mrr_at_3
value: 45.804
- type: mrr_at_5
value: 47.842
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 50.266999999999996
- type: ndcg_at_100
value: 54.967
- type: ndcg_at_1000
value: 56.976000000000006
- type: ndcg_at_3
value: 43.823
- type: ndcg_at_5
value: 47.12
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 10.057
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.125
- type: precision_at_5
value: 15.851
- type: recall_at_1
value: 31.502999999999997
- type: recall_at_10
value: 63.715999999999994
- type: recall_at_100
value: 83.61800000000001
- type: recall_at_1000
value: 96.63199999999999
- type: recall_at_3
value: 45.403
- type: recall_at_5
value: 54.481
- type: map_at_1
value: 27.833000000000002
- type: map_at_10
value: 37.330999999999996
- type: map_at_100
value: 38.580999999999996
- type: map_at_1000
value: 38.708
- type: map_at_3
value: 34.713
- type: map_at_5
value: 36.104
- type: mrr_at_1
value: 35.223
- type: mrr_at_10
value: 43.419000000000004
- type: mrr_at_100
value: 44.198
- type: mrr_at_1000
value: 44.249
- type: mrr_at_3
value: 41.614000000000004
- type: mrr_at_5
value: 42.553000000000004
- type: ndcg_at_1
value: 35.223
- type: ndcg_at_10
value: 42.687999999999995
- type: ndcg_at_100
value: 47.447
- type: ndcg_at_1000
value: 49.701
- type: ndcg_at_3
value: 39.162
- type: ndcg_at_5
value: 40.557
- type: precision_at_1
value: 35.223
- type: precision_at_10
value: 7.962
- type: precision_at_100
value: 1.304
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.184999999999999
- type: recall_at_1
value: 27.833000000000002
- type: recall_at_10
value: 51.881
- type: recall_at_100
value: 72.04
- type: recall_at_1000
value: 86.644
- type: recall_at_3
value: 40.778
- type: recall_at_5
value: 45.176
- type: map_at_1
value: 38.175
- type: map_at_10
value: 51.174
- type: map_at_100
value: 52.26499999999999
- type: map_at_1000
value: 52.315999999999995
- type: map_at_3
value: 47.897
- type: map_at_5
value: 49.703
- type: mrr_at_1
value: 43.448
- type: mrr_at_10
value: 54.505
- type: mrr_at_100
value: 55.216
- type: mrr_at_1000
value: 55.242000000000004
- type: mrr_at_3
value: 51.98500000000001
- type: mrr_at_5
value: 53.434000000000005
- type: ndcg_at_1
value: 43.448
- type: ndcg_at_10
value: 57.282
- type: ndcg_at_100
value: 61.537
- type: ndcg_at_1000
value: 62.546
- type: ndcg_at_3
value: 51.73799999999999
- type: ndcg_at_5
value: 54.324
- type: precision_at_1
value: 43.448
- type: precision_at_10
value: 9.292
- type: precision_at_100
value: 1.233
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 23.218
- type: precision_at_5
value: 15.887
- type: recall_at_1
value: 38.175
- type: recall_at_10
value: 72.00999999999999
- type: recall_at_100
value: 90.155
- type: recall_at_1000
value: 97.257
- type: recall_at_3
value: 57.133
- type: recall_at_5
value: 63.424
- type: map_at_1
value: 22.405
- type: map_at_10
value: 30.043
- type: map_at_100
value: 31.191000000000003
- type: map_at_1000
value: 31.275
- type: map_at_3
value: 27.034000000000002
- type: map_at_5
value: 28.688000000000002
- type: mrr_at_1
value: 24.068
- type: mrr_at_10
value: 31.993
- type: mrr_at_100
value: 32.992
- type: mrr_at_1000
value: 33.050000000000004
- type: mrr_at_3
value: 28.964000000000002
- type: mrr_at_5
value: 30.653000000000002
- type: ndcg_at_1
value: 24.068
- type: ndcg_at_10
value: 35.198
- type: ndcg_at_100
value: 40.709
- type: ndcg_at_1000
value: 42.855
- type: ndcg_at_3
value: 29.139
- type: ndcg_at_5
value: 32.045
- type: precision_at_1
value: 24.068
- type: precision_at_10
value: 5.65
- type: precision_at_100
value: 0.885
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 12.279
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 22.405
- type: recall_at_10
value: 49.391
- type: recall_at_100
value: 74.53699999999999
- type: recall_at_1000
value: 90.605
- type: recall_at_3
value: 33.126
- type: recall_at_5
value: 40.073
- type: map_at_1
value: 13.309999999999999
- type: map_at_10
value: 20.688000000000002
- type: map_at_100
value: 22.022
- type: map_at_1000
value: 22.152
- type: map_at_3
value: 17.954
- type: map_at_5
value: 19.439
- type: mrr_at_1
value: 16.294
- type: mrr_at_10
value: 24.479
- type: mrr_at_100
value: 25.515
- type: mrr_at_1000
value: 25.593
- type: mrr_at_3
value: 21.642
- type: mrr_at_5
value: 23.189999999999998
- type: ndcg_at_1
value: 16.294
- type: ndcg_at_10
value: 25.833000000000002
- type: ndcg_at_100
value: 32.074999999999996
- type: ndcg_at_1000
value: 35.083
- type: ndcg_at_3
value: 20.493
- type: ndcg_at_5
value: 22.949
- type: precision_at_1
value: 16.294
- type: precision_at_10
value: 5.112
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 9.908999999999999
- type: precision_at_5
value: 7.587000000000001
- type: recall_at_1
value: 13.309999999999999
- type: recall_at_10
value: 37.851
- type: recall_at_100
value: 64.835
- type: recall_at_1000
value: 86.334
- type: recall_at_3
value: 23.493
- type: recall_at_5
value: 29.528
- type: map_at_1
value: 25.857999999999997
- type: map_at_10
value: 35.503
- type: map_at_100
value: 36.957
- type: map_at_1000
value: 37.065
- type: map_at_3
value: 32.275999999999996
- type: map_at_5
value: 34.119
- type: mrr_at_1
value: 31.954
- type: mrr_at_10
value: 40.851
- type: mrr_at_100
value: 41.863
- type: mrr_at_1000
value: 41.900999999999996
- type: mrr_at_3
value: 38.129999999999995
- type: mrr_at_5
value: 39.737
- type: ndcg_at_1
value: 31.954
- type: ndcg_at_10
value: 41.343999999999994
- type: ndcg_at_100
value: 47.397
- type: ndcg_at_1000
value: 49.501
- type: ndcg_at_3
value: 36.047000000000004
- type: ndcg_at_5
value: 38.639
- type: precision_at_1
value: 31.954
- type: precision_at_10
value: 7.68
- type: precision_at_100
value: 1.247
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 17.132
- type: precision_at_5
value: 12.589
- type: recall_at_1
value: 25.857999999999997
- type: recall_at_10
value: 53.43599999999999
- type: recall_at_100
value: 78.82400000000001
- type: recall_at_1000
value: 92.78999999999999
- type: recall_at_3
value: 38.655
- type: recall_at_5
value: 45.216
- type: map_at_1
value: 24.709
- type: map_at_10
value: 34.318
- type: map_at_100
value: 35.657
- type: map_at_1000
value: 35.783
- type: map_at_3
value: 31.326999999999998
- type: map_at_5
value: 33.021
- type: mrr_at_1
value: 30.137000000000004
- type: mrr_at_10
value: 39.093
- type: mrr_at_100
value: 39.992
- type: mrr_at_1000
value: 40.056999999999995
- type: mrr_at_3
value: 36.606
- type: mrr_at_5
value: 37.861
- type: ndcg_at_1
value: 30.137000000000004
- type: ndcg_at_10
value: 39.974
- type: ndcg_at_100
value: 45.647999999999996
- type: ndcg_at_1000
value: 48.259
- type: ndcg_at_3
value: 35.028
- type: ndcg_at_5
value: 37.175999999999995
- type: precision_at_1
value: 30.137000000000004
- type: precision_at_10
value: 7.363
- type: precision_at_100
value: 1.184
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 16.857
- type: precision_at_5
value: 11.963
- type: recall_at_1
value: 24.709
- type: recall_at_10
value: 52.087
- type: recall_at_100
value: 76.125
- type: recall_at_1000
value: 93.82300000000001
- type: recall_at_3
value: 38.149
- type: recall_at_5
value: 43.984
- type: map_at_1
value: 23.40791666666667
- type: map_at_10
value: 32.458083333333335
- type: map_at_100
value: 33.691916666666664
- type: map_at_1000
value: 33.81191666666666
- type: map_at_3
value: 29.51625
- type: map_at_5
value: 31.168083333333335
- type: mrr_at_1
value: 27.96591666666666
- type: mrr_at_10
value: 36.528583333333344
- type: mrr_at_100
value: 37.404
- type: mrr_at_1000
value: 37.464333333333336
- type: mrr_at_3
value: 33.92883333333333
- type: mrr_at_5
value: 35.41933333333333
- type: ndcg_at_1
value: 27.96591666666666
- type: ndcg_at_10
value: 37.89141666666666
- type: ndcg_at_100
value: 43.23066666666666
- type: ndcg_at_1000
value: 45.63258333333333
- type: ndcg_at_3
value: 32.811249999999994
- type: ndcg_at_5
value: 35.22566666666667
- type: precision_at_1
value: 27.96591666666666
- type: precision_at_10
value: 6.834083333333332
- type: precision_at_100
value: 1.12225
- type: precision_at_1000
value: 0.15241666666666667
- type: precision_at_3
value: 15.264333333333335
- type: precision_at_5
value: 11.039416666666666
- type: recall_at_1
value: 23.40791666666667
- type: recall_at_10
value: 49.927083333333336
- type: recall_at_100
value: 73.44641666666668
- type: recall_at_1000
value: 90.19950000000001
- type: recall_at_3
value: 35.88341666666667
- type: recall_at_5
value: 42.061249999999994
- type: map_at_1
value: 19.592000000000002
- type: map_at_10
value: 26.895999999999997
- type: map_at_100
value: 27.921000000000003
- type: map_at_1000
value: 28.02
- type: map_at_3
value: 24.883
- type: map_at_5
value: 25.812
- type: mrr_at_1
value: 22.698999999999998
- type: mrr_at_10
value: 29.520999999999997
- type: mrr_at_100
value: 30.458000000000002
- type: mrr_at_1000
value: 30.526999999999997
- type: mrr_at_3
value: 27.633000000000003
- type: mrr_at_5
value: 28.483999999999998
- type: ndcg_at_1
value: 22.698999999999998
- type: ndcg_at_10
value: 31.061
- type: ndcg_at_100
value: 36.398
- type: ndcg_at_1000
value: 38.89
- type: ndcg_at_3
value: 27.149
- type: ndcg_at_5
value: 28.627000000000002
- type: precision_at_1
value: 22.698999999999998
- type: precision_at_10
value: 5.106999999999999
- type: precision_at_100
value: 0.857
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 11.963
- type: precision_at_5
value: 8.221
- type: recall_at_1
value: 19.592000000000002
- type: recall_at_10
value: 41.329
- type: recall_at_100
value: 66.094
- type: recall_at_1000
value: 84.511
- type: recall_at_3
value: 30.61
- type: recall_at_5
value: 34.213
- type: map_at_1
value: 14.71
- type: map_at_10
value: 20.965
- type: map_at_100
value: 21.994
- type: map_at_1000
value: 22.133
- type: map_at_3
value: 18.741
- type: map_at_5
value: 19.951
- type: mrr_at_1
value: 18.307000000000002
- type: mrr_at_10
value: 24.66
- type: mrr_at_100
value: 25.540000000000003
- type: mrr_at_1000
value: 25.629
- type: mrr_at_3
value: 22.511
- type: mrr_at_5
value: 23.72
- type: ndcg_at_1
value: 18.307000000000002
- type: ndcg_at_10
value: 25.153
- type: ndcg_at_100
value: 30.229
- type: ndcg_at_1000
value: 33.623
- type: ndcg_at_3
value: 21.203
- type: ndcg_at_5
value: 23.006999999999998
- type: precision_at_1
value: 18.307000000000002
- type: precision_at_10
value: 4.725
- type: precision_at_100
value: 0.8659999999999999
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.14
- type: precision_at_5
value: 7.481
- type: recall_at_1
value: 14.71
- type: recall_at_10
value: 34.087
- type: recall_at_100
value: 57.147999999999996
- type: recall_at_1000
value: 81.777
- type: recall_at_3
value: 22.996
- type: recall_at_5
value: 27.73
- type: map_at_1
value: 23.472
- type: map_at_10
value: 32.699
- type: map_at_100
value: 33.867000000000004
- type: map_at_1000
value: 33.967000000000006
- type: map_at_3
value: 29.718
- type: map_at_5
value: 31.345
- type: mrr_at_1
value: 28.265
- type: mrr_at_10
value: 36.945
- type: mrr_at_100
value: 37.794
- type: mrr_at_1000
value: 37.857
- type: mrr_at_3
value: 34.266000000000005
- type: mrr_at_5
value: 35.768
- type: ndcg_at_1
value: 28.265
- type: ndcg_at_10
value: 38.35
- type: ndcg_at_100
value: 43.739
- type: ndcg_at_1000
value: 46.087
- type: ndcg_at_3
value: 33.004
- type: ndcg_at_5
value: 35.411
- type: precision_at_1
value: 28.265
- type: precision_at_10
value: 6.715999999999999
- type: precision_at_100
value: 1.059
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 15.299
- type: precision_at_5
value: 10.951
- type: recall_at_1
value: 23.472
- type: recall_at_10
value: 51.413
- type: recall_at_100
value: 75.17
- type: recall_at_1000
value: 91.577
- type: recall_at_3
value: 36.651
- type: recall_at_5
value: 42.814
- type: map_at_1
value: 23.666
- type: map_at_10
value: 32.963
- type: map_at_100
value: 34.544999999999995
- type: map_at_1000
value: 34.792
- type: map_at_3
value: 29.74
- type: map_at_5
value: 31.5
- type: mrr_at_1
value: 29.051
- type: mrr_at_10
value: 38.013000000000005
- type: mrr_at_100
value: 38.997
- type: mrr_at_1000
value: 39.055
- type: mrr_at_3
value: 34.947
- type: mrr_at_5
value: 36.815
- type: ndcg_at_1
value: 29.051
- type: ndcg_at_10
value: 39.361000000000004
- type: ndcg_at_100
value: 45.186
- type: ndcg_at_1000
value: 47.867
- type: ndcg_at_3
value: 33.797
- type: ndcg_at_5
value: 36.456
- type: precision_at_1
value: 29.051
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 15.876000000000001
- type: precision_at_5
value: 11.779
- type: recall_at_1
value: 23.666
- type: recall_at_10
value: 51.858000000000004
- type: recall_at_100
value: 77.805
- type: recall_at_1000
value: 94.504
- type: recall_at_3
value: 36.207
- type: recall_at_5
value: 43.094
- type: map_at_1
value: 15.662
- type: map_at_10
value: 23.594
- type: map_at_100
value: 24.593999999999998
- type: map_at_1000
value: 24.694
- type: map_at_3
value: 20.925
- type: map_at_5
value: 22.817999999999998
- type: mrr_at_1
value: 17.375
- type: mrr_at_10
value: 25.734
- type: mrr_at_100
value: 26.586
- type: mrr_at_1000
value: 26.671
- type: mrr_at_3
value: 23.044
- type: mrr_at_5
value: 24.975
- type: ndcg_at_1
value: 17.375
- type: ndcg_at_10
value: 28.186
- type: ndcg_at_100
value: 33.436
- type: ndcg_at_1000
value: 36.203
- type: ndcg_at_3
value: 23.152
- type: ndcg_at_5
value: 26.397
- type: precision_at_1
value: 17.375
- type: precision_at_10
value: 4.677
- type: precision_at_100
value: 0.786
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 10.351
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 15.662
- type: recall_at_10
value: 40.066
- type: recall_at_100
value: 65.006
- type: recall_at_1000
value: 85.94000000000001
- type: recall_at_3
value: 27.400000000000002
- type: recall_at_5
value: 35.002
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.853
- type: map_at_10
value: 15.568000000000001
- type: map_at_100
value: 17.383000000000003
- type: map_at_1000
value: 17.584
- type: map_at_3
value: 12.561
- type: map_at_5
value: 14.056
- type: mrr_at_1
value: 18.958
- type: mrr_at_10
value: 28.288000000000004
- type: mrr_at_100
value: 29.432000000000002
- type: mrr_at_1000
value: 29.498
- type: mrr_at_3
value: 25.049
- type: mrr_at_5
value: 26.857
- type: ndcg_at_1
value: 18.958
- type: ndcg_at_10
value: 22.21
- type: ndcg_at_100
value: 29.596
- type: ndcg_at_1000
value: 33.583
- type: ndcg_at_3
value: 16.994999999999997
- type: ndcg_at_5
value: 18.95
- type: precision_at_1
value: 18.958
- type: precision_at_10
value: 7.192
- type: precision_at_100
value: 1.5
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 12.573
- type: precision_at_5
value: 10.202
- type: recall_at_1
value: 8.853
- type: recall_at_10
value: 28.087
- type: recall_at_100
value: 53.701
- type: recall_at_1000
value: 76.29899999999999
- type: recall_at_3
value: 15.913
- type: recall_at_5
value: 20.658
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.077
- type: map_at_10
value: 20.788999999999998
- type: map_at_100
value: 30.429000000000002
- type: map_at_1000
value: 32.143
- type: map_at_3
value: 14.692
- type: map_at_5
value: 17.139
- type: mrr_at_1
value: 70.75
- type: mrr_at_10
value: 78.036
- type: mrr_at_100
value: 78.401
- type: mrr_at_1000
value: 78.404
- type: mrr_at_3
value: 76.75
- type: mrr_at_5
value: 77.47500000000001
- type: ndcg_at_1
value: 58.12500000000001
- type: ndcg_at_10
value: 44.015
- type: ndcg_at_100
value: 49.247
- type: ndcg_at_1000
value: 56.211999999999996
- type: ndcg_at_3
value: 49.151
- type: ndcg_at_5
value: 46.195
- type: precision_at_1
value: 70.75
- type: precision_at_10
value: 35.5
- type: precision_at_100
value: 11.355
- type: precision_at_1000
value: 2.1950000000000003
- type: precision_at_3
value: 53.083000000000006
- type: precision_at_5
value: 44.800000000000004
- type: recall_at_1
value: 9.077
- type: recall_at_10
value: 26.259
- type: recall_at_100
value: 56.547000000000004
- type: recall_at_1000
value: 78.551
- type: recall_at_3
value: 16.162000000000003
- type: recall_at_5
value: 19.753999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.44500000000001
- type: f1
value: 44.67067691783401
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.182
- type: map_at_10
value: 78.223
- type: map_at_100
value: 78.498
- type: map_at_1000
value: 78.512
- type: map_at_3
value: 76.71
- type: map_at_5
value: 77.725
- type: mrr_at_1
value: 73.177
- type: mrr_at_10
value: 82.513
- type: mrr_at_100
value: 82.633
- type: mrr_at_1000
value: 82.635
- type: mrr_at_3
value: 81.376
- type: mrr_at_5
value: 82.182
- type: ndcg_at_1
value: 73.177
- type: ndcg_at_10
value: 82.829
- type: ndcg_at_100
value: 83.84
- type: ndcg_at_1000
value: 84.07900000000001
- type: ndcg_at_3
value: 80.303
- type: ndcg_at_5
value: 81.846
- type: precision_at_1
value: 73.177
- type: precision_at_10
value: 10.241999999999999
- type: precision_at_100
value: 1.099
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 31.247999999999998
- type: precision_at_5
value: 19.697
- type: recall_at_1
value: 68.182
- type: recall_at_10
value: 92.657
- type: recall_at_100
value: 96.709
- type: recall_at_1000
value: 98.184
- type: recall_at_3
value: 85.9
- type: recall_at_5
value: 89.755
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.108
- type: map_at_10
value: 33.342
- type: map_at_100
value: 35.281
- type: map_at_1000
value: 35.478
- type: map_at_3
value: 29.067
- type: map_at_5
value: 31.563000000000002
- type: mrr_at_1
value: 41.667
- type: mrr_at_10
value: 49.913000000000004
- type: mrr_at_100
value: 50.724000000000004
- type: mrr_at_1000
value: 50.766
- type: mrr_at_3
value: 47.504999999999995
- type: mrr_at_5
value: 49.033
- type: ndcg_at_1
value: 41.667
- type: ndcg_at_10
value: 41.144
- type: ndcg_at_100
value: 48.326
- type: ndcg_at_1000
value: 51.486
- type: ndcg_at_3
value: 37.486999999999995
- type: ndcg_at_5
value: 38.78
- type: precision_at_1
value: 41.667
- type: precision_at_10
value: 11.358
- type: precision_at_100
value: 1.873
- type: precision_at_1000
value: 0.244
- type: precision_at_3
value: 25
- type: precision_at_5
value: 18.519
- type: recall_at_1
value: 21.108
- type: recall_at_10
value: 47.249
- type: recall_at_100
value: 74.52
- type: recall_at_1000
value: 93.31
- type: recall_at_3
value: 33.271
- type: recall_at_5
value: 39.723000000000006
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.317
- type: map_at_10
value: 64.861
- type: map_at_100
value: 65.697
- type: map_at_1000
value: 65.755
- type: map_at_3
value: 61.258
- type: map_at_5
value: 63.590999999999994
- type: mrr_at_1
value: 80.635
- type: mrr_at_10
value: 86.528
- type: mrr_at_100
value: 86.66199999999999
- type: mrr_at_1000
value: 86.666
- type: mrr_at_3
value: 85.744
- type: mrr_at_5
value: 86.24300000000001
- type: ndcg_at_1
value: 80.635
- type: ndcg_at_10
value: 73.13199999999999
- type: ndcg_at_100
value: 75.927
- type: ndcg_at_1000
value: 76.976
- type: ndcg_at_3
value: 68.241
- type: ndcg_at_5
value: 71.071
- type: precision_at_1
value: 80.635
- type: precision_at_10
value: 15.326
- type: precision_at_100
value: 1.7500000000000002
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 43.961
- type: precision_at_5
value: 28.599999999999998
- type: recall_at_1
value: 40.317
- type: recall_at_10
value: 76.631
- type: recall_at_100
value: 87.495
- type: recall_at_1000
value: 94.362
- type: recall_at_3
value: 65.94200000000001
- type: recall_at_5
value: 71.499
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.686
- type: ap
value: 87.5577120393173
- type: f1
value: 91.6629447355139
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.702
- type: map_at_10
value: 36.414
- type: map_at_100
value: 37.561
- type: map_at_1000
value: 37.605
- type: map_at_3
value: 32.456
- type: map_at_5
value: 34.827000000000005
- type: mrr_at_1
value: 24.355
- type: mrr_at_10
value: 37.01
- type: mrr_at_100
value: 38.085
- type: mrr_at_1000
value: 38.123000000000005
- type: mrr_at_3
value: 33.117999999999995
- type: mrr_at_5
value: 35.452
- type: ndcg_at_1
value: 24.384
- type: ndcg_at_10
value: 43.456
- type: ndcg_at_100
value: 48.892
- type: ndcg_at_1000
value: 49.964
- type: ndcg_at_3
value: 35.475
- type: ndcg_at_5
value: 39.711
- type: precision_at_1
value: 24.384
- type: precision_at_10
value: 6.7940000000000005
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.052999999999999
- type: precision_at_5
value: 11.189
- type: recall_at_1
value: 23.702
- type: recall_at_10
value: 65.057
- type: recall_at_100
value: 90.021
- type: recall_at_1000
value: 98.142
- type: recall_at_3
value: 43.551
- type: recall_at_5
value: 53.738
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.62380300957591
- type: f1
value: 94.49871222100734
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.14090287277702
- type: f1
value: 60.32101258220515
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.84330867518494
- type: f1
value: 71.92248688515255
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.10692669804976
- type: f1
value: 77.9904839122866
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.822988923078444
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.38394880253403
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.82504612539082
- type: mrr
value: 32.84462298174977
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.029
- type: map_at_10
value: 14.088999999999999
- type: map_at_100
value: 17.601
- type: map_at_1000
value: 19.144
- type: map_at_3
value: 10.156
- type: map_at_5
value: 11.892
- type: mrr_at_1
value: 46.44
- type: mrr_at_10
value: 56.596999999999994
- type: mrr_at_100
value: 57.11000000000001
- type: mrr_at_1000
value: 57.14
- type: mrr_at_3
value: 54.334
- type: mrr_at_5
value: 55.774
- type: ndcg_at_1
value: 44.891999999999996
- type: ndcg_at_10
value: 37.134
- type: ndcg_at_100
value: 33.652
- type: ndcg_at_1000
value: 42.548
- type: ndcg_at_3
value: 41.851
- type: ndcg_at_5
value: 39.842
- type: precision_at_1
value: 46.44
- type: precision_at_10
value: 27.647
- type: precision_at_100
value: 8.309999999999999
- type: precision_at_1000
value: 2.146
- type: precision_at_3
value: 39.422000000000004
- type: precision_at_5
value: 34.675
- type: recall_at_1
value: 6.029
- type: recall_at_10
value: 18.907
- type: recall_at_100
value: 33.76
- type: recall_at_1000
value: 65.14999999999999
- type: recall_at_3
value: 11.584999999999999
- type: recall_at_5
value: 14.626
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.373000000000005
- type: map_at_10
value: 55.836
- type: map_at_100
value: 56.611999999999995
- type: map_at_1000
value: 56.63
- type: map_at_3
value: 51.747
- type: map_at_5
value: 54.337999999999994
- type: mrr_at_1
value: 44.147999999999996
- type: mrr_at_10
value: 58.42699999999999
- type: mrr_at_100
value: 58.902
- type: mrr_at_1000
value: 58.914
- type: mrr_at_3
value: 55.156000000000006
- type: mrr_at_5
value: 57.291000000000004
- type: ndcg_at_1
value: 44.119
- type: ndcg_at_10
value: 63.444
- type: ndcg_at_100
value: 66.40599999999999
- type: ndcg_at_1000
value: 66.822
- type: ndcg_at_3
value: 55.962
- type: ndcg_at_5
value: 60.228
- type: precision_at_1
value: 44.119
- type: precision_at_10
value: 10.006
- type: precision_at_100
value: 1.17
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.135
- type: precision_at_5
value: 17.59
- type: recall_at_1
value: 39.373000000000005
- type: recall_at_10
value: 83.78999999999999
- type: recall_at_100
value: 96.246
- type: recall_at_1000
value: 99.324
- type: recall_at_3
value: 64.71900000000001
- type: recall_at_5
value: 74.508
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.199
- type: map_at_10
value: 82.892
- type: map_at_100
value: 83.578
- type: map_at_1000
value: 83.598
- type: map_at_3
value: 79.948
- type: map_at_5
value: 81.779
- type: mrr_at_1
value: 79.67
- type: mrr_at_10
value: 86.115
- type: mrr_at_100
value: 86.249
- type: mrr_at_1000
value: 86.251
- type: mrr_at_3
value: 85.08200000000001
- type: mrr_at_5
value: 85.783
- type: ndcg_at_1
value: 79.67
- type: ndcg_at_10
value: 86.839
- type: ndcg_at_100
value: 88.252
- type: ndcg_at_1000
value: 88.401
- type: ndcg_at_3
value: 83.86200000000001
- type: ndcg_at_5
value: 85.473
- type: precision_at_1
value: 79.67
- type: precision_at_10
value: 13.19
- type: precision_at_100
value: 1.521
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.677
- type: precision_at_5
value: 24.118000000000002
- type: recall_at_1
value: 69.199
- type: recall_at_10
value: 94.321
- type: recall_at_100
value: 99.20400000000001
- type: recall_at_1000
value: 99.947
- type: recall_at_3
value: 85.787
- type: recall_at_5
value: 90.365
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.82810046856353
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.38132611783628
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.127000000000001
- type: map_at_10
value: 12.235
- type: map_at_100
value: 14.417
- type: map_at_1000
value: 14.75
- type: map_at_3
value: 8.906
- type: map_at_5
value: 10.591000000000001
- type: mrr_at_1
value: 25.2
- type: mrr_at_10
value: 35.879
- type: mrr_at_100
value: 36.935
- type: mrr_at_1000
value: 36.997
- type: mrr_at_3
value: 32.783
- type: mrr_at_5
value: 34.367999999999995
- type: ndcg_at_1
value: 25.2
- type: ndcg_at_10
value: 20.509
- type: ndcg_at_100
value: 28.67
- type: ndcg_at_1000
value: 34.42
- type: ndcg_at_3
value: 19.948
- type: ndcg_at_5
value: 17.166
- type: precision_at_1
value: 25.2
- type: precision_at_10
value: 10.440000000000001
- type: precision_at_100
value: 2.214
- type: precision_at_1000
value: 0.359
- type: precision_at_3
value: 18.533
- type: precision_at_5
value: 14.860000000000001
- type: recall_at_1
value: 5.127000000000001
- type: recall_at_10
value: 21.147
- type: recall_at_100
value: 44.946999999999996
- type: recall_at_1000
value: 72.89
- type: recall_at_3
value: 11.277
- type: recall_at_5
value: 15.042
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.0373011786213
- type: cos_sim_spearman
value: 79.27889560856613
- type: euclidean_pearson
value: 80.31186315495655
- type: euclidean_spearman
value: 79.41630415280811
- type: manhattan_pearson
value: 80.31755140442013
- type: manhattan_spearman
value: 79.43069870027611
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.8659751342045
- type: cos_sim_spearman
value: 76.95377612997667
- type: euclidean_pearson
value: 81.24552945497848
- type: euclidean_spearman
value: 77.18236963555253
- type: manhattan_pearson
value: 81.26477607759037
- type: manhattan_spearman
value: 77.13821753062756
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.34597139044875
- type: cos_sim_spearman
value: 84.124169425592
- type: euclidean_pearson
value: 83.68590721511401
- type: euclidean_spearman
value: 84.18846190846398
- type: manhattan_pearson
value: 83.57630235061498
- type: manhattan_spearman
value: 84.10244043726902
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.67641885599572
- type: cos_sim_spearman
value: 80.46450725650428
- type: euclidean_pearson
value: 81.61645042715865
- type: euclidean_spearman
value: 80.61418394236874
- type: manhattan_pearson
value: 81.55712034928871
- type: manhattan_spearman
value: 80.57905670523951
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.86650310886782
- type: cos_sim_spearman
value: 89.76081629222328
- type: euclidean_pearson
value: 89.1530747029954
- type: euclidean_spearman
value: 89.80990657280248
- type: manhattan_pearson
value: 89.10640563278132
- type: manhattan_spearman
value: 89.76282108434047
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.93864027911118
- type: cos_sim_spearman
value: 85.47096193999023
- type: euclidean_pearson
value: 85.03141840870533
- type: euclidean_spearman
value: 85.43124029598181
- type: manhattan_pearson
value: 84.99002664393512
- type: manhattan_spearman
value: 85.39169195120834
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.7045343749832
- type: cos_sim_spearman
value: 89.03262221146677
- type: euclidean_pearson
value: 89.56078218264365
- type: euclidean_spearman
value: 89.17827006466868
- type: manhattan_pearson
value: 89.52717595468582
- type: manhattan_spearman
value: 89.15878115952923
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.20191302875551
- type: cos_sim_spearman
value: 64.11446552557646
- type: euclidean_pearson
value: 64.6918197393619
- type: euclidean_spearman
value: 63.440182631197764
- type: manhattan_pearson
value: 64.55692904121835
- type: manhattan_spearman
value: 63.424877742756266
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.37793104662344
- type: cos_sim_spearman
value: 87.7357802629067
- type: euclidean_pearson
value: 87.4286301545109
- type: euclidean_spearman
value: 87.78452920777421
- type: manhattan_pearson
value: 87.42445169331255
- type: manhattan_spearman
value: 87.78537677249598
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.31465405081792
- type: mrr
value: 95.7173781193389
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 67.904
- type: map_at_100
value: 68.539
- type: map_at_1000
value: 68.562
- type: map_at_3
value: 65.415
- type: map_at_5
value: 66.788
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 68.797
- type: mrr_at_100
value: 69.236
- type: mrr_at_1000
value: 69.257
- type: mrr_at_3
value: 66.667
- type: mrr_at_5
value: 67.967
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 72.24199999999999
- type: ndcg_at_100
value: 74.86
- type: ndcg_at_1000
value: 75.354
- type: ndcg_at_3
value: 67.93400000000001
- type: ndcg_at_5
value: 70.02199999999999
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.778000000000002
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 84.383
- type: recall_at_100
value: 96.267
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 72.628
- type: recall_at_5
value: 78.094
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8029702970297
- type: cos_sim_ap
value: 94.9210324173411
- type: cos_sim_f1
value: 89.8521162672106
- type: cos_sim_precision
value: 91.67533818938605
- type: cos_sim_recall
value: 88.1
- type: dot_accuracy
value: 99.69504950495049
- type: dot_ap
value: 90.4919719146181
- type: dot_f1
value: 84.72289156626506
- type: dot_precision
value: 81.76744186046511
- type: dot_recall
value: 87.9
- type: euclidean_accuracy
value: 99.79702970297029
- type: euclidean_ap
value: 94.87827463795753
- type: euclidean_f1
value: 89.55680081507896
- type: euclidean_precision
value: 91.27725856697819
- type: euclidean_recall
value: 87.9
- type: manhattan_accuracy
value: 99.7990099009901
- type: manhattan_ap
value: 94.87587025149682
- type: manhattan_f1
value: 89.76298537569339
- type: manhattan_precision
value: 90.53916581892166
- type: manhattan_recall
value: 89
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 94.9210324173411
- type: max_f1
value: 89.8521162672106
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.92385753948724
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.671756975431144
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.677928036739004
- type: mrr
value: 51.56413133435193
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.523589340819683
- type: cos_sim_spearman
value: 30.187407518823235
- type: dot_pearson
value: 29.039713969699015
- type: dot_spearman
value: 29.114740651155508
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.211
- type: map_at_10
value: 1.6199999999999999
- type: map_at_100
value: 8.658000000000001
- type: map_at_1000
value: 21.538
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.919
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.18599999999999
- type: mrr_at_100
value: 86.18599999999999
- type: mrr_at_1000
value: 86.18599999999999
- type: mrr_at_3
value: 85
- type: mrr_at_5
value: 85.9
- type: ndcg_at_1
value: 74
- type: ndcg_at_10
value: 66.542
- type: ndcg_at_100
value: 50.163999999999994
- type: ndcg_at_1000
value: 45.696999999999996
- type: ndcg_at_3
value: 71.531
- type: ndcg_at_5
value: 70.45
- type: precision_at_1
value: 78
- type: precision_at_10
value: 69.39999999999999
- type: precision_at_100
value: 51.06
- type: precision_at_1000
value: 20.022000000000002
- type: precision_at_3
value: 76
- type: precision_at_5
value: 74.8
- type: recall_at_1
value: 0.211
- type: recall_at_10
value: 1.813
- type: recall_at_100
value: 12.098
- type: recall_at_1000
value: 42.618
- type: recall_at_3
value: 0.603
- type: recall_at_5
value: 0.987
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.2079999999999997
- type: map_at_10
value: 7.777000000000001
- type: map_at_100
value: 12.825000000000001
- type: map_at_1000
value: 14.196
- type: map_at_3
value: 4.285
- type: map_at_5
value: 6.177
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 42.635
- type: mrr_at_100
value: 43.955
- type: mrr_at_1000
value: 43.955
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.088
- type: ndcg_at_1
value: 28.571
- type: ndcg_at_10
value: 20.666999999999998
- type: ndcg_at_100
value: 31.840000000000003
- type: ndcg_at_1000
value: 43.191
- type: ndcg_at_3
value: 23.45
- type: ndcg_at_5
value: 22.994
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.4200000000000002
- type: precision_at_3
value: 23.810000000000002
- type: precision_at_5
value: 23.673
- type: recall_at_1
value: 2.2079999999999997
- type: recall_at_10
value: 13.144
- type: recall_at_100
value: 42.491
- type: recall_at_1000
value: 77.04299999999999
- type: recall_at_3
value: 5.3469999999999995
- type: recall_at_5
value: 9.139
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.9044
- type: ap
value: 14.625783489340755
- type: f1
value: 54.814936562590546
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.94227504244483
- type: f1
value: 61.22516038508854
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.602409155145864
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.94641473445789
- type: cos_sim_ap
value: 76.91572747061197
- type: cos_sim_f1
value: 70.14348097317529
- type: cos_sim_precision
value: 66.53254437869822
- type: cos_sim_recall
value: 74.1688654353562
- type: dot_accuracy
value: 84.80061989628658
- type: dot_ap
value: 70.7952548895177
- type: dot_f1
value: 65.44780728844965
- type: dot_precision
value: 61.53310104529617
- type: dot_recall
value: 69.89445910290237
- type: euclidean_accuracy
value: 86.94641473445789
- type: euclidean_ap
value: 76.80774009393652
- type: euclidean_f1
value: 70.30522503879979
- type: euclidean_precision
value: 68.94977168949772
- type: euclidean_recall
value: 71.71503957783642
- type: manhattan_accuracy
value: 86.8629671574179
- type: manhattan_ap
value: 76.76518632600317
- type: manhattan_f1
value: 70.16056518946692
- type: manhattan_precision
value: 68.360450563204
- type: manhattan_recall
value: 72.0580474934037
- type: max_accuracy
value: 86.94641473445789
- type: max_ap
value: 76.91572747061197
- type: max_f1
value: 70.30522503879979
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.10428066907285
- type: cos_sim_ap
value: 86.25114759921435
- type: cos_sim_f1
value: 78.37857884586856
- type: cos_sim_precision
value: 75.60818546078993
- type: cos_sim_recall
value: 81.35971666153372
- type: dot_accuracy
value: 87.41995575736406
- type: dot_ap
value: 81.51838010086782
- type: dot_f1
value: 74.77398015435503
- type: dot_precision
value: 71.53002390662354
- type: dot_recall
value: 78.32614721281182
- type: euclidean_accuracy
value: 89.12368533395428
- type: euclidean_ap
value: 86.33456799874504
- type: euclidean_f1
value: 78.45496750232127
- type: euclidean_precision
value: 75.78388462366364
- type: euclidean_recall
value: 81.32121958731136
- type: manhattan_accuracy
value: 89.10622113556099
- type: manhattan_ap
value: 86.31215061745333
- type: manhattan_f1
value: 78.40684906011539
- type: manhattan_precision
value: 75.89536643366722
- type: manhattan_recall
value: 81.09023714197721
- type: max_accuracy
value: 89.12368533395428
- type: max_ap
value: 86.33456799874504
- type: max_f1
value: 78.45496750232127
---
# E5-large-v2
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 24 layers and the embedding size is 1024.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-large-v2')
model = AutoModel.from_pretrained('intfloat/e5-large-v2')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-large-v2')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
jinaai/jina-embeddings-v3 | jinaai | feature-extraction | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"feature-extraction",
"sentence-similarity",
"mteb",
"sentence-transformers",
"custom_code",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
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"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2409.10173",
"license:cc-by-nc-4.0",
"model-index",
"region:eu"
] | 2024-09-05T11:56:46 | 2025-02-24T07:06:37 | 1,734,211 | 828 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- false
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
library_name: transformers
license: cc-by-nc-4.0
tags:
- feature-extraction
- sentence-similarity
- mteb
- sentence-transformers
inference: false
model-index:
- name: jina-embeddings-v3
results:
- task:
type: STS
dataset:
name: MTEB AFQMC (default)
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
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value: 41.74237700998808
- type: cosine_spearman
value: 43.4726782647566
- type: euclidean_pearson
value: 42.244585459479964
- type: euclidean_spearman
value: 43.525070045169606
- type: main_score
value: 43.4726782647566
- type: manhattan_pearson
value: 42.04616728224863
- type: manhattan_spearman
value: 43.308828270754645
- type: pearson
value: 41.74237700998808
- type: spearman
value: 43.4726782647566
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL (default)
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
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value: 50.117999999999995
- type: map_at_1
value: 24.253
- type: map_at_10
value: 40.725
- type: map_at_100
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- type: map_at_1000
value: 41.707
- type: map_at_20
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- type: map_at_3
value: 35.467
- type: map_at_5
value: 38.291
- type: mrr_at_1
value: 24.751066856330013
- type: mrr_at_10
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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value: -11.030405164830546
- type: nauc_map_at_1000_std
value: -8.902792782585117
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value: 7.509899249593199
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value: -11.023581259404406
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value: -8.892241185067272
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value: -10.810000200433278
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value: -8.987230542165776
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value: 11.37175831832417
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dataset:
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type: ai-forever/cedr-classification
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revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
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value: 9.892993775474912
- type: nAUC_P@100_diff1(MIRACL)
value: -26.330038284867552
- type: nAUC_P@100_max(MIRACL)
value: 28.45069413724051
- type: nAUC_P@100_std(MIRACL)
value: 9.892993775474928
- type: nAUC_P@10_diff1(MIRACL)
value: -17.436937353231112
- type: nAUC_P@10_max(MIRACL)
value: 24.327018012947857
- type: nAUC_P@10_std(MIRACL)
value: 11.78803527706634
- type: nAUC_P@1_diff1(MIRACL)
value: 19.1388712233292
- type: nAUC_P@1_max(MIRACL)
value: 10.871900994781642
- type: nAUC_P@1_std(MIRACL)
value: 3.218568248751811
- type: nAUC_P@20_diff1(MIRACL)
value: -22.947528755272426
- type: nAUC_P@20_max(MIRACL)
value: 27.773093471902538
- type: nAUC_P@20_std(MIRACL)
value: 14.898619107087221
- type: nAUC_P@3_diff1(MIRACL)
value: 1.4100426412400944
- type: nAUC_P@3_max(MIRACL)
value: 17.397472872058845
- type: nAUC_P@3_std(MIRACL)
value: 8.240008229861875
- type: nAUC_P@5_diff1(MIRACL)
value: -7.971349332207021
- type: nAUC_P@5_max(MIRACL)
value: 22.198441167940963
- type: nAUC_P@5_std(MIRACL)
value: 9.00265164460082
- type: nAUC_Recall@1000_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@1000_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@1000_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@100_diff1(MIRACL)
value: -38.69835271863148
- type: nAUC_Recall@100_max(MIRACL)
value: 50.9545152809108
- type: nAUC_Recall@100_std(MIRACL)
value: 20.44270887092116
- type: nAUC_Recall@10_diff1(MIRACL)
value: -0.08109036309433801
- type: nAUC_Recall@10_max(MIRACL)
value: 12.696619907773568
- type: nAUC_Recall@10_std(MIRACL)
value: 8.791982704261589
- type: nAUC_Recall@1_diff1(MIRACL)
value: 28.698973487482206
- type: nAUC_Recall@1_max(MIRACL)
value: 2.9217687660885034
- type: nAUC_Recall@1_std(MIRACL)
value: -1.1247408800976524
- type: nAUC_Recall@20_diff1(MIRACL)
value: -13.312171017942623
- type: nAUC_Recall@20_max(MIRACL)
value: 24.19847346821666
- type: nAUC_Recall@20_std(MIRACL)
value: 15.8157702609797
- type: nAUC_Recall@3_diff1(MIRACL)
value: 16.909128321353343
- type: nAUC_Recall@3_max(MIRACL)
value: 6.552122731902991
- type: nAUC_Recall@3_std(MIRACL)
value: 1.9963898223457228
- type: nAUC_Recall@5_diff1(MIRACL)
value: 9.990292655247721
- type: nAUC_Recall@5_max(MIRACL)
value: 9.361722273507574
- type: nAUC_Recall@5_std(MIRACL)
value: 3.270918827854495
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification (default)
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 30.634765625
- type: f1
value: 32.647559808678665
- type: lrap
value: 45.94319661458259
- type: main_score
value: 30.634765625
- task:
type: STS
dataset:
name: MTEB ATEC (default)
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 47.541497334563296
- type: cosine_spearman
value: 49.06268944206629
- type: euclidean_pearson
value: 51.838926748581635
- type: euclidean_spearman
value: 48.930697157135356
- type: main_score
value: 49.06268944206629
- type: manhattan_pearson
value: 51.835306769406365
- type: manhattan_spearman
value: 48.86135493444834
- type: pearson
value: 47.541497334563296
- type: spearman
value: 49.06268944206629
- task:
type: Classification
dataset:
name: MTEB AllegroReviews (default)
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
metrics:
- type: accuracy
value: 49.51292246520874
- type: f1
value: 44.14350234332397
- type: f1_weighted
value: 51.65508998354552
- type: main_score
value: 49.51292246520874
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P (default)
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: main_score
value: 63.883383458621665
- type: v_measure
value: 63.883383458621665
- type: v_measure_std
value: 2.693666879958465
- type: main_score
value: 46.85924588755251
- type: v_measure
value: 46.85924588755251
- type: v_measure_std
value: 2.1918258880872377
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 43.65721212452554
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking (default)
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: e40c8a63ce02da43200eccb5b0846fcaa888f562
metrics:
- type: map
value: 66.39013753839347
- type: mrr
value: 67.68045617786551
- type: main_score
value: 66.39013753839347
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval (default)
type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: main_score
value: 54.284
- type: map_at_1
value: 37.047000000000004
- type: map_at_10
value: 48.53
- type: map_at_100
value: 49.357
- type: map_at_1000
value: 49.39
- type: map_at_20
value: 49.064
- type: map_at_3
value: 45.675
- type: map_at_5
value: 47.441
- type: mrr_at_1
value: 37.04663212435233
- type: mrr_at_10
value: 48.5300326232969
- type: mrr_at_100
value: 49.35708199037581
- type: mrr_at_1000
value: 49.39005824603193
- type: mrr_at_20
value: 49.06417416464799
- type: mrr_at_3
value: 45.67501439263105
- type: mrr_at_5
value: 47.44099021301103
- type: nauc_map_at_1000_diff1
value: 43.32474221868009
- type: nauc_map_at_1000_max
value: 39.407334029058575
- type: nauc_map_at_1000_std
value: -2.3728154448932606
- type: nauc_map_at_100_diff1
value: 43.32336300929909
- type: nauc_map_at_100_max
value: 39.432174777554835
- type: nauc_map_at_100_std
value: -2.356396922384349
- type: nauc_map_at_10_diff1
value: 43.1606520154482
- type: nauc_map_at_10_max
value: 39.33734650558226
- type: nauc_map_at_10_std
value: -2.5156222475075256
- type: nauc_map_at_1_diff1
value: 46.2178975214499
- type: nauc_map_at_1_max
value: 36.26173199049361
- type: nauc_map_at_1_std
value: -3.0897555582816443
- type: nauc_map_at_20_diff1
value: 43.272980702916456
- type: nauc_map_at_20_max
value: 39.4896977052276
- type: nauc_map_at_20_std
value: -2.3305501742917043
- type: nauc_map_at_3_diff1
value: 43.49525042967079
- type: nauc_map_at_3_max
value: 38.66352501824728
- type: nauc_map_at_3_std
value: -3.202794391620473
- type: nauc_map_at_5_diff1
value: 43.2266692546611
- type: nauc_map_at_5_max
value: 38.77368661115743
- type: nauc_map_at_5_std
value: -3.0897532130127954
- type: nauc_mrr_at_1000_diff1
value: 43.32474221868009
- type: nauc_mrr_at_1000_max
value: 39.407334029058575
- type: nauc_mrr_at_1000_std
value: -2.3728154448932606
- type: nauc_mrr_at_100_diff1
value: 43.32336300929909
- type: nauc_mrr_at_100_max
value: 39.432174777554835
- type: nauc_mrr_at_100_std
value: -2.356396922384349
- type: nauc_mrr_at_10_diff1
value: 43.1606520154482
- type: nauc_mrr_at_10_max
value: 39.33734650558226
- type: nauc_mrr_at_10_std
value: -2.5156222475075256
- type: nauc_mrr_at_1_diff1
value: 46.2178975214499
- type: nauc_mrr_at_1_max
value: 36.26173199049361
- type: nauc_mrr_at_1_std
value: -3.0897555582816443
- type: nauc_mrr_at_20_diff1
value: 43.272980702916456
- type: nauc_mrr_at_20_max
value: 39.4896977052276
- type: nauc_mrr_at_20_std
value: -2.3305501742917043
- type: nauc_mrr_at_3_diff1
value: 43.49525042967079
- type: nauc_mrr_at_3_max
value: 38.66352501824728
- type: nauc_mrr_at_3_std
value: -3.202794391620473
- type: nauc_mrr_at_5_diff1
value: 43.2266692546611
- type: nauc_mrr_at_5_max
value: 38.77368661115743
- type: nauc_mrr_at_5_std
value: -3.0897532130127954
- type: nauc_ndcg_at_1000_diff1
value: 43.01903168202974
- type: nauc_ndcg_at_1000_max
value: 40.75496622942232
- type: nauc_ndcg_at_1000_std
value: -1.3150412981845496
- type: nauc_ndcg_at_100_diff1
value: 42.98016493758145
- type: nauc_ndcg_at_100_max
value: 41.55869635162325
- type: nauc_ndcg_at_100_std
value: -0.5355252976886055
- type: nauc_ndcg_at_10_diff1
value: 42.218755211347506
- type: nauc_ndcg_at_10_max
value: 41.305042275175765
- type: nauc_ndcg_at_10_std
value: -1.4034484444573714
- type: nauc_ndcg_at_1_diff1
value: 46.2178975214499
- type: nauc_ndcg_at_1_max
value: 36.26173199049361
- type: nauc_ndcg_at_1_std
value: -3.0897555582816443
- type: nauc_ndcg_at_20_diff1
value: 42.66574440095576
- type: nauc_ndcg_at_20_max
value: 42.014620115124515
- type: nauc_ndcg_at_20_std
value: -0.5176162553751498
- type: nauc_ndcg_at_3_diff1
value: 42.837450505106055
- type: nauc_ndcg_at_3_max
value: 39.525369733082414
- type: nauc_ndcg_at_3_std
value: -3.1605948245795155
- type: nauc_ndcg_at_5_diff1
value: 42.37951815451173
- type: nauc_ndcg_at_5_max
value: 39.78840132935179
- type: nauc_ndcg_at_5_std
value: -2.936898430768135
- type: nauc_precision_at_1000_diff1
value: 49.69224988612385
- type: nauc_precision_at_1000_max
value: 79.57897547128005
- type: nauc_precision_at_1000_std
value: 45.040371354764645
- type: nauc_precision_at_100_diff1
value: 42.70597486048422
- type: nauc_precision_at_100_max
value: 65.74628759606188
- type: nauc_precision_at_100_std
value: 25.49157745244855
- type: nauc_precision_at_10_diff1
value: 38.565609931689345
- type: nauc_precision_at_10_max
value: 50.0239696180852
- type: nauc_precision_at_10_std
value: 3.976354829503967
- type: nauc_precision_at_1_diff1
value: 46.2178975214499
- type: nauc_precision_at_1_max
value: 36.26173199049361
- type: nauc_precision_at_1_std
value: -3.0897555582816443
- type: nauc_precision_at_20_diff1
value: 40.4134718566864
- type: nauc_precision_at_20_max
value: 57.121778108665374
- type: nauc_precision_at_20_std
value: 11.46021975428544
- type: nauc_precision_at_3_diff1
value: 40.90538379461529
- type: nauc_precision_at_3_max
value: 42.18393248057992
- type: nauc_precision_at_3_std
value: -3.005249943837297
- type: nauc_precision_at_5_diff1
value: 39.60162965860782
- type: nauc_precision_at_5_max
value: 43.28317158174058
- type: nauc_precision_at_5_std
value: -2.3469094487738054
- type: nauc_recall_at_1000_diff1
value: 49.69224988612252
- type: nauc_recall_at_1000_max
value: 79.57897547127862
- type: nauc_recall_at_1000_std
value: 45.04037135476256
- type: nauc_recall_at_100_diff1
value: 42.70597486048432
- type: nauc_recall_at_100_max
value: 65.74628759606213
- type: nauc_recall_at_100_std
value: 25.491577452448727
- type: nauc_recall_at_10_diff1
value: 38.56560993168935
- type: nauc_recall_at_10_max
value: 50.02396961808522
- type: nauc_recall_at_10_std
value: 3.9763548295040314
- type: nauc_recall_at_1_diff1
value: 46.2178975214499
- type: nauc_recall_at_1_max
value: 36.26173199049361
- type: nauc_recall_at_1_std
value: -3.0897555582816443
- type: nauc_recall_at_20_diff1
value: 40.41347185668637
- type: nauc_recall_at_20_max
value: 57.12177810866533
- type: nauc_recall_at_20_std
value: 11.460219754285431
- type: nauc_recall_at_3_diff1
value: 40.90538379461527
- type: nauc_recall_at_3_max
value: 42.18393248057989
- type: nauc_recall_at_3_std
value: -3.005249943837297
- type: nauc_recall_at_5_diff1
value: 39.601629658607784
- type: nauc_recall_at_5_max
value: 43.28317158174053
- type: nauc_recall_at_5_std
value: -2.3469094487738054
- type: ndcg_at_1
value: 37.047000000000004
- type: ndcg_at_10
value: 54.284
- type: ndcg_at_100
value: 58.34
- type: ndcg_at_1000
value: 59.303
- type: ndcg_at_20
value: 56.235
- type: ndcg_at_3
value: 48.503
- type: ndcg_at_5
value: 51.686
- type: precision_at_1
value: 37.047000000000004
- type: precision_at_10
value: 7.237
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 4.005
- type: precision_at_3
value: 18.898
- type: precision_at_5
value: 12.884
- type: recall_at_1
value: 37.047000000000004
- type: recall_at_10
value: 72.366
- type: recall_at_100
value: 91.408
- type: recall_at_1000
value: 99.136
- type: recall_at_20
value: 80.095
- type: recall_at_3
value: 56.693000000000005
- type: recall_at_5
value: 64.42099999999999
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 89.49253731343283
- type: ap
value: 61.88098616359918
- type: ap_weighted
value: 61.88098616359918
- type: f1
value: 84.76516623679144
- type: f1_weighted
value: 89.92745276292968
- type: main_score
value: 89.49253731343283
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 89.61456102783727
- type: ap
value: 93.11816566733742
- type: ap_weighted
value: 93.11816566733742
- type: f1
value: 88.27635757733722
- type: f1_weighted
value: 89.82581568285453
- type: main_score
value: 89.61456102783727
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.3825
- type: ap
value: 93.393033869502
- type: ap_weighted
value: 93.393033869502
- type: f1
value: 95.38109007966307
- type: f1_weighted
value: 95.38109007966305
- type: main_score
value: 95.3825
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.768
- type: f1
value: 48.95084821944411
- type: f1_weighted
value: 48.9508482194441
- type: main_score
value: 49.768
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.071999999999996
- type: f1
value: 47.24171107487612
- type: f1_weighted
value: 47.24171107487612
- type: main_score
value: 48.071999999999996
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.102000000000004
- type: f1
value: 47.27193805278696
- type: f1_weighted
value: 47.27193805278696
- type: main_score
value: 48.102000000000004
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.30800000000001
- type: f1
value: 46.41683358017851
- type: f1_weighted
value: 46.41683358017851
- type: main_score
value: 47.30800000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.944
- type: f1
value: 44.223824487744395
- type: f1_weighted
value: 44.22382448774439
- type: main_score
value: 44.944
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 29.232000000000003
- type: map_at_10
value: 45.117000000000004
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 45.98
- type: map_at_20
value: 45.815
- type: map_at_3
value: 39.912
- type: map_at_5
value: 42.693
- type: mrr_at_1
value: 29.659000000000002
- type: mrr_at_10
value: 45.253
- type: mrr_at_100
value: 46.125
- type: mrr_at_1000
value: 46.129
- type: mrr_at_20
value: 45.964
- type: mrr_at_3
value: 40.043
- type: mrr_at_5
value: 42.870000000000005
- type: ndcg_at_1
value: 29.232000000000003
- type: ndcg_at_10
value: 54.327999999999996
- type: ndcg_at_100
value: 57.86
- type: ndcg_at_1000
value: 57.935
- type: ndcg_at_20
value: 56.794
- type: ndcg_at_3
value: 43.516
- type: ndcg_at_5
value: 48.512
- type: precision_at_1
value: 29.232000000000003
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.676
- type: precision_at_3
value: 17.994
- type: precision_at_5
value: 13.215
- type: recall_at_1
value: 29.232000000000003
- type: recall_at_10
value: 83.926
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 93.528
- type: recall_at_3
value: 53.983000000000004
- type: recall_at_5
value: 66.074
- type: main_score
value: 54.327999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 46.6636824632419
- type: v_measure
value: 46.6636824632419
- type: v_measure_std
value: 13.817129140714963
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 39.271141892800024
- type: v_measure
value: 39.271141892800024
- type: v_measure_std
value: 14.276782483454827
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.04363277324629
- type: mrr
value: 78.2372598162072
- type: main_score
value: 65.04363277324629
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.83
- type: main_score
value: 30.83
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cosine_pearson
value: 88.80382082011027
- type: cosine_spearman
value: 88.68876782169106
- type: euclidean_pearson
value: 87.00802890147176
- type: euclidean_spearman
value: 87.43211268192712
- type: main_score
value: 88.68876782169106
- type: manhattan_pearson
value: 87.14062537179474
- type: manhattan_spearman
value: 87.59115245033443
- type: pearson
value: 88.80382082011027
- type: spearman
value: 88.68876782169106
- task:
type: STS
dataset:
name: MTEB BQ (default)
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cosine_pearson
value: 61.588006604878196
- type: cosine_spearman
value: 63.20615427154465
- type: euclidean_pearson
value: 61.818547092516496
- type: euclidean_spearman
value: 63.21558009151778
- type: main_score
value: 63.20615427154465
- type: manhattan_pearson
value: 61.665588158487616
- type: manhattan_spearman
value: 63.051544488238584
- type: pearson
value: 61.588006604878196
- type: spearman
value: 63.20615427154465
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval (default)
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
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value: 64.414
- type: map_at_1
value: 14.865
- type: map_at_10
value: 21.605
- type: map_at_100
value: 22.762
- type: map_at_1000
value: 22.854
- type: map_at_20
value: 22.259999999999998
- type: map_at_3
value: 20.119999999999997
- type: map_at_5
value: 20.931
- type: mrr_at_1
value: 14.864864864864865
- type: mrr_at_10
value: 21.605176605176606
- type: mrr_at_100
value: 22.7622306460065
- type: mrr_at_1000
value: 22.85383406410312
- type: mrr_at_20
value: 22.259528463088845
- type: mrr_at_3
value: 20.12012012012012
- type: mrr_at_5
value: 20.930930930930934
- type: nauc_map_at_1000_diff1
value: 17.486265968689338
- type: nauc_map_at_1000_max
value: 22.736799291688836
- type: nauc_map_at_1000_std
value: 9.831687441977147
- type: nauc_map_at_100_diff1
value: 17.50754492049086
- type: nauc_map_at_100_max
value: 22.77693662806787
- type: nauc_map_at_100_std
value: 9.853899509675395
- type: nauc_map_at_10_diff1
value: 17.42133968580952
- type: nauc_map_at_10_max
value: 22.45861793882279
- type: nauc_map_at_10_std
value: 8.964888472915938
- type: nauc_map_at_1_diff1
value: 19.433947086968093
- type: nauc_map_at_1_max
value: 24.75657047550517
- type: nauc_map_at_1_std
value: 15.122329157218505
- type: nauc_map_at_20_diff1
value: 17.429856756008785
- type: nauc_map_at_20_max
value: 22.438850987431017
- type: nauc_map_at_20_std
value: 9.172746012213558
- type: nauc_map_at_3_diff1
value: 18.218182689678475
- type: nauc_map_at_3_max
value: 23.57169444088667
- type: nauc_map_at_3_std
value: 10.464473559366356
- type: nauc_map_at_5_diff1
value: 18.6075342519133
- type: nauc_map_at_5_max
value: 23.308845973576673
- type: nauc_map_at_5_std
value: 9.364009996445652
- type: nauc_mrr_at_1000_diff1
value: 17.486265968689338
- type: nauc_mrr_at_1000_max
value: 22.736799291688836
- type: nauc_mrr_at_1000_std
value: 9.831687441977147
- type: nauc_mrr_at_100_diff1
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- type: nauc_mrr_at_100_max
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- type: nauc_mrr_at_100_std
value: 9.853899509675395
- type: nauc_mrr_at_10_diff1
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- type: nauc_mrr_at_10_max
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- type: nauc_mrr_at_10_std
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- type: nauc_mrr_at_1_diff1
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- type: nauc_mrr_at_1_max
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- type: nauc_mrr_at_1_std
value: 15.122329157218505
- type: nauc_mrr_at_20_diff1
value: 17.429856756008785
- type: nauc_mrr_at_20_max
value: 22.438850987431017
- type: nauc_mrr_at_20_std
value: 9.172746012213558
- type: nauc_mrr_at_3_diff1
value: 18.218182689678475
- type: nauc_mrr_at_3_max
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- type: nauc_mrr_at_3_std
value: 10.464473559366356
- type: nauc_mrr_at_5_diff1
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- type: nauc_mrr_at_5_max
value: 23.308845973576673
- type: nauc_mrr_at_5_std
value: 9.364009996445652
- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_max
value: 23.308241052911495
- type: nauc_ndcg_at_1000_std
value: 11.50905911184097
- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_100_max
value: 24.323253721240974
- type: nauc_ndcg_at_100_std
value: 11.952612443651557
- type: nauc_ndcg_at_10_diff1
value: 16.030325121764594
- type: nauc_ndcg_at_10_max
value: 21.306799242079542
- type: nauc_ndcg_at_10_std
value: 6.63359364302513
- type: nauc_ndcg_at_1_diff1
value: 19.433947086968093
- type: nauc_ndcg_at_1_max
value: 24.75657047550517
- type: nauc_ndcg_at_1_std
value: 15.122329157218505
- type: nauc_ndcg_at_20_diff1
value: 16.013173605999857
- type: nauc_ndcg_at_20_max
value: 21.607217260736576
- type: nauc_ndcg_at_20_std
value: 7.319482417138996
- type: nauc_ndcg_at_3_diff1
value: 17.97958548328493
- type: nauc_ndcg_at_3_max
value: 23.58346522810145
- type: nauc_ndcg_at_3_std
value: 9.392582854708314
- type: nauc_ndcg_at_5_diff1
value: 18.734733324685287
- type: nauc_ndcg_at_5_max
value: 23.273244317623742
- type: nauc_ndcg_at_5_std
value: 7.638611545253834
- type: nauc_precision_at_1000_diff1
value: 7.919843339380295
- type: nauc_precision_at_1000_max
value: 31.575386234270486
- type: nauc_precision_at_1000_std
value: 39.332224386769404
- type: nauc_precision_at_100_diff1
value: 15.018050960000052
- type: nauc_precision_at_100_max
value: 34.98209513759861
- type: nauc_precision_at_100_std
value: 26.970034484359022
- type: nauc_precision_at_10_diff1
value: 12.102191084210922
- type: nauc_precision_at_10_max
value: 18.112541150340675
- type: nauc_precision_at_10_std
value: 0.7358784689406018
- type: nauc_precision_at_1_diff1
value: 19.433947086968093
- type: nauc_precision_at_1_max
value: 24.75657047550517
- type: nauc_precision_at_1_std
value: 15.122329157218505
- type: nauc_precision_at_20_diff1
value: 12.018814361204328
- type: nauc_precision_at_20_max
value: 19.75123746049928
- type: nauc_precision_at_20_std
value: 3.012204650582264
- type: nauc_precision_at_3_diff1
value: 17.41375604940955
- type: nauc_precision_at_3_max
value: 23.699834627021037
- type: nauc_precision_at_3_std
value: 6.793486779050103
- type: nauc_precision_at_5_diff1
value: 19.194631963780257
- type: nauc_precision_at_5_max
value: 23.31708702442155
- type: nauc_precision_at_5_std
value: 3.4591358279667332
- type: nauc_recall_at_1000_diff1
value: 7.919843339380378
- type: nauc_recall_at_1000_max
value: 31.57538623427063
- type: nauc_recall_at_1000_std
value: 39.332224386769546
- type: nauc_recall_at_100_diff1
value: 15.018050960000085
- type: nauc_recall_at_100_max
value: 34.9820951375986
- type: nauc_recall_at_100_std
value: 26.97003448435901
- type: nauc_recall_at_10_diff1
value: 12.102191084210837
- type: nauc_recall_at_10_max
value: 18.112541150340594
- type: nauc_recall_at_10_std
value: 0.7358784689405188
- type: nauc_recall_at_1_diff1
value: 19.433947086968093
- type: nauc_recall_at_1_max
value: 24.75657047550517
- type: nauc_recall_at_1_std
value: 15.122329157218505
- type: nauc_recall_at_20_diff1
value: 12.01881436120429
- type: nauc_recall_at_20_max
value: 19.751237460499222
- type: nauc_recall_at_20_std
value: 3.0122046505822135
- type: nauc_recall_at_3_diff1
value: 17.413756049409503
- type: nauc_recall_at_3_max
value: 23.699834627020998
- type: nauc_recall_at_3_std
value: 6.793486779050083
- type: nauc_recall_at_5_diff1
value: 19.194631963780203
- type: nauc_recall_at_5_max
value: 23.3170870244215
- type: nauc_recall_at_5_std
value: 3.459135827966664
- type: ndcg_at_1
value: 14.865
- type: ndcg_at_10
value: 24.764
- type: ndcg_at_100
value: 30.861
- type: ndcg_at_1000
value: 33.628
- type: ndcg_at_20
value: 27.078000000000003
- type: ndcg_at_3
value: 21.675
- type: ndcg_at_5
value: 23.148
- type: precision_at_1
value: 14.865
- type: precision_at_10
value: 3.4680000000000004
- type: precision_at_100
value: 0.644
- type: precision_at_1000
value: 0.087
- type: precision_at_20
value: 2.185
- type: precision_at_3
value: 8.709
- type: precision_at_5
value: 5.946
- type: recall_at_1
value: 14.865
- type: recall_at_10
value: 34.685
- type: recall_at_100
value: 64.414
- type: recall_at_1000
value: 86.937
- type: recall_at_20
value: 43.694
- type: recall_at_3
value: 26.125999999999998
- type: recall_at_5
value: 29.73
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.08116883116882
- type: f1
value: 84.05587055990273
- type: f1_weighted
value: 84.05587055990274
- type: main_score
value: 84.08116883116882
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 38.1941007822277
- type: v_measure
value: 38.1941007822277
- type: v_measure_std
value: 0.7502113547288178
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 34.42075599178318
- type: v_measure
value: 34.42075599178318
- type: v_measure_std
value: 0.600256720497283
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringP2P (default)
type: slvnwhrl/blurbs-clustering-p2p
config: default
split: test
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
metrics:
- type: main_score
value: 41.634627363047265
- type: v_measure
value: 41.634627363047265
- type: v_measure_std
value: 9.726923191225307
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringS2S (default)
type: slvnwhrl/blurbs-clustering-s2s
config: default
split: test
revision: 22793b6a6465bf00120ad525e38c51210858132c
metrics:
- type: main_score
value: 20.996468295584197
- type: v_measure
value: 20.996468295584197
- type: v_measure_std
value: 9.225766688272197
- task:
type: Classification
dataset:
name: MTEB CBD (default)
type: PL-MTEB/cbd
config: default
split: test
revision: 36ddb419bcffe6a5374c3891957912892916f28d
metrics:
- type: accuracy
value: 69.99
- type: ap
value: 22.57826353116948
- type: ap_weighted
value: 22.57826353116948
- type: f1
value: 59.04574955548393
- type: f1_weighted
value: 74.36235022309789
- type: main_score
value: 69.99
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E (default)
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
metrics:
- type: cosine_accuracy
value: 88.7
- type: cosine_accuracy_threshold
value: 97.37848043441772
- type: cosine_ap
value: 73.0405088928302
- type: cosine_f1
value: 63.52201257861635
- type: cosine_f1_threshold
value: 96.98888063430786
- type: cosine_precision
value: 78.90625
- type: cosine_recall
value: 53.1578947368421
- type: dot_accuracy
value: 84.89999999999999
- type: dot_accuracy_threshold
value: 43603.09753417969
- type: dot_ap
value: 56.98157569085279
- type: dot_f1
value: 57.606490872210955
- type: dot_f1_threshold
value: 40406.23779296875
- type: dot_precision
value: 46.864686468646866
- type: dot_recall
value: 74.73684210526315
- type: euclidean_accuracy
value: 88.5
- type: euclidean_accuracy_threshold
value: 498.0483055114746
- type: euclidean_ap
value: 72.97328234816734
- type: euclidean_f1
value: 63.722397476340696
- type: euclidean_f1_threshold
value: 508.6186408996582
- type: euclidean_precision
value: 79.52755905511812
- type: euclidean_recall
value: 53.1578947368421
- type: main_score
value: 73.0405088928302
- type: manhattan_accuracy
value: 88.6
- type: manhattan_accuracy_threshold
value: 12233.079528808594
- type: manhattan_ap
value: 72.92148503992615
- type: manhattan_f1
value: 63.69426751592356
- type: manhattan_f1_threshold
value: 12392.754364013672
- type: manhattan_precision
value: 80.64516129032258
- type: manhattan_recall
value: 52.63157894736842
- type: max_accuracy
value: 88.7
- type: max_ap
value: 73.0405088928302
- type: max_f1
value: 63.722397476340696
- type: max_precision
value: 80.64516129032258
- type: max_recall
value: 74.73684210526315
- type: similarity_accuracy
value: 88.7
- type: similarity_accuracy_threshold
value: 97.37848043441772
- type: similarity_ap
value: 73.0405088928302
- type: similarity_f1
value: 63.52201257861635
- type: similarity_f1_threshold
value: 96.98888063430786
- type: similarity_precision
value: 78.90625
- type: similarity_recall
value: 53.1578947368421
- task:
type: STS
dataset:
name: MTEB CDSC-R (default)
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: 1cd6abbb00df7d14be3dbd76a7dcc64b3a79a7cd
metrics:
- type: cosine_pearson
value: 92.97492495289738
- type: cosine_spearman
value: 92.63248098608472
- type: euclidean_pearson
value: 92.04712487782031
- type: euclidean_spearman
value: 92.19679486755008
- type: main_score
value: 92.63248098608472
- type: manhattan_pearson
value: 92.0101187740438
- type: manhattan_spearman
value: 92.20926859332754
- type: pearson
value: 92.97492495289738
- type: spearman
value: 92.63248098608472
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P (default)
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 39.96377851800628
- type: v_measure
value: 39.96377851800628
- type: v_measure_std
value: 0.9793033243093288
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S (default)
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 38.788850224595784
- type: v_measure
value: 38.788850224595784
- type: v_measure_std
value: 1.0712604145916924
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 77.95952507806115
- type: mrr
value: 80.8643253968254
- type: main_score
value: 77.95952507806115
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 78.21522500165045
- type: mrr
value: 81.28194444444443
- type: main_score
value: 78.21522500165045
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval (default)
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.377
- type: map_at_10
value: 46.371
- type: map_at_100
value: 47.829
- type: map_at_1000
value: 47.94
- type: map_at_20
value: 47.205000000000005
- type: map_at_3
value: 42.782
- type: map_at_5
value: 44.86
- type: mrr_at_1
value: 41.345
- type: mrr_at_10
value: 52.187
- type: mrr_at_100
value: 52.893
- type: mrr_at_1000
value: 52.929
- type: mrr_at_20
value: 52.637
- type: mrr_at_3
value: 49.714000000000006
- type: mrr_at_5
value: 51.373000000000005
- type: ndcg_at_1
value: 41.345
- type: ndcg_at_10
value: 52.946000000000005
- type: ndcg_at_100
value: 57.92699999999999
- type: ndcg_at_1000
value: 59.609
- type: ndcg_at_20
value: 54.900999999999996
- type: ndcg_at_3
value: 48.357
- type: ndcg_at_5
value: 50.739000000000004
- type: precision_at_1
value: 41.345
- type: precision_at_10
value: 10.186
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.2
- type: precision_at_20
value: 5.959
- type: precision_at_3
value: 23.796
- type: precision_at_5
value: 17.024
- type: recall_at_1
value: 33.377
- type: recall_at_10
value: 65.067
- type: recall_at_100
value: 86.04899999999999
- type: recall_at_1000
value: 96.54899999999999
- type: recall_at_20
value: 72.071
- type: recall_at_3
value: 51.349999999999994
- type: recall_at_5
value: 58.41
- type: main_score
value: 52.946000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval (default)
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 31.097
- type: map_at_10
value: 42.183
- type: map_at_100
value: 43.580999999999996
- type: map_at_1000
value: 43.718
- type: map_at_20
value: 42.921
- type: map_at_3
value: 38.963
- type: map_at_5
value: 40.815
- type: mrr_at_1
value: 39.745000000000005
- type: mrr_at_10
value: 48.736000000000004
- type: mrr_at_100
value: 49.405
- type: mrr_at_1000
value: 49.452
- type: mrr_at_20
value: 49.118
- type: mrr_at_3
value: 46.497
- type: mrr_at_5
value: 47.827999999999996
- type: ndcg_at_1
value: 39.745000000000005
- type: ndcg_at_10
value: 48.248000000000005
- type: ndcg_at_100
value: 52.956
- type: ndcg_at_1000
value: 54.99699999999999
- type: ndcg_at_20
value: 50.01
- type: ndcg_at_3
value: 43.946000000000005
- type: ndcg_at_5
value: 46.038000000000004
- type: precision_at_1
value: 39.745000000000005
- type: precision_at_10
value: 9.229
- type: precision_at_100
value: 1.5070000000000001
- type: precision_at_1000
value: 0.199
- type: precision_at_20
value: 5.489999999999999
- type: precision_at_3
value: 21.38
- type: precision_at_5
value: 15.274
- type: recall_at_1
value: 31.097
- type: recall_at_10
value: 58.617
- type: recall_at_100
value: 78.55199999999999
- type: recall_at_1000
value: 91.13900000000001
- type: recall_at_20
value: 64.92
- type: recall_at_3
value: 45.672000000000004
- type: recall_at_5
value: 51.669
- type: main_score
value: 48.248000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval (default)
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.745000000000005
- type: map_at_10
value: 52.063
- type: map_at_100
value: 53.077
- type: map_at_1000
value: 53.13
- type: map_at_20
value: 52.66
- type: map_at_3
value: 48.662
- type: map_at_5
value: 50.507000000000005
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 55.528
- type: mrr_at_100
value: 56.16100000000001
- type: mrr_at_1000
value: 56.192
- type: mrr_at_20
value: 55.923
- type: mrr_at_3
value: 52.93600000000001
- type: mrr_at_5
value: 54.435
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 58.019
- type: ndcg_at_100
value: 61.936
- type: ndcg_at_1000
value: 63.015
- type: ndcg_at_20
value: 59.691
- type: ndcg_at_3
value: 52.294
- type: ndcg_at_5
value: 55.017
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.386
- type: precision_at_100
value: 1.232
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.223
- type: precision_at_3
value: 23.177
- type: precision_at_5
value: 15.9
- type: recall_at_1
value: 39.745000000000005
- type: recall_at_10
value: 72.08099999999999
- type: recall_at_100
value: 88.85300000000001
- type: recall_at_1000
value: 96.569
- type: recall_at_20
value: 78.203
- type: recall_at_3
value: 56.957
- type: recall_at_5
value: 63.63100000000001
- type: main_score
value: 58.019
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval (default)
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.651999999999997
- type: map_at_10
value: 35.799
- type: map_at_100
value: 36.846000000000004
- type: map_at_1000
value: 36.931000000000004
- type: map_at_20
value: 36.341
- type: map_at_3
value: 32.999
- type: map_at_5
value: 34.597
- type: mrr_at_1
value: 28.814
- type: mrr_at_10
value: 37.869
- type: mrr_at_100
value: 38.728
- type: mrr_at_1000
value: 38.795
- type: mrr_at_20
value: 38.317
- type: mrr_at_3
value: 35.235
- type: mrr_at_5
value: 36.738
- type: ndcg_at_1
value: 28.814
- type: ndcg_at_10
value: 41.028
- type: ndcg_at_100
value: 46.162
- type: ndcg_at_1000
value: 48.15
- type: ndcg_at_20
value: 42.824
- type: ndcg_at_3
value: 35.621
- type: ndcg_at_5
value: 38.277
- type: precision_at_1
value: 28.814
- type: precision_at_10
value: 6.361999999999999
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.6159999999999997
- type: precision_at_3
value: 15.140999999999998
- type: precision_at_5
value: 10.712000000000002
- type: recall_at_1
value: 26.651999999999997
- type: recall_at_10
value: 55.038
- type: recall_at_100
value: 78.806
- type: recall_at_1000
value: 93.485
- type: recall_at_20
value: 61.742
- type: recall_at_3
value: 40.682
- type: recall_at_5
value: 46.855000000000004
- type: main_score
value: 41.028
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval (default)
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 17.627000000000002
- type: map_at_10
value: 26.436999999999998
- type: map_at_100
value: 27.85
- type: map_at_1000
value: 27.955999999999996
- type: map_at_20
value: 27.233
- type: map_at_3
value: 23.777
- type: map_at_5
value: 25.122
- type: mrr_at_1
value: 22.387999999999998
- type: mrr_at_10
value: 31.589
- type: mrr_at_100
value: 32.641999999999996
- type: mrr_at_1000
value: 32.696999999999996
- type: mrr_at_20
value: 32.201
- type: mrr_at_3
value: 28.98
- type: mrr_at_5
value: 30.342000000000002
- type: ndcg_at_1
value: 22.387999999999998
- type: ndcg_at_10
value: 32.129999999999995
- type: ndcg_at_100
value: 38.562999999999995
- type: ndcg_at_1000
value: 40.903
- type: ndcg_at_20
value: 34.652
- type: ndcg_at_3
value: 27.26
- type: ndcg_at_5
value: 29.235
- type: precision_at_1
value: 22.387999999999998
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 1.068
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 3.6999999999999997
- type: precision_at_3
value: 13.267000000000001
- type: precision_at_5
value: 9.403
- type: recall_at_1
value: 17.627000000000002
- type: recall_at_10
value: 44.71
- type: recall_at_100
value: 72.426
- type: recall_at_1000
value: 88.64699999999999
- type: recall_at_20
value: 53.65
- type: recall_at_3
value: 30.989
- type: recall_at_5
value: 36.237
- type: main_score
value: 32.129999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval (default)
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 30.891000000000002
- type: map_at_10
value: 41.519
- type: map_at_100
value: 42.896
- type: map_at_1000
value: 42.992999999999995
- type: map_at_20
value: 42.287
- type: map_at_3
value: 37.822
- type: map_at_5
value: 39.976
- type: mrr_at_1
value: 37.921
- type: mrr_at_10
value: 47.260999999999996
- type: mrr_at_100
value: 48.044
- type: mrr_at_1000
value: 48.08
- type: mrr_at_20
value: 47.699999999999996
- type: mrr_at_3
value: 44.513999999999996
- type: mrr_at_5
value: 46.064
- type: ndcg_at_1
value: 37.921
- type: ndcg_at_10
value: 47.806
- type: ndcg_at_100
value: 53.274
- type: ndcg_at_1000
value: 55.021
- type: ndcg_at_20
value: 49.973
- type: ndcg_at_3
value: 42.046
- type: ndcg_at_5
value: 44.835
- type: precision_at_1
value: 37.921
- type: precision_at_10
value: 8.767999999999999
- type: precision_at_100
value: 1.353
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 5.135
- type: precision_at_3
value: 20.051
- type: precision_at_5
value: 14.398
- type: recall_at_1
value: 30.891000000000002
- type: recall_at_10
value: 60.897999999999996
- type: recall_at_100
value: 83.541
- type: recall_at_1000
value: 94.825
- type: recall_at_20
value: 68.356
- type: recall_at_3
value: 44.65
- type: recall_at_5
value: 51.919000000000004
- type: main_score
value: 47.806
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval (default)
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 27.654
- type: map_at_10
value: 38.025999999999996
- type: map_at_100
value: 39.425
- type: map_at_1000
value: 39.528
- type: map_at_20
value: 38.838
- type: map_at_3
value: 34.745
- type: map_at_5
value: 36.537
- type: mrr_at_1
value: 34.018
- type: mrr_at_10
value: 43.314
- type: mrr_at_100
value: 44.283
- type: mrr_at_1000
value: 44.327
- type: mrr_at_20
value: 43.929
- type: mrr_at_3
value: 40.868
- type: mrr_at_5
value: 42.317
- type: ndcg_at_1
value: 34.018
- type: ndcg_at_10
value: 43.887
- type: ndcg_at_100
value: 49.791000000000004
- type: ndcg_at_1000
value: 51.834
- type: ndcg_at_20
value: 46.376
- type: ndcg_at_3
value: 38.769999999999996
- type: ndcg_at_5
value: 41.144
- type: precision_at_1
value: 34.018
- type: precision_at_10
value: 8.001999999999999
- type: precision_at_100
value: 1.2630000000000001
- type: precision_at_1000
value: 0.16
- type: precision_at_20
value: 4.737
- type: precision_at_3
value: 18.417
- type: precision_at_5
value: 13.150999999999998
- type: recall_at_1
value: 27.654
- type: recall_at_10
value: 56.111
- type: recall_at_100
value: 81.136
- type: recall_at_1000
value: 94.788
- type: recall_at_20
value: 65.068
- type: recall_at_3
value: 41.713
- type: recall_at_5
value: 48.106
- type: main_score
value: 43.887
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval (default)
type: CQADupstackRetrieval_is_a_combined_dataset
config: default
split: test
revision: CQADupstackRetrieval_is_a_combined_dataset
metrics:
- type: main_score
value: 42.58858333333333
- type: ndcg_at_10
value: 42.58858333333333
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval (default)
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.501
- type: map_at_10
value: 32.814
- type: map_at_100
value: 33.754
- type: map_at_1000
value: 33.859
- type: map_at_20
value: 33.324
- type: map_at_3
value: 30.758000000000003
- type: map_at_5
value: 31.936999999999998
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 35.662
- type: mrr_at_100
value: 36.443999999999996
- type: mrr_at_1000
value: 36.516999999999996
- type: mrr_at_20
value: 36.085
- type: mrr_at_3
value: 33.742
- type: mrr_at_5
value: 34.931
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 37.208000000000006
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 44.421
- type: ndcg_at_20
value: 38.917
- type: ndcg_at_3
value: 33.544000000000004
- type: ndcg_at_5
value: 35.374
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 5.92
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 3.4130000000000003
- type: precision_at_3
value: 15.031
- type: precision_at_5
value: 10.306999999999999
- type: recall_at_1
value: 24.501
- type: recall_at_10
value: 47.579
- type: recall_at_100
value: 69.045
- type: recall_at_1000
value: 88.032
- type: recall_at_20
value: 54.125
- type: recall_at_3
value: 37.202
- type: recall_at_5
value: 41.927
- type: main_score
value: 37.208000000000006
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval (default)
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.29
- type: map_at_10
value: 26.183
- type: map_at_100
value: 27.351999999999997
- type: map_at_1000
value: 27.483999999999998
- type: map_at_20
value: 26.798
- type: map_at_3
value: 23.629
- type: map_at_5
value: 24.937
- type: mrr_at_1
value: 22.299
- type: mrr_at_10
value: 30.189
- type: mrr_at_100
value: 31.098
- type: mrr_at_1000
value: 31.177
- type: mrr_at_20
value: 30.697000000000003
- type: mrr_at_3
value: 27.862
- type: mrr_at_5
value: 29.066
- type: ndcg_at_1
value: 22.299
- type: ndcg_at_10
value: 31.202
- type: ndcg_at_100
value: 36.617
- type: ndcg_at_1000
value: 39.544000000000004
- type: ndcg_at_20
value: 33.177
- type: ndcg_at_3
value: 26.639000000000003
- type: ndcg_at_5
value: 28.526
- type: precision_at_1
value: 22.299
- type: precision_at_10
value: 5.8020000000000005
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_20
value: 3.505
- type: precision_at_3
value: 12.698
- type: precision_at_5
value: 9.174
- type: recall_at_1
value: 18.29
- type: recall_at_10
value: 42.254999999999995
- type: recall_at_100
value: 66.60000000000001
- type: recall_at_1000
value: 87.31400000000001
- type: recall_at_20
value: 49.572
- type: recall_at_3
value: 29.342000000000002
- type: recall_at_5
value: 34.221000000000004
- type: main_score
value: 31.202
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval (default)
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 27.722
- type: map_at_10
value: 37.698
- type: map_at_100
value: 38.899
- type: map_at_1000
value: 38.998
- type: map_at_20
value: 38.381
- type: map_at_3
value: 34.244
- type: map_at_5
value: 36.295
- type: mrr_at_1
value: 32.183
- type: mrr_at_10
value: 41.429
- type: mrr_at_100
value: 42.308
- type: mrr_at_1000
value: 42.358000000000004
- type: mrr_at_20
value: 41.957
- type: mrr_at_3
value: 38.401999999999994
- type: mrr_at_5
value: 40.294999999999995
- type: ndcg_at_1
value: 32.183
- type: ndcg_at_10
value: 43.519000000000005
- type: ndcg_at_100
value: 48.786
- type: ndcg_at_1000
value: 50.861999999999995
- type: ndcg_at_20
value: 45.654
- type: ndcg_at_3
value: 37.521
- type: ndcg_at_5
value: 40.615
- type: precision_at_1
value: 32.183
- type: precision_at_10
value: 7.603
- type: precision_at_100
value: 1.135
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 4.408
- type: precision_at_3
value: 17.071
- type: precision_at_5
value: 12.668
- type: recall_at_1
value: 27.722
- type: recall_at_10
value: 57.230000000000004
- type: recall_at_100
value: 79.97999999999999
- type: recall_at_1000
value: 94.217
- type: recall_at_20
value: 64.864
- type: recall_at_3
value: 41.215
- type: recall_at_5
value: 48.774
- type: main_score
value: 43.519000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval (default)
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 25.852999999999998
- type: map_at_10
value: 35.394999999999996
- type: map_at_100
value: 37.291999999999994
- type: map_at_1000
value: 37.495
- type: map_at_20
value: 36.372
- type: map_at_3
value: 32.336
- type: map_at_5
value: 34.159
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 40.677
- type: mrr_at_100
value: 41.728
- type: mrr_at_1000
value: 41.778
- type: mrr_at_20
value: 41.301
- type: mrr_at_3
value: 38.208
- type: mrr_at_5
value: 39.592
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 41.559000000000005
- type: ndcg_at_100
value: 48.012
- type: ndcg_at_1000
value: 50.234
- type: ndcg_at_20
value: 44.15
- type: ndcg_at_3
value: 36.918
- type: ndcg_at_5
value: 39.227000000000004
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 8.043
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.245
- type: precision_at_20
value: 5.2170000000000005
- type: precision_at_3
value: 17.655
- type: precision_at_5
value: 12.845999999999998
- type: recall_at_1
value: 25.852999999999998
- type: recall_at_10
value: 53.093
- type: recall_at_100
value: 81.05799999999999
- type: recall_at_1000
value: 94.657
- type: recall_at_20
value: 62.748000000000005
- type: recall_at_3
value: 39.300000000000004
- type: recall_at_5
value: 45.754
- type: main_score
value: 41.559000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval (default)
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 19.23
- type: map_at_10
value: 28.128999999999998
- type: map_at_100
value: 29.195
- type: map_at_1000
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type: mteb/emotion
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value: 91.90440000000001
- task:
type: Classification
dataset:
name: MTEB InappropriatenessClassification (default)
type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
metrics:
- type: accuracy
value: 61.0498046875
- type: ap
value: 57.04240566648215
- type: ap_weighted
value: 57.04240566648215
- type: f1
value: 60.867630038606954
- type: f1_weighted
value: 60.867630038606954
- type: main_score
value: 61.0498046875
- task:
type: Classification
dataset:
name: MTEB JDReview (default)
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 83.50844277673546
- type: ap
value: 48.46732380712268
- type: ap_weighted
value: 48.46732380712268
- type: f1
value: 77.43967451387445
- type: f1_weighted
value: 84.78462929014114
- type: main_score
value: 83.50844277673546
- task:
type: Classification
dataset:
name: MTEB KinopoiskClassification (default)
type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
metrics:
- type: accuracy
value: 62.393333333333324
- type: f1
value: 61.35940129568015
- type: f1_weighted
value: 61.35940129568015
- type: main_score
value: 62.393333333333324
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type: STS
dataset:
name: MTEB LCQMC (default)
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
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value: 67.74375505907872
- type: cosine_spearman
value: 75.94582231399434
- type: euclidean_pearson
value: 74.52501692443582
- type: euclidean_spearman
value: 75.88428434746646
- type: main_score
value: 75.94582231399434
- type: manhattan_pearson
value: 74.55015441749529
- type: manhattan_spearman
value: 75.83288262176175
- type: pearson
value: 67.74375505907872
- type: spearman
value: 75.94582231399434
- task:
type: Retrieval
dataset:
name: MTEB LEMBNarrativeQARetrieval (default)
type: dwzhu/LongEmbed
config: default
split: test
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
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value: 23.093
- type: map_at_10
value: 30.227999999999998
- type: map_at_100
value: 31.423000000000002
- type: map_at_1000
value: 31.533
- type: map_at_20
value: 30.835
- type: map_at_3
value: 27.983999999999998
- type: map_at_5
value: 29.253
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value: 23.093
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- type: mrr_at_100
value: 31.423000000000002
- type: mrr_at_1000
value: 31.533
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value: 30.835
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- type: ndcg_at_10
value: 34.297
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value: 41.049
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- type: ndcg_at_20
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- type: ndcg_at_3
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- type: precision_at_1000
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- type: precision_at_3
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- type: precision_at_5
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- type: recall_at_1
value: 23.093
- type: recall_at_10
value: 47.354
- type: recall_at_100
value: 81.147
- type: recall_at_1000
value: 100.0
- type: recall_at_20
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- type: recall_at_3
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- type: recall_at_5
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- type: main_score
value: 34.297
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- type: map_at_100
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- type: map_at_1000
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- type: mrr_at_3
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- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_5
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- type: recall_at_1
value: 24.361
- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
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- type: recall_at_20
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- type: recall_at_3
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- type: recall_at_5
value: 45.972
- type: main_score
value: 39.337
- type: map_at_1
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- type: recall_at_1
value: 53.667
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- type: recall_at_20
value: 86.0
- type: recall_at_3
value: 66.0
- type: recall_at_5
value: 73.667
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value: 66.018
- task:
type: Retrieval
dataset:
name: MTEB LEMBNeedleRetrieval (default)
type: dwzhu/LongEmbed
config: default
split: test_256
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
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value: 64.0
- type: map_at_10
value: 77.083
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value: 77.265
- type: map_at_1000
value: 77.265
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value: 77.265
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value: 76.333
- type: map_at_5
value: 76.833
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value: 64.0
- type: mrr_at_10
value: 77.083
- type: mrr_at_100
value: 77.265
- type: mrr_at_1000
value: 77.265
- type: mrr_at_20
value: 77.265
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value: 76.333
- type: mrr_at_5
value: 76.833
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value: 64.0
- type: ndcg_at_10
value: 82.325
- type: ndcg_at_100
value: 82.883
- type: ndcg_at_1000
value: 82.883
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value: 82.883
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value: 80.833
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value: 81.694
- type: precision_at_1
value: 64.0
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value: 9.8
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value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 31.333
- type: precision_at_5
value: 19.2
- type: recall_at_1
value: 64.0
- type: recall_at_10
value: 98.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 94.0
- type: recall_at_5
value: 96.0
- type: main_score
value: 64.0
- type: map_at_1
value: 100.0
- type: map_at_10
value: 100.0
- type: map_at_100
value: 100.0
- type: map_at_1000
value: 100.0
- type: map_at_20
value: 100.0
- type: map_at_3
value: 100.0
- type: map_at_5
value: 100.0
- type: mrr_at_1
value: 100.0
- type: mrr_at_10
value: 100.0
- type: mrr_at_100
value: 100.0
- type: mrr_at_1000
value: 100.0
- type: mrr_at_20
value: 100.0
- type: mrr_at_3
value: 100.0
- type: mrr_at_5
value: 100.0
- type: ndcg_at_1
value: 100.0
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value: 100.0
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value: 100.0
- type: ndcg_at_1000
value: 100.0
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value: 100.0
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value: 100.0
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value: 100.0
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value: 100.0
- type: precision_at_10
value: 10.0
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value: 1.0
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value: 0.1
- type: precision_at_20
value: 5.0
- type: precision_at_3
value: 33.333
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value: 20.0
- type: recall_at_1
value: 100.0
- type: recall_at_10
value: 100.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 100.0
- type: recall_at_3
value: 100.0
- type: recall_at_5
value: 100.0
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value: 100.0
- task:
type: Retrieval
dataset:
name: MTEB LEMBSummScreenFDRetrieval (default)
type: dwzhu/LongEmbed
config: default
split: validation
revision: 6e346642246bfb4928c560ee08640dc84d074e8c
metrics:
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- type: map_at_1000
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- type: mrr_at_1000
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- type: mrr_at_3
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- type: mrr_at_5
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- type: recall_at_100
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- type: recall_at_3
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type: Retrieval
dataset:
name: MTEB MLQARetrieval (deu-deu)
type: facebook/mlqa
config: deu-deu
split: test
revision: 397ed406c1a7902140303e7faf60fff35b58d285
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dataset:
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type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
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value: 21.861
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- type: recall_at_1000
value: 97.625
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value: 40.653
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value: 49.841
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value: 40.815
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 97.4874601003192
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value: 97.19067544931094
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value: 97.49331776181019
- type: main_score
value: 97.4874601003192
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 96.89489997182305
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value: 96.51138586512977
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value: 96.89723065967186
- type: main_score
value: 96.89489997182305
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 97.17144763175452
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value: 96.81785681878274
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value: 97.1778974586874
- type: main_score
value: 97.17144763175452
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 96.30128405887879
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- type: main_score
value: 96.30128405887879
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type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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type: Classification
dataset:
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type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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type: Classification
dataset:
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type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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dataset:
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type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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dataset:
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type: mteb/masakhanews
config: eng
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
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dataset:
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type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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dataset:
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split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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dataset:
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revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 37.941836363967106
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type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (yor)
type: masakhane/masakhanews
config: yor
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
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value: 33.17083910808645
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value: 31.28169350649098
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 75.37323470073974
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 74.83523873570948
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 75.31607262945528
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 76.7955615332885
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 77.60591795561534
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (es)
type: mteb/amazon_massive_scenario
config: es
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (de)
type: mteb/amazon_massive_scenario
config: de
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
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revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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type: Classification
dataset:
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type: mteb/amazon_massive_scenario
config: ru
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dataset:
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type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
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type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P (default)
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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type: Clustering
dataset:
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type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 1.3769867487457566
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type: Retrieval
dataset:
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type: jinaai/mintakaqa
config: de
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
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value: 20.545984285353892
- type: nauc_precision_at_1000_std
value: 38.9013414992826
- type: nauc_precision_at_100_diff1
value: -5.290395978742176
- type: nauc_precision_at_100_max
value: 31.340480360546845
- type: nauc_precision_at_100_std
value: 33.6897935720505
- type: nauc_precision_at_10_diff1
value: 5.965001997926562
- type: nauc_precision_at_10_max
value: 46.12515296162247
- type: nauc_precision_at_10_std
value: 25.409433135253558
- type: nauc_precision_at_1_diff1
value: 27.297525357600776
- type: nauc_precision_at_1_max
value: 47.18835074959486
- type: nauc_precision_at_1_std
value: 18.304203168281834
- type: nauc_precision_at_20_diff1
value: 3.4438127279827744
- type: nauc_precision_at_20_max
value: 42.36095587714494
- type: nauc_precision_at_20_std
value: 27.367900512797906
- type: nauc_precision_at_3_diff1
value: 13.165017224718916
- type: nauc_precision_at_3_max
value: 50.58931825484506
- type: nauc_precision_at_3_std
value: 20.852009214609442
- type: nauc_precision_at_5_diff1
value: 7.840087177549876
- type: nauc_precision_at_5_max
value: 46.99388755575109
- type: nauc_precision_at_5_std
value: 23.048702393099834
- type: nauc_recall_at_1000_diff1
value: -15.591898698252932
- type: nauc_recall_at_1000_max
value: 20.5459842853537
- type: nauc_recall_at_1000_std
value: 38.901341499282395
- type: nauc_recall_at_100_diff1
value: -5.290395978742165
- type: nauc_recall_at_100_max
value: 31.340480360546863
- type: nauc_recall_at_100_std
value: 33.68979357205046
- type: nauc_recall_at_10_diff1
value: 5.96500199792656
- type: nauc_recall_at_10_max
value: 46.1251529616225
- type: nauc_recall_at_10_std
value: 25.409433135253543
- type: nauc_recall_at_1_diff1
value: 27.297525357600776
- type: nauc_recall_at_1_max
value: 47.18835074959486
- type: nauc_recall_at_1_std
value: 18.304203168281834
- type: nauc_recall_at_20_diff1
value: 3.4438127279827833
- type: nauc_recall_at_20_max
value: 42.36095587714498
- type: nauc_recall_at_20_std
value: 27.36790051279787
- type: nauc_recall_at_3_diff1
value: 13.165017224718916
- type: nauc_recall_at_3_max
value: 50.589318254845054
- type: nauc_recall_at_3_std
value: 20.852009214609435
- type: nauc_recall_at_5_diff1
value: 7.840087177549891
- type: nauc_recall_at_5_max
value: 46.99388755575112
- type: nauc_recall_at_5_std
value: 23.048702393099845
- type: ndcg_at_1
value: 14.701
- type: ndcg_at_10
value: 26.909
- type: ndcg_at_100
value: 32.727000000000004
- type: ndcg_at_1000
value: 36.086
- type: ndcg_at_20
value: 29.236
- type: ndcg_at_3
value: 22.004
- type: ndcg_at_5
value: 24.615000000000002
- type: precision_at_1
value: 14.701
- type: precision_at_10
value: 4.062
- type: precision_at_100
value: 0.688
- type: precision_at_1000
value: 0.096
- type: precision_at_20
value: 2.488
- type: precision_at_3
value: 9.036
- type: precision_at_5
value: 6.699
- type: recall_at_1
value: 14.701
- type: recall_at_10
value: 40.622
- type: recall_at_100
value: 68.796
- type: recall_at_1000
value: 96.314
- type: recall_at_20
value: 49.754
- type: recall_at_3
value: 27.108999999999998
- type: recall_at_5
value: 33.497
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment (default)
type: C-MTEB/MultilingualSentiment-classification
config: default
split: test
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 73.20999999999998
- type: f1
value: 73.18755986777474
- type: f1_weighted
value: 73.18755986777475
- type: main_score
value: 73.20999999999998
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 4.822
- type: map_at_10
value: 13.144
- type: map_at_100
value: 17.254
- type: map_at_1000
value: 18.931
- type: map_at_20
value: 14.834
- type: map_at_3
value: 8.975
- type: map_at_5
value: 10.922
- type: mrr_at_1
value: 47.059
- type: mrr_at_10
value: 55.806999999999995
- type: mrr_at_100
value: 56.286
- type: mrr_at_1000
value: 56.327000000000005
- type: mrr_at_20
value: 56.00000000000001
- type: mrr_at_3
value: 54.17999999999999
- type: mrr_at_5
value: 55.155
- type: ndcg_at_1
value: 44.427
- type: ndcg_at_10
value: 36.623
- type: ndcg_at_100
value: 33.664
- type: ndcg_at_1000
value: 42.538
- type: ndcg_at_20
value: 34.066
- type: ndcg_at_3
value: 41.118
- type: ndcg_at_5
value: 39.455
- type: precision_at_1
value: 46.44
- type: precision_at_10
value: 28.607
- type: precision_at_100
value: 9.189
- type: precision_at_1000
value: 2.261
- type: precision_at_20
value: 21.238
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 35.604
- type: recall_at_1
value: 4.822
- type: recall_at_10
value: 17.488999999999997
- type: recall_at_100
value: 35.052
- type: recall_at_1000
value: 66.67999999999999
- type: recall_at_20
value: 21.343999999999998
- type: recall_at_3
value: 10.259
- type: recall_at_5
value: 13.406
- type: main_score
value: 36.623
- task:
type: Retrieval
dataset:
name: MTEB NQ (default)
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 41.411
- type: map_at_10
value: 57.179
- type: map_at_100
value: 57.945
- type: map_at_1000
value: 57.967999999999996
- type: map_at_20
value: 57.687
- type: map_at_3
value: 53.46300000000001
- type: map_at_5
value: 55.696999999999996
- type: mrr_at_1
value: 46.233999999999995
- type: mrr_at_10
value: 59.831999999999994
- type: mrr_at_100
value: 60.33500000000001
- type: mrr_at_1000
value: 60.348
- type: mrr_at_20
value: 60.167
- type: mrr_at_3
value: 56.972
- type: mrr_at_5
value: 58.74
- type: ndcg_at_1
value: 46.205
- type: ndcg_at_10
value: 64.23100000000001
- type: ndcg_at_100
value: 67.242
- type: ndcg_at_1000
value: 67.72500000000001
- type: ndcg_at_20
value: 65.77300000000001
- type: ndcg_at_3
value: 57.516
- type: ndcg_at_5
value: 61.11600000000001
- type: precision_at_1
value: 46.205
- type: precision_at_10
value: 9.873
- type: precision_at_100
value: 1.158
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 5.319
- type: precision_at_3
value: 25.424999999999997
- type: precision_at_5
value: 17.375
- type: recall_at_1
value: 41.411
- type: recall_at_10
value: 82.761
- type: recall_at_100
value: 95.52199999999999
- type: recall_at_1000
value: 99.02499999999999
- type: recall_at_20
value: 88.34
- type: recall_at_3
value: 65.73
- type: recall_at_5
value: 73.894
- type: main_score
value: 64.23100000000001
- task:
type: PairClassification
dataset:
name: MTEB Ocnli (default)
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cosine_accuracy
value: 62.3714131023281
- type: cosine_accuracy_threshold
value: 79.70921993255615
- type: cosine_ap
value: 66.41380155495659
- type: cosine_f1
value: 68.89547185780786
- type: cosine_f1_threshold
value: 72.91591167449951
- type: cosine_precision
value: 57.485875706214685
- type: cosine_recall
value: 85.95564941921859
- type: dot_accuracy
value: 60.47644829453167
- type: dot_accuracy_threshold
value: 36627.362060546875
- type: dot_ap
value: 63.696303449293204
- type: dot_f1
value: 68.3986041101202
- type: dot_f1_threshold
value: 30452.72216796875
- type: dot_precision
value: 54.04411764705882
- type: dot_recall
value: 93.13621964097149
- type: euclidean_accuracy
value: 63.02111532214402
- type: euclidean_accuracy_threshold
value: 1392.76762008667
- type: euclidean_ap
value: 66.65907089443218
- type: euclidean_f1
value: 69.05036524413688
- type: euclidean_f1_threshold
value: 1711.5310668945312
- type: euclidean_precision
value: 54.29262394195889
- type: euclidean_recall
value: 94.82576557550159
- type: main_score
value: 63.02111532214402
- type: manhattan_accuracy
value: 62.75040606388739
- type: manhattan_accuracy_threshold
value: 32475.347900390625
- type: manhattan_ap
value: 66.50943585125434
- type: manhattan_f1
value: 69.08382066276802
- type: manhattan_f1_threshold
value: 41238.470458984375
- type: manhattan_precision
value: 54.75896168108776
- type: manhattan_recall
value: 93.55860612460401
- type: max_accuracy
value: 63.02111532214402
- type: max_ap
value: 66.65907089443218
- type: max_f1
value: 69.08382066276802
- type: max_precision
value: 57.485875706214685
- type: max_recall
value: 94.82576557550159
- type: similarity_accuracy
value: 62.3714131023281
- type: similarity_accuracy_threshold
value: 79.70921993255615
- type: similarity_ap
value: 66.41380155495659
- type: similarity_f1
value: 68.89547185780786
- type: similarity_f1_threshold
value: 72.91591167449951
- type: similarity_precision
value: 57.485875706214685
- type: similarity_recall
value: 85.95564941921859
- task:
type: Classification
dataset:
name: MTEB OnlineShopping (default)
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 91.88000000000001
- type: ap
value: 89.52463684448476
- type: ap_weighted
value: 89.52463684448476
- type: f1
value: 91.86313022306673
- type: f1_weighted
value: 91.87806318146912
- type: main_score
value: 91.88000000000001
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 92.65578635014838
- type: cosine_accuracy_threshold
value: 74.02530312538147
- type: cosine_ap
value: 98.3834226153613
- type: cosine_f1
value: 94.92567913890312
- type: cosine_f1_threshold
value: 74.02530312538147
- type: cosine_precision
value: 95.562435500516
- type: cosine_recall
value: 94.29735234215886
- type: dot_accuracy
value: 91.54302670623146
- type: dot_accuracy_threshold
value: 34452.29187011719
- type: dot_ap
value: 98.1237257754439
- type: dot_f1
value: 94.22400803616273
- type: dot_f1_threshold
value: 33670.41931152344
- type: dot_precision
value: 92.9633300297324
- type: dot_recall
value: 95.5193482688391
- type: euclidean_accuracy
value: 92.28486646884274
- type: euclidean_accuracy_threshold
value: 1602.8022766113281
- type: euclidean_ap
value: 98.3099021504706
- type: euclidean_f1
value: 94.75277497477296
- type: euclidean_f1_threshold
value: 1604.7462463378906
- type: euclidean_precision
value: 93.89999999999999
- type: euclidean_recall
value: 95.62118126272912
- type: main_score
value: 98.3834226153613
- type: manhattan_accuracy
value: 92.2106824925816
- type: manhattan_accuracy_threshold
value: 38872.90954589844
- type: manhattan_ap
value: 98.28694101230218
- type: manhattan_f1
value: 94.67815509376584
- type: manhattan_f1_threshold
value: 38872.90954589844
- type: manhattan_precision
value: 94.24823410696267
- type: manhattan_recall
value: 95.11201629327903
- type: max_accuracy
value: 92.65578635014838
- type: max_ap
value: 98.3834226153613
- type: max_f1
value: 94.92567913890312
- type: max_precision
value: 95.562435500516
- type: max_recall
value: 95.62118126272912
- type: similarity_accuracy
value: 92.65578635014838
- type: similarity_accuracy_threshold
value: 74.02530312538147
- type: similarity_ap
value: 98.3834226153613
- type: similarity_f1
value: 94.92567913890312
- type: similarity_f1_threshold
value: 74.02530312538147
- type: similarity_precision
value: 95.562435500516
- type: similarity_recall
value: 94.29735234215886
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (de)
type: GEM/opusparcus
config: de
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 87.72178850248403
- type: cosine_accuracy_threshold
value: 73.33863377571106
- type: cosine_ap
value: 96.98901408834976
- type: cosine_f1
value: 91.89944134078212
- type: cosine_f1_threshold
value: 71.45810127258301
- type: cosine_precision
value: 89.64577656675749
- type: cosine_recall
value: 94.26934097421203
- type: dot_accuracy
value: 86.30234208658624
- type: dot_accuracy_threshold
value: 32027.130126953125
- type: dot_ap
value: 96.12260574893256
- type: dot_f1
value: 91.31602506714414
- type: dot_f1_threshold
value: 30804.376220703125
- type: dot_precision
value: 85.93091828138164
- type: dot_recall
value: 97.42120343839542
- type: euclidean_accuracy
value: 87.9347054648687
- type: euclidean_accuracy_threshold
value: 1609.6670150756836
- type: euclidean_ap
value: 97.00238860358252
- type: euclidean_f1
value: 92.1089063221043
- type: euclidean_f1_threshold
value: 1641.8487548828125
- type: euclidean_precision
value: 89.10714285714286
- type: euclidean_recall
value: 95.31996179560649
- type: main_score
value: 97.00238860358252
- type: manhattan_accuracy
value: 87.72178850248403
- type: manhattan_accuracy_threshold
value: 40137.060546875
- type: manhattan_ap
value: 96.98653728159941
- type: manhattan_f1
value: 92.03865623561896
- type: manhattan_f1_threshold
value: 40137.060546875
- type: manhattan_precision
value: 88.80994671403198
- type: manhattan_recall
value: 95.51098376313276
- type: max_accuracy
value: 87.9347054648687
- type: max_ap
value: 97.00238860358252
- type: max_f1
value: 92.1089063221043
- type: max_precision
value: 89.64577656675749
- type: max_recall
value: 97.42120343839542
- type: similarity_accuracy
value: 87.72178850248403
- type: similarity_accuracy_threshold
value: 73.33863377571106
- type: similarity_ap
value: 96.98901408834976
- type: similarity_f1
value: 91.89944134078212
- type: similarity_f1_threshold
value: 71.45810127258301
- type: similarity_precision
value: 89.64577656675749
- type: similarity_recall
value: 94.26934097421203
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 80.92643051771117
- type: cosine_accuracy_threshold
value: 76.68856382369995
- type: cosine_ap
value: 93.74622381534307
- type: cosine_f1
value: 87.12328767123287
- type: cosine_f1_threshold
value: 71.64022922515869
- type: cosine_precision
value: 80.64243448858834
- type: cosine_recall
value: 94.73684210526315
- type: dot_accuracy
value: 80.858310626703
- type: dot_accuracy_threshold
value: 34028.3935546875
- type: dot_ap
value: 91.18448457633308
- type: dot_f1
value: 86.82606657290202
- type: dot_f1_threshold
value: 34028.3935546875
- type: dot_precision
value: 82.2380106571936
- type: dot_recall
value: 91.9563058589871
- type: euclidean_accuracy
value: 80.858310626703
- type: euclidean_accuracy_threshold
value: 1595.7651138305664
- type: euclidean_ap
value: 93.8182717829648
- type: euclidean_f1
value: 87.04044117647058
- type: euclidean_f1_threshold
value: 1609.2475891113281
- type: euclidean_precision
value: 81.00940975192472
- type: euclidean_recall
value: 94.04170804369414
- type: main_score
value: 93.8182717829648
- type: manhattan_accuracy
value: 80.99455040871935
- type: manhattan_accuracy_threshold
value: 38092.132568359375
- type: manhattan_ap
value: 93.77563401151711
- type: manhattan_f1
value: 86.91983122362869
- type: manhattan_f1_threshold
value: 38092.132568359375
- type: manhattan_precision
value: 82.32682060390763
- type: manhattan_recall
value: 92.05561072492551
- type: max_accuracy
value: 80.99455040871935
- type: max_ap
value: 93.8182717829648
- type: max_f1
value: 87.12328767123287
- type: max_precision
value: 82.32682060390763
- type: max_recall
value: 94.73684210526315
- type: similarity_accuracy
value: 80.92643051771117
- type: similarity_accuracy_threshold
value: 76.68856382369995
- type: similarity_ap
value: 93.74622381534307
- type: similarity_f1
value: 87.12328767123287
- type: similarity_f1_threshold
value: 71.64022922515869
- type: similarity_precision
value: 80.64243448858834
- type: similarity_recall
value: 94.73684210526315
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (ru)
type: GEM/opusparcus
config: ru
split: test.full
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 76.83823529411765
- type: cosine_accuracy_threshold
value: 72.70769476890564
- type: cosine_ap
value: 89.56692049908222
- type: cosine_f1
value: 83.99832003359934
- type: cosine_f1_threshold
value: 70.9052324295044
- type: cosine_precision
value: 76.16146230007617
- type: cosine_recall
value: 93.63295880149812
- type: dot_accuracy
value: 76.28676470588235
- type: dot_accuracy_threshold
value: 33740.68908691406
- type: dot_ap
value: 87.77185177141567
- type: dot_f1
value: 83.62251375370292
- type: dot_f1_threshold
value: 32726.611328125
- type: dot_precision
value: 76.29343629343629
- type: dot_recall
value: 92.50936329588015
- type: euclidean_accuracy
value: 77.32843137254902
- type: euclidean_accuracy_threshold
value: 1566.510009765625
- type: euclidean_ap
value: 89.60605626791111
- type: euclidean_f1
value: 84.06546080964686
- type: euclidean_f1_threshold
value: 1576.4202117919922
- type: euclidean_precision
value: 77.83094098883574
- type: euclidean_recall
value: 91.38576779026218
- type: main_score
value: 89.60605626791111
- type: manhattan_accuracy
value: 76.89950980392157
- type: manhattan_accuracy_threshold
value: 38202.215576171875
- type: manhattan_ap
value: 89.55766894104868
- type: manhattan_f1
value: 83.80462724935732
- type: manhattan_f1_threshold
value: 38934.375
- type: manhattan_precision
value: 77.25118483412322
- type: manhattan_recall
value: 91.57303370786516
- type: max_accuracy
value: 77.32843137254902
- type: max_ap
value: 89.60605626791111
- type: max_f1
value: 84.06546080964686
- type: max_precision
value: 77.83094098883574
- type: max_recall
value: 93.63295880149812
- type: similarity_accuracy
value: 76.83823529411765
- type: similarity_accuracy_threshold
value: 72.70769476890564
- type: similarity_ap
value: 89.56692049908222
- type: similarity_f1
value: 83.99832003359934
- type: similarity_f1_threshold
value: 70.9052324295044
- type: similarity_precision
value: 76.16146230007617
- type: similarity_recall
value: 93.63295880149812
- task:
type: Classification
dataset:
name: MTEB PAC (default)
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
metrics:
- type: accuracy
value: 68.39559803069794
- type: ap
value: 77.68074206719457
- type: ap_weighted
value: 77.68074206719457
- type: f1
value: 66.23485605467732
- type: f1_weighted
value: 69.03201442129347
- type: main_score
value: 68.39559803069794
- task:
type: STS
dataset:
name: MTEB PAWSX (default)
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cosine_pearson
value: 13.161523266433587
- type: cosine_spearman
value: 15.557333873773386
- type: euclidean_pearson
value: 17.147508431907525
- type: euclidean_spearman
value: 15.664112857732146
- type: main_score
value: 15.557333873773386
- type: manhattan_pearson
value: 17.130875906264386
- type: manhattan_spearman
value: 15.624397342229637
- type: pearson
value: 13.161523266433587
- type: spearman
value: 15.557333873773386
- task:
type: PairClassification
dataset:
name: MTEB PSC (default)
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
metrics:
- type: cosine_accuracy
value: 97.86641929499072
- type: cosine_accuracy_threshold
value: 79.0391206741333
- type: cosine_ap
value: 99.19403807771533
- type: cosine_f1
value: 96.45608628659475
- type: cosine_f1_threshold
value: 79.0391206741333
- type: cosine_precision
value: 97.50778816199377
- type: cosine_recall
value: 95.42682926829268
- type: dot_accuracy
value: 98.14471243042672
- type: dot_accuracy_threshold
value: 29808.1787109375
- type: dot_ap
value: 99.331999859971
- type: dot_f1
value: 97.01492537313433
- type: dot_f1_threshold
value: 29808.1787109375
- type: dot_precision
value: 95.02923976608187
- type: dot_recall
value: 99.08536585365853
- type: euclidean_accuracy
value: 97.49536178107606
- type: euclidean_accuracy_threshold
value: 1276.227855682373
- type: euclidean_ap
value: 98.91056467717377
- type: euclidean_f1
value: 95.83975346687212
- type: euclidean_f1_threshold
value: 1276.227855682373
- type: euclidean_precision
value: 96.88473520249221
- type: euclidean_recall
value: 94.8170731707317
- type: main_score
value: 99.331999859971
- type: manhattan_accuracy
value: 97.49536178107606
- type: manhattan_accuracy_threshold
value: 31097.674560546875
- type: manhattan_ap
value: 98.95694691792707
- type: manhattan_f1
value: 95.83975346687212
- type: manhattan_f1_threshold
value: 31097.674560546875
- type: manhattan_precision
value: 96.88473520249221
- type: manhattan_recall
value: 94.8170731707317
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.331999859971
- type: max_f1
value: 97.01492537313433
- type: max_precision
value: 97.50778816199377
- type: max_recall
value: 99.08536585365853
- type: similarity_accuracy
value: 97.86641929499072
- type: similarity_accuracy_threshold
value: 79.0391206741333
- type: similarity_ap
value: 99.19403807771533
- type: similarity_f1
value: 96.45608628659475
- type: similarity_f1_threshold
value: 79.0391206741333
- type: similarity_precision
value: 97.50778816199377
- type: similarity_recall
value: 95.42682926829268
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (en)
type: google-research-datasets/paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 61.8
- type: cosine_accuracy_threshold
value: 99.5664119720459
- type: cosine_ap
value: 60.679317786040585
- type: cosine_f1
value: 63.17354143441101
- type: cosine_f1_threshold
value: 97.22164869308472
- type: cosine_precision
value: 47.6457399103139
- type: cosine_recall
value: 93.71554575523705
- type: dot_accuracy
value: 55.7
- type: dot_accuracy_threshold
value: 48353.62548828125
- type: dot_ap
value: 48.53805970536875
- type: dot_f1
value: 62.42214532871972
- type: dot_f1_threshold
value: 38215.53955078125
- type: dot_precision
value: 45.48663640948058
- type: dot_recall
value: 99.44873208379272
- type: euclidean_accuracy
value: 61.75000000000001
- type: euclidean_accuracy_threshold
value: 189.0761137008667
- type: euclidean_ap
value: 60.55517418691518
- type: euclidean_f1
value: 63.07977736549165
- type: euclidean_f1_threshold
value: 504.3168067932129
- type: euclidean_precision
value: 47.53914988814318
- type: euclidean_recall
value: 93.71554575523705
- type: main_score
value: 60.679317786040585
- type: manhattan_accuracy
value: 61.9
- type: manhattan_accuracy_threshold
value: 4695.778274536133
- type: manhattan_ap
value: 60.48686620413608
- type: manhattan_f1
value: 62.92880855772778
- type: manhattan_f1_threshold
value: 12542.36831665039
- type: manhattan_precision
value: 47.28381374722838
- type: manhattan_recall
value: 94.04630650496141
- type: max_accuracy
value: 61.9
- type: max_ap
value: 60.679317786040585
- type: max_f1
value: 63.17354143441101
- type: max_precision
value: 47.6457399103139
- type: max_recall
value: 99.44873208379272
- type: similarity_accuracy
value: 61.8
- type: similarity_accuracy_threshold
value: 99.5664119720459
- type: similarity_ap
value: 60.679317786040585
- type: similarity_f1
value: 63.17354143441101
- type: similarity_f1_threshold
value: 97.22164869308472
- type: similarity_precision
value: 47.6457399103139
- type: similarity_recall
value: 93.71554575523705
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (de)
type: google-research-datasets/paws-x
config: de
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 60.25
- type: cosine_accuracy_threshold
value: 99.54338073730469
- type: cosine_ap
value: 56.7863613689054
- type: cosine_f1
value: 62.23499820337766
- type: cosine_f1_threshold
value: 89.95014429092407
- type: cosine_precision
value: 45.86864406779661
- type: cosine_recall
value: 96.75977653631284
- type: dot_accuracy
value: 56.8
- type: dot_accuracy_threshold
value: 47349.78332519531
- type: dot_ap
value: 49.7857806061729
- type: dot_f1
value: 62.31225986727209
- type: dot_f1_threshold
value: 30143.206787109375
- type: dot_precision
value: 45.32520325203252
- type: dot_recall
value: 99.66480446927373
- type: euclidean_accuracy
value: 60.3
- type: euclidean_accuracy_threshold
value: 219.78106498718262
- type: euclidean_ap
value: 56.731544327179606
- type: euclidean_f1
value: 62.19895287958115
- type: euclidean_f1_threshold
value: 1792.1623229980469
- type: euclidean_precision
value: 45.22842639593909
- type: euclidean_recall
value: 99.55307262569832
- type: main_score
value: 56.7863613689054
- type: manhattan_accuracy
value: 60.150000000000006
- type: manhattan_accuracy_threshold
value: 5104.503631591797
- type: manhattan_ap
value: 56.70304479768734
- type: manhattan_f1
value: 62.22067039106145
- type: manhattan_f1_threshold
value: 42839.471435546875
- type: manhattan_precision
value: 45.2513966480447
- type: manhattan_recall
value: 99.55307262569832
- type: max_accuracy
value: 60.3
- type: max_ap
value: 56.7863613689054
- type: max_f1
value: 62.31225986727209
- type: max_precision
value: 45.86864406779661
- type: max_recall
value: 99.66480446927373
- type: similarity_accuracy
value: 60.25
- type: similarity_accuracy_threshold
value: 99.54338073730469
- type: similarity_ap
value: 56.7863613689054
- type: similarity_f1
value: 62.23499820337766
- type: similarity_f1_threshold
value: 89.95014429092407
- type: similarity_precision
value: 45.86864406779661
- type: similarity_recall
value: 96.75977653631284
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (es)
type: google-research-datasets/paws-x
config: es
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 59.699999999999996
- type: cosine_accuracy_threshold
value: 99.55930709838867
- type: cosine_ap
value: 57.31662248806265
- type: cosine_f1
value: 62.444061962134256
- type: cosine_f1_threshold
value: 74.75898265838623
- type: cosine_precision
value: 45.3953953953954
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.900000000000006
- type: dot_accuracy_threshold
value: 47512.90283203125
- type: dot_ap
value: 49.39339147787568
- type: dot_f1
value: 62.487082328625554
- type: dot_f1_threshold
value: 34989.03503417969
- type: dot_precision
value: 45.44088176352705
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 59.599999999999994
- type: euclidean_accuracy_threshold
value: 200.82547664642334
- type: euclidean_ap
value: 57.19737488445163
- type: euclidean_f1
value: 62.444061962134256
- type: euclidean_f1_threshold
value: 1538.8837814331055
- type: euclidean_precision
value: 45.3953953953954
- type: euclidean_recall
value: 100.0
- type: main_score
value: 57.31662248806265
- type: manhattan_accuracy
value: 59.550000000000004
- type: manhattan_accuracy_threshold
value: 5016.501617431641
- type: manhattan_ap
value: 57.089959907945065
- type: manhattan_f1
value: 62.444061962134256
- type: manhattan_f1_threshold
value: 37523.53515625
- type: manhattan_precision
value: 45.3953953953954
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 59.699999999999996
- type: max_ap
value: 57.31662248806265
- type: max_f1
value: 62.487082328625554
- type: max_precision
value: 45.44088176352705
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 59.699999999999996
- type: similarity_accuracy_threshold
value: 99.55930709838867
- type: similarity_ap
value: 57.31662248806265
- type: similarity_f1
value: 62.444061962134256
- type: similarity_f1_threshold
value: 74.75898265838623
- type: similarity_precision
value: 45.3953953953954
- type: similarity_recall
value: 100.0
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 61.150000000000006
- type: cosine_accuracy_threshold
value: 99.36153888702393
- type: cosine_ap
value: 59.43845317938599
- type: cosine_f1
value: 62.51298026998961
- type: cosine_f1_threshold
value: 76.77866220474243
- type: cosine_precision
value: 45.468277945619334
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 55.75
- type: dot_accuracy_threshold
value: 48931.55212402344
- type: dot_ap
value: 50.15949290538757
- type: dot_f1
value: 62.53462603878117
- type: dot_f1_threshold
value: 34415.7958984375
- type: dot_precision
value: 45.4911838790932
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 61.050000000000004
- type: euclidean_accuracy_threshold
value: 240.8097267150879
- type: euclidean_ap
value: 59.367971294226216
- type: euclidean_f1
value: 62.51298026998961
- type: euclidean_f1_threshold
value: 1444.132423400879
- type: euclidean_precision
value: 45.468277945619334
- type: euclidean_recall
value: 100.0
- type: main_score
value: 59.43845317938599
- type: manhattan_accuracy
value: 60.95
- type: manhattan_accuracy_threshold
value: 5701.206207275391
- type: manhattan_ap
value: 59.30094096378774
- type: manhattan_f1
value: 62.53462603878117
- type: manhattan_f1_threshold
value: 33445.672607421875
- type: manhattan_precision
value: 45.4911838790932
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 61.150000000000006
- type: max_ap
value: 59.43845317938599
- type: max_f1
value: 62.53462603878117
- type: max_precision
value: 45.4911838790932
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 61.150000000000006
- type: similarity_accuracy_threshold
value: 99.36153888702393
- type: similarity_ap
value: 59.43845317938599
- type: similarity_f1
value: 62.51298026998961
- type: similarity_f1_threshold
value: 76.77866220474243
- type: similarity_precision
value: 45.468277945619334
- type: similarity_recall
value: 100.0
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (zh)
type: google-research-datasets/paws-x
config: zh
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 58.85
- type: cosine_accuracy_threshold
value: 99.73838329315186
- type: cosine_ap
value: 54.66913160570546
- type: cosine_f1
value: 62.32136632973162
- type: cosine_f1_threshold
value: 76.4499306678772
- type: cosine_precision
value: 45.265822784810126
- type: cosine_recall
value: 100.0
- type: dot_accuracy
value: 56.25
- type: dot_accuracy_threshold
value: 47351.9287109375
- type: dot_ap
value: 48.5266232989438
- type: dot_f1
value: 62.277951933124356
- type: dot_f1_threshold
value: 31325.28076171875
- type: dot_precision
value: 45.220030349013655
- type: dot_recall
value: 100.0
- type: euclidean_accuracy
value: 58.9
- type: euclidean_accuracy_threshold
value: 144.24468278884888
- type: euclidean_ap
value: 54.66981490353506
- type: euclidean_f1
value: 62.32136632973162
- type: euclidean_f1_threshold
value: 1484.908676147461
- type: euclidean_precision
value: 45.265822784810126
- type: euclidean_recall
value: 100.0
- type: main_score
value: 54.66981490353506
- type: manhattan_accuracy
value: 58.9
- type: manhattan_accuracy_threshold
value: 3586.785125732422
- type: manhattan_ap
value: 54.668355260247736
- type: manhattan_f1
value: 62.32136632973162
- type: manhattan_f1_threshold
value: 36031.22863769531
- type: manhattan_precision
value: 45.265822784810126
- type: manhattan_recall
value: 100.0
- type: max_accuracy
value: 58.9
- type: max_ap
value: 54.66981490353506
- type: max_f1
value: 62.32136632973162
- type: max_precision
value: 45.265822784810126
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 58.85
- type: similarity_accuracy_threshold
value: 99.73838329315186
- type: similarity_ap
value: 54.66913160570546
- type: similarity_f1
value: 62.32136632973162
- type: similarity_f1_threshold
value: 76.4499306678772
- type: similarity_precision
value: 45.265822784810126
- type: similarity_recall
value: 100.0
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN (default)
type: PL-MTEB/polemo2_in
config: default
split: test
revision: d90724373c70959f17d2331ad51fb60c71176b03
metrics:
- type: accuracy
value: 83.75346260387812
- type: f1
value: 81.98304891214909
- type: f1_weighted
value: 84.29623200830078
- type: main_score
value: 83.75346260387812
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT (default)
type: PL-MTEB/polemo2_out
config: default
split: test
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
metrics:
- type: accuracy
value: 66.53846153846153
- type: f1
value: 52.71826064368638
- type: f1_weighted
value: 69.10010124630334
- type: main_score
value: 66.53846153846153
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cosine_accuracy
value: 81.8
- type: cosine_accuracy_threshold
value: 90.47793745994568
- type: cosine_ap
value: 91.42490266080884
- type: cosine_f1
value: 85.4632587859425
- type: cosine_f1_threshold
value: 90.47793745994568
- type: cosine_precision
value: 82.56172839506173
- type: cosine_recall
value: 88.57615894039735
- type: dot_accuracy
value: 74.6
- type: dot_accuracy_threshold
value: 42102.23693847656
- type: dot_ap
value: 86.20060009096979
- type: dot_f1
value: 80.02842928216063
- type: dot_f1_threshold
value: 38970.16906738281
- type: dot_precision
value: 70.1120797011208
- type: dot_recall
value: 93.21192052980133
- type: euclidean_accuracy
value: 81.5
- type: euclidean_accuracy_threshold
value: 880.433464050293
- type: euclidean_ap
value: 91.33143477982087
- type: euclidean_f1
value: 85.44600938967135
- type: euclidean_f1_threshold
value: 964.0384674072266
- type: euclidean_precision
value: 81.00890207715133
- type: euclidean_recall
value: 90.39735099337747
- type: main_score
value: 91.42490266080884
- type: manhattan_accuracy
value: 81.3
- type: manhattan_accuracy_threshold
value: 22100.830078125
- type: manhattan_ap
value: 91.25996158651282
- type: manhattan_f1
value: 85.38102643856921
- type: manhattan_f1_threshold
value: 24043.515014648438
- type: manhattan_precision
value: 80.49853372434018
- type: manhattan_recall
value: 90.89403973509934
- type: max_accuracy
value: 81.8
- type: max_ap
value: 91.42490266080884
- type: max_f1
value: 85.4632587859425
- type: max_precision
value: 82.56172839506173
- type: max_recall
value: 93.21192052980133
- type: similarity_accuracy
value: 81.8
- type: similarity_accuracy_threshold
value: 90.47793745994568
- type: similarity_ap
value: 91.42490266080884
- type: similarity_f1
value: 85.4632587859425
- type: similarity_f1_threshold
value: 90.47793745994568
- type: similarity_precision
value: 82.56172839506173
- type: similarity_recall
value: 88.57615894039735
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval (default)
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.419
- type: map_at_10
value: 85.542
- type: map_at_100
value: 86.161
- type: map_at_1000
value: 86.175
- type: map_at_20
value: 85.949
- type: map_at_3
value: 82.623
- type: map_at_5
value: 84.5
- type: mrr_at_1
value: 82.27
- type: mrr_at_10
value: 88.21900000000001
- type: mrr_at_100
value: 88.313
- type: mrr_at_1000
value: 88.31400000000001
- type: mrr_at_20
value: 88.286
- type: mrr_at_3
value: 87.325
- type: mrr_at_5
value: 87.97500000000001
- type: ndcg_at_1
value: 82.3
- type: ndcg_at_10
value: 89.088
- type: ndcg_at_100
value: 90.217
- type: ndcg_at_1000
value: 90.29700000000001
- type: ndcg_at_20
value: 89.697
- type: ndcg_at_3
value: 86.435
- type: ndcg_at_5
value: 87.966
- type: precision_at_1
value: 82.3
- type: precision_at_10
value: 13.527000000000001
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.165000000000001
- type: precision_at_3
value: 37.92
- type: precision_at_5
value: 24.914
- type: recall_at_1
value: 71.419
- type: recall_at_10
value: 95.831
- type: recall_at_100
value: 99.64
- type: recall_at_1000
value: 99.988
- type: recall_at_20
value: 97.76599999999999
- type: recall_at_3
value: 88.081
- type: recall_at_5
value: 92.50500000000001
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value: 13.907
- type: precision_at_100
value: 1.591
- type: precision_at_1000
value: 0.166
- type: precision_at_20
value: 7.446999999999999
- type: precision_at_3
value: 35.619
- type: precision_at_5
value: 24.917
- type: recall_at_1
value: 44.187
- type: recall_at_10
value: 85.10600000000001
- type: recall_at_100
value: 95.488
- type: recall_at_1000
value: 98.831
- type: recall_at_20
value: 90.22200000000001
- type: recall_at_3
value: 68.789
- type: recall_at_5
value: 77.85499999999999
- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification (default)
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
- type: accuracy
value: 67.5830078125
- type: f1
value: 67.56931936632446
- type: f1_weighted
value: 67.57137733752779
- type: main_score
value: 67.5830078125
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS (default)
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 85.90493484626788
- type: cosine_spearman
value: 86.21965691667411
- type: euclidean_pearson
value: 86.07499842984909
- type: euclidean_spearman
value: 86.55506818735688
- type: main_score
value: 86.21965691667411
- type: manhattan_pearson
value: 85.95976420231729
- type: manhattan_spearman
value: 86.48604243661234
- type: pearson
value: 85.90493484626788
- type: spearman
value: 86.21965691667411
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 59.1943359375
- type: f1
value: 58.894480861440414
- type: f1_weighted
value: 58.903615560240866
- type: main_score
value: 59.1943359375
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P (default)
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: main_score
value: 57.99209448663228
- type: v_measure
value: 57.99209448663228
- type: v_measure_std
value: 1.0381163861993816
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 45.556640625
- type: f1
value: 45.159163104085906
- type: f1_weighted
value: 45.16098316398626
- type: main_score
value: 45.556640625
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P (default)
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: main_score
value: 50.787548070488974
- type: v_measure
value: 50.787548070488974
- type: v_measure_std
value: 0.8569958168946827
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.843
- type: map_at_10
value: 11.752
- type: map_at_100
value: 13.919
- type: map_at_1000
value: 14.198
- type: map_at_20
value: 12.898000000000001
- type: map_at_3
value: 8.603
- type: map_at_5
value: 10.069
- type: mrr_at_1
value: 23.799999999999997
- type: mrr_at_10
value: 34.449999999999996
- type: mrr_at_100
value: 35.64
- type: mrr_at_1000
value: 35.691
- type: mrr_at_20
value: 35.213
- type: mrr_at_3
value: 31.383
- type: mrr_at_5
value: 33.062999999999995
- type: ndcg_at_1
value: 23.799999999999997
- type: ndcg_at_10
value: 19.811
- type: ndcg_at_100
value: 28.108
- type: ndcg_at_1000
value: 33.1
- type: ndcg_at_20
value: 22.980999999999998
- type: ndcg_at_3
value: 19.153000000000002
- type: ndcg_at_5
value: 16.408
- type: precision_at_1
value: 23.799999999999997
- type: precision_at_10
value: 10.16
- type: precision_at_100
value: 2.1999999999999997
- type: precision_at_1000
value: 0.34099999999999997
- type: precision_at_20
value: 6.915
- type: precision_at_3
value: 17.8
- type: precision_at_5
value: 14.14
- type: recall_at_1
value: 4.843
- type: recall_at_10
value: 20.595
- type: recall_at_100
value: 44.66
- type: recall_at_1000
value: 69.152
- type: recall_at_20
value: 28.04
- type: recall_at_3
value: 10.833
- type: recall_at_5
value: 14.346999999999998
- type: main_score
value: 19.811
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL (default)
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
metrics:
- type: cosine_accuracy
value: 80.90093762739502
- type: cosine_accuracy_threshold
value: 94.40930485725403
- type: cosine_ap
value: 71.15400909912427
- type: cosine_f1
value: 66.8213457076566
- type: cosine_f1_threshold
value: 91.53673648834229
- type: cosine_precision
value: 62.4922504649721
- type: cosine_recall
value: 71.7948717948718
- type: dot_accuracy
value: 78.41418671015083
- type: dot_accuracy_threshold
value: 42924.45068359375
- type: dot_ap
value: 63.34003025365763
- type: dot_f1
value: 62.518258837277244
- type: dot_f1_threshold
value: 40900.738525390625
- type: dot_precision
value: 52.99653293709758
- type: dot_recall
value: 76.21082621082621
- type: euclidean_accuracy
value: 80.67672238075826
- type: euclidean_accuracy_threshold
value: 696.0524559020996
- type: euclidean_ap
value: 70.88762835990224
- type: euclidean_f1
value: 66.711051930759
- type: euclidean_f1_threshold
value: 878.5581588745117
- type: euclidean_precision
value: 62.625
- type: euclidean_recall
value: 71.36752136752136
- type: main_score
value: 71.15400909912427
- type: manhattan_accuracy
value: 80.65633917651854
- type: manhattan_accuracy_threshold
value: 17277.72674560547
- type: manhattan_ap
value: 70.67105336611716
- type: manhattan_f1
value: 66.51346027577151
- type: manhattan_f1_threshold
value: 21687.957763671875
- type: manhattan_precision
value: 61.69305724725944
- type: manhattan_recall
value: 72.15099715099716
- type: max_accuracy
value: 80.90093762739502
- type: max_ap
value: 71.15400909912427
- type: max_f1
value: 66.8213457076566
- type: max_precision
value: 62.625
- type: max_recall
value: 76.21082621082621
- type: similarity_accuracy
value: 80.90093762739502
- type: similarity_accuracy_threshold
value: 94.40930485725403
- type: similarity_ap
value: 71.15400909912427
- type: similarity_f1
value: 66.8213457076566
- type: similarity_f1_threshold
value: 91.53673648834229
- type: similarity_precision
value: 62.4922504649721
- type: similarity_recall
value: 71.7948717948718
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 92.3339946866199
- type: cosine_spearman
value: 89.61697355115497
- type: euclidean_pearson
value: 90.3264916449669
- type: euclidean_spearman
value: 89.36270451308866
- type: main_score
value: 89.61697355115497
- type: manhattan_pearson
value: 90.18909339052534
- type: manhattan_spearman
value: 89.28337093097377
- type: pearson
value: 92.3339946866199
- type: spearman
value: 89.61697355115497
- task:
type: STS
dataset:
name: MTEB SICK-R-PL (default)
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
metrics:
- type: cosine_pearson
value: 85.27883048457821
- type: cosine_spearman
value: 80.53204892678619
- type: euclidean_pearson
value: 82.78520705216168
- type: euclidean_spearman
value: 80.27848359873212
- type: main_score
value: 80.53204892678619
- type: manhattan_pearson
value: 82.63270640583454
- type: manhattan_spearman
value: 80.21507977473146
- type: pearson
value: 85.27883048457821
- type: spearman
value: 80.53204892678619
- task:
type: STS
dataset:
name: MTEB SICKFr (default)
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cosine_pearson
value: 88.77029361817212
- type: cosine_spearman
value: 83.9453600346894
- type: euclidean_pearson
value: 85.85331086208573
- type: euclidean_spearman
value: 83.70852031985308
- type: main_score
value: 83.9453600346894
- type: manhattan_pearson
value: 85.66222265885914
- type: manhattan_spearman
value: 83.60833111525962
- type: pearson
value: 88.77029361817212
- type: spearman
value: 83.9453600346894
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 88.76435859522375
- type: cosine_spearman
value: 82.43768167804375
- type: euclidean_pearson
value: 87.43566183874832
- type: euclidean_spearman
value: 82.82166873757507
- type: main_score
value: 82.43768167804375
- type: manhattan_pearson
value: 87.39450871380951
- type: manhattan_spearman
value: 82.89253043430163
- type: pearson
value: 88.76435859522375
- type: spearman
value: 82.43768167804375
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 88.86627241652141
- type: cosine_spearman
value: 89.49011599120688
- type: euclidean_pearson
value: 89.3314120073772
- type: euclidean_spearman
value: 89.8226502776963
- type: main_score
value: 89.49011599120688
- type: manhattan_pearson
value: 89.2252179076963
- type: manhattan_spearman
value: 89.74573844021225
- type: pearson
value: 88.86627241652141
- type: spearman
value: 89.49011599120688
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 87.22891405215968
- type: cosine_spearman
value: 84.9467188157614
- type: euclidean_pearson
value: 87.20330004726237
- type: euclidean_spearman
value: 85.34806059461808
- type: main_score
value: 84.9467188157614
- type: manhattan_pearson
value: 87.15224666107623
- type: manhattan_spearman
value: 85.34596898699708
- type: pearson
value: 87.22891405215968
- type: spearman
value: 84.9467188157614
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 88.14066430111033
- type: cosine_spearman
value: 89.31337445552545
- type: euclidean_pearson
value: 89.08039335366983
- type: euclidean_spearman
value: 89.6658762856415
- type: main_score
value: 89.31337445552545
- type: manhattan_pearson
value: 89.08057438154486
- type: manhattan_spearman
value: 89.68673984203022
- type: pearson
value: 88.14066430111033
- type: spearman
value: 89.31337445552545
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 85.14908856657084
- type: cosine_spearman
value: 86.84648320786727
- type: euclidean_pearson
value: 86.11454713131947
- type: euclidean_spearman
value: 86.77738862047961
- type: main_score
value: 86.84648320786727
- type: manhattan_pearson
value: 86.07804821916372
- type: manhattan_spearman
value: 86.78676064310474
- type: pearson
value: 85.14908856657084
- type: spearman
value: 86.84648320786727
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 89.61633502468356
- type: cosine_spearman
value: 89.99772663224805
- type: euclidean_pearson
value: 90.14056501501044
- type: euclidean_spearman
value: 90.04496896837503
- type: main_score
value: 89.99772663224805
- type: manhattan_pearson
value: 90.08964860311801
- type: manhattan_spearman
value: 90.00091712362196
- type: pearson
value: 89.61633502468356
- type: spearman
value: 89.99772663224805
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 86.44548026840202
- type: cosine_spearman
value: 87.26263108768539
- type: euclidean_pearson
value: 86.42844593583838
- type: euclidean_spearman
value: 86.89388428664364
- type: main_score
value: 87.26263108768539
- type: manhattan_pearson
value: 86.47186940800881
- type: manhattan_spearman
value: 87.02163091089946
- type: pearson
value: 86.44548026840202
- type: spearman
value: 87.26263108768539
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 87.89345132532758
- type: cosine_spearman
value: 87.96246221327699
- type: euclidean_pearson
value: 88.49013032701419
- type: euclidean_spearman
value: 87.81981265317344
- type: main_score
value: 87.96246221327699
- type: manhattan_pearson
value: 88.31360914178538
- type: manhattan_spearman
value: 87.62734530005075
- type: pearson
value: 87.89345132532758
- type: spearman
value: 87.96246221327699
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 88.4084678497171
- type: cosine_spearman
value: 88.77640638748285
- type: euclidean_pearson
value: 89.60124312475843
- type: euclidean_spearman
value: 88.4321442688528
- type: main_score
value: 88.77640638748285
- type: manhattan_pearson
value: 89.62375118021299
- type: manhattan_spearman
value: 88.46998118661577
- type: pearson
value: 88.4084678497171
- type: spearman
value: 88.77640638748285
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 87.30688801326498
- type: cosine_spearman
value: 87.55684697258378
- type: euclidean_pearson
value: 87.89672951056794
- type: euclidean_spearman
value: 87.28050429201674
- type: main_score
value: 87.55684697258378
- type: manhattan_pearson
value: 87.74292745320572
- type: manhattan_spearman
value: 87.16383993876582
- type: pearson
value: 87.30688801326498
- type: spearman
value: 87.55684697258378
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 73.46180375170147
- type: cosine_spearman
value: 73.39559590127081
- type: euclidean_pearson
value: 73.72613901293681
- type: euclidean_spearman
value: 71.85465165176795
- type: main_score
value: 73.39559590127081
- type: manhattan_pearson
value: 73.07859140869076
- type: manhattan_spearman
value: 71.22047343718893
- type: pearson
value: 73.46180375170147
- type: spearman
value: 73.39559590127081
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 62.47531620842637
- type: cosine_spearman
value: 66.22504667157702
- type: euclidean_pearson
value: 66.76201254783692
- type: euclidean_spearman
value: 66.86115760269463
- type: main_score
value: 66.22504667157702
- type: manhattan_pearson
value: 66.73847836793489
- type: manhattan_spearman
value: 66.7677116377695
- type: pearson
value: 62.47531620842637
- type: spearman
value: 66.22504667157702
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 69.89707002436481
- type: cosine_spearman
value: 72.2054865735116
- type: euclidean_pearson
value: 71.81856615570756
- type: euclidean_spearman
value: 72.72593304629407
- type: main_score
value: 72.2054865735116
- type: manhattan_pearson
value: 72.00362684700072
- type: manhattan_spearman
value: 72.62783534769964
- type: pearson
value: 69.89707002436481
- type: spearman
value: 72.2054865735116
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 81.59623734395916
- type: cosine_spearman
value: 83.28946105111358
- type: euclidean_pearson
value: 79.377330171466
- type: euclidean_spearman
value: 81.81029781662205
- type: main_score
value: 83.28946105111358
- type: manhattan_pearson
value: 78.96970881689698
- type: manhattan_spearman
value: 81.91773236079703
- type: pearson
value: 81.59623734395916
- type: spearman
value: 83.28946105111358
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 55.03825643126142
- type: cosine_spearman
value: 58.25792501780429
- type: euclidean_pearson
value: 50.38007603973409
- type: euclidean_spearman
value: 59.39961789383097
- type: main_score
value: 58.25792501780429
- type: manhattan_pearson
value: 50.518568927999155
- type: manhattan_spearman
value: 59.84185466003894
- type: pearson
value: 55.03825643126142
- type: spearman
value: 58.25792501780429
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 77.77233721490776
- type: cosine_spearman
value: 76.17596588017625
- type: euclidean_pearson
value: 74.47600468156611
- type: euclidean_spearman
value: 72.61278728057012
- type: main_score
value: 76.17596588017625
- type: manhattan_pearson
value: 74.48118910099699
- type: manhattan_spearman
value: 73.33167419101696
- type: pearson
value: 77.77233721490776
- type: spearman
value: 76.17596588017625
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 42.87453608131507
- type: cosine_spearman
value: 45.137849894401185
- type: euclidean_pearson
value: 31.66964197694796
- type: euclidean_spearman
value: 44.1014900837869
- type: main_score
value: 45.137849894401185
- type: manhattan_pearson
value: 31.007199259384745
- type: manhattan_spearman
value: 43.48181523288926
- type: pearson
value: 42.87453608131507
- type: spearman
value: 45.137849894401185
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 66.87400150638176
- type: cosine_spearman
value: 67.27861354834066
- type: euclidean_pearson
value: 66.81789582140216
- type: euclidean_spearman
value: 66.44220479858708
- type: main_score
value: 67.27861354834066
- type: manhattan_pearson
value: 66.92509859033235
- type: manhattan_spearman
value: 66.46841124185076
- type: pearson
value: 66.87400150638176
- type: spearman
value: 67.27861354834066
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 61.819804551576084
- type: cosine_spearman
value: 65.0864146772135
- type: euclidean_pearson
value: 62.518151090361876
- type: euclidean_spearman
value: 65.13608138548017
- type: main_score
value: 65.0864146772135
- type: manhattan_pearson
value: 62.51413246915267
- type: manhattan_spearman
value: 65.19077543064323
- type: pearson
value: 61.819804551576084
- type: spearman
value: 65.0864146772135
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 54.85728696035389
- type: cosine_spearman
value: 61.60906359227576
- type: euclidean_pearson
value: 52.57582587901851
- type: euclidean_spearman
value: 61.41823097598308
- type: main_score
value: 61.60906359227576
- type: manhattan_pearson
value: 52.500978361080506
- type: manhattan_spearman
value: 61.30365596659758
- type: pearson
value: 54.85728696035389
- type: spearman
value: 61.60906359227576
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 67.68016005631422
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 66.19871164667245
- type: euclidean_spearman
value: 73.24670207647144
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 67.0443525268974
- type: manhattan_spearman
value: 73.24670207647144
- type: pearson
value: 67.68016005631422
- type: spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 47.49467414030747
- type: cosine_spearman
value: 56.81512095681289
- type: euclidean_pearson
value: 48.42860221765214
- type: euclidean_spearman
value: 58.63197306329092
- type: main_score
value: 56.81512095681289
- type: manhattan_pearson
value: 48.39594959260441
- type: manhattan_spearman
value: 58.63197306329092
- type: pearson
value: 47.49467414030747
- type: spearman
value: 56.81512095681289
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 76.8364678896155
- type: cosine_spearman
value: 78.45516413087114
- type: euclidean_pearson
value: 78.62779318576634
- type: euclidean_spearman
value: 78.88760695649488
- type: main_score
value: 78.45516413087114
- type: manhattan_pearson
value: 78.62131335760031
- type: manhattan_spearman
value: 78.81861844200388
- type: pearson
value: 76.8364678896155
- type: spearman
value: 78.45516413087114
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 65.16640313911604
- type: cosine_spearman
value: 60.887608967403914
- type: euclidean_pearson
value: 67.49902244990913
- type: euclidean_spearman
value: 59.2458787136538
- type: main_score
value: 60.887608967403914
- type: manhattan_pearson
value: 67.34313506388378
- type: manhattan_spearman
value: 59.05283429200166
- type: pearson
value: 65.16640313911604
- type: spearman
value: 60.887608967403914
- task:
type: STS
dataset:
name: MTEB QBQTC (default)
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cosine_pearson
value: 34.20049144526891
- type: cosine_spearman
value: 36.41802814113771
- type: euclidean_pearson
value: 34.56994213959062
- type: euclidean_spearman
value: 36.06141660786936
- type: main_score
value: 36.41802814113771
- type: manhattan_pearson
value: 34.537041543916004
- type: manhattan_spearman
value: 36.03341892777382
- type: pearson
value: 34.20049144526891
- type: spearman
value: 36.41802814113771
- task:
type: STS
dataset:
name: MTEB STSB (default)
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cosine_pearson
value: 81.5092853013241
- type: cosine_spearman
value: 83.54005474244292
- type: euclidean_pearson
value: 83.7246578378554
- type: euclidean_spearman
value: 84.46767551087716
- type: main_score
value: 83.54005474244292
- type: manhattan_pearson
value: 83.65922665594636
- type: manhattan_spearman
value: 84.42431449101848
- type: pearson
value: 81.5092853013241
- type: spearman
value: 83.54005474244292
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.70246866744966
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956519641007
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264471425145
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246866744966
- type: spearman
value: 89.44070045346106
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (de)
type: mteb/stsb_multi_mt
config: de
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 86.83701990805217
- type: cosine_spearman
value: 87.80280785492258
- type: euclidean_pearson
value: 87.77325330043514
- type: euclidean_spearman
value: 88.3564607283144
- type: main_score
value: 87.80280785492258
- type: manhattan_pearson
value: 87.6745449945946
- type: manhattan_spearman
value: 88.30660465978795
- type: pearson
value: 86.83701990805217
- type: spearman
value: 87.80280785492258
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (zh)
type: mteb/stsb_multi_mt
config: zh
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 84.27751020600267
- type: cosine_spearman
value: 85.63500407412486
- type: euclidean_pearson
value: 85.21829891649696
- type: euclidean_spearman
value: 85.9384575715382
- type: main_score
value: 85.63500407412486
- type: manhattan_pearson
value: 85.10797194089801
- type: manhattan_spearman
value: 85.8770162042784
- type: pearson
value: 84.27751020600267
- type: spearman
value: 85.63500407412486
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 86.56833656723254
- type: cosine_spearman
value: 87.4393978501382
- type: euclidean_pearson
value: 87.45171512751267
- type: euclidean_spearman
value: 88.13106516566947
- type: main_score
value: 87.4393978501382
- type: manhattan_pearson
value: 87.33010961793333
- type: manhattan_spearman
value: 88.06707425102182
- type: pearson
value: 86.56833656723254
- type: spearman
value: 87.4393978501382
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (pl)
type: mteb/stsb_multi_mt
config: pl
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 85.45065540325523
- type: cosine_spearman
value: 85.47881076789359
- type: euclidean_pearson
value: 85.1999493863155
- type: euclidean_spearman
value: 85.7874947669187
- type: main_score
value: 85.47881076789359
- type: manhattan_pearson
value: 85.06075305990376
- type: manhattan_spearman
value: 85.71563015639558
- type: pearson
value: 85.45065540325523
- type: spearman
value: 85.47881076789359
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (es)
type: mteb/stsb_multi_mt
config: es
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 87.11952824079832
- type: cosine_spearman
value: 87.9643473573153
- type: euclidean_pearson
value: 88.11750364639971
- type: euclidean_spearman
value: 88.63695109016498
- type: main_score
value: 87.9643473573153
- type: manhattan_pearson
value: 88.00294453126699
- type: manhattan_spearman
value: 88.53750241758391
- type: pearson
value: 87.11952824079832
- type: spearman
value: 87.9643473573153
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (ru)
type: mteb/stsb_multi_mt
config: ru
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 85.99804354414991
- type: cosine_spearman
value: 86.30252111551002
- type: euclidean_pearson
value: 86.1880652037762
- type: euclidean_spearman
value: 86.69556223944502
- type: main_score
value: 86.30252111551002
- type: manhattan_pearson
value: 86.0736400320898
- type: manhattan_spearman
value: 86.61747927593393
- type: pearson
value: 85.99804354414991
- type: spearman
value: 86.30252111551002
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: mteb/stsb_multi_mt
config: en
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 87.70246861738103
- type: cosine_spearman
value: 89.44070045346106
- type: euclidean_pearson
value: 89.56956518833663
- type: euclidean_spearman
value: 89.95830112784283
- type: main_score
value: 89.44070045346106
- type: manhattan_pearson
value: 89.48264470792915
- type: manhattan_spearman
value: 89.87900732483114
- type: pearson
value: 87.70246861738103
- type: spearman
value: 89.44070045346106
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.88064122814694
- type: mrr
value: 95.84832651009123
- type: main_score
value: 84.88064122814694
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 57.289
- type: map_at_10
value: 67.88499999999999
- type: map_at_100
value: 68.477
- type: map_at_1000
value: 68.50500000000001
- type: map_at_20
value: 68.33500000000001
- type: map_at_3
value: 65.08
- type: map_at_5
value: 67.001
- type: mrr_at_1
value: 59.667
- type: mrr_at_10
value: 68.626
- type: mrr_at_100
value: 69.082
- type: mrr_at_1000
value: 69.108
- type: mrr_at_20
value: 68.958
- type: mrr_at_3
value: 66.667
- type: mrr_at_5
value: 67.983
- type: ndcg_at_1
value: 59.667
- type: ndcg_at_10
value: 72.309
- type: ndcg_at_100
value: 74.58399999999999
- type: ndcg_at_1000
value: 75.25500000000001
- type: ndcg_at_20
value: 73.656
- type: ndcg_at_3
value: 67.791
- type: ndcg_at_5
value: 70.45
- type: precision_at_1
value: 59.667
- type: precision_at_10
value: 9.567
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.083
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 57.289
- type: recall_at_10
value: 84.756
- type: recall_at_100
value: 94.5
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 89.7
- type: recall_at_3
value: 73.22800000000001
- type: recall_at_5
value: 79.444
- type: main_score
value: 72.309
- task:
type: Clustering
dataset:
name: MTEB SpanishNewsClusteringP2P (default)
type: jinaai/spanish_news_clustering
config: default
split: test
revision: bf8ca8ddc5b7da4f7004720ddf99bbe0483480e6
metrics:
- type: main_score
value: 45.04477709795154
- type: v_measure
value: 45.04477709795154
- type: v_measure_std
value: 0.0
- task:
type: Retrieval
dataset:
name: MTEB SpanishPassageRetrievalS2S (default)
type: jinaai/spanish_passage_retrieval
config: default
split: test
revision: 9cddf2ce5209ade52c2115ccfa00eb22c6d3a837
metrics:
- type: main_score
value: 69.83
- type: map_at_1
value: 15.736
- type: map_at_10
value: 52.027
- type: map_at_100
value: 65.08800000000001
- type: map_at_1000
value: 65.08800000000001
- type: map_at_20
value: 60.79900000000001
- type: map_at_3
value: 32.869
- type: map_at_5
value: 41.436
- type: mrr_at_1
value: 75.44910179640718
- type: mrr_at_10
value: 84.43446440452426
- type: mrr_at_100
value: 84.48052612723271
- type: mrr_at_1000
value: 84.48052612723271
- type: mrr_at_20
value: 84.48052612723271
- type: mrr_at_3
value: 83.13373253493013
- type: mrr_at_5
value: 84.3013972055888
- type: nauc_map_at_1000_diff1
value: 50.611540149694356
- type: nauc_map_at_1000_max
value: 2.1102430434260238
- type: nauc_map_at_1000_std
value: -18.88993521335793
- type: nauc_map_at_100_diff1
value: 50.611540149694356
- type: nauc_map_at_100_max
value: 2.1102430434260238
- type: nauc_map_at_100_std
value: -18.88993521335793
- type: nauc_map_at_10_diff1
value: 59.13518981755268
- type: nauc_map_at_10_max
value: -9.810386627392807
- type: nauc_map_at_10_std
value: -38.31810152345078
- type: nauc_map_at_1_diff1
value: 74.96782567287174
- type: nauc_map_at_1_max
value: -29.648279252607875
- type: nauc_map_at_1_std
value: -54.017459339141595
- type: nauc_map_at_20_diff1
value: 55.26694458629849
- type: nauc_map_at_20_max
value: -1.9490244535020729
- type: nauc_map_at_20_std
value: -25.22211659104076
- type: nauc_map_at_3_diff1
value: 71.67607885031732
- type: nauc_map_at_3_max
value: -25.078101661694507
- type: nauc_map_at_3_std
value: -50.55408861920259
- type: nauc_map_at_5_diff1
value: 61.50111515417668
- type: nauc_map_at_5_max
value: -16.4114670513168
- type: nauc_map_at_5_std
value: -44.391416134859135
- type: nauc_mrr_at_1000_diff1
value: 74.18848063283234
- type: nauc_mrr_at_1000_max
value: 21.929205946778005
- type: nauc_mrr_at_1000_std
value: -36.27399268489433
- type: nauc_mrr_at_100_diff1
value: 74.18848063283234
- type: nauc_mrr_at_100_max
value: 21.929205946778005
- type: nauc_mrr_at_100_std
value: -36.27399268489433
- type: nauc_mrr_at_10_diff1
value: 74.27231582268745
- type: nauc_mrr_at_10_max
value: 21.481133301135337
- type: nauc_mrr_at_10_std
value: -36.72070854872902
- type: nauc_mrr_at_1_diff1
value: 76.54855950439561
- type: nauc_mrr_at_1_max
value: 26.99938321212366
- type: nauc_mrr_at_1_std
value: -33.098742603429635
- type: nauc_mrr_at_20_diff1
value: 74.18848063283234
- type: nauc_mrr_at_20_max
value: 21.929205946778005
- type: nauc_mrr_at_20_std
value: -36.27399268489433
- type: nauc_mrr_at_3_diff1
value: 72.05379526740143
- type: nauc_mrr_at_3_max
value: 18.875831185752528
- type: nauc_mrr_at_3_std
value: -37.27302006456391
- type: nauc_mrr_at_5_diff1
value: 74.25342356682029
- type: nauc_mrr_at_5_max
value: 20.756340085088738
- type: nauc_mrr_at_5_std
value: -37.99507208540703
- type: nauc_ndcg_at_1000_diff1
value: 53.259363764380275
- type: nauc_ndcg_at_1000_max
value: 12.936954959423218
- type: nauc_ndcg_at_1000_std
value: -16.953898675672153
- type: nauc_ndcg_at_100_diff1
value: 53.259363764380275
- type: nauc_ndcg_at_100_max
value: 12.936954959423218
- type: nauc_ndcg_at_100_std
value: -16.953898675672153
- type: nauc_ndcg_at_10_diff1
value: 53.70942345413554
- type: nauc_ndcg_at_10_max
value: -3.8465093347016186
- type: nauc_ndcg_at_10_std
value: -31.208127919994755
- type: nauc_ndcg_at_1_diff1
value: 75.30551289259554
- type: nauc_ndcg_at_1_max
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type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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- type: cosine_ap
value: 96.98758090713252
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- type: cosine_precision
value: 94.58077709611452
- type: cosine_recall
value: 92.5
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value: 99.82574257425742
- type: dot_accuracy_threshold
value: 40484.73815917969
- type: dot_ap
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- type: dot_f1
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- type: dot_f1_threshold
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- type: dot_precision
value: 90.15594541910332
- type: dot_recall
value: 92.5
- type: euclidean_accuracy
value: 99.87128712871286
- type: euclidean_accuracy_threshold
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- type: euclidean_ap
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- type: euclidean_f1_threshold
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- type: euclidean_precision
value: 94.20731707317073
- type: euclidean_recall
value: 92.7
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value: 96.98758090713252
- type: manhattan_accuracy
value: 99.86930693069307
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value: 93.33333333333333
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value: 94.28571428571428
- type: manhattan_recall
value: 92.4
- type: max_accuracy
value: 99.87326732673267
- type: max_ap
value: 96.98758090713252
- type: max_f1
value: 93.52881698685542
- type: max_precision
value: 94.58077709611452
- type: max_recall
value: 92.7
- type: similarity_accuracy
value: 99.87326732673267
- type: similarity_accuracy_threshold
value: 86.0752820968628
- type: similarity_ap
value: 96.98758090713252
- type: similarity_f1
value: 93.52881698685542
- type: similarity_f1_threshold
value: 86.0752820968628
- type: similarity_precision
value: 94.58077709611452
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value: 92.5
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 65.6560129719848
- type: v_measure
value: 65.6560129719848
- type: v_measure_std
value: 4.781229811487539
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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value: 35.07546243853692
- type: v_measure
value: 35.07546243853692
- type: v_measure_std
value: 1.1978740356240998
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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value: 51.771005199508835
- type: mrr
value: 52.65443298531534
- type: main_score
value: 51.771005199508835
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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value: 29.48686238342228
- type: cosine_spearman
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- type: dot_pearson
value: 27.95853155597859
- type: dot_spearman
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- type: pearson
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- type: spearman
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type: Summarization
dataset:
name: MTEB SummEvalFr (default)
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
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value: 31.551301434917868
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type: Reranking
dataset:
name: MTEB SyntecReranking (default)
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
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type: Retrieval
dataset:
name: MTEB SyntecRetrieval (default)
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
metrics:
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- type: recall_at_1
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type: Reranking
dataset:
name: MTEB T2Reranking (default)
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
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- type: main_score
value: 65.3088203970324
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval (default)
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
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value: 83.163
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value: 26.875
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value: 75.454
- type: map_at_100
value: 79.036
- type: map_at_1000
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- type: map_at_3
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- type: map_at_5
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value: 93.988
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value: 97.801
- type: recall_at_20
value: 87.809
- type: recall_at_3
value: 54.869
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value: 68.728
- task:
type: PairClassification
dataset:
name: MTEB TERRa (default)
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
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value: 60.586319218241044
- type: cosine_accuracy_threshold
value: 82.49806761741638
- type: cosine_ap
value: 58.73198048427448
- type: cosine_f1
value: 67.37967914438502
- type: cosine_f1_threshold
value: 77.46461033821106
- type: cosine_precision
value: 57.01357466063348
- type: cosine_recall
value: 82.35294117647058
- type: dot_accuracy
value: 60.26058631921825
- type: dot_accuracy_threshold
value: 35627.020263671875
- type: dot_ap
value: 57.418783612898224
- type: dot_f1
value: 66.51982378854623
- type: dot_f1_threshold
value: 27620.843505859375
- type: dot_precision
value: 50.16611295681063
- type: dot_recall
value: 98.69281045751634
- type: euclidean_accuracy
value: 60.26058631921825
- type: euclidean_accuracy_threshold
value: 1255.4466247558594
- type: euclidean_ap
value: 58.748656145387955
- type: euclidean_f1
value: 66.99029126213591
- type: euclidean_f1_threshold
value: 1565.1330947875977
- type: euclidean_precision
value: 53.28185328185329
- type: euclidean_recall
value: 90.19607843137256
- type: main_score
value: 58.8479126365766
- type: manhattan_accuracy
value: 59.934853420195445
- type: manhattan_accuracy_threshold
value: 29897.271728515625
- type: manhattan_ap
value: 58.8479126365766
- type: manhattan_f1
value: 66.81318681318683
- type: manhattan_f1_threshold
value: 46291.802978515625
- type: manhattan_precision
value: 50.331125827814574
- type: manhattan_recall
value: 99.34640522875817
- type: max_accuracy
value: 60.586319218241044
- type: max_ap
value: 58.8479126365766
- type: max_f1
value: 67.37967914438502
- type: max_precision
value: 57.01357466063348
- type: max_recall
value: 99.34640522875817
- type: similarity_accuracy
value: 60.586319218241044
- type: similarity_accuracy_threshold
value: 82.49806761741638
- type: similarity_ap
value: 58.73198048427448
- type: similarity_f1
value: 67.37967914438502
- type: similarity_f1_threshold
value: 77.46461033821106
- type: similarity_precision
value: 57.01357466063348
- type: similarity_recall
value: 82.35294117647058
- task:
type: Classification
dataset:
name: MTEB TNews (default)
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 45.967999999999996
- type: f1
value: 44.699306100915706
- type: f1_weighted
value: 46.03730319014832
- type: main_score
value: 45.967999999999996
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
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value: 0.251
- type: map_at_10
value: 1.9480000000000002
- type: map_at_100
value: 11.082
- type: map_at_1000
value: 26.700000000000003
- type: map_at_20
value: 3.3529999999999998
- type: map_at_3
value: 0.679
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 95.786
- type: mrr_at_100
value: 95.786
- type: mrr_at_1000
value: 95.786
- type: mrr_at_20
value: 95.786
- type: mrr_at_3
value: 95.0
- type: mrr_at_5
value: 95.5
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 77.71900000000001
- type: ndcg_at_100
value: 57.726
- type: ndcg_at_1000
value: 52.737
- type: ndcg_at_20
value: 72.54
- type: ndcg_at_3
value: 83.397
- type: ndcg_at_5
value: 80.806
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 81.0
- type: precision_at_100
value: 59.199999999999996
- type: precision_at_1000
value: 23.244
- type: precision_at_20
value: 75.2
- type: precision_at_3
value: 88.0
- type: precision_at_5
value: 84.8
- type: recall_at_1
value: 0.251
- type: recall_at_10
value: 2.1229999999999998
- type: recall_at_100
value: 14.496999999999998
- type: recall_at_1000
value: 50.09
- type: recall_at_20
value: 3.8309999999999995
- type: recall_at_3
value: 0.696
- type: recall_at_5
value: 1.1400000000000001
- type: main_score
value: 77.71900000000001
- task:
type: Clustering
dataset:
name: MTEB TenKGnadClusteringP2P (default)
type: slvnwhrl/tenkgnad-clustering-p2p
config: default
split: test
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
metrics:
- type: main_score
value: 43.763609722295215
- type: v_measure
value: 43.763609722295215
- type: v_measure_std
value: 2.8751199473862457
- task:
type: Clustering
dataset:
name: MTEB TenKGnadClusteringS2S (default)
type: slvnwhrl/tenkgnad-clustering-s2s
config: default
split: test
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
metrics:
- type: main_score
value: 39.762424448504355
- type: v_measure
value: 39.762424448504355
- type: v_measure_std
value: 3.30146124979502
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P (default)
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
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value: 63.133819258289456
- type: v_measure
value: 63.133819258289456
- type: v_measure_std
value: 1.8854253356479695
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S (default)
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
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value: 58.98195851785808
- type: v_measure
value: 58.98195851785808
- type: v_measure_std
value: 1.6237600076393737
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: 3.3550000000000004
- type: map_at_10
value: 10.08
- type: map_at_100
value: 16.136
- type: map_at_1000
value: 17.605
- type: map_at_20
value: 12.561
- type: map_at_3
value: 5.641
- type: map_at_5
value: 7.3260000000000005
- type: mrr_at_1
value: 46.939
- type: mrr_at_10
value: 58.152
- type: mrr_at_100
value: 58.594
- type: mrr_at_1000
value: 58.601000000000006
- type: mrr_at_20
value: 58.279
- type: mrr_at_3
value: 55.102
- type: mrr_at_5
value: 56.531
- type: ndcg_at_1
value: 44.897999999999996
- type: ndcg_at_10
value: 26.298
- type: ndcg_at_100
value: 37.596000000000004
- type: ndcg_at_1000
value: 49.424
- type: ndcg_at_20
value: 27.066000000000003
- type: ndcg_at_3
value: 31.528
- type: ndcg_at_5
value: 28.219
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 22.245
- type: precision_at_100
value: 7.531000000000001
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_20
value: 17.041
- type: precision_at_3
value: 30.612000000000002
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 3.3550000000000004
- type: recall_at_10
value: 16.41
- type: recall_at_100
value: 47.272
- type: recall_at_1000
value: 83.584
- type: recall_at_20
value: 24.091
- type: recall_at_3
value: 6.8180000000000005
- type: recall_at_5
value: 9.677
- type: main_score
value: 26.298
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 91.2890625
- type: ap
value: 33.95547153875715
- type: ap_weighted
value: 33.95547153875715
- type: f1
value: 75.10768597556462
- type: f1_weighted
value: 92.00161208992606
- type: main_score
value: 91.2890625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 71.3978494623656
- type: f1
value: 71.7194818511814
- type: f1_weighted
value: 71.13860187349744
- type: main_score
value: 71.3978494623656
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 52.4921688720602
- type: v_measure
value: 52.4921688720602
- type: v_measure_std
value: 0.992768152658908
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 85.11652858079513
- type: cosine_accuracy_threshold
value: 87.90839910507202
- type: cosine_ap
value: 70.90459908851724
- type: cosine_f1
value: 65.66581227877457
- type: cosine_f1_threshold
value: 85.13308763504028
- type: cosine_precision
value: 61.094708153531684
- type: cosine_recall
value: 70.97625329815304
- type: dot_accuracy
value: 83.41181379269239
- type: dot_accuracy_threshold
value: 43110.113525390625
- type: dot_ap
value: 65.64869491143095
- type: dot_f1
value: 62.05308447460914
- type: dot_f1_threshold
value: 41412.542724609375
- type: dot_precision
value: 57.38623626989464
- type: dot_recall
value: 67.54617414248021
- type: euclidean_accuracy
value: 85.15229182809799
- type: euclidean_accuracy_threshold
value: 1043.08500289917
- type: euclidean_ap
value: 70.71204383269375
- type: euclidean_f1
value: 65.20304568527919
- type: euclidean_f1_threshold
value: 1179.2595863342285
- type: euclidean_precision
value: 62.81173594132029
- type: euclidean_recall
value: 67.78364116094987
- type: main_score
value: 70.90459908851724
- type: manhattan_accuracy
value: 85.1820945341837
- type: manhattan_accuracy_threshold
value: 26115.0390625
- type: manhattan_ap
value: 70.66113937117431
- type: manhattan_f1
value: 65.33383628819313
- type: manhattan_f1_threshold
value: 29105.181884765625
- type: manhattan_precision
value: 62.40691808791736
- type: manhattan_recall
value: 68.54881266490766
- type: max_accuracy
value: 85.1820945341837
- type: max_ap
value: 70.90459908851724
- type: max_f1
value: 65.66581227877457
- type: max_precision
value: 62.81173594132029
- type: max_recall
value: 70.97625329815304
- type: similarity_accuracy
value: 85.11652858079513
- type: similarity_accuracy_threshold
value: 87.90839910507202
- type: similarity_ap
value: 70.90459908851724
- type: similarity_f1
value: 65.66581227877457
- type: similarity_f1_threshold
value: 85.13308763504028
- type: similarity_precision
value: 61.094708153531684
- type: similarity_recall
value: 70.97625329815304
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 88.10299996119068
- type: cosine_accuracy_threshold
value: 84.34982895851135
- type: cosine_ap
value: 84.13755787769226
- type: cosine_f1
value: 76.0967548076923
- type: cosine_f1_threshold
value: 82.8936219215393
- type: cosine_precision
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- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fra-fra
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
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---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b>
</p>
## Quick Start
[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3-vm) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)
## Intended Usage & Model Info
`jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications.
Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation),
this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**.
Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently.
### Key Features:
- **Extended Sequence Length:** Supports up to 8192 tokens with RoPE.
- **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options:
- `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks
- `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks
- `separation`: Used for embeddings in clustering and re-ranking applications
- `classification`: Used for embeddings in classification tasks
- `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks
- **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application.
### Supported Languages:
While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages:
**Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek,
Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian,
Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
> **⚠️ Important Notice:**
> We fixed a bug in the `encode` function [#60](https://huggingface.co/jinaai/jina-embeddings-v3/discussions/60) where **Matryoshka embedding truncation** occurred *after normalization*, leading to non-normalized truncated embeddings. This issue has been resolved in the latest code revision.
>
> If you have encoded data using the previous version and wish to maintain consistency, please use the specific code revision when loading the model: `AutoModel.from_pretrained('jinaai/jina-embeddings-v3', code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', ...)`
## Usage
**<details><summary>Apply mean pooling when integrating the model.</summary>**
<p>
### Why Use Mean Pooling?
Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level.
This approach has been shown to produce high-quality sentence embeddings.
We provide an `encode` function that handles this for you automatically.
However, if you're working with the model directly, outside of the `encode` function,
you'll need to apply mean pooling manually. Here's how you can do it:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
sentences = ["How is the weather today?", "What is the current weather like today?"]
tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3")
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
task = 'retrieval.query'
task_id = model._adaptation_map[task]
adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32)
with torch.no_grad():
model_output = model(**encoded_input, adapter_mask=adapter_mask)
embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/).
Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
```bash
!pip install transformers torch einops
!pip install 'numpy<2'
```
If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100),
```bash
!pip install flash-attn --no-build-isolation
```
```python
from transformers import AutoModel
# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True)
texts = [
"Follow the white rabbit.", # English
"Sigue al conejo blanco.", # Spanish
"Suis le lapin blanc.", # French
"跟着白兔走。", # Chinese
"اتبع الأرنب الأبيض.", # Arabic
"Folge dem weißen Kaninchen.", # German
]
# When calling the `encode` function, you can choose a `task` based on the use case:
# 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching'
# Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used.
embeddings = model.encode(texts, task="text-matching")
# Compute similarities
print(embeddings[0] @ embeddings[1].T)
```
By default, the model supports a maximum sequence length of 8192 tokens.
However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(["Very long ... document"], max_length=2048)
```
In case you want to use **Matryoshka embeddings** and switch to a different dimension,
you can adjust it by passing the `truncate_dim` parameter to the `encode` function:
```python
embeddings = model.encode(['Sample text'], truncate_dim=256)
```
The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`:
```bash
!pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
task = "retrieval.query"
embeddings = model.encode(
["What is the weather like in Berlin today?"],
task=task,
prompt_name=task,
)
```
You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html).
To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'})
```
Or afterwards:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True)
model[0].default_task = 'classification'
```
This way you can fine-tune the LoRA adapter for the chosen task.
However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model:
```python
model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True})
```
This will allow fine-tuning the whole model instead of just the LoRA adapters.
**<details><summary>ONNX Inference.</summary>**
<p>
You can use ONNX for efficient inference with `jina-embeddings-v3`:
```python
import onnxruntime
import numpy as np
from transformers import AutoTokenizer, PretrainedConfig
# Mean pool function
def mean_pooling(model_output: np.ndarray, attention_mask: np.ndarray):
token_embeddings = model_output
input_mask_expanded = np.expand_dims(attention_mask, axis=-1)
input_mask_expanded = np.broadcast_to(input_mask_expanded, token_embeddings.shape)
sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
sum_mask = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None)
return sum_embeddings / sum_mask
# Load tokenizer and model config
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
# Tokenize input
input_text = tokenizer('sample text', return_tensors='np')
# ONNX session
model_path = 'jina-embeddings-v3/onnx/model.onnx'
session = onnxruntime.InferenceSession(model_path)
# Prepare inputs for ONNX model
task_type = 'text-matching'
task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
inputs = {
'input_ids': input_text['input_ids'],
'attention_mask': input_text['attention_mask'],
'task_id': task_id
}
# Run model
outputs = session.run(None, inputs)[0]
# Apply mean pooling and normalization to the model outputs
embeddings = mean_pooling(outputs, input_text["attention_mask"])
embeddings = embeddings / np.linalg.norm(embeddings, ord=2, axis=1, keepdims=True)
```
</p>
</details>
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## License
`jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/).
## Citation
If you find `jina-embeddings-v3` useful in your research, please cite the following paper:
```bibtex
@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA},
author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
year={2024},
eprint={2409.10173},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2409.10173},
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-11T15:04:22 | 2024-02-21T03:00:19 | 1,663,008 | 285 | ---
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-base-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.14925373134328
- type: ap
value: 39.32336517995478
- type: f1
value: 70.16902252611425
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.386825
- type: ap
value: 90.21276917991995
- type: f1
value: 93.37741030006174
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.846000000000004
- type: f1
value: 48.14646269778261
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.754000000000005
- type: map_at_10
value: 55.761
- type: map_at_100
value: 56.330999999999996
- type: map_at_1000
value: 56.333999999999996
- type: map_at_3
value: 51.92
- type: map_at_5
value: 54.010999999999996
- type: mrr_at_1
value: 41.181
- type: mrr_at_10
value: 55.967999999999996
- type: mrr_at_100
value: 56.538
- type: mrr_at_1000
value: 56.542
- type: mrr_at_3
value: 51.980000000000004
- type: mrr_at_5
value: 54.208999999999996
- type: ndcg_at_1
value: 40.754000000000005
- type: ndcg_at_10
value: 63.605000000000004
- type: ndcg_at_100
value: 66.05199999999999
- type: ndcg_at_1000
value: 66.12
- type: ndcg_at_3
value: 55.708
- type: ndcg_at_5
value: 59.452000000000005
- type: precision_at_1
value: 40.754000000000005
- type: precision_at_10
value: 8.841000000000001
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.238
- type: precision_at_5
value: 15.149000000000001
- type: recall_at_1
value: 40.754000000000005
- type: recall_at_10
value: 88.407
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.714
- type: recall_at_5
value: 75.747
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.74884539679369
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.8075893810716
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.128470519187736
- type: mrr
value: 74.28065778481289
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.24629081484655
- type: cos_sim_spearman
value: 86.93752309911496
- type: euclidean_pearson
value: 87.58589628573816
- type: euclidean_spearman
value: 88.05622328825284
- type: manhattan_pearson
value: 87.5594959805773
- type: manhattan_spearman
value: 88.19658793233961
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.9512987012987
- type: f1
value: 86.92515357973708
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.10263762928872
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.69711517426737
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.327
- type: map_at_10
value: 44.099
- type: map_at_100
value: 45.525
- type: map_at_1000
value: 45.641999999999996
- type: map_at_3
value: 40.47
- type: map_at_5
value: 42.36
- type: mrr_at_1
value: 39.199
- type: mrr_at_10
value: 49.651
- type: mrr_at_100
value: 50.29
- type: mrr_at_1000
value: 50.329
- type: mrr_at_3
value: 46.924
- type: mrr_at_5
value: 48.548
- type: ndcg_at_1
value: 39.199
- type: ndcg_at_10
value: 50.773
- type: ndcg_at_100
value: 55.67999999999999
- type: ndcg_at_1000
value: 57.495
- type: ndcg_at_3
value: 45.513999999999996
- type: ndcg_at_5
value: 47.703
- type: precision_at_1
value: 39.199
- type: precision_at_10
value: 9.914000000000001
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 21.984
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 32.327
- type: recall_at_10
value: 63.743
- type: recall_at_100
value: 84.538
- type: recall_at_1000
value: 96.089
- type: recall_at_3
value: 48.065000000000005
- type: recall_at_5
value: 54.519
- type: map_at_1
value: 32.671
- type: map_at_10
value: 42.954
- type: map_at_100
value: 44.151
- type: map_at_1000
value: 44.287
- type: map_at_3
value: 39.912
- type: map_at_5
value: 41.798
- type: mrr_at_1
value: 41.465
- type: mrr_at_10
value: 49.351
- type: mrr_at_100
value: 49.980000000000004
- type: mrr_at_1000
value: 50.016000000000005
- type: mrr_at_3
value: 47.144000000000005
- type: mrr_at_5
value: 48.592999999999996
- type: ndcg_at_1
value: 41.465
- type: ndcg_at_10
value: 48.565999999999995
- type: ndcg_at_100
value: 52.76499999999999
- type: ndcg_at_1000
value: 54.749
- type: ndcg_at_3
value: 44.57
- type: ndcg_at_5
value: 46.759
- type: precision_at_1
value: 41.465
- type: precision_at_10
value: 9.107999999999999
- type: precision_at_100
value: 1.433
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 21.423000000000002
- type: precision_at_5
value: 15.414
- type: recall_at_1
value: 32.671
- type: recall_at_10
value: 57.738
- type: recall_at_100
value: 75.86500000000001
- type: recall_at_1000
value: 88.36
- type: recall_at_3
value: 45.626
- type: recall_at_5
value: 51.812000000000005
- type: map_at_1
value: 41.185
- type: map_at_10
value: 53.929
- type: map_at_100
value: 54.92
- type: map_at_1000
value: 54.967999999999996
- type: map_at_3
value: 50.70400000000001
- type: map_at_5
value: 52.673
- type: mrr_at_1
value: 47.398
- type: mrr_at_10
value: 57.303000000000004
- type: mrr_at_100
value: 57.959
- type: mrr_at_1000
value: 57.985
- type: mrr_at_3
value: 54.932
- type: mrr_at_5
value: 56.464999999999996
- type: ndcg_at_1
value: 47.398
- type: ndcg_at_10
value: 59.653
- type: ndcg_at_100
value: 63.627
- type: ndcg_at_1000
value: 64.596
- type: ndcg_at_3
value: 54.455
- type: ndcg_at_5
value: 57.245000000000005
- type: precision_at_1
value: 47.398
- type: precision_at_10
value: 9.524000000000001
- type: precision_at_100
value: 1.243
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 24.389
- type: precision_at_5
value: 16.752
- type: recall_at_1
value: 41.185
- type: recall_at_10
value: 73.193
- type: recall_at_100
value: 90.357
- type: recall_at_1000
value: 97.253
- type: recall_at_3
value: 59.199999999999996
- type: recall_at_5
value: 66.118
- type: map_at_1
value: 27.27
- type: map_at_10
value: 36.223
- type: map_at_100
value: 37.218
- type: map_at_1000
value: 37.293
- type: map_at_3
value: 33.503
- type: map_at_5
value: 35.097
- type: mrr_at_1
value: 29.492
- type: mrr_at_10
value: 38.352000000000004
- type: mrr_at_100
value: 39.188
- type: mrr_at_1000
value: 39.247
- type: mrr_at_3
value: 35.876000000000005
- type: mrr_at_5
value: 37.401
- type: ndcg_at_1
value: 29.492
- type: ndcg_at_10
value: 41.239
- type: ndcg_at_100
value: 46.066
- type: ndcg_at_1000
value: 47.992000000000004
- type: ndcg_at_3
value: 36.11
- type: ndcg_at_5
value: 38.772
- type: precision_at_1
value: 29.492
- type: precision_at_10
value: 6.260000000000001
- type: precision_at_100
value: 0.914
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 15.104000000000001
- type: precision_at_5
value: 10.644
- type: recall_at_1
value: 27.27
- type: recall_at_10
value: 54.589
- type: recall_at_100
value: 76.70700000000001
- type: recall_at_1000
value: 91.158
- type: recall_at_3
value: 40.974
- type: recall_at_5
value: 47.327000000000005
- type: map_at_1
value: 17.848
- type: map_at_10
value: 26.207
- type: map_at_100
value: 27.478
- type: map_at_1000
value: 27.602
- type: map_at_3
value: 23.405
- type: map_at_5
value: 24.98
- type: mrr_at_1
value: 21.891
- type: mrr_at_10
value: 31.041999999999998
- type: mrr_at_100
value: 32.092
- type: mrr_at_1000
value: 32.151999999999994
- type: mrr_at_3
value: 28.358
- type: mrr_at_5
value: 29.969
- type: ndcg_at_1
value: 21.891
- type: ndcg_at_10
value: 31.585
- type: ndcg_at_100
value: 37.531
- type: ndcg_at_1000
value: 40.256
- type: ndcg_at_3
value: 26.508
- type: ndcg_at_5
value: 28.894
- type: precision_at_1
value: 21.891
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 0.9990000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.769
- type: precision_at_5
value: 9.279
- type: recall_at_1
value: 17.848
- type: recall_at_10
value: 43.452
- type: recall_at_100
value: 69.216
- type: recall_at_1000
value: 88.102
- type: recall_at_3
value: 29.18
- type: recall_at_5
value: 35.347
- type: map_at_1
value: 30.94
- type: map_at_10
value: 41.248000000000005
- type: map_at_100
value: 42.495
- type: map_at_1000
value: 42.602000000000004
- type: map_at_3
value: 37.939
- type: map_at_5
value: 39.924
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.041
- type: mrr_at_100
value: 47.83
- type: mrr_at_1000
value: 47.878
- type: mrr_at_3
value: 44.466
- type: mrr_at_5
value: 46.111999999999995
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.223
- type: ndcg_at_100
value: 52.394
- type: ndcg_at_1000
value: 54.432
- type: ndcg_at_3
value: 42.032000000000004
- type: ndcg_at_5
value: 44.772
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 1.2890000000000001
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 19.698
- type: precision_at_5
value: 14.013
- type: recall_at_1
value: 30.94
- type: recall_at_10
value: 59.316
- type: recall_at_100
value: 80.783
- type: recall_at_1000
value: 94.15400000000001
- type: recall_at_3
value: 44.712
- type: recall_at_5
value: 51.932
- type: map_at_1
value: 27.104
- type: map_at_10
value: 36.675999999999995
- type: map_at_100
value: 38.076
- type: map_at_1000
value: 38.189
- type: map_at_3
value: 33.733999999999995
- type: map_at_5
value: 35.287
- type: mrr_at_1
value: 33.904
- type: mrr_at_10
value: 42.55
- type: mrr_at_100
value: 43.434
- type: mrr_at_1000
value: 43.494
- type: mrr_at_3
value: 40.126
- type: mrr_at_5
value: 41.473
- type: ndcg_at_1
value: 33.904
- type: ndcg_at_10
value: 42.414
- type: ndcg_at_100
value: 48.203
- type: ndcg_at_1000
value: 50.437
- type: ndcg_at_3
value: 37.633
- type: ndcg_at_5
value: 39.67
- type: precision_at_1
value: 33.904
- type: precision_at_10
value: 7.82
- type: precision_at_100
value: 1.2409999999999999
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 17.884
- type: precision_at_5
value: 12.648000000000001
- type: recall_at_1
value: 27.104
- type: recall_at_10
value: 53.563
- type: recall_at_100
value: 78.557
- type: recall_at_1000
value: 93.533
- type: recall_at_3
value: 39.92
- type: recall_at_5
value: 45.457
- type: map_at_1
value: 27.707749999999997
- type: map_at_10
value: 36.961
- type: map_at_100
value: 38.158833333333334
- type: map_at_1000
value: 38.270333333333326
- type: map_at_3
value: 34.07183333333334
- type: map_at_5
value: 35.69533333333334
- type: mrr_at_1
value: 32.81875
- type: mrr_at_10
value: 41.293
- type: mrr_at_100
value: 42.116499999999995
- type: mrr_at_1000
value: 42.170249999999996
- type: mrr_at_3
value: 38.83983333333333
- type: mrr_at_5
value: 40.29775
- type: ndcg_at_1
value: 32.81875
- type: ndcg_at_10
value: 42.355
- type: ndcg_at_100
value: 47.41374999999999
- type: ndcg_at_1000
value: 49.5805
- type: ndcg_at_3
value: 37.52825
- type: ndcg_at_5
value: 39.83266666666667
- type: precision_at_1
value: 32.81875
- type: precision_at_10
value: 7.382416666666666
- type: precision_at_100
value: 1.1640833333333334
- type: precision_at_1000
value: 0.15383333333333335
- type: precision_at_3
value: 17.134166666666665
- type: precision_at_5
value: 12.174833333333336
- type: recall_at_1
value: 27.707749999999997
- type: recall_at_10
value: 53.945
- type: recall_at_100
value: 76.191
- type: recall_at_1000
value: 91.101
- type: recall_at_3
value: 40.39083333333334
- type: recall_at_5
value: 46.40083333333333
- type: map_at_1
value: 26.482
- type: map_at_10
value: 33.201
- type: map_at_100
value: 34.107
- type: map_at_1000
value: 34.197
- type: map_at_3
value: 31.174000000000003
- type: map_at_5
value: 32.279
- type: mrr_at_1
value: 29.908
- type: mrr_at_10
value: 36.235
- type: mrr_at_100
value: 37.04
- type: mrr_at_1000
value: 37.105
- type: mrr_at_3
value: 34.355999999999995
- type: mrr_at_5
value: 35.382999999999996
- type: ndcg_at_1
value: 29.908
- type: ndcg_at_10
value: 37.325
- type: ndcg_at_100
value: 41.795
- type: ndcg_at_1000
value: 44.105
- type: ndcg_at_3
value: 33.555
- type: ndcg_at_5
value: 35.266999999999996
- type: precision_at_1
value: 29.908
- type: precision_at_10
value: 5.721
- type: precision_at_100
value: 0.8630000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 14.008000000000001
- type: precision_at_5
value: 9.754999999999999
- type: recall_at_1
value: 26.482
- type: recall_at_10
value: 47.072
- type: recall_at_100
value: 67.27
- type: recall_at_1000
value: 84.371
- type: recall_at_3
value: 36.65
- type: recall_at_5
value: 40.774
- type: map_at_1
value: 18.815
- type: map_at_10
value: 26.369999999999997
- type: map_at_100
value: 27.458
- type: map_at_1000
value: 27.588
- type: map_at_3
value: 23.990000000000002
- type: map_at_5
value: 25.345000000000002
- type: mrr_at_1
value: 22.953000000000003
- type: mrr_at_10
value: 30.342999999999996
- type: mrr_at_100
value: 31.241000000000003
- type: mrr_at_1000
value: 31.319000000000003
- type: mrr_at_3
value: 28.16
- type: mrr_at_5
value: 29.406
- type: ndcg_at_1
value: 22.953000000000003
- type: ndcg_at_10
value: 31.151
- type: ndcg_at_100
value: 36.309000000000005
- type: ndcg_at_1000
value: 39.227000000000004
- type: ndcg_at_3
value: 26.921
- type: ndcg_at_5
value: 28.938000000000002
- type: precision_at_1
value: 22.953000000000003
- type: precision_at_10
value: 5.602
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.606
- type: precision_at_5
value: 9.119
- type: recall_at_1
value: 18.815
- type: recall_at_10
value: 41.574
- type: recall_at_100
value: 64.84400000000001
- type: recall_at_1000
value: 85.406
- type: recall_at_3
value: 29.694
- type: recall_at_5
value: 34.935
- type: map_at_1
value: 27.840999999999998
- type: map_at_10
value: 36.797999999999995
- type: map_at_100
value: 37.993
- type: map_at_1000
value: 38.086999999999996
- type: map_at_3
value: 34.050999999999995
- type: map_at_5
value: 35.379
- type: mrr_at_1
value: 32.649
- type: mrr_at_10
value: 41.025
- type: mrr_at_100
value: 41.878
- type: mrr_at_1000
value: 41.929
- type: mrr_at_3
value: 38.573
- type: mrr_at_5
value: 39.715
- type: ndcg_at_1
value: 32.649
- type: ndcg_at_10
value: 42.142
- type: ndcg_at_100
value: 47.558
- type: ndcg_at_1000
value: 49.643
- type: ndcg_at_3
value: 37.12
- type: ndcg_at_5
value: 38.983000000000004
- type: precision_at_1
value: 32.649
- type: precision_at_10
value: 7.08
- type: precision_at_100
value: 1.1039999999999999
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 16.698
- type: precision_at_5
value: 11.511000000000001
- type: recall_at_1
value: 27.840999999999998
- type: recall_at_10
value: 54.245
- type: recall_at_100
value: 77.947
- type: recall_at_1000
value: 92.36999999999999
- type: recall_at_3
value: 40.146
- type: recall_at_5
value: 44.951
- type: map_at_1
value: 26.529000000000003
- type: map_at_10
value: 35.010000000000005
- type: map_at_100
value: 36.647
- type: map_at_1000
value: 36.857
- type: map_at_3
value: 31.968000000000004
- type: map_at_5
value: 33.554
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 39.550999999999995
- type: mrr_at_100
value: 40.54
- type: mrr_at_1000
value: 40.596
- type: mrr_at_3
value: 36.726
- type: mrr_at_5
value: 38.416
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 40.675
- type: ndcg_at_100
value: 46.548
- type: ndcg_at_1000
value: 49.126
- type: ndcg_at_3
value: 35.829
- type: ndcg_at_5
value: 38.0
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 7.826
- type: precision_at_100
value: 1.538
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 16.601
- type: precision_at_5
value: 12.095
- type: recall_at_1
value: 26.529000000000003
- type: recall_at_10
value: 51.03
- type: recall_at_100
value: 77.556
- type: recall_at_1000
value: 93.804
- type: recall_at_3
value: 36.986000000000004
- type: recall_at_5
value: 43.096000000000004
- type: map_at_1
value: 23.480999999999998
- type: map_at_10
value: 30.817
- type: map_at_100
value: 31.838
- type: map_at_1000
value: 31.932
- type: map_at_3
value: 28.011999999999997
- type: map_at_5
value: 29.668
- type: mrr_at_1
value: 25.323
- type: mrr_at_10
value: 33.072
- type: mrr_at_100
value: 33.926
- type: mrr_at_1000
value: 33.993
- type: mrr_at_3
value: 30.436999999999998
- type: mrr_at_5
value: 32.092
- type: ndcg_at_1
value: 25.323
- type: ndcg_at_10
value: 35.514
- type: ndcg_at_100
value: 40.489000000000004
- type: ndcg_at_1000
value: 42.908
- type: ndcg_at_3
value: 30.092000000000002
- type: ndcg_at_5
value: 32.989000000000004
- type: precision_at_1
value: 25.323
- type: precision_at_10
value: 5.545
- type: precision_at_100
value: 0.861
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.446
- type: precision_at_5
value: 9.131
- type: recall_at_1
value: 23.480999999999998
- type: recall_at_10
value: 47.825
- type: recall_at_100
value: 70.652
- type: recall_at_1000
value: 88.612
- type: recall_at_3
value: 33.537
- type: recall_at_5
value: 40.542
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.333999999999998
- type: map_at_10
value: 22.524
- type: map_at_100
value: 24.506
- type: map_at_1000
value: 24.715
- type: map_at_3
value: 19.022
- type: map_at_5
value: 20.693
- type: mrr_at_1
value: 29.186
- type: mrr_at_10
value: 41.22
- type: mrr_at_100
value: 42.16
- type: mrr_at_1000
value: 42.192
- type: mrr_at_3
value: 38.013000000000005
- type: mrr_at_5
value: 39.704
- type: ndcg_at_1
value: 29.186
- type: ndcg_at_10
value: 31.167
- type: ndcg_at_100
value: 38.879000000000005
- type: ndcg_at_1000
value: 42.376000000000005
- type: ndcg_at_3
value: 25.817
- type: ndcg_at_5
value: 27.377000000000002
- type: precision_at_1
value: 29.186
- type: precision_at_10
value: 9.693999999999999
- type: precision_at_100
value: 1.8030000000000002
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 19.11
- type: precision_at_5
value: 14.344999999999999
- type: recall_at_1
value: 13.333999999999998
- type: recall_at_10
value: 37.092000000000006
- type: recall_at_100
value: 63.651
- type: recall_at_1000
value: 83.05
- type: recall_at_3
value: 23.74
- type: recall_at_5
value: 28.655
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.151
- type: map_at_10
value: 19.653000000000002
- type: map_at_100
value: 28.053
- type: map_at_1000
value: 29.709000000000003
- type: map_at_3
value: 14.191
- type: map_at_5
value: 16.456
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.4
- type: mrr_at_100
value: 74.715
- type: mrr_at_1000
value: 74.726
- type: mrr_at_3
value: 72.417
- type: mrr_at_5
value: 73.667
- type: ndcg_at_1
value: 54.25
- type: ndcg_at_10
value: 40.77
- type: ndcg_at_100
value: 46.359
- type: ndcg_at_1000
value: 54.193000000000005
- type: ndcg_at_3
value: 44.832
- type: ndcg_at_5
value: 42.63
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 32.175
- type: precision_at_100
value: 10.668
- type: precision_at_1000
value: 2.067
- type: precision_at_3
value: 47.667
- type: precision_at_5
value: 41.3
- type: recall_at_1
value: 9.151
- type: recall_at_10
value: 25.003999999999998
- type: recall_at_100
value: 52.976
- type: recall_at_1000
value: 78.315
- type: recall_at_3
value: 15.487
- type: recall_at_5
value: 18.999
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.89999999999999
- type: f1
value: 46.47777925067403
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 73.706
- type: map_at_10
value: 82.423
- type: map_at_100
value: 82.67999999999999
- type: map_at_1000
value: 82.694
- type: map_at_3
value: 81.328
- type: map_at_5
value: 82.001
- type: mrr_at_1
value: 79.613
- type: mrr_at_10
value: 87.07000000000001
- type: mrr_at_100
value: 87.169
- type: mrr_at_1000
value: 87.17
- type: mrr_at_3
value: 86.404
- type: mrr_at_5
value: 86.856
- type: ndcg_at_1
value: 79.613
- type: ndcg_at_10
value: 86.289
- type: ndcg_at_100
value: 87.201
- type: ndcg_at_1000
value: 87.428
- type: ndcg_at_3
value: 84.625
- type: ndcg_at_5
value: 85.53699999999999
- type: precision_at_1
value: 79.613
- type: precision_at_10
value: 10.399
- type: precision_at_100
value: 1.1079999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.473
- type: precision_at_5
value: 20.132
- type: recall_at_1
value: 73.706
- type: recall_at_10
value: 93.559
- type: recall_at_100
value: 97.188
- type: recall_at_1000
value: 98.555
- type: recall_at_3
value: 88.98700000000001
- type: recall_at_5
value: 91.373
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.841
- type: map_at_10
value: 32.643
- type: map_at_100
value: 34.575
- type: map_at_1000
value: 34.736
- type: map_at_3
value: 28.317999999999998
- type: map_at_5
value: 30.964000000000002
- type: mrr_at_1
value: 39.660000000000004
- type: mrr_at_10
value: 48.620000000000005
- type: mrr_at_100
value: 49.384
- type: mrr_at_1000
value: 49.415
- type: mrr_at_3
value: 45.988
- type: mrr_at_5
value: 47.361
- type: ndcg_at_1
value: 39.660000000000004
- type: ndcg_at_10
value: 40.646
- type: ndcg_at_100
value: 47.657
- type: ndcg_at_1000
value: 50.428
- type: ndcg_at_3
value: 36.689
- type: ndcg_at_5
value: 38.211
- type: precision_at_1
value: 39.660000000000004
- type: precision_at_10
value: 11.235000000000001
- type: precision_at_100
value: 1.8530000000000002
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 24.587999999999997
- type: precision_at_5
value: 18.395
- type: recall_at_1
value: 19.841
- type: recall_at_10
value: 48.135
- type: recall_at_100
value: 74.224
- type: recall_at_1000
value: 90.826
- type: recall_at_3
value: 33.536
- type: recall_at_5
value: 40.311
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.358
- type: map_at_10
value: 64.497
- type: map_at_100
value: 65.362
- type: map_at_1000
value: 65.41900000000001
- type: map_at_3
value: 61.06700000000001
- type: map_at_5
value: 63.317
- type: mrr_at_1
value: 80.716
- type: mrr_at_10
value: 86.10799999999999
- type: mrr_at_100
value: 86.265
- type: mrr_at_1000
value: 86.27
- type: mrr_at_3
value: 85.271
- type: mrr_at_5
value: 85.82499999999999
- type: ndcg_at_1
value: 80.716
- type: ndcg_at_10
value: 72.597
- type: ndcg_at_100
value: 75.549
- type: ndcg_at_1000
value: 76.61
- type: ndcg_at_3
value: 67.874
- type: ndcg_at_5
value: 70.655
- type: precision_at_1
value: 80.716
- type: precision_at_10
value: 15.148
- type: precision_at_100
value: 1.745
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 43.597
- type: precision_at_5
value: 28.351
- type: recall_at_1
value: 40.358
- type: recall_at_10
value: 75.739
- type: recall_at_100
value: 87.259
- type: recall_at_1000
value: 94.234
- type: recall_at_3
value: 65.39500000000001
- type: recall_at_5
value: 70.878
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.80799999999998
- type: ap
value: 86.81350378180757
- type: f1
value: 90.79901248314215
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.096
- type: map_at_10
value: 34.384
- type: map_at_100
value: 35.541
- type: map_at_1000
value: 35.589999999999996
- type: map_at_3
value: 30.496000000000002
- type: map_at_5
value: 32.718
- type: mrr_at_1
value: 22.750999999999998
- type: mrr_at_10
value: 35.024
- type: mrr_at_100
value: 36.125
- type: mrr_at_1000
value: 36.168
- type: mrr_at_3
value: 31.225
- type: mrr_at_5
value: 33.416000000000004
- type: ndcg_at_1
value: 22.750999999999998
- type: ndcg_at_10
value: 41.351
- type: ndcg_at_100
value: 46.92
- type: ndcg_at_1000
value: 48.111
- type: ndcg_at_3
value: 33.439
- type: ndcg_at_5
value: 37.407000000000004
- type: precision_at_1
value: 22.750999999999998
- type: precision_at_10
value: 6.564
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.288
- type: precision_at_5
value: 10.581999999999999
- type: recall_at_1
value: 22.096
- type: recall_at_10
value: 62.771
- type: recall_at_100
value: 88.529
- type: recall_at_1000
value: 97.55
- type: recall_at_3
value: 41.245
- type: recall_at_5
value: 50.788
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.16780665754673
- type: f1
value: 93.96331194859894
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.90606475148198
- type: f1
value: 58.58344986604187
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.14660390047075
- type: f1
value: 74.31533923533614
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.16139878950908
- type: f1
value: 80.18532656824924
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.949880906135085
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.56300351524862
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.196521894371315
- type: mrr
value: 32.22644231694389
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.783
- type: map_at_10
value: 14.549000000000001
- type: map_at_100
value: 18.433
- type: map_at_1000
value: 19.949
- type: map_at_3
value: 10.936
- type: map_at_5
value: 12.514
- type: mrr_at_1
value: 47.368
- type: mrr_at_10
value: 56.42
- type: mrr_at_100
value: 56.908
- type: mrr_at_1000
value: 56.95
- type: mrr_at_3
value: 54.283
- type: mrr_at_5
value: 55.568
- type: ndcg_at_1
value: 45.666000000000004
- type: ndcg_at_10
value: 37.389
- type: ndcg_at_100
value: 34.253
- type: ndcg_at_1000
value: 43.059999999999995
- type: ndcg_at_3
value: 42.725
- type: ndcg_at_5
value: 40.193
- type: precision_at_1
value: 47.368
- type: precision_at_10
value: 27.988000000000003
- type: precision_at_100
value: 8.672
- type: precision_at_1000
value: 2.164
- type: precision_at_3
value: 40.248
- type: precision_at_5
value: 34.737
- type: recall_at_1
value: 6.783
- type: recall_at_10
value: 17.838
- type: recall_at_100
value: 33.672000000000004
- type: recall_at_1000
value: 66.166
- type: recall_at_3
value: 11.849
- type: recall_at_5
value: 14.205000000000002
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.698999999999998
- type: map_at_10
value: 46.556
- type: map_at_100
value: 47.652
- type: map_at_1000
value: 47.68
- type: map_at_3
value: 42.492000000000004
- type: map_at_5
value: 44.763999999999996
- type: mrr_at_1
value: 35.747
- type: mrr_at_10
value: 49.242999999999995
- type: mrr_at_100
value: 50.052
- type: mrr_at_1000
value: 50.068
- type: mrr_at_3
value: 45.867000000000004
- type: mrr_at_5
value: 47.778999999999996
- type: ndcg_at_1
value: 35.717999999999996
- type: ndcg_at_10
value: 54.14600000000001
- type: ndcg_at_100
value: 58.672999999999995
- type: ndcg_at_1000
value: 59.279
- type: ndcg_at_3
value: 46.407
- type: ndcg_at_5
value: 50.181
- type: precision_at_1
value: 35.717999999999996
- type: precision_at_10
value: 8.844000000000001
- type: precision_at_100
value: 1.139
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.993000000000002
- type: precision_at_5
value: 14.791000000000002
- type: recall_at_1
value: 31.698999999999998
- type: recall_at_10
value: 74.693
- type: recall_at_100
value: 94.15299999999999
- type: recall_at_1000
value: 98.585
- type: recall_at_3
value: 54.388999999999996
- type: recall_at_5
value: 63.08200000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.283
- type: map_at_10
value: 85.24000000000001
- type: map_at_100
value: 85.882
- type: map_at_1000
value: 85.897
- type: map_at_3
value: 82.326
- type: map_at_5
value: 84.177
- type: mrr_at_1
value: 82.21000000000001
- type: mrr_at_10
value: 88.228
- type: mrr_at_100
value: 88.32
- type: mrr_at_1000
value: 88.32
- type: mrr_at_3
value: 87.323
- type: mrr_at_5
value: 87.94800000000001
- type: ndcg_at_1
value: 82.17999999999999
- type: ndcg_at_10
value: 88.9
- type: ndcg_at_100
value: 90.079
- type: ndcg_at_1000
value: 90.158
- type: ndcg_at_3
value: 86.18299999999999
- type: ndcg_at_5
value: 87.71799999999999
- type: precision_at_1
value: 82.17999999999999
- type: precision_at_10
value: 13.464
- type: precision_at_100
value: 1.533
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.693
- type: precision_at_5
value: 24.792
- type: recall_at_1
value: 71.283
- type: recall_at_10
value: 95.742
- type: recall_at_100
value: 99.67200000000001
- type: recall_at_1000
value: 99.981
- type: recall_at_3
value: 87.888
- type: recall_at_5
value: 92.24
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.24267063669042
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.88056988932578
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.903
- type: map_at_10
value: 13.202
- type: map_at_100
value: 15.5
- type: map_at_1000
value: 15.870999999999999
- type: map_at_3
value: 9.407
- type: map_at_5
value: 11.238
- type: mrr_at_1
value: 24.2
- type: mrr_at_10
value: 35.867
- type: mrr_at_100
value: 37.001
- type: mrr_at_1000
value: 37.043
- type: mrr_at_3
value: 32.5
- type: mrr_at_5
value: 34.35
- type: ndcg_at_1
value: 24.2
- type: ndcg_at_10
value: 21.731
- type: ndcg_at_100
value: 30.7
- type: ndcg_at_1000
value: 36.618
- type: ndcg_at_3
value: 20.72
- type: ndcg_at_5
value: 17.954
- type: precision_at_1
value: 24.2
- type: precision_at_10
value: 11.33
- type: precision_at_100
value: 2.4410000000000003
- type: precision_at_1000
value: 0.386
- type: precision_at_3
value: 19.667
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 4.903
- type: recall_at_10
value: 22.962
- type: recall_at_100
value: 49.563
- type: recall_at_1000
value: 78.238
- type: recall_at_3
value: 11.953
- type: recall_at_5
value: 16.067999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.12694254604078
- type: cos_sim_spearman
value: 80.30141815181918
- type: euclidean_pearson
value: 81.34015449877128
- type: euclidean_spearman
value: 80.13984197010849
- type: manhattan_pearson
value: 81.31767068124086
- type: manhattan_spearman
value: 80.11720513114103
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.13112984010417
- type: cos_sim_spearman
value: 78.03063573402875
- type: euclidean_pearson
value: 83.51928418844804
- type: euclidean_spearman
value: 78.4045235411144
- type: manhattan_pearson
value: 83.49981637388689
- type: manhattan_spearman
value: 78.4042575139372
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.50327987379504
- type: cos_sim_spearman
value: 84.18556767756205
- type: euclidean_pearson
value: 82.69684424327679
- type: euclidean_spearman
value: 83.5368106038335
- type: manhattan_pearson
value: 82.57967581007374
- type: manhattan_spearman
value: 83.43009053133697
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.50756863007814
- type: cos_sim_spearman
value: 82.27204331279108
- type: euclidean_pearson
value: 81.39535251429741
- type: euclidean_spearman
value: 81.84386626336239
- type: manhattan_pearson
value: 81.34281737280695
- type: manhattan_spearman
value: 81.81149375673166
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.8727714856726
- type: cos_sim_spearman
value: 87.95738287792312
- type: euclidean_pearson
value: 86.62920602795887
- type: euclidean_spearman
value: 87.05207355381243
- type: manhattan_pearson
value: 86.53587918472225
- type: manhattan_spearman
value: 86.95382961029586
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.52240359769479
- type: cos_sim_spearman
value: 85.47685776238286
- type: euclidean_pearson
value: 84.25815333483058
- type: euclidean_spearman
value: 85.27415639683198
- type: manhattan_pearson
value: 84.29127757025637
- type: manhattan_spearman
value: 85.30226224917351
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.42501708915708
- type: cos_sim_spearman
value: 86.42276182795041
- type: euclidean_pearson
value: 86.5408207354761
- type: euclidean_spearman
value: 85.46096321750838
- type: manhattan_pearson
value: 86.54177303026881
- type: manhattan_spearman
value: 85.50313151916117
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.86521089250766
- type: cos_sim_spearman
value: 65.94868540323003
- type: euclidean_pearson
value: 67.16569626533084
- type: euclidean_spearman
value: 66.37667004134917
- type: manhattan_pearson
value: 67.1482365102333
- type: manhattan_spearman
value: 66.53240122580029
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.64746265365318
- type: cos_sim_spearman
value: 86.41888825906786
- type: euclidean_pearson
value: 85.27453642725811
- type: euclidean_spearman
value: 85.94095796602544
- type: manhattan_pearson
value: 85.28643660505334
- type: manhattan_spearman
value: 85.95028003260744
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.48903153618527
- type: mrr
value: 96.41081503826601
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.594
- type: map_at_10
value: 69.296
- type: map_at_100
value: 69.782
- type: map_at_1000
value: 69.795
- type: map_at_3
value: 66.23
- type: map_at_5
value: 68.293
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 70.339
- type: mrr_at_100
value: 70.708
- type: mrr_at_1000
value: 70.722
- type: mrr_at_3
value: 68.0
- type: mrr_at_5
value: 69.56700000000001
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 74.039
- type: ndcg_at_100
value: 76.103
- type: ndcg_at_1000
value: 76.47800000000001
- type: ndcg_at_3
value: 68.967
- type: ndcg_at_5
value: 71.96900000000001
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.866999999999999
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 18.2
- type: recall_at_1
value: 58.594
- type: recall_at_10
value: 87.422
- type: recall_at_100
value: 96.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 74.217
- type: recall_at_5
value: 81.539
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85049504950496
- type: cos_sim_ap
value: 96.33111544137081
- type: cos_sim_f1
value: 92.35443037974684
- type: cos_sim_precision
value: 93.53846153846153
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.82376237623762
- type: dot_ap
value: 95.38082527310888
- type: dot_f1
value: 90.90909090909092
- type: dot_precision
value: 92.90187891440502
- type: dot_recall
value: 89.0
- type: euclidean_accuracy
value: 99.84851485148515
- type: euclidean_ap
value: 96.32316003996347
- type: euclidean_f1
value: 92.2071392659628
- type: euclidean_precision
value: 92.71991911021233
- type: euclidean_recall
value: 91.7
- type: manhattan_accuracy
value: 99.84851485148515
- type: manhattan_ap
value: 96.3655668249217
- type: manhattan_f1
value: 92.18356026222895
- type: manhattan_precision
value: 92.98067141403867
- type: manhattan_recall
value: 91.4
- type: max_accuracy
value: 99.85049504950496
- type: max_ap
value: 96.3655668249217
- type: max_f1
value: 92.35443037974684
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.94861371629051
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.009430451385
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.61164066427969
- type: mrr
value: 55.49710603938544
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.622620124907662
- type: cos_sim_spearman
value: 31.0678351356163
- type: dot_pearson
value: 30.863727693306814
- type: dot_spearman
value: 31.230306567021255
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 2.011
- type: map_at_100
value: 10.974
- type: map_at_1000
value: 25.819
- type: map_at_3
value: 0.6649999999999999
- type: map_at_5
value: 1.076
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 78.07300000000001
- type: ndcg_at_100
value: 58.231
- type: ndcg_at_1000
value: 51.153000000000006
- type: ndcg_at_3
value: 81.123
- type: ndcg_at_5
value: 81.059
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 83.0
- type: precision_at_100
value: 59.38
- type: precision_at_1000
value: 22.55
- type: precision_at_3
value: 87.333
- type: precision_at_5
value: 86.8
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.2079999999999997
- type: recall_at_100
value: 14.069
- type: recall_at_1000
value: 47.678
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.161
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.809
- type: map_at_10
value: 10.394
- type: map_at_100
value: 16.598
- type: map_at_1000
value: 18.142
- type: map_at_3
value: 5.572
- type: map_at_5
value: 7.1370000000000005
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 46.564
- type: mrr_at_100
value: 47.469
- type: mrr_at_1000
value: 47.469
- type: mrr_at_3
value: 42.177
- type: mrr_at_5
value: 44.524
- type: ndcg_at_1
value: 30.612000000000002
- type: ndcg_at_10
value: 25.701
- type: ndcg_at_100
value: 37.532
- type: ndcg_at_1000
value: 48.757
- type: ndcg_at_3
value: 28.199999999999996
- type: ndcg_at_5
value: 25.987
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.9799999999999995
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_3
value: 29.932
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 2.809
- type: recall_at_10
value: 16.887
- type: recall_at_100
value: 48.67
- type: recall_at_1000
value: 82.89699999999999
- type: recall_at_3
value: 6.521000000000001
- type: recall_at_5
value: 9.609
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.57860000000001
- type: ap
value: 13.82629211536393
- type: f1
value: 54.59860966183956
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.38030560271647
- type: f1
value: 59.69685552567865
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.4736717043405
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.92853311080646
- type: cos_sim_ap
value: 77.67872502591382
- type: cos_sim_f1
value: 70.33941236068895
- type: cos_sim_precision
value: 67.63273258645884
- type: cos_sim_recall
value: 73.27176781002639
- type: dot_accuracy
value: 85.79603027954938
- type: dot_ap
value: 73.73786190233379
- type: dot_f1
value: 67.3437901774235
- type: dot_precision
value: 65.67201604814443
- type: dot_recall
value: 69.10290237467018
- type: euclidean_accuracy
value: 86.94045419324074
- type: euclidean_ap
value: 77.6687791535167
- type: euclidean_f1
value: 70.47209214023542
- type: euclidean_precision
value: 67.7207492094381
- type: euclidean_recall
value: 73.45646437994723
- type: manhattan_accuracy
value: 86.87488823985218
- type: manhattan_ap
value: 77.63373392430728
- type: manhattan_f1
value: 70.40920716112532
- type: manhattan_precision
value: 68.31265508684864
- type: manhattan_recall
value: 72.63852242744063
- type: max_accuracy
value: 86.94045419324074
- type: max_ap
value: 77.67872502591382
- type: max_f1
value: 70.47209214023542
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.67155664221679
- type: cos_sim_ap
value: 85.64591703003417
- type: cos_sim_f1
value: 77.59531005352656
- type: cos_sim_precision
value: 73.60967184801382
- type: cos_sim_recall
value: 82.03726516784724
- type: dot_accuracy
value: 88.41541506578181
- type: dot_ap
value: 84.6482788957769
- type: dot_f1
value: 77.04748541466657
- type: dot_precision
value: 74.02440754931176
- type: dot_recall
value: 80.3279950723745
- type: euclidean_accuracy
value: 88.63080684596576
- type: euclidean_ap
value: 85.44570045321562
- type: euclidean_f1
value: 77.28769403336106
- type: euclidean_precision
value: 72.90600040958427
- type: euclidean_recall
value: 82.22975053895904
- type: manhattan_accuracy
value: 88.59393798269105
- type: manhattan_ap
value: 85.40271361038187
- type: manhattan_f1
value: 77.17606419344392
- type: manhattan_precision
value: 72.4447747078295
- type: manhattan_recall
value: 82.5685247921158
- type: max_accuracy
value: 88.67155664221679
- type: max_ap
value: 85.64591703003417
- type: max_f1
value: 77.59531005352656
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
#### Usage of the ONNX files
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
model_output_ort = model_ort(**encoded_input)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# model_output and model_output_ort are identical
```
#### Usage via infinity
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gte-multilingual-base | Alibaba-NLP | sentence-similarity | [
"sentence-transformers",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"multilingual",
"sentence-similarity",
"custom_code",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"pa",
"pl",
"pt",
"qu",
"ro",
"ru",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"vi",
"yo",
"zh",
"arxiv:2407.19669",
"arxiv:2210.09984",
"arxiv:2402.03216",
"arxiv:2007.15207",
"arxiv:2104.08663",
"arxiv:2402.07440",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-20T08:37:28 | 2025-03-17T05:40:01 | 1,376,850 | 212 | ---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- multilingual
- sentence-similarity
model-index:
- name: gte-multilingual-base (dense)
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 33.66681726329994
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_spearman
value: 43.54760696384009
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_spearman
value: 48.91186363417501
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 41.689860834990064
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 54.20241337977897
- type: v_measure
value: 44.34083695608643
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
metrics:
- type: map
value: 64.91495250072002
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: ndcg_at_10
value: 53.638
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.95522388059702
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 80.717625
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.64199999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.108
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.169999999999995
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.56799999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.75000000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 33.342000000000006
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_10
value: 58.231
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
- type: ndcg_at_10
value: 53.166000000000004
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.01900557959478
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 41.06626465345723
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.87514497610431
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 81.21450112991194
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_spearman
value: 51.71589543397271
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: ndcg_at_10
value: 26.115
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 98.6169102296451
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 97.89603052314916
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 97.12388869645537
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: f1
value: 98.15692469720906
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.36038961038962
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.5903826674123
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.21474277151329
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 62.519999999999996
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 74.90132799162956
- task:
type: STS
dataset:
name: MTEB CDSC-R
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_spearman
value: 90.30727955142524
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 37.94850105022274
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 38.11958675421534
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 86.10950950485399
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 87.28038294231966
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: ndcg_at_10
value: 47.099000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: ndcg_at_10
value: 45.973000000000006
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: ndcg_at_10
value: 55.606
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: ndcg_at_10
value: 36.638
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: ndcg_at_10
value: 30.711
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: ndcg_at_10
value: 44.523
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: ndcg_at_10
value: 37.940000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: ndcg_at_10
value: 38.12183333333333
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: ndcg_at_10
value: 32.684000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: ndcg_at_10
value: 26.735
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: ndcg_at_10
value: 36.933
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: ndcg_at_10
value: 33.747
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: ndcg_at_10
value: 28.872999999999998
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: ndcg_at_10
value: 34.833
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: ndcg_at_10
value: 43.78
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_ap
value: 84.00640599186677
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: ndcg_at_10
value: 80.60000000000001
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: ndcg_at_10
value: 40.116
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: clarin-knext/dbpedia-pl
config: default
split: test
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
metrics:
- type: ndcg_at_10
value: 32.498
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: ndcg_at_10
value: 87.547
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: ndcg_at_10
value: 64.85
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.949999999999996
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: ndcg_at_10
value: 92.111
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: clarin-knext/fiqa-pl
config: default
split: test
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
metrics:
- type: ndcg_at_10
value: 28.962
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: ndcg_at_10
value: 45.005
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 25.133776435657595
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: ndcg_at_10
value: 63.036
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: clarin-knext/hotpotqa-pl
config: default
split: test
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
metrics:
- type: ndcg_at_10
value: 56.904999999999994
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 44.59407464409388
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 74.912
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 79.26829268292683
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_spearman
value: 74.8601229809791
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: mlsum
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.331902754246556
- type: v_measure
value: 40.92029335502153
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 32.19266316591337
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: ndcg_at_10
value: 79.346
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: ndcg_at_10
value: 39.922999999999995
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: clarin-knext/msmarco-pl
config: default
split: test
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
metrics:
- type: ndcg_at_10
value: 55.620999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.53989968080255
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.26993519301212
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.87725150100067
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.48512370811149
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.45141627823591
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 83.45750452079565
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.57637938896488
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.50803043110736
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.6577718478986
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 64.05887879736925
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.27070634636071
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.04520795660037
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 80.66350710900474
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 44.016506455899425
- type: v_measure
value: 40.67730129573544
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.94552790854068
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 49.273705447209146
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.490921318090116
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.97511768661733
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.5689307330195
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 48.34902488231337
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.6684599865501
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.54539340954942
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.08675184936112
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.12508406186953
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.41425689307331
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.59515803631474
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.90517821116342
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.91526563550774
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.198386012104905
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.04371217215869
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.31203765971756
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.521183591123055
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (id)
type: mteb/amazon_massive_intent
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.06254203093476
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (is)
type: mteb/amazon_massive_intent
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dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.72629455279085
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.16072629455278
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.92199058507062
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.40484196368527
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.61398789509079
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: ndcg_at_10
value: 61.934999999999995
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.052031054565205
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.969909524076794
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.7530992892652
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: ndcg_at_10
value: 34.705999999999996
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ar)
type: Shitao/MLDR
config: ar
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 55.166000000000004
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (de)
type: Shitao/MLDR
config: de
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 55.155
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (en)
type: Shitao/MLDR
config: en
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 50.993
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (es)
type: Shitao/MLDR
config: es
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 81.228
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (fr)
type: Shitao/MLDR
config: fr
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 76.19
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (hi)
type: Shitao/MLDR
config: hi
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 45.206
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (it)
type: Shitao/MLDR
config: it
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 66.741
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ja)
type: Shitao/MLDR
config: ja
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 52.111
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ko)
type: Shitao/MLDR
config: ko
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 46.733000000000004
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (pt)
type: Shitao/MLDR
config: pt
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 79.105
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (ru)
type: Shitao/MLDR
config: ru
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 64.21
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (th)
type: Shitao/MLDR
config: th
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 35.467
- task:
type: Retrieval
dataset:
name: MTEB MultiLongDocRetrieval (zh)
type: Shitao/MLDR
config: zh
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 27.419
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 61.02000000000001
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: ndcg_at_10
value: 36.65
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: clarin-knext/nfcorpus-pl
config: default
split: test
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
metrics:
- type: ndcg_at_10
value: 26.831
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: ndcg_at_10
value: 58.111000000000004
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
- type: ndcg_at_10
value: 43.126999999999995
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_ap
value: 72.67630697316041
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 84.85000000000001
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_ap
value: 100
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 65.99189110918043
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_spearman
value: 16.124364530596228
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 92.43431057460192
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 99.06090138049724
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_ap
value: 58.9314954874314
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.59833795013851
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 44.73684210526315
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_spearman
value: 39.36450754137984
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
- type: ndcg_at_10
value: 80.76299999999999
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 88.022
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.719165988934385
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.25390069273025
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: ndcg_at_10
value: 18.243000000000002
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
metrics:
- type: ndcg_at_10
value: 14.219000000000001
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_ap
value: 75.4022630307816
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 79.34269390198548
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_spearman
value: 74.0651660446132
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_spearman
value: 78.62693119733123
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 77.50660544631359
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 85.55415077723738
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.67550814479077
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.94601412322764
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 84.33844259337481
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 81.58650681159105
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 78.82472265884256
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 76.43637938260397
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 84.71008299464059
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 88.88074713413747
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 76.36405640457285
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 83.84737910084762
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 87.03931621433031
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 84.43335591752246
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 83.85268648747021
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 82.45786516224341
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 67.20227303970304
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 60.892838305537126
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 72.01876318464508
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 42.3879320510127
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 65.54048784845729
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 58.55244068334867
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 66.48710288440624
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 66.585754901838
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 81.03001290557805
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 62.28001859884359
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 79.64106342105019
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 78.27915339361124
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 78.28574268257462
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 72.92658860751482
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 74.83418886368217
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_spearman
value: 56.01064022625769
- task:
type: STS
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type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 53.64332829635126
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type: STS
dataset:
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type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
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value: 73.24670207647144
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dataset:
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type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
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value: 80.7157790971544
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dataset:
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type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 86.45763616928973
- task:
type: STS
dataset:
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type: stsb_multi_mt
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
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value: 84.4335500335282
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type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 84.15276484499303
- task:
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dataset:
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type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_10
value: 73.433
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
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value: 58.919999999999995
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type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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value: 95.40564890916419
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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value: 63.41856697730145
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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value: 31.709285904909112
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type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.09341030060322
- task:
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type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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value: 30.58262517835034
- task:
type: Summarization
dataset:
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type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
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value: 29.744542072951358
- task:
type: Reranking
dataset:
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config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
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value: 88.03333333333333
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type: Retrieval
dataset:
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split: test
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
metrics:
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value: 83.043
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dataset:
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config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
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value: 67.08577894804324
- task:
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config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
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value: 84.718
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dataset:
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type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 48.726
- task:
type: Retrieval
dataset:
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config: default
split: test
revision: None
metrics:
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value: 57.56
- task:
type: Retrieval
dataset:
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type: clarin-knext/trec-covid-pl
config: default
split: test
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.765
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dataset:
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 73.69942196531792
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 32.86585365853657
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 34.36077879427633
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type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 52.571845212690285
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type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.13107263107262
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type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.33333333333333
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type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 42.87370133925458
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type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 20.394327616827614
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type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 84.29967426710098
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type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.80666666666667
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type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 67.23062271062273
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type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 78.08398950131233
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type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 77.85166666666666
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type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 67.63004001231148
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type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 89.77000000000001
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type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 40.2654503616042
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type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 83.90333333333334
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type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 77.80666666666666
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 84.08
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 60.43098607367475
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 88.19333333333333
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 90.55352798053529
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: f1
value: 88.44999999999999
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 57.25416429643288
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 56.616646560243524
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: ndcg_at_10
value: 22.819
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.02579999999999
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.60045274476514
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.346666699466205
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_ap
value: 71.88199004440489
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_ap
value: 85.41587779677383
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: ndcg_at_10
value: 72.792
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 82.58000000000001
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: ndcg_at_10
value: 67.327
---
## gte-multilingual-base
The **gte-multilingual-base** model is the latest in the [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) (General Text Embedding) family of models, featuring several key attributes:
- **High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size.
- **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed.
- **Long Context**: Supports text lengths up to **8192** tokens.
- **Multilingual Capability**: Supports over **70** languages.
- **Elastic Dense Embedding**: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency.
- **Sparse Vectors**: In addition to dense representations, it can also generate sparse vectors.
**Paper**: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
## Model Information
- Model Size: 305M
- Embedding Dimension: 768
- Max Input Tokens: 8192
## Usage
- **It is recommended to install xformers and enable unpadding for acceleration,
refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
- **How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)**
- **How to use with [TEI](https://github.com/huggingface/text-embeddings-inference): [refs/pr/7](https://huggingface.co/Alibaba-NLP/gte-multilingual-base/discussions/7#66bfb82ea03b764ca92a2221)**
### Get Dense Embeddings with Transformers
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
dimension=768 # The output dimension of the output embedding, should be in [128, 768]
embeddings = outputs.last_hidden_state[:, 0][:dimension]
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
```
### Use with sentence-transformers
```python
# Requires sentence-transformers>=3.0.0
from sentence_transformers import SentenceTransformer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
model_name_or_path="Alibaba-NLP/gte-multilingual-base"
model = SentenceTransformer(model_name_or_path, trust_remote_code=True)
embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768)
# sim scores
scores = model.similarity(embeddings[:1], embeddings[1:])
print(scores.tolist())
# [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]]
```
### Use with infinity
Usage via docker and [infinity](https://github.com/michaelfeil/infinity), MIT Licensed.
```
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.69 \
v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997
```
### Use with custom code to get dense embeddings and sparse token weights
```python
# You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py
from gte_embedding import GTEEmbeddidng
model_name_or_path = 'Alibaba-NLP/gte-multilingual-base'
model = GTEEmbeddidng(model_name_or_path)
query = "中国的首都在哪儿"
docs = [
"what is the capital of China?",
"how to implement quick sort in python?",
"北京",
"快排算法介绍"
]
embs = model.encode(docs, return_dense=True,return_sparse=True)
print('dense_embeddings vecs', embs['dense_embeddings'])
print('token_weights', embs['token_weights'])
pairs = [(query, doc) for doc in docs]
dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0)
sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0)
hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3)
print('dense_scores', dense_scores)
print('sparse_scores', sparse_scores)
print('hybrid_scores', hybrid_scores)
# dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125]
# sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084]
# hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252]
```
## Evaluation
We validated the performance of the **gte-multilingual-base** model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), among others.
### Retrieval Task
Retrieval results on [MIRACL](https://arxiv.org/abs/2210.09984) and [MLDR](https://arxiv.org/abs/2402.03216) (multilingual), [MKQA](https://arxiv.org/abs/2007.15207) (crosslingual), [BEIR](https://arxiv.org/abs/2104.08663) and [LoCo](https://arxiv.org/abs/2402.07440) (English).
- Detail results on [MLDR](https://arxiv.org/abs/2402.03216)
- Detail results on [LoCo](https://arxiv.org/abs/2402.07440)
### MTEB
Results on MTEB English, Chinese, French, Polish
**More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669)**.
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Citation
If you find our paper or models helpful, please consider cite:
```
@inproceedings{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track},
pages={1393--1412},
year={2024}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
nomic-ai/nomic-embed-text-v1 | nomic-ai | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"en",
"arxiv:2402.01613",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-31T20:26:50 | 2024-09-26T14:42:37 | 1,037,781 | 498 | ---
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
new_version: nomic-ai/nomic-embed-text-v1.5
model-index:
- name: epoch_0_model
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.8507462686567
- type: ap
value: 40.592189159090495
- type: f1
value: 71.01634655512476
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.51892500000001
- type: ap
value: 88.50346762975335
- type: f1
value: 91.50342077459624
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.364
- type: f1
value: 46.72708080922794
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.178
- type: map_at_10
value: 40.244
- type: map_at_100
value: 41.321999999999996
- type: map_at_1000
value: 41.331
- type: map_at_3
value: 35.016999999999996
- type: map_at_5
value: 37.99
- type: mrr_at_1
value: 25.605
- type: mrr_at_10
value: 40.422000000000004
- type: mrr_at_100
value: 41.507
- type: mrr_at_1000
value: 41.516
- type: mrr_at_3
value: 35.23
- type: mrr_at_5
value: 38.15
- type: ndcg_at_1
value: 25.178
- type: ndcg_at_10
value: 49.258
- type: ndcg_at_100
value: 53.776
- type: ndcg_at_1000
value: 53.995000000000005
- type: ndcg_at_3
value: 38.429
- type: ndcg_at_5
value: 43.803
- type: precision_at_1
value: 25.178
- type: precision_at_10
value: 7.831
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.121
- type: precision_at_5
value: 12.29
- type: recall_at_1
value: 25.178
- type: recall_at_10
value: 78.307
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 48.364000000000004
- type: recall_at_5
value: 61.451
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.93034494751465
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.64579480054327
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.601310529222054
- type: mrr
value: 75.04484896451656
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.57797718095814
- type: cos_sim_spearman
value: 86.47064499110101
- type: euclidean_pearson
value: 87.4559602783142
- type: euclidean_spearman
value: 86.47064499110101
- type: manhattan_pearson
value: 87.7232764230245
- type: manhattan_spearman
value: 86.91222131777742
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.5422077922078
- type: f1
value: 84.47657456950589
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.48953561974464
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.75995857510105
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.008000000000003
- type: map_at_10
value: 39.51
- type: map_at_100
value: 40.841
- type: map_at_1000
value: 40.973
- type: map_at_3
value: 36.248999999999995
- type: map_at_5
value: 38.096999999999994
- type: mrr_at_1
value: 36.481
- type: mrr_at_10
value: 44.818000000000005
- type: mrr_at_100
value: 45.64
- type: mrr_at_1000
value: 45.687
- type: mrr_at_3
value: 42.036
- type: mrr_at_5
value: 43.782
- type: ndcg_at_1
value: 36.481
- type: ndcg_at_10
value: 45.152
- type: ndcg_at_100
value: 50.449
- type: ndcg_at_1000
value: 52.76499999999999
- type: ndcg_at_3
value: 40.161
- type: ndcg_at_5
value: 42.577999999999996
- type: precision_at_1
value: 36.481
- type: precision_at_10
value: 8.369
- type: precision_at_100
value: 1.373
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 18.693
- type: precision_at_5
value: 13.533999999999999
- type: recall_at_1
value: 30.008000000000003
- type: recall_at_10
value: 56.108999999999995
- type: recall_at_100
value: 78.55499999999999
- type: recall_at_1000
value: 93.659
- type: recall_at_3
value: 41.754999999999995
- type: recall_at_5
value: 48.296
- type: map_at_1
value: 30.262
- type: map_at_10
value: 40.139
- type: map_at_100
value: 41.394
- type: map_at_1000
value: 41.526
- type: map_at_3
value: 37.155
- type: map_at_5
value: 38.785
- type: mrr_at_1
value: 38.153
- type: mrr_at_10
value: 46.369
- type: mrr_at_100
value: 47.072
- type: mrr_at_1000
value: 47.111999999999995
- type: mrr_at_3
value: 44.268
- type: mrr_at_5
value: 45.389
- type: ndcg_at_1
value: 38.153
- type: ndcg_at_10
value: 45.925
- type: ndcg_at_100
value: 50.394000000000005
- type: ndcg_at_1000
value: 52.37500000000001
- type: ndcg_at_3
value: 41.754000000000005
- type: ndcg_at_5
value: 43.574
- type: precision_at_1
value: 38.153
- type: precision_at_10
value: 8.796
- type: precision_at_100
value: 1.432
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 20.318
- type: precision_at_5
value: 14.395
- type: recall_at_1
value: 30.262
- type: recall_at_10
value: 55.72200000000001
- type: recall_at_100
value: 74.97500000000001
- type: recall_at_1000
value: 87.342
- type: recall_at_3
value: 43.129
- type: recall_at_5
value: 48.336
- type: map_at_1
value: 39.951
- type: map_at_10
value: 51.248000000000005
- type: map_at_100
value: 52.188
- type: map_at_1000
value: 52.247
- type: map_at_3
value: 48.211
- type: map_at_5
value: 49.797000000000004
- type: mrr_at_1
value: 45.329
- type: mrr_at_10
value: 54.749
- type: mrr_at_100
value: 55.367999999999995
- type: mrr_at_1000
value: 55.400000000000006
- type: mrr_at_3
value: 52.382
- type: mrr_at_5
value: 53.649
- type: ndcg_at_1
value: 45.329
- type: ndcg_at_10
value: 56.847
- type: ndcg_at_100
value: 60.738
- type: ndcg_at_1000
value: 61.976
- type: ndcg_at_3
value: 51.59
- type: ndcg_at_5
value: 53.915
- type: precision_at_1
value: 45.329
- type: precision_at_10
value: 8.959
- type: precision_at_100
value: 1.187
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 22.612
- type: precision_at_5
value: 15.273
- type: recall_at_1
value: 39.951
- type: recall_at_10
value: 70.053
- type: recall_at_100
value: 86.996
- type: recall_at_1000
value: 95.707
- type: recall_at_3
value: 56.032000000000004
- type: recall_at_5
value: 61.629999999999995
- type: map_at_1
value: 25.566
- type: map_at_10
value: 33.207
- type: map_at_100
value: 34.166000000000004
- type: map_at_1000
value: 34.245
- type: map_at_3
value: 30.94
- type: map_at_5
value: 32.01
- type: mrr_at_1
value: 27.345000000000002
- type: mrr_at_10
value: 35.193000000000005
- type: mrr_at_100
value: 35.965
- type: mrr_at_1000
value: 36.028999999999996
- type: mrr_at_3
value: 32.806000000000004
- type: mrr_at_5
value: 34.021
- type: ndcg_at_1
value: 27.345000000000002
- type: ndcg_at_10
value: 37.891999999999996
- type: ndcg_at_100
value: 42.664
- type: ndcg_at_1000
value: 44.757000000000005
- type: ndcg_at_3
value: 33.123000000000005
- type: ndcg_at_5
value: 35.035
- type: precision_at_1
value: 27.345000000000002
- type: precision_at_10
value: 5.763
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 13.71
- type: precision_at_5
value: 9.401
- type: recall_at_1
value: 25.566
- type: recall_at_10
value: 50.563
- type: recall_at_100
value: 72.86399999999999
- type: recall_at_1000
value: 88.68599999999999
- type: recall_at_3
value: 37.43
- type: recall_at_5
value: 41.894999999999996
- type: map_at_1
value: 16.663
- type: map_at_10
value: 23.552
- type: map_at_100
value: 24.538
- type: map_at_1000
value: 24.661
- type: map_at_3
value: 21.085
- type: map_at_5
value: 22.391
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 27.643
- type: mrr_at_100
value: 28.499999999999996
- type: mrr_at_1000
value: 28.582
- type: mrr_at_3
value: 25.083
- type: mrr_at_5
value: 26.544
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 28.272000000000002
- type: ndcg_at_100
value: 33.353
- type: ndcg_at_1000
value: 36.454
- type: ndcg_at_3
value: 23.579
- type: ndcg_at_5
value: 25.685000000000002
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 5.187
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 10.987
- type: precision_at_5
value: 8.06
- type: recall_at_1
value: 16.663
- type: recall_at_10
value: 38.808
- type: recall_at_100
value: 61.305
- type: recall_at_1000
value: 83.571
- type: recall_at_3
value: 25.907999999999998
- type: recall_at_5
value: 31.214
- type: map_at_1
value: 27.695999999999998
- type: map_at_10
value: 37.018
- type: map_at_100
value: 38.263000000000005
- type: map_at_1000
value: 38.371
- type: map_at_3
value: 34.226
- type: map_at_5
value: 35.809999999999995
- type: mrr_at_1
value: 32.916000000000004
- type: mrr_at_10
value: 42.067
- type: mrr_at_100
value: 42.925000000000004
- type: mrr_at_1000
value: 42.978
- type: mrr_at_3
value: 39.637
- type: mrr_at_5
value: 41.134
- type: ndcg_at_1
value: 32.916000000000004
- type: ndcg_at_10
value: 42.539
- type: ndcg_at_100
value: 47.873
- type: ndcg_at_1000
value: 50.08200000000001
- type: ndcg_at_3
value: 37.852999999999994
- type: ndcg_at_5
value: 40.201
- type: precision_at_1
value: 32.916000000000004
- type: precision_at_10
value: 7.5840000000000005
- type: precision_at_100
value: 1.199
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 17.485
- type: precision_at_5
value: 12.512
- type: recall_at_1
value: 27.695999999999998
- type: recall_at_10
value: 53.638
- type: recall_at_100
value: 76.116
- type: recall_at_1000
value: 91.069
- type: recall_at_3
value: 41.13
- type: recall_at_5
value: 46.872
- type: map_at_1
value: 24.108
- type: map_at_10
value: 33.372
- type: map_at_100
value: 34.656
- type: map_at_1000
value: 34.768
- type: map_at_3
value: 30.830999999999996
- type: map_at_5
value: 32.204
- type: mrr_at_1
value: 29.110000000000003
- type: mrr_at_10
value: 37.979
- type: mrr_at_100
value: 38.933
- type: mrr_at_1000
value: 38.988
- type: mrr_at_3
value: 35.731
- type: mrr_at_5
value: 36.963
- type: ndcg_at_1
value: 29.110000000000003
- type: ndcg_at_10
value: 38.635000000000005
- type: ndcg_at_100
value: 44.324999999999996
- type: ndcg_at_1000
value: 46.747
- type: ndcg_at_3
value: 34.37
- type: ndcg_at_5
value: 36.228
- type: precision_at_1
value: 29.110000000000003
- type: precision_at_10
value: 6.963
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 16.400000000000002
- type: precision_at_5
value: 11.552999999999999
- type: recall_at_1
value: 24.108
- type: recall_at_10
value: 49.597
- type: recall_at_100
value: 73.88900000000001
- type: recall_at_1000
value: 90.62400000000001
- type: recall_at_3
value: 37.662
- type: recall_at_5
value: 42.565
- type: map_at_1
value: 25.00791666666667
- type: map_at_10
value: 33.287749999999996
- type: map_at_100
value: 34.41141666666667
- type: map_at_1000
value: 34.52583333333333
- type: map_at_3
value: 30.734416666666668
- type: map_at_5
value: 32.137166666666666
- type: mrr_at_1
value: 29.305666666666664
- type: mrr_at_10
value: 37.22966666666666
- type: mrr_at_100
value: 38.066583333333334
- type: mrr_at_1000
value: 38.12616666666667
- type: mrr_at_3
value: 34.92275
- type: mrr_at_5
value: 36.23333333333334
- type: ndcg_at_1
value: 29.305666666666664
- type: ndcg_at_10
value: 38.25533333333333
- type: ndcg_at_100
value: 43.25266666666666
- type: ndcg_at_1000
value: 45.63583333333334
- type: ndcg_at_3
value: 33.777166666666666
- type: ndcg_at_5
value: 35.85
- type: precision_at_1
value: 29.305666666666664
- type: precision_at_10
value: 6.596416666666667
- type: precision_at_100
value: 1.0784166666666668
- type: precision_at_1000
value: 0.14666666666666664
- type: precision_at_3
value: 15.31075
- type: precision_at_5
value: 10.830916666666667
- type: recall_at_1
value: 25.00791666666667
- type: recall_at_10
value: 49.10933333333333
- type: recall_at_100
value: 71.09216666666667
- type: recall_at_1000
value: 87.77725000000001
- type: recall_at_3
value: 36.660916666666665
- type: recall_at_5
value: 41.94149999999999
- type: map_at_1
value: 23.521
- type: map_at_10
value: 30.043
- type: map_at_100
value: 30.936000000000003
- type: map_at_1000
value: 31.022
- type: map_at_3
value: 27.926000000000002
- type: map_at_5
value: 29.076999999999998
- type: mrr_at_1
value: 26.227
- type: mrr_at_10
value: 32.822
- type: mrr_at_100
value: 33.61
- type: mrr_at_1000
value: 33.672000000000004
- type: mrr_at_3
value: 30.776999999999997
- type: mrr_at_5
value: 31.866
- type: ndcg_at_1
value: 26.227
- type: ndcg_at_10
value: 34.041
- type: ndcg_at_100
value: 38.394
- type: ndcg_at_1000
value: 40.732
- type: ndcg_at_3
value: 30.037999999999997
- type: ndcg_at_5
value: 31.845000000000002
- type: precision_at_1
value: 26.227
- type: precision_at_10
value: 5.244999999999999
- type: precision_at_100
value: 0.808
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 12.679000000000002
- type: precision_at_5
value: 8.773
- type: recall_at_1
value: 23.521
- type: recall_at_10
value: 43.633
- type: recall_at_100
value: 63.126000000000005
- type: recall_at_1000
value: 80.765
- type: recall_at_3
value: 32.614
- type: recall_at_5
value: 37.15
- type: map_at_1
value: 16.236
- type: map_at_10
value: 22.898
- type: map_at_100
value: 23.878
- type: map_at_1000
value: 24.009
- type: map_at_3
value: 20.87
- type: map_at_5
value: 22.025
- type: mrr_at_1
value: 19.339000000000002
- type: mrr_at_10
value: 26.382
- type: mrr_at_100
value: 27.245
- type: mrr_at_1000
value: 27.33
- type: mrr_at_3
value: 24.386
- type: mrr_at_5
value: 25.496000000000002
- type: ndcg_at_1
value: 19.339000000000002
- type: ndcg_at_10
value: 27.139999999999997
- type: ndcg_at_100
value: 31.944
- type: ndcg_at_1000
value: 35.077999999999996
- type: ndcg_at_3
value: 23.424
- type: ndcg_at_5
value: 25.188
- type: precision_at_1
value: 19.339000000000002
- type: precision_at_10
value: 4.8309999999999995
- type: precision_at_100
value: 0.845
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 10.874
- type: precision_at_5
value: 7.825
- type: recall_at_1
value: 16.236
- type: recall_at_10
value: 36.513
- type: recall_at_100
value: 57.999
- type: recall_at_1000
value: 80.512
- type: recall_at_3
value: 26.179999999999996
- type: recall_at_5
value: 30.712
- type: map_at_1
value: 24.11
- type: map_at_10
value: 31.566
- type: map_at_100
value: 32.647
- type: map_at_1000
value: 32.753
- type: map_at_3
value: 29.24
- type: map_at_5
value: 30.564999999999998
- type: mrr_at_1
value: 28.265
- type: mrr_at_10
value: 35.504000000000005
- type: mrr_at_100
value: 36.436
- type: mrr_at_1000
value: 36.503
- type: mrr_at_3
value: 33.349000000000004
- type: mrr_at_5
value: 34.622
- type: ndcg_at_1
value: 28.265
- type: ndcg_at_10
value: 36.192
- type: ndcg_at_100
value: 41.388000000000005
- type: ndcg_at_1000
value: 43.948
- type: ndcg_at_3
value: 31.959
- type: ndcg_at_5
value: 33.998
- type: precision_at_1
value: 28.265
- type: precision_at_10
value: 5.989
- type: precision_at_100
value: 0.9650000000000001
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 14.335
- type: precision_at_5
value: 10.112
- type: recall_at_1
value: 24.11
- type: recall_at_10
value: 46.418
- type: recall_at_100
value: 69.314
- type: recall_at_1000
value: 87.397
- type: recall_at_3
value: 34.724
- type: recall_at_5
value: 39.925
- type: map_at_1
value: 22.091
- type: map_at_10
value: 29.948999999999998
- type: map_at_100
value: 31.502000000000002
- type: map_at_1000
value: 31.713
- type: map_at_3
value: 27.464
- type: map_at_5
value: 28.968
- type: mrr_at_1
value: 26.482
- type: mrr_at_10
value: 34.009
- type: mrr_at_100
value: 35.081
- type: mrr_at_1000
value: 35.138000000000005
- type: mrr_at_3
value: 31.785000000000004
- type: mrr_at_5
value: 33.178999999999995
- type: ndcg_at_1
value: 26.482
- type: ndcg_at_10
value: 35.008
- type: ndcg_at_100
value: 41.272999999999996
- type: ndcg_at_1000
value: 43.972
- type: ndcg_at_3
value: 30.804
- type: ndcg_at_5
value: 33.046
- type: precision_at_1
value: 26.482
- type: precision_at_10
value: 6.462
- type: precision_at_100
value: 1.431
- type: precision_at_1000
value: 0.22899999999999998
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.474
- type: recall_at_1
value: 22.091
- type: recall_at_10
value: 45.125
- type: recall_at_100
value: 72.313
- type: recall_at_1000
value: 89.503
- type: recall_at_3
value: 33.158
- type: recall_at_5
value: 39.086999999999996
- type: map_at_1
value: 19.883
- type: map_at_10
value: 26.951000000000004
- type: map_at_100
value: 27.927999999999997
- type: map_at_1000
value: 28.022000000000002
- type: map_at_3
value: 24.616
- type: map_at_5
value: 25.917
- type: mrr_at_1
value: 21.996
- type: mrr_at_10
value: 29.221000000000004
- type: mrr_at_100
value: 30.024
- type: mrr_at_1000
value: 30.095
- type: mrr_at_3
value: 26.833000000000002
- type: mrr_at_5
value: 28.155
- type: ndcg_at_1
value: 21.996
- type: ndcg_at_10
value: 31.421
- type: ndcg_at_100
value: 36.237
- type: ndcg_at_1000
value: 38.744
- type: ndcg_at_3
value: 26.671
- type: ndcg_at_5
value: 28.907
- type: precision_at_1
value: 21.996
- type: precision_at_10
value: 5.009
- type: precision_at_100
value: 0.799
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 11.275
- type: precision_at_5
value: 8.059
- type: recall_at_1
value: 19.883
- type: recall_at_10
value: 43.132999999999996
- type: recall_at_100
value: 65.654
- type: recall_at_1000
value: 84.492
- type: recall_at_3
value: 30.209000000000003
- type: recall_at_5
value: 35.616
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.756
- type: map_at_10
value: 30.378
- type: map_at_100
value: 32.537
- type: map_at_1000
value: 32.717
- type: map_at_3
value: 25.599
- type: map_at_5
value: 28.372999999999998
- type: mrr_at_1
value: 41.303
- type: mrr_at_10
value: 53.483999999999995
- type: mrr_at_100
value: 54.106
- type: mrr_at_1000
value: 54.127
- type: mrr_at_3
value: 50.315
- type: mrr_at_5
value: 52.396
- type: ndcg_at_1
value: 41.303
- type: ndcg_at_10
value: 40.503
- type: ndcg_at_100
value: 47.821000000000005
- type: ndcg_at_1000
value: 50.788
- type: ndcg_at_3
value: 34.364
- type: ndcg_at_5
value: 36.818
- type: precision_at_1
value: 41.303
- type: precision_at_10
value: 12.463000000000001
- type: precision_at_100
value: 2.037
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 25.798
- type: precision_at_5
value: 19.896
- type: recall_at_1
value: 17.756
- type: recall_at_10
value: 46.102
- type: recall_at_100
value: 70.819
- type: recall_at_1000
value: 87.21799999999999
- type: recall_at_3
value: 30.646
- type: recall_at_5
value: 38.022
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.033
- type: map_at_10
value: 20.584
- type: map_at_100
value: 29.518
- type: map_at_1000
value: 31.186000000000003
- type: map_at_3
value: 14.468
- type: map_at_5
value: 17.177
- type: mrr_at_1
value: 69.75
- type: mrr_at_10
value: 77.025
- type: mrr_at_100
value: 77.36699999999999
- type: mrr_at_1000
value: 77.373
- type: mrr_at_3
value: 75.583
- type: mrr_at_5
value: 76.396
- type: ndcg_at_1
value: 58.5
- type: ndcg_at_10
value: 45.033
- type: ndcg_at_100
value: 49.071
- type: ndcg_at_1000
value: 56.056
- type: ndcg_at_3
value: 49.936
- type: ndcg_at_5
value: 47.471999999999994
- type: precision_at_1
value: 69.75
- type: precision_at_10
value: 35.775
- type: precision_at_100
value: 11.594999999999999
- type: precision_at_1000
value: 2.062
- type: precision_at_3
value: 52.5
- type: precision_at_5
value: 45.300000000000004
- type: recall_at_1
value: 9.033
- type: recall_at_10
value: 26.596999999999998
- type: recall_at_100
value: 54.607000000000006
- type: recall_at_1000
value: 76.961
- type: recall_at_3
value: 15.754999999999999
- type: recall_at_5
value: 20.033
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.345000000000006
- type: f1
value: 43.4514918068706
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.29100000000001
- type: map_at_10
value: 81.059
- type: map_at_100
value: 81.341
- type: map_at_1000
value: 81.355
- type: map_at_3
value: 79.74799999999999
- type: map_at_5
value: 80.612
- type: mrr_at_1
value: 76.40299999999999
- type: mrr_at_10
value: 84.615
- type: mrr_at_100
value: 84.745
- type: mrr_at_1000
value: 84.748
- type: mrr_at_3
value: 83.776
- type: mrr_at_5
value: 84.343
- type: ndcg_at_1
value: 76.40299999999999
- type: ndcg_at_10
value: 84.981
- type: ndcg_at_100
value: 86.00999999999999
- type: ndcg_at_1000
value: 86.252
- type: ndcg_at_3
value: 82.97
- type: ndcg_at_5
value: 84.152
- type: precision_at_1
value: 76.40299999999999
- type: precision_at_10
value: 10.446
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 32.147999999999996
- type: precision_at_5
value: 20.135
- type: recall_at_1
value: 71.29100000000001
- type: recall_at_10
value: 93.232
- type: recall_at_100
value: 97.363
- type: recall_at_1000
value: 98.905
- type: recall_at_3
value: 87.893
- type: recall_at_5
value: 90.804
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.667
- type: map_at_10
value: 30.853
- type: map_at_100
value: 32.494
- type: map_at_1000
value: 32.677
- type: map_at_3
value: 26.91
- type: map_at_5
value: 29.099000000000004
- type: mrr_at_1
value: 37.191
- type: mrr_at_10
value: 46.171
- type: mrr_at_100
value: 47.056
- type: mrr_at_1000
value: 47.099000000000004
- type: mrr_at_3
value: 44.059
- type: mrr_at_5
value: 45.147
- type: ndcg_at_1
value: 37.191
- type: ndcg_at_10
value: 38.437
- type: ndcg_at_100
value: 44.62
- type: ndcg_at_1000
value: 47.795
- type: ndcg_at_3
value: 35.003
- type: ndcg_at_5
value: 36.006
- type: precision_at_1
value: 37.191
- type: precision_at_10
value: 10.586
- type: precision_at_100
value: 1.688
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 23.302
- type: precision_at_5
value: 17.006
- type: recall_at_1
value: 18.667
- type: recall_at_10
value: 45.367000000000004
- type: recall_at_100
value: 68.207
- type: recall_at_1000
value: 87.072
- type: recall_at_3
value: 32.129000000000005
- type: recall_at_5
value: 37.719
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.494
- type: map_at_10
value: 66.223
- type: map_at_100
value: 67.062
- type: map_at_1000
value: 67.11500000000001
- type: map_at_3
value: 62.867
- type: map_at_5
value: 64.994
- type: mrr_at_1
value: 78.987
- type: mrr_at_10
value: 84.585
- type: mrr_at_100
value: 84.773
- type: mrr_at_1000
value: 84.77900000000001
- type: mrr_at_3
value: 83.592
- type: mrr_at_5
value: 84.235
- type: ndcg_at_1
value: 78.987
- type: ndcg_at_10
value: 73.64
- type: ndcg_at_100
value: 76.519
- type: ndcg_at_1000
value: 77.51
- type: ndcg_at_3
value: 68.893
- type: ndcg_at_5
value: 71.585
- type: precision_at_1
value: 78.987
- type: precision_at_10
value: 15.529000000000002
- type: precision_at_100
value: 1.7770000000000001
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 44.808
- type: precision_at_5
value: 29.006999999999998
- type: recall_at_1
value: 39.494
- type: recall_at_10
value: 77.643
- type: recall_at_100
value: 88.825
- type: recall_at_1000
value: 95.321
- type: recall_at_3
value: 67.211
- type: recall_at_5
value: 72.519
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.55959999999999
- type: ap
value: 80.7246500384617
- type: f1
value: 85.52336485065454
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.631
- type: map_at_10
value: 36.264
- type: map_at_100
value: 37.428
- type: map_at_1000
value: 37.472
- type: map_at_3
value: 32.537
- type: map_at_5
value: 34.746
- type: mrr_at_1
value: 24.312
- type: mrr_at_10
value: 36.858000000000004
- type: mrr_at_100
value: 37.966
- type: mrr_at_1000
value: 38.004
- type: mrr_at_3
value: 33.188
- type: mrr_at_5
value: 35.367
- type: ndcg_at_1
value: 24.312
- type: ndcg_at_10
value: 43.126999999999995
- type: ndcg_at_100
value: 48.642
- type: ndcg_at_1000
value: 49.741
- type: ndcg_at_3
value: 35.589
- type: ndcg_at_5
value: 39.515
- type: precision_at_1
value: 24.312
- type: precision_at_10
value: 6.699
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.153
- type: precision_at_5
value: 11.065999999999999
- type: recall_at_1
value: 23.631
- type: recall_at_10
value: 64.145
- type: recall_at_100
value: 89.41
- type: recall_at_1000
value: 97.83500000000001
- type: recall_at_3
value: 43.769000000000005
- type: recall_at_5
value: 53.169
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.4108527131783
- type: f1
value: 93.1415880261038
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.24806201550388
- type: f1
value: 60.531916308197175
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.71553463349024
- type: f1
value: 71.70753174900791
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.79757901815736
- type: f1
value: 77.83719850433258
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.74193296622113
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.64257594108566
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.811018518883625
- type: mrr
value: 31.910376577445003
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.409
- type: map_at_10
value: 13.093
- type: map_at_100
value: 16.256999999999998
- type: map_at_1000
value: 17.617
- type: map_at_3
value: 9.555
- type: map_at_5
value: 11.428
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 54.179
- type: mrr_at_100
value: 54.812000000000005
- type: mrr_at_1000
value: 54.840999999999994
- type: mrr_at_3
value: 51.909000000000006
- type: mrr_at_5
value: 53.519000000000005
- type: ndcg_at_1
value: 43.189
- type: ndcg_at_10
value: 35.028
- type: ndcg_at_100
value: 31.226
- type: ndcg_at_1000
value: 39.678000000000004
- type: ndcg_at_3
value: 40.596
- type: ndcg_at_5
value: 38.75
- type: precision_at_1
value: 44.582
- type: precision_at_10
value: 25.974999999999998
- type: precision_at_100
value: 7.793
- type: precision_at_1000
value: 2.036
- type: precision_at_3
value: 38.493
- type: precision_at_5
value: 33.994
- type: recall_at_1
value: 5.409
- type: recall_at_10
value: 16.875999999999998
- type: recall_at_100
value: 30.316
- type: recall_at_1000
value: 60.891
- type: recall_at_3
value: 10.688
- type: recall_at_5
value: 13.832
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.375
- type: map_at_10
value: 51.991
- type: map_at_100
value: 52.91400000000001
- type: map_at_1000
value: 52.93600000000001
- type: map_at_3
value: 48.014
- type: map_at_5
value: 50.381
- type: mrr_at_1
value: 40.759
- type: mrr_at_10
value: 54.617000000000004
- type: mrr_at_100
value: 55.301
- type: mrr_at_1000
value: 55.315000000000005
- type: mrr_at_3
value: 51.516
- type: mrr_at_5
value: 53.435
- type: ndcg_at_1
value: 40.759
- type: ndcg_at_10
value: 59.384
- type: ndcg_at_100
value: 63.157
- type: ndcg_at_1000
value: 63.654999999999994
- type: ndcg_at_3
value: 52.114000000000004
- type: ndcg_at_5
value: 55.986000000000004
- type: precision_at_1
value: 40.759
- type: precision_at_10
value: 9.411999999999999
- type: precision_at_100
value: 1.153
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.329
- type: precision_at_5
value: 16.256999999999998
- type: recall_at_1
value: 36.375
- type: recall_at_10
value: 79.053
- type: recall_at_100
value: 95.167
- type: recall_at_1000
value: 98.82
- type: recall_at_3
value: 60.475
- type: recall_at_5
value: 69.327
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.256
- type: map_at_10
value: 83.8
- type: map_at_100
value: 84.425
- type: map_at_1000
value: 84.444
- type: map_at_3
value: 80.906
- type: map_at_5
value: 82.717
- type: mrr_at_1
value: 80.97999999999999
- type: mrr_at_10
value: 87.161
- type: mrr_at_100
value: 87.262
- type: mrr_at_1000
value: 87.263
- type: mrr_at_3
value: 86.175
- type: mrr_at_5
value: 86.848
- type: ndcg_at_1
value: 80.97999999999999
- type: ndcg_at_10
value: 87.697
- type: ndcg_at_100
value: 88.959
- type: ndcg_at_1000
value: 89.09899999999999
- type: ndcg_at_3
value: 84.83800000000001
- type: ndcg_at_5
value: 86.401
- type: precision_at_1
value: 80.97999999999999
- type: precision_at_10
value: 13.261000000000001
- type: precision_at_100
value: 1.5150000000000001
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 37.01
- type: precision_at_5
value: 24.298000000000002
- type: recall_at_1
value: 70.256
- type: recall_at_10
value: 94.935
- type: recall_at_100
value: 99.274
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.602
- type: recall_at_5
value: 91.133
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.322692497613104
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.895813503775074
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.338
- type: map_at_10
value: 10.767
- type: map_at_100
value: 12.537999999999998
- type: map_at_1000
value: 12.803999999999998
- type: map_at_3
value: 7.788
- type: map_at_5
value: 9.302000000000001
- type: mrr_at_1
value: 21.4
- type: mrr_at_10
value: 31.637999999999998
- type: mrr_at_100
value: 32.688
- type: mrr_at_1000
value: 32.756
- type: mrr_at_3
value: 28.433000000000003
- type: mrr_at_5
value: 30.178
- type: ndcg_at_1
value: 21.4
- type: ndcg_at_10
value: 18.293
- type: ndcg_at_100
value: 25.274
- type: ndcg_at_1000
value: 30.284
- type: ndcg_at_3
value: 17.391000000000002
- type: ndcg_at_5
value: 15.146999999999998
- type: precision_at_1
value: 21.4
- type: precision_at_10
value: 9.48
- type: precision_at_100
value: 1.949
- type: precision_at_1000
value: 0.316
- type: precision_at_3
value: 16.167
- type: precision_at_5
value: 13.22
- type: recall_at_1
value: 4.338
- type: recall_at_10
value: 19.213
- type: recall_at_100
value: 39.562999999999995
- type: recall_at_1000
value: 64.08
- type: recall_at_3
value: 9.828000000000001
- type: recall_at_5
value: 13.383000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.42568163642142
- type: cos_sim_spearman
value: 78.5797159641342
- type: euclidean_pearson
value: 80.22151260811604
- type: euclidean_spearman
value: 78.5797151953878
- type: manhattan_pearson
value: 80.21224215864788
- type: manhattan_spearman
value: 78.55641478381344
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.44020710812569
- type: cos_sim_spearman
value: 78.91631735081286
- type: euclidean_pearson
value: 81.64188964182102
- type: euclidean_spearman
value: 78.91633286881678
- type: manhattan_pearson
value: 81.69294748512496
- type: manhattan_spearman
value: 78.93438558002656
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.27165426412311
- type: cos_sim_spearman
value: 85.40429140249618
- type: euclidean_pearson
value: 84.7509580724893
- type: euclidean_spearman
value: 85.40429140249618
- type: manhattan_pearson
value: 84.76488289321308
- type: manhattan_spearman
value: 85.4256793698708
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.138851760732
- type: cos_sim_spearman
value: 81.64101363896586
- type: euclidean_pearson
value: 82.55165038934942
- type: euclidean_spearman
value: 81.64105257080502
- type: manhattan_pearson
value: 82.52802949883335
- type: manhattan_spearman
value: 81.61255430718158
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.0654695484029
- type: cos_sim_spearman
value: 87.20408521902229
- type: euclidean_pearson
value: 86.8110651362115
- type: euclidean_spearman
value: 87.20408521902229
- type: manhattan_pearson
value: 86.77984656478691
- type: manhattan_spearman
value: 87.1719947099227
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.77823915496512
- type: cos_sim_spearman
value: 85.43566325729779
- type: euclidean_pearson
value: 84.5396956658821
- type: euclidean_spearman
value: 85.43566325729779
- type: manhattan_pearson
value: 84.5665398848169
- type: manhattan_spearman
value: 85.44375870303232
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.20030208471798
- type: cos_sim_spearman
value: 87.20485505076539
- type: euclidean_pearson
value: 88.10588324368722
- type: euclidean_spearman
value: 87.20485505076539
- type: manhattan_pearson
value: 87.92324770415183
- type: manhattan_spearman
value: 87.0571314561877
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.06093161604453
- type: cos_sim_spearman
value: 64.2163140357722
- type: euclidean_pearson
value: 65.27589680994006
- type: euclidean_spearman
value: 64.2163140357722
- type: manhattan_pearson
value: 65.45904383711101
- type: manhattan_spearman
value: 64.55404716679305
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.32976164578706
- type: cos_sim_spearman
value: 85.54302197678368
- type: euclidean_pearson
value: 85.26307149193056
- type: euclidean_spearman
value: 85.54302197678368
- type: manhattan_pearson
value: 85.26647282029371
- type: manhattan_spearman
value: 85.5316135265568
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.44675968318754
- type: mrr
value: 94.92741826075158
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 56.34400000000001
- type: map_at_10
value: 65.927
- type: map_at_100
value: 66.431
- type: map_at_1000
value: 66.461
- type: map_at_3
value: 63.529
- type: map_at_5
value: 64.818
- type: mrr_at_1
value: 59.333000000000006
- type: mrr_at_10
value: 67.54599999999999
- type: mrr_at_100
value: 67.892
- type: mrr_at_1000
value: 67.917
- type: mrr_at_3
value: 65.778
- type: mrr_at_5
value: 66.794
- type: ndcg_at_1
value: 59.333000000000006
- type: ndcg_at_10
value: 70.5
- type: ndcg_at_100
value: 72.688
- type: ndcg_at_1000
value: 73.483
- type: ndcg_at_3
value: 66.338
- type: ndcg_at_5
value: 68.265
- type: precision_at_1
value: 59.333000000000006
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.889
- type: precision_at_5
value: 16.866999999999997
- type: recall_at_1
value: 56.34400000000001
- type: recall_at_10
value: 82.789
- type: recall_at_100
value: 92.767
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 71.64399999999999
- type: recall_at_5
value: 76.322
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.75742574257426
- type: cos_sim_ap
value: 93.52081548447406
- type: cos_sim_f1
value: 87.33850129198966
- type: cos_sim_precision
value: 90.37433155080214
- type: cos_sim_recall
value: 84.5
- type: dot_accuracy
value: 99.75742574257426
- type: dot_ap
value: 93.52081548447406
- type: dot_f1
value: 87.33850129198966
- type: dot_precision
value: 90.37433155080214
- type: dot_recall
value: 84.5
- type: euclidean_accuracy
value: 99.75742574257426
- type: euclidean_ap
value: 93.52081548447406
- type: euclidean_f1
value: 87.33850129198966
- type: euclidean_precision
value: 90.37433155080214
- type: euclidean_recall
value: 84.5
- type: manhattan_accuracy
value: 99.75841584158415
- type: manhattan_ap
value: 93.4975678585854
- type: manhattan_f1
value: 87.26708074534162
- type: manhattan_precision
value: 90.45064377682404
- type: manhattan_recall
value: 84.3
- type: max_accuracy
value: 99.75841584158415
- type: max_ap
value: 93.52081548447406
- type: max_f1
value: 87.33850129198966
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.31437036686651
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.25569319007206
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.90474939720706
- type: mrr
value: 50.568115503777264
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.866828641244712
- type: cos_sim_spearman
value: 30.077555055873866
- type: dot_pearson
value: 29.866832988572266
- type: dot_spearman
value: 30.077555055873866
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.232
- type: map_at_10
value: 2.094
- type: map_at_100
value: 11.971
- type: map_at_1000
value: 28.158
- type: map_at_3
value: 0.688
- type: map_at_5
value: 1.114
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.4
- type: mrr_at_100
value: 93.4
- type: mrr_at_1000
value: 93.4
- type: mrr_at_3
value: 93
- type: mrr_at_5
value: 93.4
- type: ndcg_at_1
value: 84
- type: ndcg_at_10
value: 79.923
- type: ndcg_at_100
value: 61.17
- type: ndcg_at_1000
value: 53.03
- type: ndcg_at_3
value: 84.592
- type: ndcg_at_5
value: 82.821
- type: precision_at_1
value: 88
- type: precision_at_10
value: 85
- type: precision_at_100
value: 63.019999999999996
- type: precision_at_1000
value: 23.554
- type: precision_at_3
value: 89.333
- type: precision_at_5
value: 87.2
- type: recall_at_1
value: 0.232
- type: recall_at_10
value: 2.255
- type: recall_at_100
value: 14.823
- type: recall_at_1000
value: 49.456
- type: recall_at_3
value: 0.718
- type: recall_at_5
value: 1.175
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.547
- type: map_at_10
value: 11.375
- type: map_at_100
value: 18.194
- type: map_at_1000
value: 19.749
- type: map_at_3
value: 5.825
- type: map_at_5
value: 8.581
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 51.32
- type: mrr_at_100
value: 51.747
- type: mrr_at_1000
value: 51.747
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 48.605
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 28.151
- type: ndcg_at_100
value: 39.438
- type: ndcg_at_1000
value: 50.769
- type: ndcg_at_3
value: 30.758999999999997
- type: ndcg_at_5
value: 30.366
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 25.714
- type: precision_at_100
value: 8.041
- type: precision_at_1000
value: 1.555
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 31.837
- type: recall_at_1
value: 2.547
- type: recall_at_10
value: 18.19
- type: recall_at_100
value: 49.538
- type: recall_at_1000
value: 83.86
- type: recall_at_3
value: 7.329
- type: recall_at_5
value: 11.532
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.4952
- type: ap
value: 14.793362635531409
- type: f1
value: 55.204635551516915
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.5365025466893
- type: f1
value: 61.81742556334845
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.05531070301185
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.51725576682364
- type: cos_sim_ap
value: 75.2292304265163
- type: cos_sim_f1
value: 69.54022988505749
- type: cos_sim_precision
value: 63.65629110039457
- type: cos_sim_recall
value: 76.62269129287598
- type: dot_accuracy
value: 86.51725576682364
- type: dot_ap
value: 75.22922386081054
- type: dot_f1
value: 69.54022988505749
- type: dot_precision
value: 63.65629110039457
- type: dot_recall
value: 76.62269129287598
- type: euclidean_accuracy
value: 86.51725576682364
- type: euclidean_ap
value: 75.22925730473472
- type: euclidean_f1
value: 69.54022988505749
- type: euclidean_precision
value: 63.65629110039457
- type: euclidean_recall
value: 76.62269129287598
- type: manhattan_accuracy
value: 86.52321630804077
- type: manhattan_ap
value: 75.20608115037336
- type: manhattan_f1
value: 69.60000000000001
- type: manhattan_precision
value: 64.37219730941705
- type: manhattan_recall
value: 75.75197889182058
- type: max_accuracy
value: 86.52321630804077
- type: max_ap
value: 75.22925730473472
- type: max_f1
value: 69.60000000000001
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.34877944657896
- type: cos_sim_ap
value: 86.71257569277373
- type: cos_sim_f1
value: 79.10386355986088
- type: cos_sim_precision
value: 76.91468470434214
- type: cos_sim_recall
value: 81.4213119802895
- type: dot_accuracy
value: 89.34877944657896
- type: dot_ap
value: 86.71257133133368
- type: dot_f1
value: 79.10386355986088
- type: dot_precision
value: 76.91468470434214
- type: dot_recall
value: 81.4213119802895
- type: euclidean_accuracy
value: 89.34877944657896
- type: euclidean_ap
value: 86.71257651501476
- type: euclidean_f1
value: 79.10386355986088
- type: euclidean_precision
value: 76.91468470434214
- type: euclidean_recall
value: 81.4213119802895
- type: manhattan_accuracy
value: 89.35848177901967
- type: manhattan_ap
value: 86.69330615469126
- type: manhattan_f1
value: 79.13867741453949
- type: manhattan_precision
value: 76.78881807647741
- type: manhattan_recall
value: 81.63689559593472
- type: max_accuracy
value: 89.35848177901967
- type: max_ap
value: 86.71257651501476
- type: max_f1
value: 79.13867741453949
---
# nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder
`nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
# Performance Benchmarks
| Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data |
| :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- |
| nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ |
| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ |
| text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ |
| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ |
**Exciting Update!**: `nomic-embed-text-v1` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal!
## Usage
**Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed.
For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`.
## Task instruction prefixes
### `search_document`
#### Purpose: embed texts as documents from a dataset
This prefix is used for embedding texts as documents, for example as documents for a RAG index.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
embeddings = model.encode(sentences)
print(embeddings)
```
### `search_query`
#### Purpose: embed texts as questions to answer
This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
```
### `clustering`
#### Purpose: embed texts to group them into clusters
This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['clustering: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
```
### `classification`
#### Purpose: embed texts to classify them
This prefix is used for embedding texts into vectors that will be used as features for a classification model
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['classification: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
```
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
```
### Transformers
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
```diff
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
```
### Transformers.js
```js
import { pipeline } from '@xenova/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', {
quantized: false, // Comment out this line to use the quantized version
});
// Compute sentence embeddings
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(embeddings);
```
## Nomic API
The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
Generating embeddings with the `nomic` Python client is as easy as
```python
from nomic import embed
output = embed.text(
texts=['Nomic Embedding API', '#keepAIOpen'],
model='nomic-embed-text-v1',
task_type='search_document'
)
print(output)
```
For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
## Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
# Join the Nomic Community
- Nomic: [https://nomic.ai](https://nomic.ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
# Citation
If you find the model, dataset, or training code useful, please cite our work
```bibtex
@misc{nussbaum2024nomic,
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
year={2024},
eprint={2402.01613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"en",
"arxiv:2205.13147",
"arxiv:2402.01613",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-10T06:32:35 | 2025-01-16T22:29:18 | 986,761 | 577 | ---
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
model-index:
- name: epoch_0_model
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.20895522388058
- type: ap
value: 38.57605549557802
- type: f1
value: 69.35586565857854
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.8144
- type: ap
value: 88.65222882032363
- type: f1
value: 91.80426301643274
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.162000000000006
- type: f1
value: 46.59329642263158
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.253
- type: map_at_10
value: 38.962
- type: map_at_100
value: 40.081
- type: map_at_1000
value: 40.089000000000006
- type: map_at_3
value: 33.499
- type: map_at_5
value: 36.351
- type: mrr_at_1
value: 24.609
- type: mrr_at_10
value: 39.099000000000004
- type: mrr_at_100
value: 40.211000000000006
- type: mrr_at_1000
value: 40.219
- type: mrr_at_3
value: 33.677
- type: mrr_at_5
value: 36.469
- type: ndcg_at_1
value: 24.253
- type: ndcg_at_10
value: 48.010999999999996
- type: ndcg_at_100
value: 52.756
- type: ndcg_at_1000
value: 52.964999999999996
- type: ndcg_at_3
value: 36.564
- type: ndcg_at_5
value: 41.711999999999996
- type: precision_at_1
value: 24.253
- type: precision_at_10
value: 7.738
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.149000000000001
- type: precision_at_5
value: 11.593
- type: recall_at_1
value: 24.253
- type: recall_at_10
value: 77.383
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 45.448
- type: recall_at_5
value: 57.965999999999994
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.69069567851087
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.35185490976283
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.71274951450321
- type: mrr
value: 76.06032625423207
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.73980520022269
- type: cos_sim_spearman
value: 84.24649792685918
- type: euclidean_pearson
value: 85.85197641158186
- type: euclidean_spearman
value: 84.24649792685918
- type: manhattan_pearson
value: 86.26809552711346
- type: manhattan_spearman
value: 84.56397504030865
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.25324675324674
- type: f1
value: 84.17872280892557
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.770253446400886
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.94307095497281
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.164
- type: map_at_10
value: 42.641
- type: map_at_100
value: 43.947
- type: map_at_1000
value: 44.074999999999996
- type: map_at_3
value: 39.592
- type: map_at_5
value: 41.204
- type: mrr_at_1
value: 39.628
- type: mrr_at_10
value: 48.625
- type: mrr_at_100
value: 49.368
- type: mrr_at_1000
value: 49.413000000000004
- type: mrr_at_3
value: 46.400000000000006
- type: mrr_at_5
value: 47.68
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 48.564
- type: ndcg_at_100
value: 53.507000000000005
- type: ndcg_at_1000
value: 55.635999999999996
- type: ndcg_at_3
value: 44.471
- type: ndcg_at_5
value: 46.137
- type: precision_at_1
value: 39.628
- type: precision_at_10
value: 8.856
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 21.268
- type: precision_at_5
value: 14.649000000000001
- type: recall_at_1
value: 32.164
- type: recall_at_10
value: 59.609
- type: recall_at_100
value: 80.521
- type: recall_at_1000
value: 94.245
- type: recall_at_3
value: 46.521
- type: recall_at_5
value: 52.083999999999996
- type: map_at_1
value: 31.526
- type: map_at_10
value: 41.581
- type: map_at_100
value: 42.815999999999995
- type: map_at_1000
value: 42.936
- type: map_at_3
value: 38.605000000000004
- type: map_at_5
value: 40.351
- type: mrr_at_1
value: 39.489999999999995
- type: mrr_at_10
value: 47.829
- type: mrr_at_100
value: 48.512
- type: mrr_at_1000
value: 48.552
- type: mrr_at_3
value: 45.754
- type: mrr_at_5
value: 46.986
- type: ndcg_at_1
value: 39.489999999999995
- type: ndcg_at_10
value: 47.269
- type: ndcg_at_100
value: 51.564
- type: ndcg_at_1000
value: 53.53099999999999
- type: ndcg_at_3
value: 43.301
- type: ndcg_at_5
value: 45.239000000000004
- type: precision_at_1
value: 39.489999999999995
- type: precision_at_10
value: 8.93
- type: precision_at_100
value: 1.415
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.892
- type: precision_at_5
value: 14.865999999999998
- type: recall_at_1
value: 31.526
- type: recall_at_10
value: 56.76
- type: recall_at_100
value: 75.029
- type: recall_at_1000
value: 87.491
- type: recall_at_3
value: 44.786
- type: recall_at_5
value: 50.254
- type: map_at_1
value: 40.987
- type: map_at_10
value: 52.827
- type: map_at_100
value: 53.751000000000005
- type: map_at_1000
value: 53.81
- type: map_at_3
value: 49.844
- type: map_at_5
value: 51.473
- type: mrr_at_1
value: 46.833999999999996
- type: mrr_at_10
value: 56.389
- type: mrr_at_100
value: 57.003
- type: mrr_at_1000
value: 57.034
- type: mrr_at_3
value: 54.17999999999999
- type: mrr_at_5
value: 55.486999999999995
- type: ndcg_at_1
value: 46.833999999999996
- type: ndcg_at_10
value: 58.372
- type: ndcg_at_100
value: 62.068
- type: ndcg_at_1000
value: 63.288
- type: ndcg_at_3
value: 53.400000000000006
- type: ndcg_at_5
value: 55.766000000000005
- type: precision_at_1
value: 46.833999999999996
- type: precision_at_10
value: 9.191
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 23.448
- type: precision_at_5
value: 15.862000000000002
- type: recall_at_1
value: 40.987
- type: recall_at_10
value: 71.146
- type: recall_at_100
value: 87.035
- type: recall_at_1000
value: 95.633
- type: recall_at_3
value: 58.025999999999996
- type: recall_at_5
value: 63.815999999999995
- type: map_at_1
value: 24.587
- type: map_at_10
value: 33.114
- type: map_at_100
value: 34.043
- type: map_at_1000
value: 34.123999999999995
- type: map_at_3
value: 30.45
- type: map_at_5
value: 31.813999999999997
- type: mrr_at_1
value: 26.554
- type: mrr_at_10
value: 35.148
- type: mrr_at_100
value: 35.926
- type: mrr_at_1000
value: 35.991
- type: mrr_at_3
value: 32.599000000000004
- type: mrr_at_5
value: 33.893
- type: ndcg_at_1
value: 26.554
- type: ndcg_at_10
value: 38.132
- type: ndcg_at_100
value: 42.78
- type: ndcg_at_1000
value: 44.919
- type: ndcg_at_3
value: 32.833
- type: ndcg_at_5
value: 35.168
- type: precision_at_1
value: 26.554
- type: precision_at_10
value: 5.921
- type: precision_at_100
value: 0.8659999999999999
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.861
- type: precision_at_5
value: 9.605
- type: recall_at_1
value: 24.587
- type: recall_at_10
value: 51.690000000000005
- type: recall_at_100
value: 73.428
- type: recall_at_1000
value: 89.551
- type: recall_at_3
value: 37.336999999999996
- type: recall_at_5
value: 43.047000000000004
- type: map_at_1
value: 16.715
- type: map_at_10
value: 24.251
- type: map_at_100
value: 25.326999999999998
- type: map_at_1000
value: 25.455
- type: map_at_3
value: 21.912000000000003
- type: map_at_5
value: 23.257
- type: mrr_at_1
value: 20.274
- type: mrr_at_10
value: 28.552
- type: mrr_at_100
value: 29.42
- type: mrr_at_1000
value: 29.497
- type: mrr_at_3
value: 26.14
- type: mrr_at_5
value: 27.502
- type: ndcg_at_1
value: 20.274
- type: ndcg_at_10
value: 29.088
- type: ndcg_at_100
value: 34.293
- type: ndcg_at_1000
value: 37.271
- type: ndcg_at_3
value: 24.708
- type: ndcg_at_5
value: 26.809
- type: precision_at_1
value: 20.274
- type: precision_at_10
value: 5.361
- type: precision_at_100
value: 0.915
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.733
- type: precision_at_5
value: 8.556999999999999
- type: recall_at_1
value: 16.715
- type: recall_at_10
value: 39.587
- type: recall_at_100
value: 62.336000000000006
- type: recall_at_1000
value: 83.453
- type: recall_at_3
value: 27.839999999999996
- type: recall_at_5
value: 32.952999999999996
- type: map_at_1
value: 28.793000000000003
- type: map_at_10
value: 38.582
- type: map_at_100
value: 39.881
- type: map_at_1000
value: 39.987
- type: map_at_3
value: 35.851
- type: map_at_5
value: 37.289
- type: mrr_at_1
value: 34.455999999999996
- type: mrr_at_10
value: 43.909
- type: mrr_at_100
value: 44.74
- type: mrr_at_1000
value: 44.786
- type: mrr_at_3
value: 41.659
- type: mrr_at_5
value: 43.010999999999996
- type: ndcg_at_1
value: 34.455999999999996
- type: ndcg_at_10
value: 44.266
- type: ndcg_at_100
value: 49.639
- type: ndcg_at_1000
value: 51.644
- type: ndcg_at_3
value: 39.865
- type: ndcg_at_5
value: 41.887
- type: precision_at_1
value: 34.455999999999996
- type: precision_at_10
value: 7.843999999999999
- type: precision_at_100
value: 1.243
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 18.831999999999997
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 28.793000000000003
- type: recall_at_10
value: 55.68300000000001
- type: recall_at_100
value: 77.99000000000001
- type: recall_at_1000
value: 91.183
- type: recall_at_3
value: 43.293
- type: recall_at_5
value: 48.618
- type: map_at_1
value: 25.907000000000004
- type: map_at_10
value: 35.519
- type: map_at_100
value: 36.806
- type: map_at_1000
value: 36.912
- type: map_at_3
value: 32.748
- type: map_at_5
value: 34.232
- type: mrr_at_1
value: 31.621
- type: mrr_at_10
value: 40.687
- type: mrr_at_100
value: 41.583
- type: mrr_at_1000
value: 41.638999999999996
- type: mrr_at_3
value: 38.527
- type: mrr_at_5
value: 39.612
- type: ndcg_at_1
value: 31.621
- type: ndcg_at_10
value: 41.003
- type: ndcg_at_100
value: 46.617999999999995
- type: ndcg_at_1000
value: 48.82
- type: ndcg_at_3
value: 36.542
- type: ndcg_at_5
value: 38.368
- type: precision_at_1
value: 31.621
- type: precision_at_10
value: 7.396999999999999
- type: precision_at_100
value: 1.191
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 17.39
- type: precision_at_5
value: 12.1
- type: recall_at_1
value: 25.907000000000004
- type: recall_at_10
value: 52.115
- type: recall_at_100
value: 76.238
- type: recall_at_1000
value: 91.218
- type: recall_at_3
value: 39.417
- type: recall_at_5
value: 44.435
- type: map_at_1
value: 25.732166666666668
- type: map_at_10
value: 34.51616666666667
- type: map_at_100
value: 35.67241666666666
- type: map_at_1000
value: 35.78675
- type: map_at_3
value: 31.953416666666662
- type: map_at_5
value: 33.333
- type: mrr_at_1
value: 30.300166666666673
- type: mrr_at_10
value: 38.6255
- type: mrr_at_100
value: 39.46183333333334
- type: mrr_at_1000
value: 39.519999999999996
- type: mrr_at_3
value: 36.41299999999999
- type: mrr_at_5
value: 37.6365
- type: ndcg_at_1
value: 30.300166666666673
- type: ndcg_at_10
value: 39.61466666666667
- type: ndcg_at_100
value: 44.60808333333334
- type: ndcg_at_1000
value: 46.91708333333334
- type: ndcg_at_3
value: 35.26558333333333
- type: ndcg_at_5
value: 37.220000000000006
- type: precision_at_1
value: 30.300166666666673
- type: precision_at_10
value: 6.837416666666667
- type: precision_at_100
value: 1.10425
- type: precision_at_1000
value: 0.14875
- type: precision_at_3
value: 16.13716666666667
- type: precision_at_5
value: 11.2815
- type: recall_at_1
value: 25.732166666666668
- type: recall_at_10
value: 50.578916666666665
- type: recall_at_100
value: 72.42183333333334
- type: recall_at_1000
value: 88.48766666666667
- type: recall_at_3
value: 38.41325
- type: recall_at_5
value: 43.515750000000004
- type: map_at_1
value: 23.951
- type: map_at_10
value: 30.974
- type: map_at_100
value: 31.804
- type: map_at_1000
value: 31.900000000000002
- type: map_at_3
value: 28.762
- type: map_at_5
value: 29.94
- type: mrr_at_1
value: 26.534000000000002
- type: mrr_at_10
value: 33.553
- type: mrr_at_100
value: 34.297
- type: mrr_at_1000
value: 34.36
- type: mrr_at_3
value: 31.391000000000002
- type: mrr_at_5
value: 32.525999999999996
- type: ndcg_at_1
value: 26.534000000000002
- type: ndcg_at_10
value: 35.112
- type: ndcg_at_100
value: 39.28
- type: ndcg_at_1000
value: 41.723
- type: ndcg_at_3
value: 30.902
- type: ndcg_at_5
value: 32.759
- type: precision_at_1
value: 26.534000000000002
- type: precision_at_10
value: 5.445
- type: precision_at_100
value: 0.819
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.951
- type: recall_at_10
value: 45.24
- type: recall_at_100
value: 64.12299999999999
- type: recall_at_1000
value: 82.28999999999999
- type: recall_at_3
value: 33.806000000000004
- type: recall_at_5
value: 38.277
- type: map_at_1
value: 16.829
- type: map_at_10
value: 23.684
- type: map_at_100
value: 24.683
- type: map_at_1000
value: 24.81
- type: map_at_3
value: 21.554000000000002
- type: map_at_5
value: 22.768
- type: mrr_at_1
value: 20.096
- type: mrr_at_10
value: 27.230999999999998
- type: mrr_at_100
value: 28.083999999999996
- type: mrr_at_1000
value: 28.166000000000004
- type: mrr_at_3
value: 25.212
- type: mrr_at_5
value: 26.32
- type: ndcg_at_1
value: 20.096
- type: ndcg_at_10
value: 27.989000000000004
- type: ndcg_at_100
value: 32.847
- type: ndcg_at_1000
value: 35.896
- type: ndcg_at_3
value: 24.116
- type: ndcg_at_5
value: 25.964
- type: precision_at_1
value: 20.096
- type: precision_at_10
value: 5
- type: precision_at_100
value: 0.8750000000000001
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 11.207
- type: precision_at_5
value: 8.08
- type: recall_at_1
value: 16.829
- type: recall_at_10
value: 37.407000000000004
- type: recall_at_100
value: 59.101000000000006
- type: recall_at_1000
value: 81.024
- type: recall_at_3
value: 26.739
- type: recall_at_5
value: 31.524
- type: map_at_1
value: 24.138
- type: map_at_10
value: 32.275999999999996
- type: map_at_100
value: 33.416000000000004
- type: map_at_1000
value: 33.527
- type: map_at_3
value: 29.854000000000003
- type: map_at_5
value: 31.096
- type: mrr_at_1
value: 28.450999999999997
- type: mrr_at_10
value: 36.214
- type: mrr_at_100
value: 37.134
- type: mrr_at_1000
value: 37.198
- type: mrr_at_3
value: 34.001999999999995
- type: mrr_at_5
value: 35.187000000000005
- type: ndcg_at_1
value: 28.450999999999997
- type: ndcg_at_10
value: 37.166
- type: ndcg_at_100
value: 42.454
- type: ndcg_at_1000
value: 44.976
- type: ndcg_at_3
value: 32.796
- type: ndcg_at_5
value: 34.631
- type: precision_at_1
value: 28.450999999999997
- type: precision_at_10
value: 6.241
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 14.801
- type: precision_at_5
value: 10.280000000000001
- type: recall_at_1
value: 24.138
- type: recall_at_10
value: 48.111
- type: recall_at_100
value: 71.245
- type: recall_at_1000
value: 88.986
- type: recall_at_3
value: 36.119
- type: recall_at_5
value: 40.846
- type: map_at_1
value: 23.244
- type: map_at_10
value: 31.227
- type: map_at_100
value: 33.007
- type: map_at_1000
value: 33.223
- type: map_at_3
value: 28.924
- type: map_at_5
value: 30.017
- type: mrr_at_1
value: 27.668
- type: mrr_at_10
value: 35.524
- type: mrr_at_100
value: 36.699
- type: mrr_at_1000
value: 36.759
- type: mrr_at_3
value: 33.366
- type: mrr_at_5
value: 34.552
- type: ndcg_at_1
value: 27.668
- type: ndcg_at_10
value: 36.381
- type: ndcg_at_100
value: 43.062
- type: ndcg_at_1000
value: 45.656
- type: ndcg_at_3
value: 32.501999999999995
- type: ndcg_at_5
value: 34.105999999999995
- type: precision_at_1
value: 27.668
- type: precision_at_10
value: 6.798
- type: precision_at_100
value: 1.492
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 15.152
- type: precision_at_5
value: 10.791
- type: recall_at_1
value: 23.244
- type: recall_at_10
value: 45.979
- type: recall_at_100
value: 74.822
- type: recall_at_1000
value: 91.078
- type: recall_at_3
value: 34.925
- type: recall_at_5
value: 39.126
- type: map_at_1
value: 19.945
- type: map_at_10
value: 27.517999999999997
- type: map_at_100
value: 28.588
- type: map_at_1000
value: 28.682000000000002
- type: map_at_3
value: 25.345000000000002
- type: map_at_5
value: 26.555
- type: mrr_at_1
value: 21.996
- type: mrr_at_10
value: 29.845
- type: mrr_at_100
value: 30.775999999999996
- type: mrr_at_1000
value: 30.845
- type: mrr_at_3
value: 27.726
- type: mrr_at_5
value: 28.882
- type: ndcg_at_1
value: 21.996
- type: ndcg_at_10
value: 32.034
- type: ndcg_at_100
value: 37.185
- type: ndcg_at_1000
value: 39.645
- type: ndcg_at_3
value: 27.750999999999998
- type: ndcg_at_5
value: 29.805999999999997
- type: precision_at_1
value: 21.996
- type: precision_at_10
value: 5.065
- type: precision_at_100
value: 0.819
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.076
- type: precision_at_5
value: 8.392
- type: recall_at_1
value: 19.945
- type: recall_at_10
value: 43.62
- type: recall_at_100
value: 67.194
- type: recall_at_1000
value: 85.7
- type: recall_at_3
value: 32.15
- type: recall_at_5
value: 37.208999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.279
- type: map_at_10
value: 31.052999999999997
- type: map_at_100
value: 33.125
- type: map_at_1000
value: 33.306000000000004
- type: map_at_3
value: 26.208
- type: map_at_5
value: 28.857
- type: mrr_at_1
value: 42.671
- type: mrr_at_10
value: 54.557
- type: mrr_at_100
value: 55.142
- type: mrr_at_1000
value: 55.169000000000004
- type: mrr_at_3
value: 51.488
- type: mrr_at_5
value: 53.439
- type: ndcg_at_1
value: 42.671
- type: ndcg_at_10
value: 41.276
- type: ndcg_at_100
value: 48.376000000000005
- type: ndcg_at_1000
value: 51.318
- type: ndcg_at_3
value: 35.068
- type: ndcg_at_5
value: 37.242
- type: precision_at_1
value: 42.671
- type: precision_at_10
value: 12.638
- type: precision_at_100
value: 2.045
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 26.08
- type: precision_at_5
value: 19.805
- type: recall_at_1
value: 18.279
- type: recall_at_10
value: 46.946
- type: recall_at_100
value: 70.97200000000001
- type: recall_at_1000
value: 87.107
- type: recall_at_3
value: 31.147999999999996
- type: recall_at_5
value: 38.099
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.573
- type: map_at_10
value: 19.747
- type: map_at_100
value: 28.205000000000002
- type: map_at_1000
value: 29.831000000000003
- type: map_at_3
value: 14.109
- type: map_at_5
value: 16.448999999999998
- type: mrr_at_1
value: 71
- type: mrr_at_10
value: 77.68599999999999
- type: mrr_at_100
value: 77.995
- type: mrr_at_1000
value: 78.00200000000001
- type: mrr_at_3
value: 76.292
- type: mrr_at_5
value: 77.029
- type: ndcg_at_1
value: 59.12500000000001
- type: ndcg_at_10
value: 43.9
- type: ndcg_at_100
value: 47.863
- type: ndcg_at_1000
value: 54.848
- type: ndcg_at_3
value: 49.803999999999995
- type: ndcg_at_5
value: 46.317
- type: precision_at_1
value: 71
- type: precision_at_10
value: 34.4
- type: precision_at_100
value: 11.063
- type: precision_at_1000
value: 1.989
- type: precision_at_3
value: 52.333
- type: precision_at_5
value: 43.7
- type: recall_at_1
value: 8.573
- type: recall_at_10
value: 25.615
- type: recall_at_100
value: 53.385000000000005
- type: recall_at_1000
value: 75.46000000000001
- type: recall_at_3
value: 15.429
- type: recall_at_5
value: 19.357
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.989999999999995
- type: f1
value: 42.776314451497555
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.13499999999999
- type: map_at_10
value: 82.825
- type: map_at_100
value: 83.096
- type: map_at_1000
value: 83.111
- type: map_at_3
value: 81.748
- type: map_at_5
value: 82.446
- type: mrr_at_1
value: 79.553
- type: mrr_at_10
value: 86.654
- type: mrr_at_100
value: 86.774
- type: mrr_at_1000
value: 86.778
- type: mrr_at_3
value: 85.981
- type: mrr_at_5
value: 86.462
- type: ndcg_at_1
value: 79.553
- type: ndcg_at_10
value: 86.345
- type: ndcg_at_100
value: 87.32
- type: ndcg_at_1000
value: 87.58200000000001
- type: ndcg_at_3
value: 84.719
- type: ndcg_at_5
value: 85.677
- type: precision_at_1
value: 79.553
- type: precision_at_10
value: 10.402000000000001
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.413
- type: precision_at_5
value: 20.138
- type: recall_at_1
value: 74.13499999999999
- type: recall_at_10
value: 93.215
- type: recall_at_100
value: 97.083
- type: recall_at_1000
value: 98.732
- type: recall_at_3
value: 88.79
- type: recall_at_5
value: 91.259
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.298000000000002
- type: map_at_10
value: 29.901
- type: map_at_100
value: 31.528
- type: map_at_1000
value: 31.713
- type: map_at_3
value: 25.740000000000002
- type: map_at_5
value: 28.227999999999998
- type: mrr_at_1
value: 36.728
- type: mrr_at_10
value: 45.401
- type: mrr_at_100
value: 46.27
- type: mrr_at_1000
value: 46.315
- type: mrr_at_3
value: 42.978
- type: mrr_at_5
value: 44.29
- type: ndcg_at_1
value: 36.728
- type: ndcg_at_10
value: 37.456
- type: ndcg_at_100
value: 43.832
- type: ndcg_at_1000
value: 47
- type: ndcg_at_3
value: 33.694
- type: ndcg_at_5
value: 35.085
- type: precision_at_1
value: 36.728
- type: precision_at_10
value: 10.386
- type: precision_at_100
value: 1.701
- type: precision_at_1000
value: 0.22599999999999998
- type: precision_at_3
value: 22.479
- type: precision_at_5
value: 16.605
- type: recall_at_1
value: 18.298000000000002
- type: recall_at_10
value: 44.369
- type: recall_at_100
value: 68.098
- type: recall_at_1000
value: 87.21900000000001
- type: recall_at_3
value: 30.215999999999998
- type: recall_at_5
value: 36.861
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.568
- type: map_at_10
value: 65.061
- type: map_at_100
value: 65.896
- type: map_at_1000
value: 65.95100000000001
- type: map_at_3
value: 61.831
- type: map_at_5
value: 63.849000000000004
- type: mrr_at_1
value: 79.136
- type: mrr_at_10
value: 84.58200000000001
- type: mrr_at_100
value: 84.765
- type: mrr_at_1000
value: 84.772
- type: mrr_at_3
value: 83.684
- type: mrr_at_5
value: 84.223
- type: ndcg_at_1
value: 79.136
- type: ndcg_at_10
value: 72.622
- type: ndcg_at_100
value: 75.539
- type: ndcg_at_1000
value: 76.613
- type: ndcg_at_3
value: 68.065
- type: ndcg_at_5
value: 70.58
- type: precision_at_1
value: 79.136
- type: precision_at_10
value: 15.215
- type: precision_at_100
value: 1.7500000000000002
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 44.011
- type: precision_at_5
value: 28.388999999999996
- type: recall_at_1
value: 39.568
- type: recall_at_10
value: 76.077
- type: recall_at_100
value: 87.481
- type: recall_at_1000
value: 94.56400000000001
- type: recall_at_3
value: 66.01599999999999
- type: recall_at_5
value: 70.97200000000001
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.312
- type: ap
value: 80.36296867333715
- type: f1
value: 85.26613311552218
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.363999999999997
- type: map_at_10
value: 35.711999999999996
- type: map_at_100
value: 36.876999999999995
- type: map_at_1000
value: 36.923
- type: map_at_3
value: 32.034
- type: map_at_5
value: 34.159
- type: mrr_at_1
value: 24.04
- type: mrr_at_10
value: 36.345
- type: mrr_at_100
value: 37.441
- type: mrr_at_1000
value: 37.480000000000004
- type: mrr_at_3
value: 32.713
- type: mrr_at_5
value: 34.824
- type: ndcg_at_1
value: 24.026
- type: ndcg_at_10
value: 42.531
- type: ndcg_at_100
value: 48.081
- type: ndcg_at_1000
value: 49.213
- type: ndcg_at_3
value: 35.044
- type: ndcg_at_5
value: 38.834
- type: precision_at_1
value: 24.026
- type: precision_at_10
value: 6.622999999999999
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.909
- type: precision_at_5
value: 10.871
- type: recall_at_1
value: 23.363999999999997
- type: recall_at_10
value: 63.426
- type: recall_at_100
value: 88.96300000000001
- type: recall_at_1000
value: 97.637
- type: recall_at_3
value: 43.095
- type: recall_at_5
value: 52.178000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.0095759233926
- type: f1
value: 92.78387794667408
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.0296397628819
- type: f1
value: 58.45699589820874
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.45662407531944
- type: f1
value: 71.42364781421813
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.07800941492937
- type: f1
value: 77.22799045640845
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.531234379250606
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.941490381193802
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.3115090856725
- type: mrr
value: 31.290667638675757
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.465
- type: map_at_10
value: 13.03
- type: map_at_100
value: 16.057
- type: map_at_1000
value: 17.49
- type: map_at_3
value: 9.553
- type: map_at_5
value: 11.204
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 53.269
- type: mrr_at_100
value: 53.72
- type: mrr_at_1000
value: 53.761
- type: mrr_at_3
value: 50.929
- type: mrr_at_5
value: 52.461
- type: ndcg_at_1
value: 42.26
- type: ndcg_at_10
value: 34.673
- type: ndcg_at_100
value: 30.759999999999998
- type: ndcg_at_1000
value: 39.728
- type: ndcg_at_3
value: 40.349000000000004
- type: ndcg_at_5
value: 37.915
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.789
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.596000000000004
- type: precision_at_5
value: 33.251
- type: recall_at_1
value: 5.465
- type: recall_at_10
value: 17.148
- type: recall_at_100
value: 29.768
- type: recall_at_1000
value: 62.239
- type: recall_at_3
value: 10.577
- type: recall_at_5
value: 13.315
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.008
- type: map_at_10
value: 52.467
- type: map_at_100
value: 53.342999999999996
- type: map_at_1000
value: 53.366
- type: map_at_3
value: 48.412
- type: map_at_5
value: 50.875
- type: mrr_at_1
value: 41.541
- type: mrr_at_10
value: 54.967
- type: mrr_at_100
value: 55.611
- type: mrr_at_1000
value: 55.627
- type: mrr_at_3
value: 51.824999999999996
- type: mrr_at_5
value: 53.763000000000005
- type: ndcg_at_1
value: 41.541
- type: ndcg_at_10
value: 59.724999999999994
- type: ndcg_at_100
value: 63.38700000000001
- type: ndcg_at_1000
value: 63.883
- type: ndcg_at_3
value: 52.331
- type: ndcg_at_5
value: 56.327000000000005
- type: precision_at_1
value: 41.541
- type: precision_at_10
value: 9.447
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.262
- type: precision_at_5
value: 16.314999999999998
- type: recall_at_1
value: 37.008
- type: recall_at_10
value: 79.145
- type: recall_at_100
value: 94.986
- type: recall_at_1000
value: 98.607
- type: recall_at_3
value: 60.277
- type: recall_at_5
value: 69.407
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.402
- type: map_at_10
value: 84.181
- type: map_at_100
value: 84.796
- type: map_at_1000
value: 84.81400000000001
- type: map_at_3
value: 81.209
- type: map_at_5
value: 83.085
- type: mrr_at_1
value: 81.02000000000001
- type: mrr_at_10
value: 87.263
- type: mrr_at_100
value: 87.36
- type: mrr_at_1000
value: 87.36
- type: mrr_at_3
value: 86.235
- type: mrr_at_5
value: 86.945
- type: ndcg_at_1
value: 81.01
- type: ndcg_at_10
value: 87.99900000000001
- type: ndcg_at_100
value: 89.217
- type: ndcg_at_1000
value: 89.33
- type: ndcg_at_3
value: 85.053
- type: ndcg_at_5
value: 86.703
- type: precision_at_1
value: 81.01
- type: precision_at_10
value: 13.336
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 24.44
- type: recall_at_1
value: 70.402
- type: recall_at_10
value: 95.214
- type: recall_at_100
value: 99.438
- type: recall_at_1000
value: 99.928
- type: recall_at_3
value: 86.75699999999999
- type: recall_at_5
value: 91.44099999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.51721502758904
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.054808572333016
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.578
- type: map_at_10
value: 11.036999999999999
- type: map_at_100
value: 12.879999999999999
- type: map_at_1000
value: 13.150999999999998
- type: map_at_3
value: 8.133
- type: map_at_5
value: 9.559
- type: mrr_at_1
value: 22.6
- type: mrr_at_10
value: 32.68
- type: mrr_at_100
value: 33.789
- type: mrr_at_1000
value: 33.854
- type: mrr_at_3
value: 29.7
- type: mrr_at_5
value: 31.480000000000004
- type: ndcg_at_1
value: 22.6
- type: ndcg_at_10
value: 18.616
- type: ndcg_at_100
value: 25.883
- type: ndcg_at_1000
value: 30.944
- type: ndcg_at_3
value: 18.136
- type: ndcg_at_5
value: 15.625
- type: precision_at_1
value: 22.6
- type: precision_at_10
value: 9.48
- type: precision_at_100
value: 1.991
- type: precision_at_1000
value: 0.321
- type: precision_at_3
value: 16.8
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.578
- type: recall_at_10
value: 19.213
- type: recall_at_100
value: 40.397
- type: recall_at_1000
value: 65.2
- type: recall_at_3
value: 10.208
- type: recall_at_5
value: 13.718
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.44288351714071
- type: cos_sim_spearman
value: 79.37995604564952
- type: euclidean_pearson
value: 81.1078874670718
- type: euclidean_spearman
value: 79.37995905980499
- type: manhattan_pearson
value: 81.03697527288986
- type: manhattan_spearman
value: 79.33490235296236
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.95557650436523
- type: cos_sim_spearman
value: 78.5190672399868
- type: euclidean_pearson
value: 81.58064025904707
- type: euclidean_spearman
value: 78.5190672399868
- type: manhattan_pearson
value: 81.52857930619889
- type: manhattan_spearman
value: 78.50421361308034
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.79128416228737
- type: cos_sim_spearman
value: 86.05402451477147
- type: euclidean_pearson
value: 85.46280267054289
- type: euclidean_spearman
value: 86.05402451477147
- type: manhattan_pearson
value: 85.46278563858236
- type: manhattan_spearman
value: 86.08079590861004
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.20623089568763
- type: cos_sim_spearman
value: 81.53786907061009
- type: euclidean_pearson
value: 82.82272250091494
- type: euclidean_spearman
value: 81.53786907061009
- type: manhattan_pearson
value: 82.78850494027013
- type: manhattan_spearman
value: 81.5135618083407
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.46366618397936
- type: cos_sim_spearman
value: 86.96566013336908
- type: euclidean_pearson
value: 86.62651697548931
- type: euclidean_spearman
value: 86.96565526364454
- type: manhattan_pearson
value: 86.58812160258009
- type: manhattan_spearman
value: 86.9336484321288
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.51858358641559
- type: cos_sim_spearman
value: 84.7652527954999
- type: euclidean_pearson
value: 84.23914783766861
- type: euclidean_spearman
value: 84.7652527954999
- type: manhattan_pearson
value: 84.22749648503171
- type: manhattan_spearman
value: 84.74527996746386
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.28026563313065
- type: cos_sim_spearman
value: 87.46928143824915
- type: euclidean_pearson
value: 88.30558762000372
- type: euclidean_spearman
value: 87.46928143824915
- type: manhattan_pearson
value: 88.10513330809331
- type: manhattan_spearman
value: 87.21069787834173
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.376497134587375
- type: cos_sim_spearman
value: 65.0159550112516
- type: euclidean_pearson
value: 65.64572120879598
- type: euclidean_spearman
value: 65.0159550112516
- type: manhattan_pearson
value: 65.88143604989976
- type: manhattan_spearman
value: 65.17547297222434
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.22876368947644
- type: cos_sim_spearman
value: 85.46935577445318
- type: euclidean_pearson
value: 85.32830231392005
- type: euclidean_spearman
value: 85.46935577445318
- type: manhattan_pearson
value: 85.30353211758495
- type: manhattan_spearman
value: 85.42821085956945
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.60986667767133
- type: mrr
value: 94.29432314236236
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.528
- type: map_at_10
value: 65.187
- type: map_at_100
value: 65.62599999999999
- type: map_at_1000
value: 65.657
- type: map_at_3
value: 62.352
- type: map_at_5
value: 64.025
- type: mrr_at_1
value: 57.333
- type: mrr_at_10
value: 66.577
- type: mrr_at_100
value: 66.88
- type: mrr_at_1000
value: 66.908
- type: mrr_at_3
value: 64.556
- type: mrr_at_5
value: 65.739
- type: ndcg_at_1
value: 57.333
- type: ndcg_at_10
value: 70.275
- type: ndcg_at_100
value: 72.136
- type: ndcg_at_1000
value: 72.963
- type: ndcg_at_3
value: 65.414
- type: ndcg_at_5
value: 67.831
- type: precision_at_1
value: 57.333
- type: precision_at_10
value: 9.5
- type: precision_at_100
value: 1.057
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.2
- type: recall_at_1
value: 54.528
- type: recall_at_10
value: 84.356
- type: recall_at_100
value: 92.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.283
- type: recall_at_5
value: 77.14999999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74158415841585
- type: cos_sim_ap
value: 92.90048959850317
- type: cos_sim_f1
value: 86.35650810245687
- type: cos_sim_precision
value: 90.4709748083242
- type: cos_sim_recall
value: 82.6
- type: dot_accuracy
value: 99.74158415841585
- type: dot_ap
value: 92.90048959850317
- type: dot_f1
value: 86.35650810245687
- type: dot_precision
value: 90.4709748083242
- type: dot_recall
value: 82.6
- type: euclidean_accuracy
value: 99.74158415841585
- type: euclidean_ap
value: 92.90048959850317
- type: euclidean_f1
value: 86.35650810245687
- type: euclidean_precision
value: 90.4709748083242
- type: euclidean_recall
value: 82.6
- type: manhattan_accuracy
value: 99.74158415841585
- type: manhattan_ap
value: 92.87344692947894
- type: manhattan_f1
value: 86.38497652582159
- type: manhattan_precision
value: 90.29443838604145
- type: manhattan_recall
value: 82.8
- type: max_accuracy
value: 99.74158415841585
- type: max_ap
value: 92.90048959850317
- type: max_f1
value: 86.38497652582159
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.191648770424216
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.02944668730218
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.466386167525265
- type: mrr
value: 51.19071492233257
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.198022505886435
- type: cos_sim_spearman
value: 30.40170257939193
- type: dot_pearson
value: 30.198015316402614
- type: dot_spearman
value: 30.40170257939193
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.242
- type: map_at_10
value: 2.17
- type: map_at_100
value: 12.221
- type: map_at_1000
value: 28.63
- type: map_at_3
value: 0.728
- type: map_at_5
value: 1.185
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 97
- type: mrr_at_100
value: 97
- type: mrr_at_1000
value: 97
- type: mrr_at_3
value: 97
- type: mrr_at_5
value: 97
- type: ndcg_at_1
value: 89
- type: ndcg_at_10
value: 82.30499999999999
- type: ndcg_at_100
value: 61.839999999999996
- type: ndcg_at_1000
value: 53.381
- type: ndcg_at_3
value: 88.877
- type: ndcg_at_5
value: 86.05199999999999
- type: precision_at_1
value: 94
- type: precision_at_10
value: 87
- type: precision_at_100
value: 63.38
- type: precision_at_1000
value: 23.498
- type: precision_at_3
value: 94
- type: precision_at_5
value: 92
- type: recall_at_1
value: 0.242
- type: recall_at_10
value: 2.302
- type: recall_at_100
value: 14.979000000000001
- type: recall_at_1000
value: 49.638
- type: recall_at_3
value: 0.753
- type: recall_at_5
value: 1.226
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.006
- type: map_at_10
value: 11.805
- type: map_at_100
value: 18.146
- type: map_at_1000
value: 19.788
- type: map_at_3
value: 5.914
- type: map_at_5
value: 8.801
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 56.36600000000001
- type: mrr_at_100
value: 56.721999999999994
- type: mrr_at_1000
value: 56.721999999999994
- type: mrr_at_3
value: 52.041000000000004
- type: mrr_at_5
value: 54.796
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_10
value: 29.863
- type: ndcg_at_100
value: 39.571
- type: ndcg_at_1000
value: 51.385999999999996
- type: ndcg_at_3
value: 32.578
- type: ndcg_at_5
value: 32.351
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 26.531
- type: precision_at_100
value: 7.796
- type: precision_at_1000
value: 1.555
- type: precision_at_3
value: 32.653
- type: precision_at_5
value: 33.061
- type: recall_at_1
value: 3.006
- type: recall_at_10
value: 18.738
- type: recall_at_100
value: 48.058
- type: recall_at_1000
value: 83.41300000000001
- type: recall_at_3
value: 7.166
- type: recall_at_5
value: 12.102
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.4178
- type: ap
value: 14.648781342150446
- type: f1
value: 55.07299194946378
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.919637804187886
- type: f1
value: 61.24122013967399
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.207896583685695
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.23114978840078
- type: cos_sim_ap
value: 74.26624727825818
- type: cos_sim_f1
value: 68.72377190817083
- type: cos_sim_precision
value: 64.56400742115028
- type: cos_sim_recall
value: 73.45646437994723
- type: dot_accuracy
value: 86.23114978840078
- type: dot_ap
value: 74.26624032659652
- type: dot_f1
value: 68.72377190817083
- type: dot_precision
value: 64.56400742115028
- type: dot_recall
value: 73.45646437994723
- type: euclidean_accuracy
value: 86.23114978840078
- type: euclidean_ap
value: 74.26624714480556
- type: euclidean_f1
value: 68.72377190817083
- type: euclidean_precision
value: 64.56400742115028
- type: euclidean_recall
value: 73.45646437994723
- type: manhattan_accuracy
value: 86.16558383501221
- type: manhattan_ap
value: 74.2091943976357
- type: manhattan_f1
value: 68.64221520524654
- type: manhattan_precision
value: 63.59135913591359
- type: manhattan_recall
value: 74.5646437994723
- type: max_accuracy
value: 86.23114978840078
- type: max_ap
value: 74.26624727825818
- type: max_f1
value: 68.72377190817083
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.3681841114604
- type: cos_sim_ap
value: 86.65166387498546
- type: cos_sim_f1
value: 79.02581944698774
- type: cos_sim_precision
value: 75.35796605434099
- type: cos_sim_recall
value: 83.06898675700647
- type: dot_accuracy
value: 89.3681841114604
- type: dot_ap
value: 86.65166019802056
- type: dot_f1
value: 79.02581944698774
- type: dot_precision
value: 75.35796605434099
- type: dot_recall
value: 83.06898675700647
- type: euclidean_accuracy
value: 89.3681841114604
- type: euclidean_ap
value: 86.65166462876266
- type: euclidean_f1
value: 79.02581944698774
- type: euclidean_precision
value: 75.35796605434099
- type: euclidean_recall
value: 83.06898675700647
- type: manhattan_accuracy
value: 89.36624364497226
- type: manhattan_ap
value: 86.65076471274106
- type: manhattan_f1
value: 79.07408783532733
- type: manhattan_precision
value: 76.41102972856527
- type: manhattan_recall
value: 81.92947336002464
- type: max_accuracy
value: 89.3681841114604
- type: max_ap
value: 86.65166462876266
- type: max_f1
value: 79.07408783532733
---
# nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning
**Exciting Update!**: `nomic-embed-text-v1.5` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) is aligned to the embedding space of `nomic-embed-text-v1.5`, meaning any text embedding is multimodal!
## Usage
**Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed.
For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`.
## Task instruction prefixes
### `search_document`
#### Purpose: embed texts as documents from a dataset
This prefix is used for embedding texts as documents, for example as documents for a RAG index.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
embeddings = model.encode(sentences)
print(embeddings)
```
### `search_query`
#### Purpose: embed texts as questions to answer
This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)
```
### `clustering`
#### Purpose: embed texts to group them into clusters
This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['clustering: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
```
### `classification`
#### Purpose: embed texts to classify them
This prefix is used for embedding texts into vectors that will be used as features for a classification model
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['classification: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)
```
### Sentence Transformers
```python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
matryoshka_dim = 512
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences, convert_to_tensor=True)
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```
### Transformers
```diff
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, safe_serialization=True)
model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+ matryoshka_dim = 512
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+ embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
+ embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
```diff
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
```
### Transformers.js
```js
import { pipeline, layer_norm } from '@huggingface/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1.5');
// Define sentences
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
// Compute sentence embeddings
let embeddings = await extractor(texts, { pooling: 'mean' });
console.log(embeddings); // Tensor of shape [2, 768]
const matryoshka_dim = 512;
embeddings = layer_norm(embeddings, [embeddings.dims[1]])
.slice(null, [0, matryoshka_dim])
.normalize(2, -1);
console.log(embeddings.tolist());
```
## Nomic API
The easiest way to use Nomic Embed is through the Nomic Embedding API.
Generating embeddings with the `nomic` Python client is as easy as
```python
from nomic import embed
output = embed.text(
texts=['Nomic Embedding API', '#keepAIOpen'],
model='nomic-embed-text-v1.5',
task_type='search_document',
dimensionality=256,
)
print(output)
```
For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
## Infinity
Usage with [Infinity](https://github.com/michaelfeil/infinity).
```bash
docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \
michaelf34/infinity:0.0.70 \
v2 --model-id nomic-ai/nomic-embed-text-v1.5 --revision "main" --dtype float16 --batch-size 8 --engine torch --port 7997 --no-bettertransformer
```
## Adjusting Dimensionality
`nomic-embed-text-v1.5` is an improvement upon [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1) that utilizes [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which gives developers the flexibility to trade off the embedding size for a negligible reduction in performance.
| Name | SeqLen | Dimension | MTEB |
| :-------------------------------:| :----- | :-------- | :------: |
| nomic-embed-text-v1 | 8192 | 768 | **62.39** |
| nomic-embed-text-v1.5 | 8192 | 768 | 62.28 |
| nomic-embed-text-v1.5 | 8192 | 512 | 61.96 |
| nomic-embed-text-v1.5 | 8192 | 256 | 61.04 |
| nomic-embed-text-v1.5 | 8192 | 128 | 59.34 |
| nomic-embed-text-v1.5 | 8192 | 64 | 56.10 |
## Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-matryoshka).
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
# Join the Nomic Community
- Nomic: [https://nomic.ai](https://nomic.ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
# Citation
If you find the model, dataset, or training code useful, please cite our work
```bibtex
@misc{nussbaum2024nomic,
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
year={2024},
eprint={2402.01613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-reranker-base | BAAI | text-classification | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"mteb",
"text-embeddings-inference",
"text-classification",
"en",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"region:us"
] | 2023-09-11T12:30:04 | 2024-06-24T14:10:03 | 979,604 | 176 | ---
language:
- en
- zh
library_name: sentence-transformers
license: mit
pipeline_tag: text-classification
tags:
- mteb
- text-embeddings-inference
model-index:
- name: bge-reranker-base
results:
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 81.27206722525007
- type: mrr
value: 84.14238095238095
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 84.10369934291236
- type: mrr
value: 86.79376984126984
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 35.4600511272538
- type: mrr
value: 34.60238095238095
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 67.27728847727172
- type: mrr
value: 77.1315192743764
---
**We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).**
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Embedding Model**: [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [llm rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 3/18/2024: Release new [rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation.
- 3/18/2024: Release [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data.
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503)
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results.
Hard negatives also are needed to fine-tune reranker. Refer to this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) for the fine-tuning for reranker
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
#### Usage reranker with the ONNX files
```python
from optimum.onnxruntime import ORTModelForSequenceClassification # type: ignore
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
model_ort = ORTModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base', file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
# Tokenize sentences
encoded_input = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
scores_ort = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float()
# Compute token embeddings
with torch.inference_mode():
scores = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float()
# scores and scores_ort are identical
```
#### Usage reranker with infinity
Its also possible to deploy the onnx/torch files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
query='what is a panda?'
docs = ['The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear', "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-reranker-base", device="cpu", engine="torch" # or engine="optimum" for onnx
))
async def main():
async with engine:
ranking, usage = await engine.rerank(query=query, docs=docs)
print(list(zip(ranking, docs)))
asyncio.run(main())
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. | [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
BAAI/bge-reranker-large | BAAI | feature-extraction | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"text-classification",
"mteb",
"feature-extraction",
"en",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T07:39:18 | 2024-05-11T13:39:02 | 917,242 | 383 | ---
language:
- en
- zh
license: mit
pipeline_tag: feature-extraction
tags:
- mteb
model-index:
- name: bge-reranker-base
results:
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 81.27206722525007
- type: mrr
value: 84.14238095238095
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 84.10369934291236
- type: mrr
value: 86.79376984126984
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 35.4600511272538
- type: mrr
value: 34.60238095238095
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 67.27728847727172
- type: mrr
value: 77.1315192743764
---
**We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).**
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Embedding Model**: [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [llm rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 3/18/2024: Release new [rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation.
- 3/18/2024: Release [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/visual), equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data.
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503)
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results.
Hard negatives also are needed to fine-tune reranker. Refer to this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) for the fine-tuning for reranker
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
#### Usage reranker with the ONNX files
```python
from optimum.onnxruntime import ORTModelForSequenceClassification # type: ignore
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base')
model_ort = ORTModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base', file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
# Tokenize sentences
encoded_input = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt')
scores_ort = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float()
# Compute token embeddings
with torch.inference_mode():
scores = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float()
# scores and scores_ort are identical
```
#### Usage reranker with infinity
Its also possible to deploy the onnx/torch files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
query='what is a panda?'
docs = ['The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear', "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-reranker-base", device="cpu", engine="torch" # or engine="optimum" for onnx
))
async def main():
async with engine:
ranking, usage = await engine.rerank(query=query, docs=docs)
print(list(zip(ranking, docs)))
asyncio.run(main())
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. | [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
jinaai/jina-embeddings-v2-small-en | jinaai | feature-extraction | [
"sentence-transformers",
"pytorch",
"coreml",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"custom_code",
"en",
"dataset:jinaai/negation-dataset",
"arxiv:2108.12409",
"arxiv:2310.19923",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"region:us"
] | 2023-09-27T20:17:27 | 2025-01-06T16:26:03 | 842,417 | 133 | ---
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
inference: false
model-index:
- name: jina-embedding-s-en-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.35820895522387
- type: ap
value: 33.99931933598115
- type: f1
value: 65.3853685535555
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 82.90140000000001
- type: ap
value: 78.01434597815617
- type: f1
value: 82.83357802722676
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.88999999999999
- type: f1
value: 39.209432767163456
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.257
- type: map_at_10
value: 37.946000000000005
- type: map_at_100
value: 39.17
- type: map_at_1000
value: 39.181
- type: map_at_3
value: 32.99
- type: map_at_5
value: 35.467999999999996
- type: mrr_at_1
value: 23.541999999999998
- type: mrr_at_10
value: 38.057
- type: mrr_at_100
value: 39.289
- type: mrr_at_1000
value: 39.299
- type: mrr_at_3
value: 33.096
- type: mrr_at_5
value: 35.628
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 46.729
- type: ndcg_at_100
value: 51.900999999999996
- type: ndcg_at_1000
value: 52.16
- type: ndcg_at_3
value: 36.323
- type: ndcg_at_5
value: 40.766999999999996
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 7.510999999999999
- type: precision_at_100
value: 0.976
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 15.339
- type: precision_at_5
value: 11.350999999999999
- type: recall_at_1
value: 23.257
- type: recall_at_10
value: 75.107
- type: recall_at_100
value: 97.58200000000001
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 46.017
- type: recall_at_5
value: 56.757000000000005
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.02420878391967
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.16136856000258
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.61809790513646
- type: mrr
value: 73.07215406938397
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.0167350090749
- type: cos_sim_spearman
value: 80.51569002630401
- type: euclidean_pearson
value: 81.46820525099726
- type: euclidean_spearman
value: 80.51569002630401
- type: manhattan_pearson
value: 81.35596555056757
- type: manhattan_spearman
value: 80.12592210903303
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.25
- type: f1
value: 77.34950913540605
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.57238596005698
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.066444306196683
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.891000000000002
- type: map_at_10
value: 42.772
- type: map_at_100
value: 44.108999999999995
- type: map_at_1000
value: 44.236
- type: map_at_3
value: 39.289
- type: map_at_5
value: 41.113
- type: mrr_at_1
value: 39.342
- type: mrr_at_10
value: 48.852000000000004
- type: mrr_at_100
value: 49.534
- type: mrr_at_1000
value: 49.582
- type: mrr_at_3
value: 46.089999999999996
- type: mrr_at_5
value: 47.685
- type: ndcg_at_1
value: 39.342
- type: ndcg_at_10
value: 48.988
- type: ndcg_at_100
value: 53.854
- type: ndcg_at_1000
value: 55.955
- type: ndcg_at_3
value: 43.877
- type: ndcg_at_5
value: 46.027
- type: precision_at_1
value: 39.342
- type: precision_at_10
value: 9.285
- type: precision_at_100
value: 1.488
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 20.696
- type: precision_at_5
value: 14.878
- type: recall_at_1
value: 31.891000000000002
- type: recall_at_10
value: 60.608
- type: recall_at_100
value: 81.025
- type: recall_at_1000
value: 94.883
- type: recall_at_3
value: 45.694
- type: recall_at_5
value: 51.684
- type: map_at_1
value: 28.778
- type: map_at_10
value: 37.632
- type: map_at_100
value: 38.800000000000004
- type: map_at_1000
value: 38.934999999999995
- type: map_at_3
value: 35.293
- type: map_at_5
value: 36.547000000000004
- type: mrr_at_1
value: 35.35
- type: mrr_at_10
value: 42.936
- type: mrr_at_100
value: 43.69
- type: mrr_at_1000
value: 43.739
- type: mrr_at_3
value: 41.062
- type: mrr_at_5
value: 42.097
- type: ndcg_at_1
value: 35.35
- type: ndcg_at_10
value: 42.528
- type: ndcg_at_100
value: 46.983000000000004
- type: ndcg_at_1000
value: 49.187999999999995
- type: ndcg_at_3
value: 39.271
- type: ndcg_at_5
value: 40.654
- type: precision_at_1
value: 35.35
- type: precision_at_10
value: 7.828
- type: precision_at_100
value: 1.3010000000000002
- type: precision_at_1000
value: 0.17700000000000002
- type: precision_at_3
value: 18.96
- type: precision_at_5
value: 13.120999999999999
- type: recall_at_1
value: 28.778
- type: recall_at_10
value: 50.775000000000006
- type: recall_at_100
value: 69.66799999999999
- type: recall_at_1000
value: 83.638
- type: recall_at_3
value: 40.757
- type: recall_at_5
value: 44.86
- type: map_at_1
value: 37.584
- type: map_at_10
value: 49.69
- type: map_at_100
value: 50.639
- type: map_at_1000
value: 50.702999999999996
- type: map_at_3
value: 46.61
- type: map_at_5
value: 48.486000000000004
- type: mrr_at_1
value: 43.009
- type: mrr_at_10
value: 52.949999999999996
- type: mrr_at_100
value: 53.618
- type: mrr_at_1000
value: 53.65299999999999
- type: mrr_at_3
value: 50.605999999999995
- type: mrr_at_5
value: 52.095
- type: ndcg_at_1
value: 43.009
- type: ndcg_at_10
value: 55.278000000000006
- type: ndcg_at_100
value: 59.134
- type: ndcg_at_1000
value: 60.528999999999996
- type: ndcg_at_3
value: 50.184
- type: ndcg_at_5
value: 52.919000000000004
- type: precision_at_1
value: 43.009
- type: precision_at_10
value: 8.821
- type: precision_at_100
value: 1.161
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 22.424
- type: precision_at_5
value: 15.436
- type: recall_at_1
value: 37.584
- type: recall_at_10
value: 68.514
- type: recall_at_100
value: 85.099
- type: recall_at_1000
value: 95.123
- type: recall_at_3
value: 55.007
- type: recall_at_5
value: 61.714999999999996
- type: map_at_1
value: 24.7
- type: map_at_10
value: 32.804
- type: map_at_100
value: 33.738
- type: map_at_1000
value: 33.825
- type: map_at_3
value: 30.639
- type: map_at_5
value: 31.781
- type: mrr_at_1
value: 26.328000000000003
- type: mrr_at_10
value: 34.679
- type: mrr_at_100
value: 35.510000000000005
- type: mrr_at_1000
value: 35.577999999999996
- type: mrr_at_3
value: 32.58
- type: mrr_at_5
value: 33.687
- type: ndcg_at_1
value: 26.328000000000003
- type: ndcg_at_10
value: 37.313
- type: ndcg_at_100
value: 42.004000000000005
- type: ndcg_at_1000
value: 44.232
- type: ndcg_at_3
value: 33.076
- type: ndcg_at_5
value: 34.966
- type: precision_at_1
value: 26.328000000000003
- type: precision_at_10
value: 5.627
- type: precision_at_100
value: 0.8410000000000001
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 14.011000000000001
- type: precision_at_5
value: 9.582
- type: recall_at_1
value: 24.7
- type: recall_at_10
value: 49.324
- type: recall_at_100
value: 71.018
- type: recall_at_1000
value: 87.905
- type: recall_at_3
value: 37.7
- type: recall_at_5
value: 42.281
- type: map_at_1
value: 14.350999999999999
- type: map_at_10
value: 21.745
- type: map_at_100
value: 22.731
- type: map_at_1000
value: 22.852
- type: map_at_3
value: 19.245
- type: map_at_5
value: 20.788
- type: mrr_at_1
value: 18.159
- type: mrr_at_10
value: 25.833000000000002
- type: mrr_at_100
value: 26.728
- type: mrr_at_1000
value: 26.802
- type: mrr_at_3
value: 23.383000000000003
- type: mrr_at_5
value: 24.887999999999998
- type: ndcg_at_1
value: 18.159
- type: ndcg_at_10
value: 26.518000000000004
- type: ndcg_at_100
value: 31.473000000000003
- type: ndcg_at_1000
value: 34.576
- type: ndcg_at_3
value: 21.907
- type: ndcg_at_5
value: 24.39
- type: precision_at_1
value: 18.159
- type: precision_at_10
value: 4.938
- type: precision_at_100
value: 0.853
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 10.655000000000001
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 14.350999999999999
- type: recall_at_10
value: 37.284
- type: recall_at_100
value: 59.11300000000001
- type: recall_at_1000
value: 81.634
- type: recall_at_3
value: 24.753
- type: recall_at_5
value: 30.979
- type: map_at_1
value: 26.978
- type: map_at_10
value: 36.276
- type: map_at_100
value: 37.547000000000004
- type: map_at_1000
value: 37.678
- type: map_at_3
value: 33.674
- type: map_at_5
value: 35.119
- type: mrr_at_1
value: 32.916000000000004
- type: mrr_at_10
value: 41.798
- type: mrr_at_100
value: 42.72
- type: mrr_at_1000
value: 42.778
- type: mrr_at_3
value: 39.493
- type: mrr_at_5
value: 40.927
- type: ndcg_at_1
value: 32.916000000000004
- type: ndcg_at_10
value: 41.81
- type: ndcg_at_100
value: 47.284
- type: ndcg_at_1000
value: 49.702
- type: ndcg_at_3
value: 37.486999999999995
- type: ndcg_at_5
value: 39.597
- type: precision_at_1
value: 32.916000000000004
- type: precision_at_10
value: 7.411
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.581
- type: precision_at_5
value: 12.397
- type: recall_at_1
value: 26.978
- type: recall_at_10
value: 52.869
- type: recall_at_100
value: 75.78399999999999
- type: recall_at_1000
value: 91.545
- type: recall_at_3
value: 40.717
- type: recall_at_5
value: 46.168
- type: map_at_1
value: 24.641
- type: map_at_10
value: 32.916000000000004
- type: map_at_100
value: 34.165
- type: map_at_1000
value: 34.286
- type: map_at_3
value: 30.335
- type: map_at_5
value: 31.569000000000003
- type: mrr_at_1
value: 30.593999999999998
- type: mrr_at_10
value: 38.448
- type: mrr_at_100
value: 39.299
- type: mrr_at_1000
value: 39.362
- type: mrr_at_3
value: 36.244
- type: mrr_at_5
value: 37.232
- type: ndcg_at_1
value: 30.593999999999998
- type: ndcg_at_10
value: 38.2
- type: ndcg_at_100
value: 43.742
- type: ndcg_at_1000
value: 46.217000000000006
- type: ndcg_at_3
value: 33.925
- type: ndcg_at_5
value: 35.394
- type: precision_at_1
value: 30.593999999999998
- type: precision_at_10
value: 6.895
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 16.096
- type: precision_at_5
value: 11.05
- type: recall_at_1
value: 24.641
- type: recall_at_10
value: 48.588
- type: recall_at_100
value: 72.841
- type: recall_at_1000
value: 89.535
- type: recall_at_3
value: 36.087
- type: recall_at_5
value: 40.346
- type: map_at_1
value: 24.79425
- type: map_at_10
value: 33.12033333333333
- type: map_at_100
value: 34.221333333333334
- type: map_at_1000
value: 34.3435
- type: map_at_3
value: 30.636583333333338
- type: map_at_5
value: 31.974083333333326
- type: mrr_at_1
value: 29.242416666666664
- type: mrr_at_10
value: 37.11675
- type: mrr_at_100
value: 37.93783333333334
- type: mrr_at_1000
value: 38.003083333333336
- type: mrr_at_3
value: 34.904666666666664
- type: mrr_at_5
value: 36.12916666666667
- type: ndcg_at_1
value: 29.242416666666664
- type: ndcg_at_10
value: 38.03416666666667
- type: ndcg_at_100
value: 42.86674999999999
- type: ndcg_at_1000
value: 45.34550000000001
- type: ndcg_at_3
value: 33.76466666666666
- type: ndcg_at_5
value: 35.668666666666674
- type: precision_at_1
value: 29.242416666666664
- type: precision_at_10
value: 6.589833333333334
- type: precision_at_100
value: 1.0693333333333332
- type: precision_at_1000
value: 0.14641666666666667
- type: precision_at_3
value: 15.430749999999998
- type: precision_at_5
value: 10.833833333333333
- type: recall_at_1
value: 24.79425
- type: recall_at_10
value: 48.582916666666655
- type: recall_at_100
value: 69.88499999999999
- type: recall_at_1000
value: 87.211
- type: recall_at_3
value: 36.625499999999995
- type: recall_at_5
value: 41.553999999999995
- type: map_at_1
value: 22.767
- type: map_at_10
value: 28.450999999999997
- type: map_at_100
value: 29.332
- type: map_at_1000
value: 29.426000000000002
- type: map_at_3
value: 26.379
- type: map_at_5
value: 27.584999999999997
- type: mrr_at_1
value: 25.46
- type: mrr_at_10
value: 30.974
- type: mrr_at_100
value: 31.784000000000002
- type: mrr_at_1000
value: 31.857999999999997
- type: mrr_at_3
value: 28.962
- type: mrr_at_5
value: 30.066
- type: ndcg_at_1
value: 25.46
- type: ndcg_at_10
value: 32.041
- type: ndcg_at_100
value: 36.522
- type: ndcg_at_1000
value: 39.101
- type: ndcg_at_3
value: 28.152
- type: ndcg_at_5
value: 30.03
- type: precision_at_1
value: 25.46
- type: precision_at_10
value: 4.893
- type: precision_at_100
value: 0.77
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 11.605
- type: precision_at_5
value: 8.19
- type: recall_at_1
value: 22.767
- type: recall_at_10
value: 40.71
- type: recall_at_100
value: 61.334999999999994
- type: recall_at_1000
value: 80.567
- type: recall_at_3
value: 30.198000000000004
- type: recall_at_5
value: 34.803
- type: map_at_1
value: 16.722
- type: map_at_10
value: 22.794
- type: map_at_100
value: 23.7
- type: map_at_1000
value: 23.822
- type: map_at_3
value: 20.781
- type: map_at_5
value: 22.024
- type: mrr_at_1
value: 20.061999999999998
- type: mrr_at_10
value: 26.346999999999998
- type: mrr_at_100
value: 27.153
- type: mrr_at_1000
value: 27.233
- type: mrr_at_3
value: 24.375
- type: mrr_at_5
value: 25.593
- type: ndcg_at_1
value: 20.061999999999998
- type: ndcg_at_10
value: 26.785999999999998
- type: ndcg_at_100
value: 31.319999999999997
- type: ndcg_at_1000
value: 34.346
- type: ndcg_at_3
value: 23.219
- type: ndcg_at_5
value: 25.107000000000003
- type: precision_at_1
value: 20.061999999999998
- type: precision_at_10
value: 4.78
- type: precision_at_100
value: 0.83
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 10.874
- type: precision_at_5
value: 7.956
- type: recall_at_1
value: 16.722
- type: recall_at_10
value: 35.204
- type: recall_at_100
value: 55.797
- type: recall_at_1000
value: 77.689
- type: recall_at_3
value: 25.245
- type: recall_at_5
value: 30.115
- type: map_at_1
value: 24.842
- type: map_at_10
value: 32.917
- type: map_at_100
value: 33.961000000000006
- type: map_at_1000
value: 34.069
- type: map_at_3
value: 30.595
- type: map_at_5
value: 31.837
- type: mrr_at_1
value: 29.011
- type: mrr_at_10
value: 36.977
- type: mrr_at_100
value: 37.814
- type: mrr_at_1000
value: 37.885999999999996
- type: mrr_at_3
value: 34.966
- type: mrr_at_5
value: 36.043
- type: ndcg_at_1
value: 29.011
- type: ndcg_at_10
value: 37.735
- type: ndcg_at_100
value: 42.683
- type: ndcg_at_1000
value: 45.198
- type: ndcg_at_3
value: 33.650000000000006
- type: ndcg_at_5
value: 35.386
- type: precision_at_1
value: 29.011
- type: precision_at_10
value: 6.259
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 15.329999999999998
- type: precision_at_5
value: 10.541
- type: recall_at_1
value: 24.842
- type: recall_at_10
value: 48.304
- type: recall_at_100
value: 70.04899999999999
- type: recall_at_1000
value: 87.82600000000001
- type: recall_at_3
value: 36.922
- type: recall_at_5
value: 41.449999999999996
- type: map_at_1
value: 24.252000000000002
- type: map_at_10
value: 32.293
- type: map_at_100
value: 33.816
- type: map_at_1000
value: 34.053
- type: map_at_3
value: 29.781999999999996
- type: map_at_5
value: 31.008000000000003
- type: mrr_at_1
value: 29.051
- type: mrr_at_10
value: 36.722
- type: mrr_at_100
value: 37.663000000000004
- type: mrr_at_1000
value: 37.734
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 35.609
- type: ndcg_at_1
value: 29.051
- type: ndcg_at_10
value: 37.775999999999996
- type: ndcg_at_100
value: 43.221
- type: ndcg_at_1000
value: 46.116
- type: ndcg_at_3
value: 33.403
- type: ndcg_at_5
value: 35.118
- type: precision_at_1
value: 29.051
- type: precision_at_10
value: 7.332
- type: precision_at_100
value: 1.49
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 15.415000000000001
- type: precision_at_5
value: 11.107
- type: recall_at_1
value: 24.252000000000002
- type: recall_at_10
value: 47.861
- type: recall_at_100
value: 72.21600000000001
- type: recall_at_1000
value: 90.886
- type: recall_at_3
value: 35.533
- type: recall_at_5
value: 39.959
- type: map_at_1
value: 20.025000000000002
- type: map_at_10
value: 27.154
- type: map_at_100
value: 28.118
- type: map_at_1000
value: 28.237000000000002
- type: map_at_3
value: 25.017
- type: map_at_5
value: 25.832
- type: mrr_at_1
value: 21.627
- type: mrr_at_10
value: 28.884999999999998
- type: mrr_at_100
value: 29.741
- type: mrr_at_1000
value: 29.831999999999997
- type: mrr_at_3
value: 26.741
- type: mrr_at_5
value: 27.628000000000004
- type: ndcg_at_1
value: 21.627
- type: ndcg_at_10
value: 31.436999999999998
- type: ndcg_at_100
value: 36.181000000000004
- type: ndcg_at_1000
value: 38.986
- type: ndcg_at_3
value: 27.025
- type: ndcg_at_5
value: 28.436
- type: precision_at_1
value: 21.627
- type: precision_at_10
value: 5.009
- type: precision_at_100
value: 0.7929999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 11.522
- type: precision_at_5
value: 7.763000000000001
- type: recall_at_1
value: 20.025000000000002
- type: recall_at_10
value: 42.954
- type: recall_at_100
value: 64.67500000000001
- type: recall_at_1000
value: 85.301
- type: recall_at_3
value: 30.892999999999997
- type: recall_at_5
value: 34.288000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.079
- type: map_at_10
value: 16.930999999999997
- type: map_at_100
value: 18.398999999999997
- type: map_at_1000
value: 18.561
- type: map_at_3
value: 14.294
- type: map_at_5
value: 15.579
- type: mrr_at_1
value: 22.606
- type: mrr_at_10
value: 32.513
- type: mrr_at_100
value: 33.463
- type: mrr_at_1000
value: 33.513999999999996
- type: mrr_at_3
value: 29.479
- type: mrr_at_5
value: 31.3
- type: ndcg_at_1
value: 22.606
- type: ndcg_at_10
value: 24.053
- type: ndcg_at_100
value: 30.258000000000003
- type: ndcg_at_1000
value: 33.516
- type: ndcg_at_3
value: 19.721
- type: ndcg_at_5
value: 21.144
- type: precision_at_1
value: 22.606
- type: precision_at_10
value: 7.55
- type: precision_at_100
value: 1.399
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 14.701
- type: precision_at_5
value: 11.192
- type: recall_at_1
value: 10.079
- type: recall_at_10
value: 28.970000000000002
- type: recall_at_100
value: 50.805
- type: recall_at_1000
value: 69.378
- type: recall_at_3
value: 18.199
- type: recall_at_5
value: 22.442
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.794
- type: map_at_10
value: 15.165999999999999
- type: map_at_100
value: 20.508000000000003
- type: map_at_1000
value: 21.809
- type: map_at_3
value: 11.568000000000001
- type: map_at_5
value: 13.059000000000001
- type: mrr_at_1
value: 56.49999999999999
- type: mrr_at_10
value: 65.90899999999999
- type: mrr_at_100
value: 66.352
- type: mrr_at_1000
value: 66.369
- type: mrr_at_3
value: 64.0
- type: mrr_at_5
value: 65.10000000000001
- type: ndcg_at_1
value: 44.25
- type: ndcg_at_10
value: 32.649
- type: ndcg_at_100
value: 36.668
- type: ndcg_at_1000
value: 43.918
- type: ndcg_at_3
value: 37.096000000000004
- type: ndcg_at_5
value: 34.048
- type: precision_at_1
value: 56.49999999999999
- type: precision_at_10
value: 25.45
- type: precision_at_100
value: 8.055
- type: precision_at_1000
value: 1.7489999999999999
- type: precision_at_3
value: 41.0
- type: precision_at_5
value: 32.85
- type: recall_at_1
value: 7.794
- type: recall_at_10
value: 20.101
- type: recall_at_100
value: 42.448
- type: recall_at_1000
value: 65.88000000000001
- type: recall_at_3
value: 12.753
- type: recall_at_5
value: 15.307
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.01
- type: f1
value: 38.659680951114964
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.713
- type: map_at_10
value: 61.79
- type: map_at_100
value: 62.28
- type: map_at_1000
value: 62.297000000000004
- type: map_at_3
value: 59.361
- type: map_at_5
value: 60.92100000000001
- type: mrr_at_1
value: 53.405
- type: mrr_at_10
value: 65.79899999999999
- type: mrr_at_100
value: 66.219
- type: mrr_at_1000
value: 66.227
- type: mrr_at_3
value: 63.431000000000004
- type: mrr_at_5
value: 64.98
- type: ndcg_at_1
value: 53.405
- type: ndcg_at_10
value: 68.01899999999999
- type: ndcg_at_100
value: 70.197
- type: ndcg_at_1000
value: 70.571
- type: ndcg_at_3
value: 63.352
- type: ndcg_at_5
value: 66.018
- type: precision_at_1
value: 53.405
- type: precision_at_10
value: 9.119
- type: precision_at_100
value: 1.03
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 25.602999999999998
- type: precision_at_5
value: 16.835
- type: recall_at_1
value: 49.713
- type: recall_at_10
value: 83.306
- type: recall_at_100
value: 92.92
- type: recall_at_1000
value: 95.577
- type: recall_at_3
value: 70.798
- type: recall_at_5
value: 77.254
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.310000000000002
- type: map_at_10
value: 26.204
- type: map_at_100
value: 27.932000000000002
- type: map_at_1000
value: 28.121000000000002
- type: map_at_3
value: 22.481
- type: map_at_5
value: 24.678
- type: mrr_at_1
value: 29.784
- type: mrr_at_10
value: 39.582
- type: mrr_at_100
value: 40.52
- type: mrr_at_1000
value: 40.568
- type: mrr_at_3
value: 37.114000000000004
- type: mrr_at_5
value: 38.596000000000004
- type: ndcg_at_1
value: 29.784
- type: ndcg_at_10
value: 33.432
- type: ndcg_at_100
value: 40.281
- type: ndcg_at_1000
value: 43.653999999999996
- type: ndcg_at_3
value: 29.612
- type: ndcg_at_5
value: 31.223
- type: precision_at_1
value: 29.784
- type: precision_at_10
value: 9.645
- type: precision_at_100
value: 1.645
- type: precision_at_1000
value: 0.22499999999999998
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 15.401000000000002
- type: recall_at_1
value: 15.310000000000002
- type: recall_at_10
value: 40.499
- type: recall_at_100
value: 66.643
- type: recall_at_1000
value: 87.059
- type: recall_at_3
value: 27.492
- type: recall_at_5
value: 33.748
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.599000000000004
- type: map_at_10
value: 47.347
- type: map_at_100
value: 48.191
- type: map_at_1000
value: 48.263
- type: map_at_3
value: 44.698
- type: map_at_5
value: 46.278999999999996
- type: mrr_at_1
value: 67.19800000000001
- type: mrr_at_10
value: 74.054
- type: mrr_at_100
value: 74.376
- type: mrr_at_1000
value: 74.392
- type: mrr_at_3
value: 72.849
- type: mrr_at_5
value: 73.643
- type: ndcg_at_1
value: 67.19800000000001
- type: ndcg_at_10
value: 56.482
- type: ndcg_at_100
value: 59.694
- type: ndcg_at_1000
value: 61.204
- type: ndcg_at_3
value: 52.43299999999999
- type: ndcg_at_5
value: 54.608000000000004
- type: precision_at_1
value: 67.19800000000001
- type: precision_at_10
value: 11.613999999999999
- type: precision_at_100
value: 1.415
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 32.726
- type: precision_at_5
value: 21.349999999999998
- type: recall_at_1
value: 33.599000000000004
- type: recall_at_10
value: 58.069
- type: recall_at_100
value: 70.736
- type: recall_at_1000
value: 80.804
- type: recall_at_3
value: 49.088
- type: recall_at_5
value: 53.376000000000005
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 73.64359999999999
- type: ap
value: 67.54685976014599
- type: f1
value: 73.55148707559482
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.502
- type: map_at_10
value: 30.816
- type: map_at_100
value: 32.007999999999996
- type: map_at_1000
value: 32.067
- type: map_at_3
value: 27.215
- type: map_at_5
value: 29.304000000000002
- type: mrr_at_1
value: 20.072000000000003
- type: mrr_at_10
value: 31.406
- type: mrr_at_100
value: 32.549
- type: mrr_at_1000
value: 32.602
- type: mrr_at_3
value: 27.839000000000002
- type: mrr_at_5
value: 29.926000000000002
- type: ndcg_at_1
value: 20.086000000000002
- type: ndcg_at_10
value: 37.282
- type: ndcg_at_100
value: 43.206
- type: ndcg_at_1000
value: 44.690000000000005
- type: ndcg_at_3
value: 29.932
- type: ndcg_at_5
value: 33.668
- type: precision_at_1
value: 20.086000000000002
- type: precision_at_10
value: 5.961
- type: precision_at_100
value: 0.898
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 12.856000000000002
- type: precision_at_5
value: 9.596
- type: recall_at_1
value: 19.502
- type: recall_at_10
value: 57.182
- type: recall_at_100
value: 84.952
- type: recall_at_1000
value: 96.34700000000001
- type: recall_at_3
value: 37.193
- type: recall_at_5
value: 46.157
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.96488828089375
- type: f1
value: 93.32119260543482
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.4965800273598
- type: f1
value: 49.34896217536082
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.60928043039678
- type: f1
value: 64.34244712074538
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.75453934095493
- type: f1
value: 68.39224867489249
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.862573504920082
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.511123551196803
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.99145104942086
- type: mrr
value: 32.03606480418627
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.015
- type: map_at_10
value: 11.054
- type: map_at_100
value: 13.773
- type: map_at_1000
value: 15.082999999999998
- type: map_at_3
value: 8.253
- type: map_at_5
value: 9.508999999999999
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.44499999999999
- type: mrr_at_100
value: 51.080000000000005
- type: mrr_at_1000
value: 51.129999999999995
- type: mrr_at_3
value: 48.555
- type: mrr_at_5
value: 49.84
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 30.403000000000002
- type: ndcg_at_100
value: 28.216
- type: ndcg_at_1000
value: 37.021
- type: ndcg_at_3
value: 35.53
- type: ndcg_at_5
value: 33.202999999999996
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.353
- type: precision_at_100
value: 7.266
- type: precision_at_1000
value: 2.011
- type: precision_at_3
value: 32.921
- type: precision_at_5
value: 28.297
- type: recall_at_1
value: 5.015
- type: recall_at_10
value: 14.393
- type: recall_at_100
value: 28.893
- type: recall_at_1000
value: 60.18
- type: recall_at_3
value: 9.184000000000001
- type: recall_at_5
value: 11.39
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.524
- type: map_at_10
value: 44.182
- type: map_at_100
value: 45.228
- type: map_at_1000
value: 45.265
- type: map_at_3
value: 39.978
- type: map_at_5
value: 42.482
- type: mrr_at_1
value: 33.256
- type: mrr_at_10
value: 46.661
- type: mrr_at_100
value: 47.47
- type: mrr_at_1000
value: 47.496
- type: mrr_at_3
value: 43.187999999999995
- type: mrr_at_5
value: 45.330999999999996
- type: ndcg_at_1
value: 33.227000000000004
- type: ndcg_at_10
value: 51.589
- type: ndcg_at_100
value: 56.043
- type: ndcg_at_1000
value: 56.937000000000005
- type: ndcg_at_3
value: 43.751
- type: ndcg_at_5
value: 47.937000000000005
- type: precision_at_1
value: 33.227000000000004
- type: precision_at_10
value: 8.556999999999999
- type: precision_at_100
value: 1.103
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.921
- type: precision_at_5
value: 14.396999999999998
- type: recall_at_1
value: 29.524
- type: recall_at_10
value: 71.615
- type: recall_at_100
value: 91.056
- type: recall_at_1000
value: 97.72800000000001
- type: recall_at_3
value: 51.451
- type: recall_at_5
value: 61.119
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.596
- type: map_at_10
value: 83.281
- type: map_at_100
value: 83.952
- type: map_at_1000
value: 83.97200000000001
- type: map_at_3
value: 80.315
- type: map_at_5
value: 82.223
- type: mrr_at_1
value: 80.17
- type: mrr_at_10
value: 86.522
- type: mrr_at_100
value: 86.644
- type: mrr_at_1000
value: 86.64500000000001
- type: mrr_at_3
value: 85.438
- type: mrr_at_5
value: 86.21799999999999
- type: ndcg_at_1
value: 80.19
- type: ndcg_at_10
value: 87.19
- type: ndcg_at_100
value: 88.567
- type: ndcg_at_1000
value: 88.70400000000001
- type: ndcg_at_3
value: 84.17999999999999
- type: ndcg_at_5
value: 85.931
- type: precision_at_1
value: 80.19
- type: precision_at_10
value: 13.209000000000001
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.717
- type: precision_at_5
value: 24.248
- type: recall_at_1
value: 69.596
- type: recall_at_10
value: 94.533
- type: recall_at_100
value: 99.322
- type: recall_at_1000
value: 99.965
- type: recall_at_3
value: 85.911
- type: recall_at_5
value: 90.809
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 49.27650627571912
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 57.08550946534183
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.568
- type: map_at_10
value: 10.862
- type: map_at_100
value: 12.757
- type: map_at_1000
value: 13.031
- type: map_at_3
value: 7.960000000000001
- type: map_at_5
value: 9.337
- type: mrr_at_1
value: 22.5
- type: mrr_at_10
value: 32.6
- type: mrr_at_100
value: 33.603
- type: mrr_at_1000
value: 33.672000000000004
- type: mrr_at_3
value: 29.299999999999997
- type: mrr_at_5
value: 31.25
- type: ndcg_at_1
value: 22.5
- type: ndcg_at_10
value: 18.605
- type: ndcg_at_100
value: 26.029999999999998
- type: ndcg_at_1000
value: 31.256
- type: ndcg_at_3
value: 17.873
- type: ndcg_at_5
value: 15.511
- type: precision_at_1
value: 22.5
- type: precision_at_10
value: 9.58
- type: precision_at_100
value: 2.033
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 16.633
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.568
- type: recall_at_10
value: 19.402
- type: recall_at_100
value: 41.277
- type: recall_at_1000
value: 66.963
- type: recall_at_3
value: 10.112
- type: recall_at_5
value: 13.712
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.31992291680787
- type: cos_sim_spearman
value: 76.7212346922664
- type: euclidean_pearson
value: 80.42189271706478
- type: euclidean_spearman
value: 76.7212342532493
- type: manhattan_pearson
value: 80.33171093031578
- type: manhattan_spearman
value: 76.63192883074694
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.16654278886763
- type: cos_sim_spearman
value: 73.66390263429565
- type: euclidean_pearson
value: 79.7485360086639
- type: euclidean_spearman
value: 73.66389870373436
- type: manhattan_pearson
value: 79.73652237443706
- type: manhattan_spearman
value: 73.65296117151647
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.40389689929246
- type: cos_sim_spearman
value: 83.29727595993955
- type: euclidean_pearson
value: 82.23970587854079
- type: euclidean_spearman
value: 83.29727595993955
- type: manhattan_pearson
value: 82.18823600831897
- type: manhattan_spearman
value: 83.20746192209594
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.73505246913413
- type: cos_sim_spearman
value: 79.1686548248754
- type: euclidean_pearson
value: 80.48889135993412
- type: euclidean_spearman
value: 79.16864112930354
- type: manhattan_pearson
value: 80.40720651057302
- type: manhattan_spearman
value: 79.0640155089286
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.3953512879065
- type: cos_sim_spearman
value: 87.29947322714338
- type: euclidean_pearson
value: 86.59759438529645
- type: euclidean_spearman
value: 87.29947511092824
- type: manhattan_pearson
value: 86.52097806169155
- type: manhattan_spearman
value: 87.22987242146534
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.48565753792056
- type: cos_sim_spearman
value: 83.6049720319893
- type: euclidean_pearson
value: 82.56452023172913
- type: euclidean_spearman
value: 83.60490168191697
- type: manhattan_pearson
value: 82.58079941137872
- type: manhattan_spearman
value: 83.60975807374051
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.18239976618212
- type: cos_sim_spearman
value: 88.23061724730616
- type: euclidean_pearson
value: 87.78482472776658
- type: euclidean_spearman
value: 88.23061724730616
- type: manhattan_pearson
value: 87.75059641730239
- type: manhattan_spearman
value: 88.22527413524622
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.42816418706765
- type: cos_sim_spearman
value: 63.4569864520124
- type: euclidean_pearson
value: 64.35405409953853
- type: euclidean_spearman
value: 63.4569864520124
- type: manhattan_pearson
value: 63.96649236073056
- type: manhattan_spearman
value: 63.01448583722708
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.41659638047614
- type: cos_sim_spearman
value: 84.03893866106175
- type: euclidean_pearson
value: 84.2251203953798
- type: euclidean_spearman
value: 84.03893866106175
- type: manhattan_pearson
value: 84.22733643205514
- type: manhattan_spearman
value: 84.06504411263612
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.75608022582414
- type: mrr
value: 94.0947732369301
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.161
- type: map_at_10
value: 59.458999999999996
- type: map_at_100
value: 60.156
- type: map_at_1000
value: 60.194
- type: map_at_3
value: 56.45400000000001
- type: map_at_5
value: 58.165
- type: mrr_at_1
value: 53.333
- type: mrr_at_10
value: 61.050000000000004
- type: mrr_at_100
value: 61.586
- type: mrr_at_1000
value: 61.624
- type: mrr_at_3
value: 58.889
- type: mrr_at_5
value: 60.122
- type: ndcg_at_1
value: 53.333
- type: ndcg_at_10
value: 63.888999999999996
- type: ndcg_at_100
value: 66.963
- type: ndcg_at_1000
value: 68.062
- type: ndcg_at_3
value: 59.01
- type: ndcg_at_5
value: 61.373999999999995
- type: precision_at_1
value: 53.333
- type: precision_at_10
value: 8.633000000000001
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 23.111
- type: precision_at_5
value: 15.467
- type: recall_at_1
value: 50.161
- type: recall_at_10
value: 75.922
- type: recall_at_100
value: 90.0
- type: recall_at_1000
value: 98.667
- type: recall_at_3
value: 62.90599999999999
- type: recall_at_5
value: 68.828
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81188118811882
- type: cos_sim_ap
value: 95.11619225962413
- type: cos_sim_f1
value: 90.35840484603736
- type: cos_sim_precision
value: 91.23343527013252
- type: cos_sim_recall
value: 89.5
- type: dot_accuracy
value: 99.81188118811882
- type: dot_ap
value: 95.11619225962413
- type: dot_f1
value: 90.35840484603736
- type: dot_precision
value: 91.23343527013252
- type: dot_recall
value: 89.5
- type: euclidean_accuracy
value: 99.81188118811882
- type: euclidean_ap
value: 95.11619225962413
- type: euclidean_f1
value: 90.35840484603736
- type: euclidean_precision
value: 91.23343527013252
- type: euclidean_recall
value: 89.5
- type: manhattan_accuracy
value: 99.80891089108911
- type: manhattan_ap
value: 95.07294266220966
- type: manhattan_f1
value: 90.21794221996959
- type: manhattan_precision
value: 91.46968139773895
- type: manhattan_recall
value: 89.0
- type: max_accuracy
value: 99.81188118811882
- type: max_ap
value: 95.11619225962413
- type: max_f1
value: 90.35840484603736
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.3481874105239
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.421291695525
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.98746633276634
- type: mrr
value: 50.63143249724133
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.009961979844036
- type: cos_sim_spearman
value: 30.558416108881044
- type: dot_pearson
value: 31.009964941134253
- type: dot_spearman
value: 30.545760761761393
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.207
- type: map_at_10
value: 1.6
- type: map_at_100
value: 8.594
- type: map_at_1000
value: 20.213
- type: map_at_3
value: 0.585
- type: map_at_5
value: 0.9039999999999999
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 87.4
- type: mrr_at_100
value: 87.4
- type: mrr_at_1000
value: 87.4
- type: mrr_at_3
value: 86.667
- type: mrr_at_5
value: 87.06700000000001
- type: ndcg_at_1
value: 73.0
- type: ndcg_at_10
value: 65.18
- type: ndcg_at_100
value: 49.631
- type: ndcg_at_1000
value: 43.498999999999995
- type: ndcg_at_3
value: 71.83800000000001
- type: ndcg_at_5
value: 69.271
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 69.19999999999999
- type: precision_at_100
value: 50.980000000000004
- type: precision_at_1000
value: 19.426
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.207
- type: recall_at_10
value: 1.822
- type: recall_at_100
value: 11.849
- type: recall_at_1000
value: 40.492
- type: recall_at_3
value: 0.622
- type: recall_at_5
value: 0.9809999999999999
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.001
- type: map_at_10
value: 10.376000000000001
- type: map_at_100
value: 16.936999999999998
- type: map_at_1000
value: 18.615000000000002
- type: map_at_3
value: 5.335999999999999
- type: map_at_5
value: 7.374
- type: mrr_at_1
value: 20.408
- type: mrr_at_10
value: 38.29
- type: mrr_at_100
value: 39.33
- type: mrr_at_1000
value: 39.347
- type: mrr_at_3
value: 32.993
- type: mrr_at_5
value: 36.973
- type: ndcg_at_1
value: 17.347
- type: ndcg_at_10
value: 23.515
- type: ndcg_at_100
value: 37.457
- type: ndcg_at_1000
value: 49.439
- type: ndcg_at_3
value: 22.762999999999998
- type: ndcg_at_5
value: 22.622
- type: precision_at_1
value: 20.408
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_100
value: 8.184
- type: precision_at_1000
value: 1.608
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 2.001
- type: recall_at_10
value: 17.422
- type: recall_at_100
value: 51.532999999999994
- type: recall_at_1000
value: 87.466
- type: recall_at_3
value: 6.861000000000001
- type: recall_at_5
value: 10.502
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.54419999999999
- type: ap
value: 14.372170450843907
- type: f1
value: 54.94420257390529
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.402942840973395
- type: f1
value: 59.4166538875571
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 41.569064336457906
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.31322644096085
- type: cos_sim_ap
value: 72.14518894837381
- type: cos_sim_f1
value: 66.67489813557229
- type: cos_sim_precision
value: 62.65954977953121
- type: cos_sim_recall
value: 71.2401055408971
- type: dot_accuracy
value: 85.31322644096085
- type: dot_ap
value: 72.14521480685293
- type: dot_f1
value: 66.67489813557229
- type: dot_precision
value: 62.65954977953121
- type: dot_recall
value: 71.2401055408971
- type: euclidean_accuracy
value: 85.31322644096085
- type: euclidean_ap
value: 72.14520820485349
- type: euclidean_f1
value: 66.67489813557229
- type: euclidean_precision
value: 62.65954977953121
- type: euclidean_recall
value: 71.2401055408971
- type: manhattan_accuracy
value: 85.21785778148656
- type: manhattan_ap
value: 72.01177147657364
- type: manhattan_f1
value: 66.62594673833374
- type: manhattan_precision
value: 62.0336669699727
- type: manhattan_recall
value: 71.95250659630607
- type: max_accuracy
value: 85.31322644096085
- type: max_ap
value: 72.14521480685293
- type: max_f1
value: 66.67489813557229
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.12756626693057
- type: cos_sim_ap
value: 86.05430786440826
- type: cos_sim_f1
value: 78.27759692216631
- type: cos_sim_precision
value: 75.33466248931929
- type: cos_sim_recall
value: 81.45980905451185
- type: dot_accuracy
value: 89.12950673341872
- type: dot_ap
value: 86.05431161145492
- type: dot_f1
value: 78.27759692216631
- type: dot_precision
value: 75.33466248931929
- type: dot_recall
value: 81.45980905451185
- type: euclidean_accuracy
value: 89.12756626693057
- type: euclidean_ap
value: 86.05431303247397
- type: euclidean_f1
value: 78.27759692216631
- type: euclidean_precision
value: 75.33466248931929
- type: euclidean_recall
value: 81.45980905451185
- type: manhattan_accuracy
value: 89.04994760740482
- type: manhattan_ap
value: 86.00860610892074
- type: manhattan_f1
value: 78.1846776005392
- type: manhattan_precision
value: 76.10438839480975
- type: manhattan_recall
value: 80.3818909762858
- type: max_accuracy
value: 89.12950673341872
- type: max_ap
value: 86.05431303247397
- type: max_f1
value: 78.27759692216631
---
<!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
## Quick Start
The easiest way to starting using `jina-embeddings-v2-small-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
## Intended Usage & Model Info
`jina-embeddings-v2-small-en` is an English, monolingual **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset.
The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance.
Additionally, we provide the following embedding models:
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
## Data & Parameters
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
## Usage
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>
### Why mean pooling?
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.
However, if you would like to do it without using the default `encode` function:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['How is the weather today?', 'What is the current weather like today?']
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
You can use Jina Embedding models directly from transformers package.
```python
!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))
```
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(
['Very long ... document'],
max_length=2048
)
```
The latest sentence-transformers also supports Jina embeddings:
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"jinaai/jina-embeddings-v2-small-en", # switch to en/zh for English or Chinese
trust_remote_code=True
)
# control your input sequence length up to 8192
model.max_seq_length = 1024
embeddings = model.encode([
'How is the weather today?',
'What is the current weather like today?'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
## Alternatives to Using Transformers Package
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
## RAG Performance
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
## Plans
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
2. Multimodal embedding models enable Multimodal RAG applications.
3. High-performt rerankers.
## Trouble Shooting
**Loading of Model Code failed**
If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
```bash
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
```
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
```
@misc{günther2023jina,
title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents},
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
year={2023},
eprint={2310.19923},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/multilingual-e5-large-instruct | intfloat | feature-extraction | [
"sentence-transformers",
"onnx",
"safetensors",
"xlm-roberta",
"feature-extraction",
"mteb",
"transformers",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2401.00368",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-08T12:59:32 | 2025-02-17T04:16:16 | 812,767 | 382 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: multilingual-e5-large-instruct
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.23880597014924
- type: ap
value: 39.07351965022687
- type: f1
value: 70.04836733862683
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.71306209850107
- type: ap
value: 79.01499914759529
- type: f1
value: 64.81951817560703
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.85307346326837
- type: ap
value: 22.447519885878737
- type: f1
value: 61.0162730745633
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.04925053533191
- type: ap
value: 23.44983217128922
- type: f1
value: 62.5723230907759
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.28742500000001
- type: ap
value: 94.8449918887462
- type: f1
value: 96.28680923610432
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 56.716
- type: f1
value: 55.76510398266401
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 52.99999999999999
- type: f1
value: 52.00829994765178
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.806000000000004
- type: f1
value: 48.082345914983634
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.507999999999996
- type: f1
value: 47.68752844642045
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.709999999999994
- type: f1
value: 47.05870376637181
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.662000000000006
- type: f1
value: 43.42371965372771
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.721
- type: map_at_10
value: 49.221
- type: map_at_100
value: 49.884
- type: map_at_1000
value: 49.888
- type: map_at_3
value: 44.31
- type: map_at_5
value: 47.276
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 49.5
- type: mrr_at_100
value: 50.163000000000004
- type: mrr_at_1000
value: 50.166
- type: mrr_at_3
value: 44.618
- type: mrr_at_5
value: 47.541
- type: ndcg_at_1
value: 31.721
- type: ndcg_at_10
value: 58.384
- type: ndcg_at_100
value: 61.111000000000004
- type: ndcg_at_1000
value: 61.187999999999995
- type: ndcg_at_3
value: 48.386
- type: ndcg_at_5
value: 53.708999999999996
- type: precision_at_1
value: 31.721
- type: precision_at_10
value: 8.741
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.609
- type: recall_at_1
value: 31.721
- type: recall_at_10
value: 87.411
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.044
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.40419580759799
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.48593255007969
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.889179122289995
- type: mrr
value: 77.61146286769556
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.15075203727929
- type: cos_sim_spearman
value: 86.9622224570873
- type: euclidean_pearson
value: 86.70473853624121
- type: euclidean_spearman
value: 86.9622224570873
- type: manhattan_pearson
value: 86.21089380980065
- type: manhattan_spearman
value: 86.75318154937008
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.65553235908142
- type: f1
value: 99.60681976339595
- type: precision
value: 99.58246346555325
- type: recall
value: 99.65553235908142
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26260180497468
- type: f1
value: 99.14520507740848
- type: precision
value: 99.08650671362535
- type: recall
value: 99.26260180497468
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.07412538967787
- type: f1
value: 97.86629719431936
- type: precision
value: 97.76238309664012
- type: recall
value: 98.07412538967787
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.42074776197998
- type: f1
value: 99.38564156573635
- type: precision
value: 99.36808846761454
- type: recall
value: 99.42074776197998
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.73376623376623
- type: f1
value: 85.68480707214599
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.935218072113855
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.276389017675264
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.764166666666668
- type: map_at_10
value: 37.298166666666674
- type: map_at_100
value: 38.530166666666666
- type: map_at_1000
value: 38.64416666666667
- type: map_at_3
value: 34.484833333333334
- type: map_at_5
value: 36.0385
- type: mrr_at_1
value: 32.93558333333333
- type: mrr_at_10
value: 41.589749999999995
- type: mrr_at_100
value: 42.425333333333334
- type: mrr_at_1000
value: 42.476333333333336
- type: mrr_at_3
value: 39.26825
- type: mrr_at_5
value: 40.567083333333336
- type: ndcg_at_1
value: 32.93558333333333
- type: ndcg_at_10
value: 42.706583333333334
- type: ndcg_at_100
value: 47.82483333333333
- type: ndcg_at_1000
value: 49.95733333333334
- type: ndcg_at_3
value: 38.064750000000004
- type: ndcg_at_5
value: 40.18158333333333
- type: precision_at_1
value: 32.93558333333333
- type: precision_at_10
value: 7.459833333333334
- type: precision_at_100
value: 1.1830833333333335
- type: precision_at_1000
value: 0.15608333333333332
- type: precision_at_3
value: 17.5235
- type: precision_at_5
value: 12.349833333333333
- type: recall_at_1
value: 27.764166666666668
- type: recall_at_10
value: 54.31775
- type: recall_at_100
value: 76.74350000000001
- type: recall_at_1000
value: 91.45208333333332
- type: recall_at_3
value: 41.23425
- type: recall_at_5
value: 46.73983333333334
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.969
- type: map_at_10
value: 21.584999999999997
- type: map_at_100
value: 23.3
- type: map_at_1000
value: 23.5
- type: map_at_3
value: 18.218999999999998
- type: map_at_5
value: 19.983
- type: mrr_at_1
value: 29.316
- type: mrr_at_10
value: 40.033
- type: mrr_at_100
value: 40.96
- type: mrr_at_1000
value: 41.001
- type: mrr_at_3
value: 37.123
- type: mrr_at_5
value: 38.757999999999996
- type: ndcg_at_1
value: 29.316
- type: ndcg_at_10
value: 29.858
- type: ndcg_at_100
value: 36.756
- type: ndcg_at_1000
value: 40.245999999999995
- type: ndcg_at_3
value: 24.822
- type: ndcg_at_5
value: 26.565
- type: precision_at_1
value: 29.316
- type: precision_at_10
value: 9.186
- type: precision_at_100
value: 1.6549999999999998
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 18.436
- type: precision_at_5
value: 13.876
- type: recall_at_1
value: 12.969
- type: recall_at_10
value: 35.142
- type: recall_at_100
value: 59.143
- type: recall_at_1000
value: 78.594
- type: recall_at_3
value: 22.604
- type: recall_at_5
value: 27.883000000000003
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.527999999999999
- type: map_at_10
value: 17.974999999999998
- type: map_at_100
value: 25.665
- type: map_at_1000
value: 27.406000000000002
- type: map_at_3
value: 13.017999999999999
- type: map_at_5
value: 15.137
- type: mrr_at_1
value: 62.5
- type: mrr_at_10
value: 71.891
- type: mrr_at_100
value: 72.294
- type: mrr_at_1000
value: 72.296
- type: mrr_at_3
value: 69.958
- type: mrr_at_5
value: 71.121
- type: ndcg_at_1
value: 50.875
- type: ndcg_at_10
value: 38.36
- type: ndcg_at_100
value: 44.235
- type: ndcg_at_1000
value: 52.154
- type: ndcg_at_3
value: 43.008
- type: ndcg_at_5
value: 40.083999999999996
- type: precision_at_1
value: 62.5
- type: precision_at_10
value: 30.0
- type: precision_at_100
value: 10.038
- type: precision_at_1000
value: 2.0869999999999997
- type: precision_at_3
value: 46.833000000000006
- type: precision_at_5
value: 38.800000000000004
- type: recall_at_1
value: 8.527999999999999
- type: recall_at_10
value: 23.828
- type: recall_at_100
value: 52.322
- type: recall_at_1000
value: 77.143
- type: recall_at_3
value: 14.136000000000001
- type: recall_at_5
value: 17.761
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.51
- type: f1
value: 47.632159862049896
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.734
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.735
- type: map_at_1000
value: 72.75
- type: map_at_3
value: 70.41199999999999
- type: map_at_5
value: 71.80499999999999
- type: mrr_at_1
value: 65.212
- type: mrr_at_10
value: 76.613
- type: mrr_at_100
value: 76.79899999999999
- type: mrr_at_1000
value: 76.801
- type: mrr_at_3
value: 74.8
- type: mrr_at_5
value: 76.12400000000001
- type: ndcg_at_1
value: 65.212
- type: ndcg_at_10
value: 77.988
- type: ndcg_at_100
value: 79.167
- type: ndcg_at_1000
value: 79.452
- type: ndcg_at_3
value: 74.362
- type: ndcg_at_5
value: 76.666
- type: precision_at_1
value: 65.212
- type: precision_at_10
value: 10.003
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 29.518
- type: precision_at_5
value: 19.016
- type: recall_at_1
value: 60.734
- type: recall_at_10
value: 90.824
- type: recall_at_100
value: 95.71600000000001
- type: recall_at_1000
value: 97.577
- type: recall_at_3
value: 81.243
- type: recall_at_5
value: 86.90299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.845
- type: map_at_10
value: 39.281
- type: map_at_100
value: 41.422
- type: map_at_1000
value: 41.593
- type: map_at_3
value: 34.467
- type: map_at_5
value: 37.017
- type: mrr_at_1
value: 47.531
- type: mrr_at_10
value: 56.204
- type: mrr_at_100
value: 56.928999999999995
- type: mrr_at_1000
value: 56.962999999999994
- type: mrr_at_3
value: 54.115
- type: mrr_at_5
value: 55.373000000000005
- type: ndcg_at_1
value: 47.531
- type: ndcg_at_10
value: 47.711999999999996
- type: ndcg_at_100
value: 54.510999999999996
- type: ndcg_at_1000
value: 57.103
- type: ndcg_at_3
value: 44.145
- type: ndcg_at_5
value: 45.032
- type: precision_at_1
value: 47.531
- type: precision_at_10
value: 13.194
- type: precision_at_100
value: 2.045
- type: precision_at_1000
value: 0.249
- type: precision_at_3
value: 29.424
- type: precision_at_5
value: 21.451
- type: recall_at_1
value: 23.845
- type: recall_at_10
value: 54.967
- type: recall_at_100
value: 79.11399999999999
- type: recall_at_1000
value: 94.56700000000001
- type: recall_at_3
value: 40.256
- type: recall_at_5
value: 46.215
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.819
- type: map_at_10
value: 60.889
- type: map_at_100
value: 61.717999999999996
- type: map_at_1000
value: 61.778
- type: map_at_3
value: 57.254000000000005
- type: map_at_5
value: 59.541
- type: mrr_at_1
value: 75.638
- type: mrr_at_10
value: 82.173
- type: mrr_at_100
value: 82.362
- type: mrr_at_1000
value: 82.37
- type: mrr_at_3
value: 81.089
- type: mrr_at_5
value: 81.827
- type: ndcg_at_1
value: 75.638
- type: ndcg_at_10
value: 69.317
- type: ndcg_at_100
value: 72.221
- type: ndcg_at_1000
value: 73.382
- type: ndcg_at_3
value: 64.14
- type: ndcg_at_5
value: 67.07600000000001
- type: precision_at_1
value: 75.638
- type: precision_at_10
value: 14.704999999999998
- type: precision_at_100
value: 1.698
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 41.394999999999996
- type: precision_at_5
value: 27.162999999999997
- type: recall_at_1
value: 37.819
- type: recall_at_10
value: 73.52499999999999
- type: recall_at_100
value: 84.875
- type: recall_at_1000
value: 92.559
- type: recall_at_3
value: 62.092999999999996
- type: recall_at_5
value: 67.907
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.60079999999999
- type: ap
value: 92.67396345347356
- type: f1
value: 94.5988098167121
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.285
- type: map_at_10
value: 33.436
- type: map_at_100
value: 34.63
- type: map_at_1000
value: 34.681
- type: map_at_3
value: 29.412
- type: map_at_5
value: 31.715
- type: mrr_at_1
value: 21.848
- type: mrr_at_10
value: 33.979
- type: mrr_at_100
value: 35.118
- type: mrr_at_1000
value: 35.162
- type: mrr_at_3
value: 30.036
- type: mrr_at_5
value: 32.298
- type: ndcg_at_1
value: 21.862000000000002
- type: ndcg_at_10
value: 40.43
- type: ndcg_at_100
value: 46.17
- type: ndcg_at_1000
value: 47.412
- type: ndcg_at_3
value: 32.221
- type: ndcg_at_5
value: 36.332
- type: precision_at_1
value: 21.862000000000002
- type: precision_at_10
value: 6.491
- type: precision_at_100
value: 0.935
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.744
- type: precision_at_5
value: 10.331999999999999
- type: recall_at_1
value: 21.285
- type: recall_at_10
value: 62.083
- type: recall_at_100
value: 88.576
- type: recall_at_1000
value: 98.006
- type: recall_at_3
value: 39.729
- type: recall_at_5
value: 49.608000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.92612859097127
- type: f1
value: 93.82370333372853
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.67681036911807
- type: f1
value: 92.14191382411472
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.26817878585723
- type: f1
value: 91.92824250337878
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.96554963983714
- type: f1
value: 90.02859329630792
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.02509860164935
- type: f1
value: 89.30665159182062
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.55515370705244
- type: f1
value: 87.94449232331907
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.4623803009576
- type: f1
value: 66.06738378772725
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
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dataset:
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type: mteb/amazon_massive_scenario
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.9670477471419
- type: f1
value: 77.37318805793146
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fa)
type: mteb/amazon_massive_scenario
config: fa
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.09683927370544
- type: f1
value: 77.69773737430847
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fi)
type: mteb/amazon_massive_scenario
config: fi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.20847343644922
- type: f1
value: 75.17071738727348
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.07464694014796
- type: f1
value: 77.16136207698571
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.53396099529255
- type: f1
value: 73.58296404484122
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.75319435104237
- type: f1
value: 75.24674707850833
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.0948217888366
- type: f1
value: 76.47559490205028
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.07599193006052
- type: f1
value: 70.76028043093511
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.10490921318089
- type: f1
value: 77.01215275283272
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.25756556825824
- type: f1
value: 70.20605314648762
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.08137188971082
- type: f1
value: 77.3899269057439
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.35440484196369
- type: f1
value: 79.58964690002772
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.42299932750504
- type: f1
value: 68.07844356925413
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.15669132481507
- type: f1
value: 65.89383352608513
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.11432414256894
- type: f1
value: 57.69910594559806
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.24747814391392
- type: f1
value: 70.42455553830918
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.46267652992603
- type: f1
value: 76.8854559308316
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.24815063887021
- type: f1
value: 72.77805034658074
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.11566913248151
- type: f1
value: 73.86147988001356
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.0168123739072
- type: f1
value: 69.38515920054571
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.41156691324814
- type: f1
value: 73.43474953408237
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.39609952925353
- type: f1
value: 67.29731681109291
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.20914593140552
- type: f1
value: 77.07066497935367
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.52387357094821
- type: f1
value: 78.5259569473291
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.6913248150639
- type: f1
value: 76.91201656350455
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.1217215870881
- type: f1
value: 77.41179937912504
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.25891055817083
- type: f1
value: 75.8089244542887
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.70679219905851
- type: f1
value: 78.21459594517711
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.83523873570948
- type: f1
value: 74.86847028401978
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.71755211835911
- type: f1
value: 74.0214326485662
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.06523201075991
- type: f1
value: 79.10545620325138
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.91862811028918
- type: f1
value: 66.50386121217983
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.93140551445865
- type: f1
value: 70.755435928495
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.40753194351042
- type: f1
value: 71.61816115782923
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.1815736381977
- type: f1
value: 75.08016717887205
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.86482851378614
- type: f1
value: 72.39521180006291
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.46940147948891
- type: f1
value: 76.70044085362349
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.89307330195024
- type: f1
value: 71.5721825332298
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.7511768661735
- type: f1
value: 75.17918654541515
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69535978480162
- type: f1
value: 78.90019070153316
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.45729657027572
- type: f1
value: 76.19578371794672
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.92715354123554
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 35.53536244162518
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.08507884504006
- type: mrr
value: 34.32436977159129
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.935
- type: map_at_10
value: 13.297
- type: map_at_100
value: 16.907
- type: map_at_1000
value: 18.391
- type: map_at_3
value: 9.626999999999999
- type: map_at_5
value: 11.190999999999999
- type: mrr_at_1
value: 46.129999999999995
- type: mrr_at_10
value: 54.346000000000004
- type: mrr_at_100
value: 55.067
- type: mrr_at_1000
value: 55.1
- type: mrr_at_3
value: 51.961
- type: mrr_at_5
value: 53.246
- type: ndcg_at_1
value: 44.118
- type: ndcg_at_10
value: 35.534
- type: ndcg_at_100
value: 32.946999999999996
- type: ndcg_at_1000
value: 41.599000000000004
- type: ndcg_at_3
value: 40.25
- type: ndcg_at_5
value: 37.978
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.842
- type: precision_at_100
value: 8.427
- type: precision_at_1000
value: 2.128
- type: precision_at_3
value: 37.977
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.935
- type: recall_at_10
value: 17.211000000000002
- type: recall_at_100
value: 34.33
- type: recall_at_1000
value: 65.551
- type: recall_at_3
value: 10.483
- type: recall_at_5
value: 13.078999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.231
- type: map_at_10
value: 50.202000000000005
- type: map_at_100
value: 51.154999999999994
- type: map_at_1000
value: 51.181
- type: map_at_3
value: 45.774
- type: map_at_5
value: 48.522
- type: mrr_at_1
value: 39.687
- type: mrr_at_10
value: 52.88
- type: mrr_at_100
value: 53.569
- type: mrr_at_1000
value: 53.58500000000001
- type: mrr_at_3
value: 49.228
- type: mrr_at_5
value: 51.525
- type: ndcg_at_1
value: 39.687
- type: ndcg_at_10
value: 57.754000000000005
- type: ndcg_at_100
value: 61.597
- type: ndcg_at_1000
value: 62.18900000000001
- type: ndcg_at_3
value: 49.55
- type: ndcg_at_5
value: 54.11899999999999
- type: precision_at_1
value: 39.687
- type: precision_at_10
value: 9.313
- type: precision_at_100
value: 1.146
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.229
- type: precision_at_5
value: 15.939
- type: recall_at_1
value: 35.231
- type: recall_at_10
value: 78.083
- type: recall_at_100
value: 94.42099999999999
- type: recall_at_1000
value: 98.81
- type: recall_at_3
value: 57.047000000000004
- type: recall_at_5
value: 67.637
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.241
- type: map_at_10
value: 85.462
- type: map_at_100
value: 86.083
- type: map_at_1000
value: 86.09700000000001
- type: map_at_3
value: 82.49499999999999
- type: map_at_5
value: 84.392
- type: mrr_at_1
value: 82.09
- type: mrr_at_10
value: 88.301
- type: mrr_at_100
value: 88.383
- type: mrr_at_1000
value: 88.384
- type: mrr_at_3
value: 87.37
- type: mrr_at_5
value: 88.035
- type: ndcg_at_1
value: 82.12
- type: ndcg_at_10
value: 89.149
- type: ndcg_at_100
value: 90.235
- type: ndcg_at_1000
value: 90.307
- type: ndcg_at_3
value: 86.37599999999999
- type: ndcg_at_5
value: 87.964
- type: precision_at_1
value: 82.12
- type: precision_at_10
value: 13.56
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.88
- type: precision_at_5
value: 24.92
- type: recall_at_1
value: 71.241
- type: recall_at_10
value: 96.128
- type: recall_at_100
value: 99.696
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.181
- type: recall_at_5
value: 92.694
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.59757799655151
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.27391998854624
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.243
- type: map_at_10
value: 10.965
- type: map_at_100
value: 12.934999999999999
- type: map_at_1000
value: 13.256
- type: map_at_3
value: 7.907
- type: map_at_5
value: 9.435
- type: mrr_at_1
value: 20.9
- type: mrr_at_10
value: 31.849
- type: mrr_at_100
value: 32.964
- type: mrr_at_1000
value: 33.024
- type: mrr_at_3
value: 28.517
- type: mrr_at_5
value: 30.381999999999998
- type: ndcg_at_1
value: 20.9
- type: ndcg_at_10
value: 18.723
- type: ndcg_at_100
value: 26.384999999999998
- type: ndcg_at_1000
value: 32.114
- type: ndcg_at_3
value: 17.753
- type: ndcg_at_5
value: 15.558
- type: precision_at_1
value: 20.9
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 2.078
- type: precision_at_1000
value: 0.345
- type: precision_at_3
value: 16.900000000000002
- type: precision_at_5
value: 13.88
- type: recall_at_1
value: 4.243
- type: recall_at_10
value: 19.885
- type: recall_at_100
value: 42.17
- type: recall_at_1000
value: 70.12
- type: recall_at_3
value: 10.288
- type: recall_at_5
value: 14.072000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.84209174935282
- type: cos_sim_spearman
value: 81.73248048438833
- type: euclidean_pearson
value: 83.02810070308149
- type: euclidean_spearman
value: 81.73248295679514
- type: manhattan_pearson
value: 82.95368060376002
- type: manhattan_spearman
value: 81.60277910998718
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 88.52628804556943
- type: cos_sim_spearman
value: 82.5713913555672
- type: euclidean_pearson
value: 85.8796774746988
- type: euclidean_spearman
value: 82.57137506803424
- type: manhattan_pearson
value: 85.79671002960058
- type: manhattan_spearman
value: 82.49445981618027
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.23682503505542
- type: cos_sim_spearman
value: 87.15008956711806
- type: euclidean_pearson
value: 86.79805401524959
- type: euclidean_spearman
value: 87.15008956711806
- type: manhattan_pearson
value: 86.65298502699244
- type: manhattan_spearman
value: 86.97677821948562
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.63370304677802
- type: cos_sim_spearman
value: 84.97105553540318
- type: euclidean_pearson
value: 85.28896108687721
- type: euclidean_spearman
value: 84.97105553540318
- type: manhattan_pearson
value: 85.09663190337331
- type: manhattan_spearman
value: 84.79126831644619
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.2614838800733
- type: cos_sim_spearman
value: 91.0509162991835
- type: euclidean_pearson
value: 90.33098317533373
- type: euclidean_spearman
value: 91.05091625871644
- type: manhattan_pearson
value: 90.26250435151107
- type: manhattan_spearman
value: 90.97999594417519
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.80480973335091
- type: cos_sim_spearman
value: 87.313695492969
- type: euclidean_pearson
value: 86.49267251576939
- type: euclidean_spearman
value: 87.313695492969
- type: manhattan_pearson
value: 86.44019901831935
- type: manhattan_spearman
value: 87.24205395460392
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.05662789380672
- type: cos_sim_spearman
value: 90.02759424426651
- type: euclidean_pearson
value: 90.4042483422981
- type: euclidean_spearman
value: 90.02759424426651
- type: manhattan_pearson
value: 90.51446975000226
- type: manhattan_spearman
value: 90.08832889933616
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.5975528273532
- type: cos_sim_spearman
value: 67.62969861411354
- type: euclidean_pearson
value: 69.224275734323
- type: euclidean_spearman
value: 67.62969861411354
- type: manhattan_pearson
value: 69.3761447059927
- type: manhattan_spearman
value: 67.90921005611467
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.11244327231684
- type: cos_sim_spearman
value: 88.37902438979035
- type: euclidean_pearson
value: 87.86054279847336
- type: euclidean_spearman
value: 88.37902438979035
- type: manhattan_pearson
value: 87.77257757320378
- type: manhattan_spearman
value: 88.25208966098123
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.87174608143563
- type: mrr
value: 96.12836872640794
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 67.258
- type: map_at_100
value: 67.757
- type: map_at_1000
value: 67.78800000000001
- type: map_at_3
value: 64.602
- type: map_at_5
value: 65.64
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.441
- type: mrr_at_100
value: 68.825
- type: mrr_at_1000
value: 68.853
- type: mrr_at_3
value: 66.444
- type: mrr_at_5
value: 67.26100000000001
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.852
- type: ndcg_at_100
value: 73.9
- type: ndcg_at_1000
value: 74.628
- type: ndcg_at_3
value: 67.093
- type: ndcg_at_5
value: 68.58
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.733
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 84.967
- type: recall_at_100
value: 93.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.589
- type: recall_at_5
value: 75.483
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.66633663366336
- type: cos_sim_ap
value: 91.17685358899108
- type: cos_sim_f1
value: 82.16818642350559
- type: cos_sim_precision
value: 83.26488706365504
- type: cos_sim_recall
value: 81.10000000000001
- type: dot_accuracy
value: 99.66633663366336
- type: dot_ap
value: 91.17663411119032
- type: dot_f1
value: 82.16818642350559
- type: dot_precision
value: 83.26488706365504
- type: dot_recall
value: 81.10000000000001
- type: euclidean_accuracy
value: 99.66633663366336
- type: euclidean_ap
value: 91.17685189882275
- type: euclidean_f1
value: 82.16818642350559
- type: euclidean_precision
value: 83.26488706365504
- type: euclidean_recall
value: 81.10000000000001
- type: manhattan_accuracy
value: 99.66633663366336
- type: manhattan_ap
value: 91.2241619496737
- type: manhattan_f1
value: 82.20472440944883
- type: manhattan_precision
value: 86.51933701657458
- type: manhattan_recall
value: 78.3
- type: max_accuracy
value: 99.66633663366336
- type: max_ap
value: 91.2241619496737
- type: max_f1
value: 82.20472440944883
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.85101268897951
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 42.461184054706905
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.44542568873886
- type: mrr
value: 52.33656151854681
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.75982974997539
- type: cos_sim_spearman
value: 30.385405026539914
- type: dot_pearson
value: 30.75982433546523
- type: dot_spearman
value: 30.385405026539914
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22799999999999998
- type: map_at_10
value: 2.064
- type: map_at_100
value: 13.056000000000001
- type: map_at_1000
value: 31.747999999999998
- type: map_at_3
value: 0.67
- type: map_at_5
value: 1.097
- type: mrr_at_1
value: 90.0
- type: mrr_at_10
value: 94.667
- type: mrr_at_100
value: 94.667
- type: mrr_at_1000
value: 94.667
- type: mrr_at_3
value: 94.667
- type: mrr_at_5
value: 94.667
- type: ndcg_at_1
value: 86.0
- type: ndcg_at_10
value: 82.0
- type: ndcg_at_100
value: 64.307
- type: ndcg_at_1000
value: 57.023999999999994
- type: ndcg_at_3
value: 85.816
- type: ndcg_at_5
value: 84.904
- type: precision_at_1
value: 90.0
- type: precision_at_10
value: 85.8
- type: precision_at_100
value: 66.46
- type: precision_at_1000
value: 25.202
- type: precision_at_3
value: 90.0
- type: precision_at_5
value: 89.2
- type: recall_at_1
value: 0.22799999999999998
- type: recall_at_10
value: 2.235
- type: recall_at_100
value: 16.185
- type: recall_at_1000
value: 53.620999999999995
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.172
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.75
- type: precision
value: 96.45
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.54913294797689
- type: f1
value: 82.46628131021194
- type: precision
value: 81.1175337186898
- type: recall
value: 85.54913294797689
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.21951219512195
- type: f1
value: 77.33333333333334
- type: precision
value: 75.54878048780488
- type: recall
value: 81.21951219512195
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.26666666666665
- type: precision
value: 98.1
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.5
- type: f1
value: 99.33333333333333
- type: precision
value: 99.25
- type: recall
value: 99.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.2
- type: precision
value: 96.89999999999999
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.18333333333334
- type: precision
value: 96.88333333333333
- type: recall
value: 97.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.61194029850746
- type: f1
value: 72.81094527363183
- type: precision
value: 70.83333333333333
- type: recall
value: 77.61194029850746
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.91666666666667
- type: precision
value: 91.08333333333334
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.29268292682927
- type: f1
value: 85.27642276422765
- type: precision
value: 84.01277584204414
- type: recall
value: 88.29268292682927
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.0
- type: precision
value: 94.46666666666668
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.681652490887
- type: f1
value: 91.90765492102065
- type: precision
value: 91.05913325232888
- type: recall
value: 93.681652490887
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.17391304347827
- type: f1
value: 89.97101449275361
- type: precision
value: 88.96811594202899
- type: recall
value: 92.17391304347827
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.43478260869566
- type: f1
value: 87.72173913043478
- type: precision
value: 86.42028985507245
- type: recall
value: 90.43478260869566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.03
- type: precision
value: 86.95
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.4
- type: f1
value: 91.45666666666666
- type: precision
value: 90.525
- type: recall
value: 93.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.9059107358263
- type: f1
value: 78.32557872364869
- type: precision
value: 76.78260286824823
- type: recall
value: 81.9059107358263
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.58333333333333
- type: precision
value: 91.73333333333332
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.10000000000001
- type: f1
value: 74.50500000000001
- type: precision
value: 72.58928571428571
- type: recall
value: 79.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.6
- type: f1
value: 95.55
- type: precision
value: 95.05
- type: recall
value: 96.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.0952380952381
- type: f1
value: 77.98458049886621
- type: precision
value: 76.1968253968254
- type: recall
value: 82.0952380952381
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.9
- type: f1
value: 84.99190476190476
- type: precision
value: 83.65
- type: recall
value: 87.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.56666666666666
- type: precision
value: 94.01666666666667
- type: recall
value: 95.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.6
- type: f1
value: 98.2
- type: precision
value: 98.0
- type: recall
value: 98.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.38333333333334
- type: precision
value: 93.78333333333335
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.5
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type: BitextMining
dataset:
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config: swg-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: arq-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: aze-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: nno-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: mhr-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.2
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: amh-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 83.4368530020704
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: epo-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 17.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.31297709923665
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type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 99.12663755458514
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.0
- type: f1
value: 89.77999999999999
- type: precision
value: 88.78333333333333
- type: recall
value: 92.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.89265536723164
- type: f1
value: 95.85687382297553
- type: precision
value: 95.33898305084746
- type: recall
value: 96.89265536723164
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.6
- type: f1
value: 11.820611790170615
- type: precision
value: 11.022616224355355
- type: recall
value: 14.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.93333333333334
- type: precision
value: 94.48666666666666
- type: recall
value: 95.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.6
- type: f1
value: 84.72333333333334
- type: precision
value: 83.44166666666666
- type: recall
value: 87.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.8
- type: f1
value: 93.47333333333333
- type: precision
value: 92.875
- type: recall
value: 94.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.6
- type: f1
value: 95.71666666666665
- type: precision
value: 95.28333333333335
- type: recall
value: 96.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.8
- type: f1
value: 14.511074040901628
- type: precision
value: 13.503791000666002
- type: recall
value: 17.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.10187667560321
- type: f1
value: 92.46648793565683
- type: precision
value: 91.71134941912423
- type: recall
value: 94.10187667560321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.0
- type: f1
value: 96.11666666666666
- type: precision
value: 95.68333333333334
- type: recall
value: 97.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.72727272727273
- type: f1
value: 66.58949745906267
- type: precision
value: 63.86693017127799
- type: recall
value: 72.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.14084507042254
- type: f1
value: 88.26291079812206
- type: precision
value: 87.32394366197182
- type: recall
value: 90.14084507042254
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 64.67065868263472
- type: f1
value: 58.2876627696987
- type: precision
value: 55.79255774165953
- type: recall
value: 64.67065868263472
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.6
- type: f1
value: 94.41666666666667
- type: precision
value: 93.85
- type: recall
value: 95.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.172413793103445
- type: f1
value: 49.63992493549144
- type: precision
value: 47.71405113769646
- type: recall
value: 55.172413793103445
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.46478873239437
- type: f1
value: 73.4417616811983
- type: precision
value: 71.91607981220658
- type: recall
value: 77.46478873239437
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.61538461538461
- type: f1
value: 80.91452991452994
- type: precision
value: 79.33760683760683
- type: recall
value: 84.61538461538461
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 98.2
- type: f1
value: 97.6
- type: precision
value: 97.3
- type: recall
value: 98.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.5741127348643
- type: f1
value: 72.00417536534445
- type: precision
value: 70.53467872883321
- type: recall
value: 75.5741127348643
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 62.2
- type: f1
value: 55.577460317460314
- type: precision
value: 52.98583333333333
- type: recall
value: 62.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.18241042345277
- type: f1
value: 90.6468124709167
- type: precision
value: 89.95656894679696
- type: recall
value: 92.18241042345277
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.13333333333333
- type: precision
value: 94.66666666666667
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 95.85000000000001
- type: precision
value: 95.39999999999999
- type: recall
value: 96.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.1259842519685
- type: f1
value: 89.76377952755905
- type: precision
value: 88.71391076115485
- type: recall
value: 92.1259842519685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.49
- type: precision
value: 91.725
- type: recall
value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.5623268698061
- type: f1
value: 73.27364463791058
- type: precision
value: 71.51947852086357
- type: recall
value: 77.5623268698061
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.56666666666666
- type: precision
value: 96.16666666666667
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.34615384615384
- type: f1
value: 61.092032967032964
- type: precision
value: 59.27197802197802
- type: recall
value: 66.34615384615384
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.41190476190476
- type: precision
value: 92.7
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.13333333333333
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.97333333333334
- type: precision
value: 91.14166666666667
- type: recall
value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.21698113207547
- type: f1
value: 90.3796046720575
- type: precision
value: 89.56367924528303
- type: recall
value: 92.21698113207547
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.91666666666667
- type: precision
value: 96.6
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.44525547445255
- type: f1
value: 96.71532846715328
- type: precision
value: 96.35036496350365
- type: recall
value: 97.44525547445255
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.34000000000002
- type: precision
value: 91.49166666666667
- type: recall
value: 94.1
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.2910000000000004
- type: map_at_10
value: 10.373000000000001
- type: map_at_100
value: 15.612
- type: map_at_1000
value: 17.06
- type: map_at_3
value: 6.119
- type: map_at_5
value: 7.917000000000001
- type: mrr_at_1
value: 44.897999999999996
- type: mrr_at_10
value: 56.054
- type: mrr_at_100
value: 56.82000000000001
- type: mrr_at_1000
value: 56.82000000000001
- type: mrr_at_3
value: 52.381
- type: mrr_at_5
value: 53.81
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 27.249000000000002
- type: ndcg_at_100
value: 36.529
- type: ndcg_at_1000
value: 48.136
- type: ndcg_at_3
value: 33.938
- type: ndcg_at_5
value: 29.951
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 22.653000000000002
- type: precision_at_100
value: 7.000000000000001
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 32.653
- type: precision_at_5
value: 27.755000000000003
- type: recall_at_1
value: 3.2910000000000004
- type: recall_at_10
value: 16.16
- type: recall_at_100
value: 43.908
- type: recall_at_1000
value: 79.823
- type: recall_at_3
value: 7.156
- type: recall_at_5
value: 10.204
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.05879999999999
- type: ap
value: 14.609748142799111
- type: f1
value: 54.878956295843096
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.61799660441426
- type: f1
value: 64.8698191961434
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.32860036611885
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.34714192048638
- type: cos_sim_ap
value: 80.26732975975634
- type: cos_sim_f1
value: 73.53415148134374
- type: cos_sim_precision
value: 69.34767360299276
- type: cos_sim_recall
value: 78.25857519788919
- type: dot_accuracy
value: 88.34714192048638
- type: dot_ap
value: 80.26733698491206
- type: dot_f1
value: 73.53415148134374
- type: dot_precision
value: 69.34767360299276
- type: dot_recall
value: 78.25857519788919
- type: euclidean_accuracy
value: 88.34714192048638
- type: euclidean_ap
value: 80.26734337771738
- type: euclidean_f1
value: 73.53415148134374
- type: euclidean_precision
value: 69.34767360299276
- type: euclidean_recall
value: 78.25857519788919
- type: manhattan_accuracy
value: 88.30541813196639
- type: manhattan_ap
value: 80.19415808104145
- type: manhattan_f1
value: 73.55143870713441
- type: manhattan_precision
value: 73.25307511122743
- type: manhattan_recall
value: 73.85224274406332
- type: max_accuracy
value: 88.34714192048638
- type: max_ap
value: 80.26734337771738
- type: max_f1
value: 73.55143870713441
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.81061047075717
- type: cos_sim_ap
value: 87.11747055081017
- type: cos_sim_f1
value: 80.04355498817256
- type: cos_sim_precision
value: 78.1165262000733
- type: cos_sim_recall
value: 82.06806282722513
- type: dot_accuracy
value: 89.81061047075717
- type: dot_ap
value: 87.11746902745236
- type: dot_f1
value: 80.04355498817256
- type: dot_precision
value: 78.1165262000733
- type: dot_recall
value: 82.06806282722513
- type: euclidean_accuracy
value: 89.81061047075717
- type: euclidean_ap
value: 87.11746919324248
- type: euclidean_f1
value: 80.04355498817256
- type: euclidean_precision
value: 78.1165262000733
- type: euclidean_recall
value: 82.06806282722513
- type: manhattan_accuracy
value: 89.79508673885202
- type: manhattan_ap
value: 87.11074390832218
- type: manhattan_f1
value: 80.13002540726349
- type: manhattan_precision
value: 77.83826945412311
- type: manhattan_recall
value: 82.56082537727133
- type: max_accuracy
value: 89.81061047075717
- type: max_ap
value: 87.11747055081017
- type: max_f1
value: 80.13002540726349
---
## Multilingual-E5-large-instruct
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset.
### Transformers
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]
```
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, '南瓜的家常做法')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
input_texts = queries + documents
model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]]
```
### Infinity
Usage with [Infinity](https://github.com/michaelfeil/infinity):
```bash
docker run --gpus all -v $PWD/data:/app/.cache -e HF_TOKEN=$HF_TOKEN -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id intfloat/multilingual-e5-large-instruct --revision "main" --dtype float16 --batch-size 32 -engine torch --port 7997
```
## Supported Languages
This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
**First stage**: contrastive pre-training with 1 billion weakly supervised text pairs.
**Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper.
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## FAQ
**1. Do I need to add instructions to the query?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
The task definition should be a one-sentence instruction that describes the task.
This is a way to customize text embeddings for different scenarios through natural language instructions.
Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation.
On the other hand, there is no need to add instructions to the document side.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/multilingual-e5-base | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-19T10:26:40 | 2025-02-17T03:23:43 | 578,159 | 263 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: multilingual-e5-base
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.97014925373135
- type: ap
value: 43.69351129103008
- type: f1
value: 73.38075030070492
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.7237687366167
- type: ap
value: 82.22089859962671
- type: f1
value: 69.95532758884401
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.65517241379312
- type: ap
value: 28.507918657094738
- type: f1
value: 66.84516013726119
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.32976445396146
- type: ap
value: 20.720481637566014
- type: f1
value: 59.78002763416003
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.63775
- type: ap
value: 87.22277903861716
- type: f1
value: 90.60378636386807
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.546
- type: f1
value: 44.05666638370923
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 41.828
- type: f1
value: 41.2710255644252
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.534
- type: f1
value: 39.820743174270326
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 39.684
- type: f1
value: 39.11052682815307
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.436
- type: f1
value: 37.07082931930871
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.226000000000006
- type: f1
value: 36.65372077739185
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.831000000000003
- type: map_at_10
value: 36.42
- type: map_at_100
value: 37.699
- type: map_at_1000
value: 37.724000000000004
- type: map_at_3
value: 32.207
- type: map_at_5
value: 34.312
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 36.574
- type: mrr_at_100
value: 37.854
- type: mrr_at_1000
value: 37.878
- type: mrr_at_3
value: 32.385000000000005
- type: mrr_at_5
value: 34.48
- type: ndcg_at_1
value: 22.831000000000003
- type: ndcg_at_10
value: 44.230000000000004
- type: ndcg_at_100
value: 49.974000000000004
- type: ndcg_at_1000
value: 50.522999999999996
- type: ndcg_at_3
value: 35.363
- type: ndcg_at_5
value: 39.164
- type: precision_at_1
value: 22.831000000000003
- type: precision_at_10
value: 6.935
- type: precision_at_100
value: 0.9520000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.841
- type: precision_at_5
value: 10.754
- type: recall_at_1
value: 22.831000000000003
- type: recall_at_10
value: 69.346
- type: recall_at_100
value: 95.235
- type: recall_at_1000
value: 99.36
- type: recall_at_3
value: 44.523
- type: recall_at_5
value: 53.769999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 40.27789869854063
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.41979463347428
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.22752045109304
- type: mrr
value: 71.51112430198303
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.71147646622866
- type: cos_sim_spearman
value: 85.059167046486
- type: euclidean_pearson
value: 75.88421613600647
- type: euclidean_spearman
value: 75.12821787150585
- type: manhattan_pearson
value: 75.22005646957604
- type: manhattan_spearman
value: 74.42880434453272
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.23799582463465
- type: f1
value: 99.12665274878218
- type: precision
value: 99.07098121085595
- type: recall
value: 99.23799582463465
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.88685890380806
- type: f1
value: 97.59336708489249
- type: precision
value: 97.44662117543473
- type: recall
value: 97.88685890380806
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 97.47142362313821
- type: f1
value: 97.1989377670015
- type: precision
value: 97.06384944001847
- type: recall
value: 97.47142362313821
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.4728804634018
- type: f1
value: 98.2973494821836
- type: precision
value: 98.2095839915745
- type: recall
value: 98.4728804634018
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 82.74025974025975
- type: f1
value: 82.67420447730439
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.0380848063507
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 29.45956405670166
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.122
- type: map_at_10
value: 42.03
- type: map_at_100
value: 43.364000000000004
- type: map_at_1000
value: 43.474000000000004
- type: map_at_3
value: 38.804
- type: map_at_5
value: 40.585
- type: mrr_at_1
value: 39.914
- type: mrr_at_10
value: 48.227
- type: mrr_at_100
value: 49.018
- type: mrr_at_1000
value: 49.064
- type: mrr_at_3
value: 45.994
- type: mrr_at_5
value: 47.396
- type: ndcg_at_1
value: 39.914
- type: ndcg_at_10
value: 47.825
- type: ndcg_at_100
value: 52.852
- type: ndcg_at_1000
value: 54.891
- type: ndcg_at_3
value: 43.517
- type: ndcg_at_5
value: 45.493
- type: precision_at_1
value: 39.914
- type: precision_at_10
value: 8.956
- type: precision_at_100
value: 1.388
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 20.791999999999998
- type: precision_at_5
value: 14.821000000000002
- type: recall_at_1
value: 32.122
- type: recall_at_10
value: 58.294999999999995
- type: recall_at_100
value: 79.726
- type: recall_at_1000
value: 93.099
- type: recall_at_3
value: 45.017
- type: recall_at_5
value: 51.002
- type: map_at_1
value: 29.677999999999997
- type: map_at_10
value: 38.684000000000005
- type: map_at_100
value: 39.812999999999995
- type: map_at_1000
value: 39.945
- type: map_at_3
value: 35.831
- type: map_at_5
value: 37.446
- type: mrr_at_1
value: 37.771
- type: mrr_at_10
value: 44.936
- type: mrr_at_100
value: 45.583
- type: mrr_at_1000
value: 45.634
- type: mrr_at_3
value: 42.771
- type: mrr_at_5
value: 43.994
- type: ndcg_at_1
value: 37.771
- type: ndcg_at_10
value: 44.059
- type: ndcg_at_100
value: 48.192
- type: ndcg_at_1000
value: 50.375
- type: ndcg_at_3
value: 40.172000000000004
- type: ndcg_at_5
value: 41.899
- type: precision_at_1
value: 37.771
- type: precision_at_10
value: 8.286999999999999
- type: precision_at_100
value: 1.322
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 19.406000000000002
- type: precision_at_5
value: 13.745
- type: recall_at_1
value: 29.677999999999997
- type: recall_at_10
value: 53.071
- type: recall_at_100
value: 70.812
- type: recall_at_1000
value: 84.841
- type: recall_at_3
value: 41.016000000000005
- type: recall_at_5
value: 46.22
- type: map_at_1
value: 42.675000000000004
- type: map_at_10
value: 53.93599999999999
- type: map_at_100
value: 54.806999999999995
- type: map_at_1000
value: 54.867
- type: map_at_3
value: 50.934000000000005
- type: map_at_5
value: 52.583
- type: mrr_at_1
value: 48.339
- type: mrr_at_10
value: 57.265
- type: mrr_at_100
value: 57.873
- type: mrr_at_1000
value: 57.906
- type: mrr_at_3
value: 55.193000000000005
- type: mrr_at_5
value: 56.303000000000004
- type: ndcg_at_1
value: 48.339
- type: ndcg_at_10
value: 59.19799999999999
- type: ndcg_at_100
value: 62.743
- type: ndcg_at_1000
value: 63.99399999999999
- type: ndcg_at_3
value: 54.367
- type: ndcg_at_5
value: 56.548
- type: precision_at_1
value: 48.339
- type: precision_at_10
value: 9.216000000000001
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 23.72
- type: precision_at_5
value: 16.025
- type: recall_at_1
value: 42.675000000000004
- type: recall_at_10
value: 71.437
- type: recall_at_100
value: 86.803
- type: recall_at_1000
value: 95.581
- type: recall_at_3
value: 58.434
- type: recall_at_5
value: 63.754
- type: map_at_1
value: 23.518
- type: map_at_10
value: 30.648999999999997
- type: map_at_100
value: 31.508999999999997
- type: map_at_1000
value: 31.604
- type: map_at_3
value: 28.247
- type: map_at_5
value: 29.65
- type: mrr_at_1
value: 25.650000000000002
- type: mrr_at_10
value: 32.771
- type: mrr_at_100
value: 33.554
- type: mrr_at_1000
value: 33.629999999999995
- type: mrr_at_3
value: 30.433
- type: mrr_at_5
value: 31.812
- type: ndcg_at_1
value: 25.650000000000002
- type: ndcg_at_10
value: 34.929
- type: ndcg_at_100
value: 39.382
- type: ndcg_at_1000
value: 41.913
- type: ndcg_at_3
value: 30.292
- type: ndcg_at_5
value: 32.629999999999995
- type: precision_at_1
value: 25.650000000000002
- type: precision_at_10
value: 5.311
- type: precision_at_100
value: 0.792
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 12.58
- type: precision_at_5
value: 8.994
- type: recall_at_1
value: 23.518
- type: recall_at_10
value: 46.19
- type: recall_at_100
value: 67.123
- type: recall_at_1000
value: 86.442
- type: recall_at_3
value: 33.678000000000004
- type: recall_at_5
value: 39.244
- type: map_at_1
value: 15.891
- type: map_at_10
value: 22.464000000000002
- type: map_at_100
value: 23.483
- type: map_at_1000
value: 23.613
- type: map_at_3
value: 20.080000000000002
- type: map_at_5
value: 21.526
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 26.712999999999997
- type: mrr_at_100
value: 27.650000000000002
- type: mrr_at_1000
value: 27.737000000000002
- type: mrr_at_3
value: 24.274
- type: mrr_at_5
value: 25.711000000000002
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 27.028999999999996
- type: ndcg_at_100
value: 32.064
- type: ndcg_at_1000
value: 35.188
- type: ndcg_at_3
value: 22.512999999999998
- type: ndcg_at_5
value: 24.89
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 4.776
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 10.531
- type: precision_at_5
value: 7.811
- type: recall_at_1
value: 15.891
- type: recall_at_10
value: 37.261
- type: recall_at_100
value: 59.12
- type: recall_at_1000
value: 81.356
- type: recall_at_3
value: 24.741
- type: recall_at_5
value: 30.753999999999998
- type: map_at_1
value: 27.544
- type: map_at_10
value: 36.283
- type: map_at_100
value: 37.467
- type: map_at_1000
value: 37.574000000000005
- type: map_at_3
value: 33.528999999999996
- type: map_at_5
value: 35.028999999999996
- type: mrr_at_1
value: 34.166999999999994
- type: mrr_at_10
value: 41.866
- type: mrr_at_100
value: 42.666
- type: mrr_at_1000
value: 42.716
- type: mrr_at_3
value: 39.541
- type: mrr_at_5
value: 40.768
- type: ndcg_at_1
value: 34.166999999999994
- type: ndcg_at_10
value: 41.577
- type: ndcg_at_100
value: 46.687
- type: ndcg_at_1000
value: 48.967
- type: ndcg_at_3
value: 37.177
- type: ndcg_at_5
value: 39.097
- type: precision_at_1
value: 34.166999999999994
- type: precision_at_10
value: 7.420999999999999
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 17.291999999999998
- type: precision_at_5
value: 12.166
- type: recall_at_1
value: 27.544
- type: recall_at_10
value: 51.99399999999999
- type: recall_at_100
value: 73.738
- type: recall_at_1000
value: 89.33
- type: recall_at_3
value: 39.179
- type: recall_at_5
value: 44.385999999999996
- type: map_at_1
value: 26.661
- type: map_at_10
value: 35.475
- type: map_at_100
value: 36.626999999999995
- type: map_at_1000
value: 36.741
- type: map_at_3
value: 32.818000000000005
- type: map_at_5
value: 34.397
- type: mrr_at_1
value: 32.647999999999996
- type: mrr_at_10
value: 40.784
- type: mrr_at_100
value: 41.602
- type: mrr_at_1000
value: 41.661
- type: mrr_at_3
value: 38.68
- type: mrr_at_5
value: 39.838
- type: ndcg_at_1
value: 32.647999999999996
- type: ndcg_at_10
value: 40.697
- type: ndcg_at_100
value: 45.799
- type: ndcg_at_1000
value: 48.235
- type: ndcg_at_3
value: 36.516
- type: ndcg_at_5
value: 38.515
- type: precision_at_1
value: 32.647999999999996
- type: precision_at_10
value: 7.202999999999999
- type: precision_at_100
value: 1.1360000000000001
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 17.314
- type: precision_at_5
value: 12.145999999999999
- type: recall_at_1
value: 26.661
- type: recall_at_10
value: 50.995000000000005
- type: recall_at_100
value: 73.065
- type: recall_at_1000
value: 89.781
- type: recall_at_3
value: 39.073
- type: recall_at_5
value: 44.395
- type: map_at_1
value: 25.946583333333333
- type: map_at_10
value: 33.79725
- type: map_at_100
value: 34.86408333333333
- type: map_at_1000
value: 34.9795
- type: map_at_3
value: 31.259999999999998
- type: map_at_5
value: 32.71541666666666
- type: mrr_at_1
value: 30.863749999999996
- type: mrr_at_10
value: 37.99183333333333
- type: mrr_at_100
value: 38.790499999999994
- type: mrr_at_1000
value: 38.85575000000001
- type: mrr_at_3
value: 35.82083333333333
- type: mrr_at_5
value: 37.07533333333333
- type: ndcg_at_1
value: 30.863749999999996
- type: ndcg_at_10
value: 38.52141666666667
- type: ndcg_at_100
value: 43.17966666666667
- type: ndcg_at_1000
value: 45.64608333333333
- type: ndcg_at_3
value: 34.333000000000006
- type: ndcg_at_5
value: 36.34975
- type: precision_at_1
value: 30.863749999999996
- type: precision_at_10
value: 6.598999999999999
- type: precision_at_100
value: 1.0502500000000001
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 15.557583333333334
- type: precision_at_5
value: 11.020000000000001
- type: recall_at_1
value: 25.946583333333333
- type: recall_at_10
value: 48.36991666666666
- type: recall_at_100
value: 69.02408333333334
- type: recall_at_1000
value: 86.43858333333331
- type: recall_at_3
value: 36.4965
- type: recall_at_5
value: 41.76258333333334
- type: map_at_1
value: 22.431
- type: map_at_10
value: 28.889
- type: map_at_100
value: 29.642000000000003
- type: map_at_1000
value: 29.742
- type: map_at_3
value: 26.998
- type: map_at_5
value: 28.172000000000004
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 31.763
- type: mrr_at_100
value: 32.443
- type: mrr_at_1000
value: 32.531
- type: mrr_at_3
value: 29.959000000000003
- type: mrr_at_5
value: 31.063000000000002
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 32.586999999999996
- type: ndcg_at_100
value: 36.5
- type: ndcg_at_1000
value: 39.133
- type: ndcg_at_3
value: 29.25
- type: ndcg_at_5
value: 31.023
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 4.954
- type: precision_at_100
value: 0.747
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.741999999999999
- type: recall_at_1
value: 22.431
- type: recall_at_10
value: 41.134
- type: recall_at_100
value: 59.28600000000001
- type: recall_at_1000
value: 78.857
- type: recall_at_3
value: 31.926
- type: recall_at_5
value: 36.335
- type: map_at_1
value: 17.586
- type: map_at_10
value: 23.304
- type: map_at_100
value: 24.159
- type: map_at_1000
value: 24.281
- type: map_at_3
value: 21.316
- type: map_at_5
value: 22.383
- type: mrr_at_1
value: 21.645
- type: mrr_at_10
value: 27.365000000000002
- type: mrr_at_100
value: 28.108
- type: mrr_at_1000
value: 28.192
- type: mrr_at_3
value: 25.482
- type: mrr_at_5
value: 26.479999999999997
- type: ndcg_at_1
value: 21.645
- type: ndcg_at_10
value: 27.306
- type: ndcg_at_100
value: 31.496000000000002
- type: ndcg_at_1000
value: 34.53
- type: ndcg_at_3
value: 23.73
- type: ndcg_at_5
value: 25.294
- type: precision_at_1
value: 21.645
- type: precision_at_10
value: 4.797
- type: precision_at_100
value: 0.8059999999999999
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 10.850999999999999
- type: precision_at_5
value: 7.736
- type: recall_at_1
value: 17.586
- type: recall_at_10
value: 35.481
- type: recall_at_100
value: 54.534000000000006
- type: recall_at_1000
value: 76.456
- type: recall_at_3
value: 25.335
- type: recall_at_5
value: 29.473
- type: map_at_1
value: 25.095
- type: map_at_10
value: 32.374
- type: map_at_100
value: 33.537
- type: map_at_1000
value: 33.634
- type: map_at_3
value: 30.089
- type: map_at_5
value: 31.433
- type: mrr_at_1
value: 29.198
- type: mrr_at_10
value: 36.01
- type: mrr_at_100
value: 37.022
- type: mrr_at_1000
value: 37.083
- type: mrr_at_3
value: 33.94
- type: mrr_at_5
value: 35.148
- type: ndcg_at_1
value: 29.198
- type: ndcg_at_10
value: 36.729
- type: ndcg_at_100
value: 42.114000000000004
- type: ndcg_at_1000
value: 44.592
- type: ndcg_at_3
value: 32.644
- type: ndcg_at_5
value: 34.652
- type: precision_at_1
value: 29.198
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 0.967
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 14.396999999999998
- type: precision_at_5
value: 10.093
- type: recall_at_1
value: 25.095
- type: recall_at_10
value: 46.392
- type: recall_at_100
value: 69.706
- type: recall_at_1000
value: 87.738
- type: recall_at_3
value: 35.303000000000004
- type: recall_at_5
value: 40.441
- type: map_at_1
value: 26.857999999999997
- type: map_at_10
value: 34.066
- type: map_at_100
value: 35.671
- type: map_at_1000
value: 35.881
- type: map_at_3
value: 31.304
- type: map_at_5
value: 32.885
- type: mrr_at_1
value: 32.411
- type: mrr_at_10
value: 38.987
- type: mrr_at_100
value: 39.894
- type: mrr_at_1000
value: 39.959
- type: mrr_at_3
value: 36.626999999999995
- type: mrr_at_5
value: 38.011
- type: ndcg_at_1
value: 32.411
- type: ndcg_at_10
value: 39.208
- type: ndcg_at_100
value: 44.626
- type: ndcg_at_1000
value: 47.43
- type: ndcg_at_3
value: 35.091
- type: ndcg_at_5
value: 37.119
- type: precision_at_1
value: 32.411
- type: precision_at_10
value: 7.51
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 16.14
- type: precision_at_5
value: 11.976
- type: recall_at_1
value: 26.857999999999997
- type: recall_at_10
value: 47.407
- type: recall_at_100
value: 72.236
- type: recall_at_1000
value: 90.77
- type: recall_at_3
value: 35.125
- type: recall_at_5
value: 40.522999999999996
- type: map_at_1
value: 21.3
- type: map_at_10
value: 27.412999999999997
- type: map_at_100
value: 28.29
- type: map_at_1000
value: 28.398
- type: map_at_3
value: 25.169999999999998
- type: map_at_5
value: 26.496
- type: mrr_at_1
value: 23.29
- type: mrr_at_10
value: 29.215000000000003
- type: mrr_at_100
value: 30.073
- type: mrr_at_1000
value: 30.156
- type: mrr_at_3
value: 26.956000000000003
- type: mrr_at_5
value: 28.38
- type: ndcg_at_1
value: 23.29
- type: ndcg_at_10
value: 31.113000000000003
- type: ndcg_at_100
value: 35.701
- type: ndcg_at_1000
value: 38.505
- type: ndcg_at_3
value: 26.727
- type: ndcg_at_5
value: 29.037000000000003
- type: precision_at_1
value: 23.29
- type: precision_at_10
value: 4.787
- type: precision_at_100
value: 0.763
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 11.091
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 21.3
- type: recall_at_10
value: 40.782000000000004
- type: recall_at_100
value: 62.13999999999999
- type: recall_at_1000
value: 83.012
- type: recall_at_3
value: 29.131
- type: recall_at_5
value: 34.624
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.631
- type: map_at_10
value: 16.634999999999998
- type: map_at_100
value: 18.23
- type: map_at_1000
value: 18.419
- type: map_at_3
value: 13.66
- type: map_at_5
value: 15.173
- type: mrr_at_1
value: 21.368000000000002
- type: mrr_at_10
value: 31.56
- type: mrr_at_100
value: 32.58
- type: mrr_at_1000
value: 32.633
- type: mrr_at_3
value: 28.241
- type: mrr_at_5
value: 30.225
- type: ndcg_at_1
value: 21.368000000000002
- type: ndcg_at_10
value: 23.855999999999998
- type: ndcg_at_100
value: 30.686999999999998
- type: ndcg_at_1000
value: 34.327000000000005
- type: ndcg_at_3
value: 18.781
- type: ndcg_at_5
value: 20.73
- type: precision_at_1
value: 21.368000000000002
- type: precision_at_10
value: 7.564
- type: precision_at_100
value: 1.496
- type: precision_at_1000
value: 0.217
- type: precision_at_3
value: 13.876
- type: precision_at_5
value: 11.062
- type: recall_at_1
value: 9.631
- type: recall_at_10
value: 29.517
- type: recall_at_100
value: 53.452
- type: recall_at_1000
value: 74.115
- type: recall_at_3
value: 17.605999999999998
- type: recall_at_5
value: 22.505
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.885
- type: map_at_10
value: 18.798000000000002
- type: map_at_100
value: 26.316
- type: map_at_1000
value: 27.869
- type: map_at_3
value: 13.719000000000001
- type: map_at_5
value: 15.716
- type: mrr_at_1
value: 66
- type: mrr_at_10
value: 74.263
- type: mrr_at_100
value: 74.519
- type: mrr_at_1000
value: 74.531
- type: mrr_at_3
value: 72.458
- type: mrr_at_5
value: 73.321
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.355999999999995
- type: ndcg_at_100
value: 44.366
- type: ndcg_at_1000
value: 51.771
- type: ndcg_at_3
value: 45.195
- type: ndcg_at_5
value: 42.187000000000005
- type: precision_at_1
value: 66
- type: precision_at_10
value: 31.75
- type: precision_at_100
value: 10.11
- type: precision_at_1000
value: 1.9800000000000002
- type: precision_at_3
value: 48.167
- type: precision_at_5
value: 40.050000000000004
- type: recall_at_1
value: 8.885
- type: recall_at_10
value: 24.471999999999998
- type: recall_at_100
value: 49.669000000000004
- type: recall_at_1000
value: 73.383
- type: recall_at_3
value: 14.872
- type: recall_at_5
value: 18.262999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.18
- type: f1
value: 40.26878691789978
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.751999999999995
- type: map_at_10
value: 74.131
- type: map_at_100
value: 74.407
- type: map_at_1000
value: 74.423
- type: map_at_3
value: 72.329
- type: map_at_5
value: 73.555
- type: mrr_at_1
value: 67.282
- type: mrr_at_10
value: 78.292
- type: mrr_at_100
value: 78.455
- type: mrr_at_1000
value: 78.458
- type: mrr_at_3
value: 76.755
- type: mrr_at_5
value: 77.839
- type: ndcg_at_1
value: 67.282
- type: ndcg_at_10
value: 79.443
- type: ndcg_at_100
value: 80.529
- type: ndcg_at_1000
value: 80.812
- type: ndcg_at_3
value: 76.281
- type: ndcg_at_5
value: 78.235
- type: precision_at_1
value: 67.282
- type: precision_at_10
value: 10.078
- type: precision_at_100
value: 1.082
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 30.178
- type: precision_at_5
value: 19.232
- type: recall_at_1
value: 62.751999999999995
- type: recall_at_10
value: 91.521
- type: recall_at_100
value: 95.997
- type: recall_at_1000
value: 97.775
- type: recall_at_3
value: 83.131
- type: recall_at_5
value: 87.93299999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.861
- type: map_at_10
value: 30.252000000000002
- type: map_at_100
value: 32.082
- type: map_at_1000
value: 32.261
- type: map_at_3
value: 25.909
- type: map_at_5
value: 28.296
- type: mrr_at_1
value: 37.346000000000004
- type: mrr_at_10
value: 45.802
- type: mrr_at_100
value: 46.611999999999995
- type: mrr_at_1000
value: 46.659
- type: mrr_at_3
value: 43.056
- type: mrr_at_5
value: 44.637
- type: ndcg_at_1
value: 37.346000000000004
- type: ndcg_at_10
value: 38.169
- type: ndcg_at_100
value: 44.864
- type: ndcg_at_1000
value: 47.974
- type: ndcg_at_3
value: 33.619
- type: ndcg_at_5
value: 35.317
- type: precision_at_1
value: 37.346000000000004
- type: precision_at_10
value: 10.693999999999999
- type: precision_at_100
value: 1.775
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 22.325
- type: precision_at_5
value: 16.852
- type: recall_at_1
value: 18.861
- type: recall_at_10
value: 45.672000000000004
- type: recall_at_100
value: 70.60499999999999
- type: recall_at_1000
value: 89.216
- type: recall_at_3
value: 30.361
- type: recall_at_5
value: 36.998999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.852999999999994
- type: map_at_10
value: 59.961
- type: map_at_100
value: 60.78
- type: map_at_1000
value: 60.843
- type: map_at_3
value: 56.39999999999999
- type: map_at_5
value: 58.646
- type: mrr_at_1
value: 75.70599999999999
- type: mrr_at_10
value: 82.321
- type: mrr_at_100
value: 82.516
- type: mrr_at_1000
value: 82.525
- type: mrr_at_3
value: 81.317
- type: mrr_at_5
value: 81.922
- type: ndcg_at_1
value: 75.70599999999999
- type: ndcg_at_10
value: 68.557
- type: ndcg_at_100
value: 71.485
- type: ndcg_at_1000
value: 72.71600000000001
- type: ndcg_at_3
value: 63.524
- type: ndcg_at_5
value: 66.338
- type: precision_at_1
value: 75.70599999999999
- type: precision_at_10
value: 14.463000000000001
- type: precision_at_100
value: 1.677
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 40.806
- type: precision_at_5
value: 26.709
- type: recall_at_1
value: 37.852999999999994
- type: recall_at_10
value: 72.316
- type: recall_at_100
value: 83.842
- type: recall_at_1000
value: 91.999
- type: recall_at_3
value: 61.209
- type: recall_at_5
value: 66.77199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.46039999999999
- type: ap
value: 79.9812521351881
- type: f1
value: 85.31722909702084
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.704
- type: map_at_10
value: 35.329
- type: map_at_100
value: 36.494
- type: map_at_1000
value: 36.541000000000004
- type: map_at_3
value: 31.476
- type: map_at_5
value: 33.731
- type: mrr_at_1
value: 23.294999999999998
- type: mrr_at_10
value: 35.859
- type: mrr_at_100
value: 36.968
- type: mrr_at_1000
value: 37.008
- type: mrr_at_3
value: 32.085
- type: mrr_at_5
value: 34.299
- type: ndcg_at_1
value: 23.324
- type: ndcg_at_10
value: 42.274
- type: ndcg_at_100
value: 47.839999999999996
- type: ndcg_at_1000
value: 48.971
- type: ndcg_at_3
value: 34.454
- type: ndcg_at_5
value: 38.464
- type: precision_at_1
value: 23.324
- type: precision_at_10
value: 6.648
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.674999999999999
- type: precision_at_5
value: 10.850999999999999
- type: recall_at_1
value: 22.704
- type: recall_at_10
value: 63.660000000000004
- type: recall_at_100
value: 89.29899999999999
- type: recall_at_1000
value: 97.88900000000001
- type: recall_at_3
value: 42.441
- type: recall_at_5
value: 52.04
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.1326949384405
- type: f1
value: 92.89743579612082
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.62524654832347
- type: f1
value: 88.65106082263151
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.59039359573046
- type: f1
value: 90.31532892105662
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.21046038208581
- type: f1
value: 86.41459529813113
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.3180351380423
- type: f1
value: 86.71383078226444
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.24231464737792
- type: f1
value: 86.31845567592403
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.27131782945736
- type: f1
value: 57.52079940417103
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.2341504649197
- type: f1
value: 51.349951558039244
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.27418278852569
- type: f1
value: 50.1714985749095
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.68243031631694
- type: f1
value: 50.1066160836192
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.2362854069559
- type: f1
value: 48.821279948766424
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.71428571428571
- type: f1
value: 53.94611389496195
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.97646267652992
- type: f1
value: 57.26797883561521
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 53.65501008742435
- type: f1
value: 50.416258382177034
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.45796906523201
- type: f1
value: 53.306690547422185
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.59246805648957
- type: f1
value: 59.818381969051494
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 61.126429051782104
- type: f1
value: 58.25993593933026
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 50.057162071284466
- type: f1
value: 46.96095728790911
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.64425016812375
- type: f1
value: 62.858291698755764
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.08944182918628
- type: f1
value: 62.44639030604241
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.68056489576328
- type: f1
value: 61.775326758789504
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.11163416274377
- type: f1
value: 69.70789096927015
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.40282447881641
- type: f1
value: 66.38492065671895
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.24613315400134
- type: f1
value: 64.3348019501336
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.78345662407531
- type: f1
value: 62.21279452354622
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.9455279085407
- type: f1
value: 65.48193124964094
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.05110961667788
- type: f1
value: 58.097856564684534
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.95292535305985
- type: f1
value: 62.09182174767901
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.97310020174848
- type: f1
value: 61.14252567730396
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.08069939475453
- type: f1
value: 57.044041742492034
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (id)
type: mteb/amazon_massive_intent
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.63752521856085
- type: f1
value: 63.889340907205316
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (is)
type: mteb/amazon_massive_intent
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 56.385339609952936
- type: f1
value: 53.449033750088304
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (it)
type: mteb/amazon_massive_intent
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.93073301950234
- type: f1
value: 65.9884357824104
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ja)
type: mteb/amazon_massive_intent
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.94418291862812
- type: f1
value: 66.48740222583132
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (jv)
type: mteb/amazon_massive_intent
config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 54.26025554808339
- type: f1
value: 50.19562815100793
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ka)
type: mteb/amazon_massive_intent
config: ka
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 48.98789509078682
- type: f1
value: 46.65788438676836
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (km)
type: mteb/amazon_massive_intent
config: km
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.68728984532616
- type: f1
value: 41.642419349541996
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (kn)
type: mteb/amazon_massive_intent
config: kn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.19300605245461
- type: f1
value: 55.8626492442437
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ko)
type: mteb/amazon_massive_intent
config: ko
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.33826496301278
- type: f1
value: 63.89499791648792
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (lv)
type: mteb/amazon_massive_intent
config: lv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 60.33960995292536
- type: f1
value: 57.15242464180892
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ml)
type: mteb/amazon_massive_intent
config: ml
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.09347679892402
- type: f1
value: 59.64733214063841
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (mn)
type: mteb/amazon_massive_intent
config: mn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.75924680564896
- type: f1
value: 55.96585692366827
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ms)
type: mteb/amazon_massive_intent
config: ms
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 62.48486886348352
- type: f1
value: 59.45143559032946
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (my)
type: mteb/amazon_massive_intent
config: my
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 58.56422326832549
- type: f1
value: 54.96368702901926
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (nb)
type: mteb/amazon_massive_intent
config: nb
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.18022864828512
- type: f1
value: 63.05369805040634
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (nl)
type: mteb/amazon_massive_intent
config: nl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 67.30329522528581
- type: f1
value: 64.06084612020727
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.36919973100201
- type: f1
value: 65.12154124788887
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pt)
type: mteb/amazon_massive_intent
config: pt
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.98117014122394
- type: f1
value: 66.41847559806962
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ro)
type: mteb/amazon_massive_intent
config: ro
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.53799596503026
- type: f1
value: 62.17067330740817
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.01815736381977
- type: f1
value: 66.24988369607843
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (sl)
type: mteb/amazon_massive_intent
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split: test
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type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.96683513343383
- type: mrr
value: 31.967364078714834
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.5280000000000005
- type: map_at_10
value: 11.793
- type: map_at_100
value: 14.496999999999998
- type: map_at_1000
value: 15.783
- type: map_at_3
value: 8.838
- type: map_at_5
value: 10.07
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 51.531000000000006
- type: mrr_at_100
value: 52.205
- type: mrr_at_1000
value: 52.242999999999995
- type: mrr_at_3
value: 49.431999999999995
- type: mrr_at_5
value: 50.470000000000006
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 32.464999999999996
- type: ndcg_at_100
value: 28.927999999999997
- type: ndcg_at_1000
value: 37.629000000000005
- type: ndcg_at_3
value: 37.845
- type: ndcg_at_5
value: 35.147
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 23.932000000000002
- type: precision_at_100
value: 7.17
- type: precision_at_1000
value: 1.967
- type: precision_at_3
value: 35.397
- type: precision_at_5
value: 29.907
- type: recall_at_1
value: 5.5280000000000005
- type: recall_at_10
value: 15.568000000000001
- type: recall_at_100
value: 28.54
- type: recall_at_1000
value: 59.864
- type: recall_at_3
value: 9.822000000000001
- type: recall_at_5
value: 11.726
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.041000000000004
- type: map_at_10
value: 52.664
- type: map_at_100
value: 53.477
- type: map_at_1000
value: 53.505
- type: map_at_3
value: 48.510999999999996
- type: map_at_5
value: 51.036
- type: mrr_at_1
value: 41.338
- type: mrr_at_10
value: 55.071000000000005
- type: mrr_at_100
value: 55.672
- type: mrr_at_1000
value: 55.689
- type: mrr_at_3
value: 51.82
- type: mrr_at_5
value: 53.852
- type: ndcg_at_1
value: 41.338
- type: ndcg_at_10
value: 60.01800000000001
- type: ndcg_at_100
value: 63.409000000000006
- type: ndcg_at_1000
value: 64.017
- type: ndcg_at_3
value: 52.44799999999999
- type: ndcg_at_5
value: 56.571000000000005
- type: precision_at_1
value: 41.338
- type: precision_at_10
value: 9.531
- type: precision_at_100
value: 1.145
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.416
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 37.041000000000004
- type: recall_at_10
value: 79.76299999999999
- type: recall_at_100
value: 94.39
- type: recall_at_1000
value: 98.851
- type: recall_at_3
value: 60.465
- type: recall_at_5
value: 69.906
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.952
- type: map_at_10
value: 83.758
- type: map_at_100
value: 84.406
- type: map_at_1000
value: 84.425
- type: map_at_3
value: 80.839
- type: map_at_5
value: 82.646
- type: mrr_at_1
value: 80.62
- type: mrr_at_10
value: 86.947
- type: mrr_at_100
value: 87.063
- type: mrr_at_1000
value: 87.064
- type: mrr_at_3
value: 85.96000000000001
- type: mrr_at_5
value: 86.619
- type: ndcg_at_1
value: 80.63
- type: ndcg_at_10
value: 87.64800000000001
- type: ndcg_at_100
value: 88.929
- type: ndcg_at_1000
value: 89.054
- type: ndcg_at_3
value: 84.765
- type: ndcg_at_5
value: 86.291
- type: precision_at_1
value: 80.63
- type: precision_at_10
value: 13.314
- type: precision_at_100
value: 1.525
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.1
- type: precision_at_5
value: 24.372
- type: recall_at_1
value: 69.952
- type: recall_at_10
value: 94.955
- type: recall_at_100
value: 99.38
- type: recall_at_1000
value: 99.96000000000001
- type: recall_at_3
value: 86.60600000000001
- type: recall_at_5
value: 90.997
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.41329517878427
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.171278362748666
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.213
- type: map_at_10
value: 9.895
- type: map_at_100
value: 11.776
- type: map_at_1000
value: 12.084
- type: map_at_3
value: 7.2669999999999995
- type: map_at_5
value: 8.620999999999999
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 31.112000000000002
- type: mrr_at_100
value: 32.274
- type: mrr_at_1000
value: 32.35
- type: mrr_at_3
value: 28.133000000000003
- type: mrr_at_5
value: 29.892999999999997
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.163999999999998
- type: ndcg_at_100
value: 24.738
- type: ndcg_at_1000
value: 30.316
- type: ndcg_at_3
value: 16.665
- type: ndcg_at_5
value: 14.478
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 8.74
- type: precision_at_100
value: 1.963
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.6
- type: recall_at_1
value: 4.213
- type: recall_at_10
value: 17.698
- type: recall_at_100
value: 39.838
- type: recall_at_1000
value: 66.893
- type: recall_at_3
value: 9.418
- type: recall_at_5
value: 12.773000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.90453315738294
- type: cos_sim_spearman
value: 78.51197850080254
- type: euclidean_pearson
value: 80.09647123597748
- type: euclidean_spearman
value: 78.63548011514061
- type: manhattan_pearson
value: 80.10645285675231
- type: manhattan_spearman
value: 78.57861806068901
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.2616156846401
- type: cos_sim_spearman
value: 76.69713867850156
- type: euclidean_pearson
value: 77.97948563800394
- type: euclidean_spearman
value: 74.2371211567807
- type: manhattan_pearson
value: 77.69697879669705
- type: manhattan_spearman
value: 73.86529778022278
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.0293269315045
- type: cos_sim_spearman
value: 78.02555120584198
- type: euclidean_pearson
value: 78.25398100379078
- type: euclidean_spearman
value: 78.66963870599464
- type: manhattan_pearson
value: 78.14314682167348
- type: manhattan_spearman
value: 78.57692322969135
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.16989925136942
- type: cos_sim_spearman
value: 76.5996225327091
- type: euclidean_pearson
value: 77.8319003279786
- type: euclidean_spearman
value: 76.42824009468998
- type: manhattan_pearson
value: 77.69118862737736
- type: manhattan_spearman
value: 76.25568104762812
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.42012286935325
- type: cos_sim_spearman
value: 88.15654297884122
- type: euclidean_pearson
value: 87.34082819427852
- type: euclidean_spearman
value: 88.06333589547084
- type: manhattan_pearson
value: 87.25115596784842
- type: manhattan_spearman
value: 87.9559927695203
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.88222044996712
- type: cos_sim_spearman
value: 84.28476589061077
- type: euclidean_pearson
value: 83.17399758058309
- type: euclidean_spearman
value: 83.85497357244542
- type: manhattan_pearson
value: 83.0308397703786
- type: manhattan_spearman
value: 83.71554539935046
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.20682986257339
- type: cos_sim_spearman
value: 79.94567120362092
- type: euclidean_pearson
value: 79.43122480368902
- type: euclidean_spearman
value: 79.94802077264987
- type: manhattan_pearson
value: 79.32653021527081
- type: manhattan_spearman
value: 79.80961146709178
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.46578144394383
- type: cos_sim_spearman
value: 74.52496637472179
- type: euclidean_pearson
value: 72.2903807076809
- type: euclidean_spearman
value: 73.55549359771645
- type: manhattan_pearson
value: 72.09324837709393
- type: manhattan_spearman
value: 73.36743103606581
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 71.37272335116
- type: cos_sim_spearman
value: 71.26702117766037
- type: euclidean_pearson
value: 67.114829954434
- type: euclidean_spearman
value: 66.37938893947761
- type: manhattan_pearson
value: 66.79688574095246
- type: manhattan_spearman
value: 66.17292828079667
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.61016770129092
- type: cos_sim_spearman
value: 82.08515426632214
- type: euclidean_pearson
value: 80.557340361131
- type: euclidean_spearman
value: 80.37585812266175
- type: manhattan_pearson
value: 80.6782873404285
- type: manhattan_spearman
value: 80.6678073032024
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.00150745350108
- type: cos_sim_spearman
value: 87.83441972211425
- type: euclidean_pearson
value: 87.94826702308792
- type: euclidean_spearman
value: 87.46143974860725
- type: manhattan_pearson
value: 87.97560344306105
- type: manhattan_spearman
value: 87.5267102829796
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 64.76325252267235
- type: cos_sim_spearman
value: 63.32615095463905
- type: euclidean_pearson
value: 64.07920669155716
- type: euclidean_spearman
value: 61.21409893072176
- type: manhattan_pearson
value: 64.26308625680016
- type: manhattan_spearman
value: 61.2438185254079
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.82644463022595
- type: cos_sim_spearman
value: 76.50381269945073
- type: euclidean_pearson
value: 75.1328548315934
- type: euclidean_spearman
value: 75.63761139408453
- type: manhattan_pearson
value: 75.18610101241407
- type: manhattan_spearman
value: 75.30669266354164
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.49994164686832
- type: cos_sim_spearman
value: 86.73743986245549
- type: euclidean_pearson
value: 86.8272894387145
- type: euclidean_spearman
value: 85.97608491000507
- type: manhattan_pearson
value: 86.74960140396779
- type: manhattan_spearman
value: 85.79285984190273
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.58172210788469
- type: cos_sim_spearman
value: 80.17516468334607
- type: euclidean_pearson
value: 77.56537843470504
- type: euclidean_spearman
value: 77.57264627395521
- type: manhattan_pearson
value: 78.09703521695943
- type: manhattan_spearman
value: 78.15942760916954
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.7589932931751
- type: cos_sim_spearman
value: 80.15210089028162
- type: euclidean_pearson
value: 77.54135223516057
- type: euclidean_spearman
value: 77.52697996368764
- type: manhattan_pearson
value: 77.65734439572518
- type: manhattan_spearman
value: 77.77702992016121
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.16682365511267
- type: cos_sim_spearman
value: 79.25311267628506
- type: euclidean_pearson
value: 77.54882036762244
- type: euclidean_spearman
value: 77.33212935194827
- type: manhattan_pearson
value: 77.98405516064015
- type: manhattan_spearman
value: 77.85075717865719
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.10473294775917
- type: cos_sim_spearman
value: 61.82780474476838
- type: euclidean_pearson
value: 45.885111672377256
- type: euclidean_spearman
value: 56.88306351932454
- type: manhattan_pearson
value: 46.101218127323186
- type: manhattan_spearman
value: 56.80953694186333
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.781923079584146
- type: cos_sim_spearman
value: 55.95098449691107
- type: euclidean_pearson
value: 25.4571031323205
- type: euclidean_spearman
value: 49.859978118078935
- type: manhattan_pearson
value: 25.624938455041384
- type: manhattan_spearman
value: 49.99546185049401
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.00618133997907
- type: cos_sim_spearman
value: 66.57896677718321
- type: euclidean_pearson
value: 42.60118466388821
- type: euclidean_spearman
value: 62.8210759715209
- type: manhattan_pearson
value: 42.63446860604094
- type: manhattan_spearman
value: 62.73803068925271
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.460759121626943
- type: cos_sim_spearman
value: 34.13459007469131
- type: euclidean_pearson
value: 6.0917739325525195
- type: euclidean_spearman
value: 27.9947262664867
- type: manhattan_pearson
value: 6.16877864169911
- type: manhattan_spearman
value: 28.00664163971514
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.42546621771696
- type: cos_sim_spearman
value: 63.699663168970474
- type: euclidean_pearson
value: 38.12085278789738
- type: euclidean_spearman
value: 58.12329140741536
- type: manhattan_pearson
value: 37.97364549443335
- type: manhattan_spearman
value: 57.81545502318733
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 46.82241380954213
- type: cos_sim_spearman
value: 57.86569456006391
- type: euclidean_pearson
value: 31.80480070178813
- type: euclidean_spearman
value: 52.484000620130104
- type: manhattan_pearson
value: 31.952708554646097
- type: manhattan_spearman
value: 52.8560972356195
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 52.00447170498087
- type: cos_sim_spearman
value: 60.664116225735164
- type: euclidean_pearson
value: 33.87382555421702
- type: euclidean_spearman
value: 55.74649067458667
- type: manhattan_pearson
value: 33.99117246759437
- type: manhattan_spearman
value: 55.98749034923899
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.06497233105448
- type: cos_sim_spearman
value: 65.62968801135676
- type: euclidean_pearson
value: 47.482076613243905
- type: euclidean_spearman
value: 62.65137791498299
- type: manhattan_pearson
value: 47.57052626104093
- type: manhattan_spearman
value: 62.436916516613294
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.49397298562575
- type: cos_sim_spearman
value: 74.79604041187868
- type: euclidean_pearson
value: 49.661891561317795
- type: euclidean_spearman
value: 70.31535537621006
- type: manhattan_pearson
value: 49.553715741850006
- type: manhattan_spearman
value: 70.24779344636806
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.640574515348696
- type: cos_sim_spearman
value: 54.927959317689
- type: euclidean_pearson
value: 29.00139666967476
- type: euclidean_spearman
value: 41.86386566971605
- type: manhattan_pearson
value: 29.47411067730344
- type: manhattan_spearman
value: 42.337438424952786
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.14095292259312
- type: cos_sim_spearman
value: 73.99017581234789
- type: euclidean_pearson
value: 46.46304297872084
- type: euclidean_spearman
value: 60.91834114800041
- type: manhattan_pearson
value: 47.07072666338692
- type: manhattan_spearman
value: 61.70415727977926
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.27184653359575
- type: cos_sim_spearman
value: 77.76070252418626
- type: euclidean_pearson
value: 62.30586577544778
- type: euclidean_spearman
value: 75.14246629110978
- type: manhattan_pearson
value: 62.328196884927046
- type: manhattan_spearman
value: 75.1282792981433
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.59448528829957
- type: cos_sim_spearman
value: 70.37277734222123
- type: euclidean_pearson
value: 57.63145565721123
- type: euclidean_spearman
value: 66.10113048304427
- type: manhattan_pearson
value: 57.18897811586808
- type: manhattan_spearman
value: 66.5595511215901
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.37520607720838
- type: cos_sim_spearman
value: 69.92282148997948
- type: euclidean_pearson
value: 40.55768770125291
- type: euclidean_spearman
value: 55.189128944669605
- type: manhattan_pearson
value: 41.03566433468883
- type: manhattan_spearman
value: 55.61251893174558
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 57.791929533771835
- type: cos_sim_spearman
value: 66.45819707662093
- type: euclidean_pearson
value: 39.03686018511092
- type: euclidean_spearman
value: 56.01282695640428
- type: manhattan_pearson
value: 38.91586623619632
- type: manhattan_spearman
value: 56.69394943612747
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.82224468473866
- type: cos_sim_spearman
value: 59.467307194781164
- type: euclidean_pearson
value: 27.428459190256145
- type: euclidean_spearman
value: 60.83463107397519
- type: manhattan_pearson
value: 27.487391578496638
- type: manhattan_spearman
value: 61.281380460246496
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 16.306666792752644
- type: cos_sim_spearman
value: 39.35486427252405
- type: euclidean_pearson
value: -2.7887154897955435
- type: euclidean_spearman
value: 27.1296051831719
- type: manhattan_pearson
value: -3.202291270581297
- type: manhattan_spearman
value: 26.32895849218158
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.67006803805076
- type: cos_sim_spearman
value: 73.24670207647144
- type: euclidean_pearson
value: 46.91884681500483
- type: euclidean_spearman
value: 16.903085094570333
- type: manhattan_pearson
value: 46.88391675325812
- type: manhattan_spearman
value: 28.17180849095055
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.79555591223837
- type: cos_sim_spearman
value: 85.63658602085185
- type: euclidean_pearson
value: 85.22080894037671
- type: euclidean_spearman
value: 85.54113580167038
- type: manhattan_pearson
value: 85.1639505960118
- type: manhattan_spearman
value: 85.43502665436196
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.73900991689766
- type: mrr
value: 94.81624131133934
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.678000000000004
- type: map_at_10
value: 65.135
- type: map_at_100
value: 65.824
- type: map_at_1000
value: 65.852
- type: map_at_3
value: 62.736000000000004
- type: map_at_5
value: 64.411
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 66.5
- type: mrr_at_100
value: 67.053
- type: mrr_at_1000
value: 67.08
- type: mrr_at_3
value: 64.944
- type: mrr_at_5
value: 65.89399999999999
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 69.34700000000001
- type: ndcg_at_100
value: 72.32
- type: ndcg_at_1000
value: 73.014
- type: ndcg_at_3
value: 65.578
- type: ndcg_at_5
value: 67.738
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 55.678000000000004
- type: recall_at_10
value: 80.72200000000001
- type: recall_at_100
value: 93.93299999999999
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 70.783
- type: recall_at_5
value: 75.978
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.74653465346535
- type: cos_sim_ap
value: 93.01476369929063
- type: cos_sim_f1
value: 86.93009118541033
- type: cos_sim_precision
value: 88.09034907597535
- type: cos_sim_recall
value: 85.8
- type: dot_accuracy
value: 99.22970297029703
- type: dot_ap
value: 51.58725659485144
- type: dot_f1
value: 53.51351351351352
- type: dot_precision
value: 58.235294117647065
- type: dot_recall
value: 49.5
- type: euclidean_accuracy
value: 99.74356435643564
- type: euclidean_ap
value: 92.40332894384368
- type: euclidean_f1
value: 86.97838109602817
- type: euclidean_precision
value: 87.46208291203236
- type: euclidean_recall
value: 86.5
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 92.01320815721121
- type: manhattan_f1
value: 86.4135864135864
- type: manhattan_precision
value: 86.32734530938124
- type: manhattan_recall
value: 86.5
- type: max_accuracy
value: 99.74653465346535
- type: max_ap
value: 93.01476369929063
- type: max_f1
value: 86.97838109602817
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.2660514302523
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.4637783572547
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.41377758357637
- type: mrr
value: 50.138451213818854
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 28.887846011166594
- type: cos_sim_spearman
value: 30.10823258355903
- type: dot_pearson
value: 12.888049550236385
- type: dot_spearman
value: 12.827495903098123
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.667
- type: map_at_100
value: 9.15
- type: map_at_1000
value: 22.927
- type: map_at_3
value: 0.573
- type: map_at_5
value: 0.915
- type: mrr_at_1
value: 80
- type: mrr_at_10
value: 87.167
- type: mrr_at_100
value: 87.167
- type: mrr_at_1000
value: 87.167
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 87.167
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 69.757
- type: ndcg_at_100
value: 52.402
- type: ndcg_at_1000
value: 47.737
- type: ndcg_at_3
value: 71.866
- type: ndcg_at_5
value: 72.225
- type: precision_at_1
value: 80
- type: precision_at_10
value: 75
- type: precision_at_100
value: 53.959999999999994
- type: precision_at_1000
value: 21.568
- type: precision_at_3
value: 76.667
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.9189999999999998
- type: recall_at_100
value: 12.589
- type: recall_at_1000
value: 45.312000000000005
- type: recall_at_3
value: 0.61
- type: recall_at_5
value: 1.019
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.10000000000001
- type: f1
value: 90.06
- type: precision
value: 89.17333333333333
- type: recall
value: 92.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 56.06936416184971
- type: f1
value: 50.87508028259473
- type: precision
value: 48.97398843930635
- type: recall
value: 56.06936416184971
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.3170731707317
- type: f1
value: 52.96080139372822
- type: precision
value: 51.67861124382864
- type: recall
value: 57.3170731707317
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.67333333333333
- type: precision
value: 91.90833333333333
- type: recall
value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97.07333333333332
- type: precision
value: 96.79500000000002
- type: recall
value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.2
- type: precision
value: 92.48333333333333
- type: recall
value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.9
- type: f1
value: 91.26666666666667
- type: precision
value: 90.59444444444445
- type: recall
value: 92.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 34.32835820895522
- type: f1
value: 29.074180380150533
- type: precision
value: 28.068207322920596
- type: recall
value: 34.32835820895522
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.5
- type: f1
value: 74.3945115995116
- type: precision
value: 72.82967843459222
- type: recall
value: 78.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.34146341463415
- type: f1
value: 61.2469400518181
- type: precision
value: 59.63977756660683
- type: recall
value: 66.34146341463415
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 80.9
- type: f1
value: 76.90349206349207
- type: precision
value: 75.32921568627451
- type: recall
value: 80.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.93317132442284
- type: f1
value: 81.92519105034295
- type: precision
value: 80.71283920615635
- type: recall
value: 84.93317132442284
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 71.1304347826087
- type: f1
value: 65.22394755003451
- type: precision
value: 62.912422360248435
- type: recall
value: 71.1304347826087
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.82608695652173
- type: f1
value: 75.55693581780538
- type: precision
value: 73.79420289855072
- type: recall
value: 79.82608695652173
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74
- type: f1
value: 70.51022222222223
- type: precision
value: 69.29673599347512
- type: recall
value: 74
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.7
- type: f1
value: 74.14238095238095
- type: precision
value: 72.27214285714285
- type: recall
value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.97466827503016
- type: f1
value: 43.080330405420874
- type: precision
value: 41.36505499593557
- type: recall
value: 48.97466827503016
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.60000000000001
- type: f1
value: 86.62333333333333
- type: precision
value: 85.225
- type: recall
value: 89.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.2
- type: f1
value: 39.5761253006253
- type: precision
value: 37.991358436312
- type: recall
value: 45.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.70333333333333
- type: precision
value: 85.53166666666667
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 42.774116796477045
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value: 50.095238095238095
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type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 63.4
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value: 58.35967261904762
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value: 56.54857142857143
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value: 63.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.2
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value: 87.075
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value: 86.12095238095239
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value: 89.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96.8
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value: 95.50833333333333
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value: 96.8
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type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.9
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value: 88.6288888888889
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value: 87.61607142857142
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value: 90.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.2
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value: 60.54377630539395
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value: 58.89434482711381
- type: recall
value: 65.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87
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value: 84.32412698412699
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value: 87
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type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 68.7
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value: 63.07883541295306
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value: 61.06117424242426
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value: 68.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.7
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value: 90.86666666666667
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value: 93.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.7
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value: 96.96666666666667
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value: 96.61666666666667
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value: 97.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.27493261455525
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value: 85.90745732255168
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value: 84.91389637616052
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value: 88.27493261455525
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type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.5982905982906
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value: 90.5982905982906
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type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 89.5
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value: 86.90769841269842
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value: 85.80178571428571
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value: 89.5
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type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 82.5
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value: 78.36796536796538
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value: 76.82196969696969
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value: 82.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.48846960167715
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value: 66.78771089148448
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value: 64.98302885095339
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value: 71.48846960167715
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.1
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value: 92.50333333333333
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value: 91.77499999999999
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value: 94.1
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type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 65.35065777283677
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value: 71.20622568093385
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type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 48.717948717948715
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value: 42.04721204721204
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value: 48.717948717948715
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type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.5
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value: 53.8564991863928
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value: 52.40329436122275
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value: 58.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.8
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value: 88.29
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value: 87.09166666666667
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value: 90.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 60.98065939771546
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value: 67.28971962616822
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 78.7
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value: 75.5264472455649
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value: 74.38205086580086
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value: 78.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 88.7
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value: 86.10809523809525
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value: 85.07602564102565
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value: 88.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 56.99999999999999
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value: 51.53985162713104
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value: 56.99999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94
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value: 92.45333333333333
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value: 94
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type: BitextMining
dataset:
name: MTEB Tatoeba (ita-eng)
type: mteb/tatoeba-bitext-mining
config: ita-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.30000000000001
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value: 89.83333333333331
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value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cmn-eng)
type: mteb/tatoeba-bitext-mining
config: cmn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.69999999999999
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value: 93.34555555555555
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value: 92.75416666666668
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value: 94.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lvs-eng)
type: mteb/tatoeba-bitext-mining
config: lvs-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.2
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value: 76.6563035113035
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value: 75.3014652014652
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value: 80.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (glg-eng)
type: mteb/tatoeba-bitext-mining
config: glg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 84.7
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value: 82.78689263765207
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value: 82.06705086580087
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value: 84.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ceb-eng)
type: mteb/tatoeba-bitext-mining
config: ceb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 50.33333333333333
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type: BitextMining
dataset:
name: MTEB Tatoeba (bre-eng)
type: mteb/tatoeba-bitext-mining
config: bre-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 6.6000000000000005
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type: BitextMining
dataset:
name: MTEB Tatoeba (ben-eng)
type: mteb/tatoeba-bitext-mining
config: ben-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85
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value: 79.25
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value: 85
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type: BitextMining
dataset:
name: MTEB Tatoeba (swg-eng)
type: mteb/tatoeba-bitext-mining
config: swg-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 47.32142857142857
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type: BitextMining
dataset:
name: MTEB Tatoeba (arq-eng)
type: mteb/tatoeba-bitext-mining
config: arq-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 30.735455543358945
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value: 25.397823220451283
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value: 30.735455543358945
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kab-eng)
type: mteb/tatoeba-bitext-mining
config: kab-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 25.1
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value: 21.975989896371022
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value: 21.059885632257203
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value: 25.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fra-eng)
type: mteb/tatoeba-bitext-mining
config: fra-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.3
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value: 92.75666666666666
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value: 92.06166666666665
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value: 94.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (por-eng)
type: mteb/tatoeba-bitext-mining
config: por-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.1
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value: 92.74
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value: 92.09166666666667
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value: 94.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tat-eng)
type: mteb/tatoeba-bitext-mining
config: tat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 71.3
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value: 66.922442002442
- type: precision
value: 65.38249567099568
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value: 71.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (oci-eng)
type: mteb/tatoeba-bitext-mining
config: oci-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 40.300000000000004
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value: 35.78682789299971
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value: 34.66425128716588
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value: 40.300000000000004
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type: BitextMining
dataset:
name: MTEB Tatoeba (pol-eng)
type: mteb/tatoeba-bitext-mining
config: pol-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 96
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value: 94.82333333333334
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value: 94.27833333333334
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value: 96
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type: BitextMining
dataset:
name: MTEB Tatoeba (war-eng)
type: mteb/tatoeba-bitext-mining
config: war-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 51.1
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value: 47.179074753133584
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value: 46.06461044702424
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value: 51.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (aze-eng)
type: mteb/tatoeba-bitext-mining
config: aze-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 87.7
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value: 84.71
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value: 83.46166666666667
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value: 87.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (vie-eng)
type: mteb/tatoeba-bitext-mining
config: vie-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.8
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value: 94.68333333333334
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value: 94.13333333333334
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value: 95.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nno-eng)
type: mteb/tatoeba-bitext-mining
config: nno-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 85.39999999999999
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value: 82.5577380952381
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value: 81.36833333333334
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value: 85.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cha-eng)
type: mteb/tatoeba-bitext-mining
config: cha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 21.16788321167883
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value: 16.948865627297987
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value: 15.971932568647897
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value: 21.16788321167883
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mhr-eng)
type: mteb/tatoeba-bitext-mining
config: mhr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.9
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value: 5.515526831658907
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value: 5.141966366966367
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value: 6.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dan-eng)
type: mteb/tatoeba-bitext-mining
config: dan-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.2
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value: 91.39666666666668
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value: 90.58666666666667
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value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ell-eng)
type: mteb/tatoeba-bitext-mining
config: ell-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
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value: 89.95666666666666
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value: 88.92833333333333
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value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (amh-eng)
type: mteb/tatoeba-bitext-mining
config: amh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 79.76190476190477
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value: 74.93386243386244
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value: 73.11011904761904
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value: 79.76190476190477
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pam-eng)
type: mteb/tatoeba-bitext-mining
config: pam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 8.799999999999999
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value: 6.921439712248537
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value: 6.489885109680683
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value: 8.799999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hsb-eng)
type: mteb/tatoeba-bitext-mining
config: hsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 45.75569358178054
- type: f1
value: 40.34699501312631
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value: 38.57886764719063
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value: 45.75569358178054
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (srp-eng)
type: mteb/tatoeba-bitext-mining
config: srp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.4
- type: f1
value: 89.08333333333333
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value: 88.01666666666668
- type: recall
value: 91.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (epo-eng)
type: mteb/tatoeba-bitext-mining
config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.60000000000001
- type: f1
value: 92.06690476190477
- type: precision
value: 91.45095238095239
- type: recall
value: 93.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kzj-eng)
type: mteb/tatoeba-bitext-mining
config: kzj-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.5
- type: f1
value: 6.200363129378736
- type: precision
value: 5.89115314822466
- type: recall
value: 7.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (awa-eng)
type: mteb/tatoeba-bitext-mining
config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.59307359307358
- type: f1
value: 68.38933553219267
- type: precision
value: 66.62698412698413
- type: recall
value: 73.59307359307358
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fao-eng)
type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.8473282442748
- type: f1
value: 64.72373682297346
- type: precision
value: 62.82834214131924
- type: recall
value: 69.8473282442748
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mal-eng)
type: mteb/tatoeba-bitext-mining
config: mal-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.5254730713246
- type: f1
value: 96.72489082969432
- type: precision
value: 96.33672974284326
- type: recall
value: 97.5254730713246
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ile-eng)
type: mteb/tatoeba-bitext-mining
config: ile-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.6
- type: f1
value: 72.42746031746033
- type: precision
value: 71.14036630036631
- type: recall
value: 75.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bos-eng)
type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.24293785310734
- type: f1
value: 88.86064030131826
- type: precision
value: 87.73540489642184
- type: recall
value: 91.24293785310734
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cor-eng)
type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.2
- type: f1
value: 4.383083659794954
- type: precision
value: 4.027861324289673
- type: recall
value: 6.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 84.09428571428572
- type: precision
value: 83.00333333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.699999999999996
- type: f1
value: 56.1584972394755
- type: precision
value: 54.713456330903135
- type: recall
value: 60.699999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.2
- type: f1
value: 80.66190476190475
- type: precision
value: 79.19690476190476
- type: recall
value: 84.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.33
- type: precision
value: 90.45
- type: recall
value: 93.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.3
- type: f1
value: 5.126828976748276
- type: precision
value: 4.853614328966668
- type: recall
value: 6.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.76943699731903
- type: f1
value: 77.82873739308057
- type: precision
value: 76.27622452019234
- type: recall
value: 81.76943699731903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.30000000000001
- type: f1
value: 90.29666666666665
- type: precision
value: 89.40333333333334
- type: recall
value: 92.30000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 29.249011857707508
- type: f1
value: 24.561866096392947
- type: precision
value: 23.356583740215456
- type: recall
value: 29.249011857707508
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.46478873239437
- type: f1
value: 73.23943661971832
- type: precision
value: 71.66666666666667
- type: recall
value: 77.46478873239437
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 20.35928143712575
- type: f1
value: 15.997867865075824
- type: precision
value: 14.882104658301346
- type: recall
value: 20.35928143712575
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.2
- type: f1
value: 90.25999999999999
- type: precision
value: 89.45333333333335
- type: recall
value: 92.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 23.15270935960591
- type: f1
value: 19.65673625772148
- type: precision
value: 18.793705293464992
- type: recall
value: 23.15270935960591
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.154929577464785
- type: f1
value: 52.3868463305083
- type: precision
value: 50.14938113529662
- type: recall
value: 59.154929577464785
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.51282051282051
- type: f1
value: 66.8089133089133
- type: precision
value: 65.37645687645687
- type: recall
value: 70.51282051282051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93
- type: precision
value: 92.23333333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.62212943632568
- type: f1
value: 34.3278276962583
- type: precision
value: 33.07646935732408
- type: recall
value: 38.62212943632568
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 28.1
- type: f1
value: 23.579609223054604
- type: precision
value: 22.39622774921555
- type: recall
value: 28.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.27361563517914
- type: f1
value: 85.12486427795874
- type: precision
value: 83.71335504885994
- type: recall
value: 88.27361563517914
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.6
- type: f1
value: 86.39928571428571
- type: precision
value: 85.4947557997558
- type: recall
value: 88.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.77952380952381
- type: precision
value: 82.67602564102565
- type: recall
value: 86.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.52755905511812
- type: f1
value: 75.3055868016498
- type: precision
value: 73.81889763779527
- type: recall
value: 79.52755905511812
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.76261904761905
- type: precision
value: 72.11670995670995
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.8781163434903
- type: f1
value: 47.25804051288816
- type: precision
value: 45.0603482390186
- type: recall
value: 53.8781163434903
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.10000000000001
- type: f1
value: 88.88
- type: precision
value: 87.96333333333334
- type: recall
value: 91.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 38.46153846153847
- type: f1
value: 34.43978243978244
- type: precision
value: 33.429487179487175
- type: recall
value: 38.46153846153847
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.9
- type: f1
value: 86.19888888888887
- type: precision
value: 85.07440476190476
- type: recall
value: 88.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.9
- type: f1
value: 82.58857142857143
- type: precision
value: 81.15666666666667
- type: recall
value: 85.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.8
- type: f1
value: 83.36999999999999
- type: precision
value: 81.86833333333333
- type: recall
value: 86.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.51415094339622
- type: f1
value: 63.195000099481234
- type: precision
value: 61.394033442972116
- type: recall
value: 68.51415094339622
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 86.14603174603175
- type: precision
value: 85.1162037037037
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.62043795620438
- type: f1
value: 94.40389294403892
- type: precision
value: 93.7956204379562
- type: recall
value: 95.62043795620438
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.8
- type: f1
value: 78.6532178932179
- type: precision
value: 77.46348795840176
- type: recall
value: 81.8
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.603
- type: map_at_10
value: 8.5
- type: map_at_100
value: 12.985
- type: map_at_1000
value: 14.466999999999999
- type: map_at_3
value: 4.859999999999999
- type: map_at_5
value: 5.817
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 42.331
- type: mrr_at_100
value: 43.592999999999996
- type: mrr_at_1000
value: 43.592999999999996
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 39.966
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 21.353
- type: ndcg_at_100
value: 31.087999999999997
- type: ndcg_at_1000
value: 43.163000000000004
- type: ndcg_at_3
value: 22.999
- type: ndcg_at_5
value: 21.451
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 19.387999999999998
- type: precision_at_100
value: 6.265
- type: precision_at_1000
value: 1.4160000000000001
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 2.603
- type: recall_at_10
value: 14.474
- type: recall_at_100
value: 40.287
- type: recall_at_1000
value: 76.606
- type: recall_at_3
value: 5.978
- type: recall_at_5
value: 7.819
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.7848
- type: ap
value: 13.661023167088224
- type: f1
value: 53.61686134460943
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.28183361629882
- type: f1
value: 61.55481034919965
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 35.972128420092396
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59933241938367
- type: cos_sim_ap
value: 72.20760361208136
- type: cos_sim_f1
value: 66.4447731755424
- type: cos_sim_precision
value: 62.35539102267469
- type: cos_sim_recall
value: 71.10817941952506
- type: dot_accuracy
value: 78.98313166835548
- type: dot_ap
value: 44.492521645493795
- type: dot_f1
value: 45.814889336016094
- type: dot_precision
value: 37.02439024390244
- type: dot_recall
value: 60.07915567282321
- type: euclidean_accuracy
value: 85.3907134767837
- type: euclidean_ap
value: 71.53847289080343
- type: euclidean_f1
value: 65.95952206778834
- type: euclidean_precision
value: 61.31006346328196
- type: euclidean_recall
value: 71.37203166226914
- type: manhattan_accuracy
value: 85.40859510043511
- type: manhattan_ap
value: 71.49664104395515
- type: manhattan_f1
value: 65.98569969356485
- type: manhattan_precision
value: 63.928748144482924
- type: manhattan_recall
value: 68.17941952506597
- type: max_accuracy
value: 85.59933241938367
- type: max_ap
value: 72.20760361208136
- type: max_f1
value: 66.4447731755424
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.83261536073273
- type: cos_sim_ap
value: 85.48178133644264
- type: cos_sim_f1
value: 77.87816307403935
- type: cos_sim_precision
value: 75.88953021114926
- type: cos_sim_recall
value: 79.97382198952879
- type: dot_accuracy
value: 79.76287499514883
- type: dot_ap
value: 59.17438838475084
- type: dot_f1
value: 56.34566667855996
- type: dot_precision
value: 52.50349092359864
- type: dot_recall
value: 60.794579611949494
- type: euclidean_accuracy
value: 88.76857996662397
- type: euclidean_ap
value: 85.22764834359887
- type: euclidean_f1
value: 77.65379751543554
- type: euclidean_precision
value: 75.11152683839401
- type: euclidean_recall
value: 80.37419156144134
- type: manhattan_accuracy
value: 88.6987231730508
- type: manhattan_ap
value: 85.18907981724007
- type: manhattan_f1
value: 77.51967028849757
- type: manhattan_precision
value: 75.49992701795358
- type: manhattan_recall
value: 79.65044656606098
- type: max_accuracy
value: 88.83261536073273
- type: max_ap
value: 85.48178133644264
- type: max_f1
value: 77.87816307403935
---
## Multilingual-E5-base
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 12 layers and the embedding size is 768.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-base')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-base')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"TRANSLATION",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
avsolatorio/GIST-Embedding-v0 | avsolatorio | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2402.16829",
"arxiv:2212.09741",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-31T16:41:20 | 2024-02-28T00:31:27 | 548,257 | 25 | ---
language:
- en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
model-index:
- name: GIST-Embedding-v0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.95522388059702
- type: ap
value: 38.940434354439276
- type: f1
value: 69.88686275888114
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.51357499999999
- type: ap
value: 90.30414241486682
- type: f1
value: 93.50552829047328
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.446000000000005
- type: f1
value: 49.76432659699279
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.265
- type: map_at_10
value: 54.236
- type: map_at_100
value: 54.81399999999999
- type: map_at_1000
value: 54.81700000000001
- type: map_at_3
value: 49.881
- type: map_at_5
value: 52.431000000000004
- type: mrr_at_1
value: 38.265
- type: mrr_at_10
value: 54.152
- type: mrr_at_100
value: 54.730000000000004
- type: mrr_at_1000
value: 54.733
- type: mrr_at_3
value: 49.644
- type: mrr_at_5
value: 52.32599999999999
- type: ndcg_at_1
value: 38.265
- type: ndcg_at_10
value: 62.62
- type: ndcg_at_100
value: 64.96600000000001
- type: ndcg_at_1000
value: 65.035
- type: ndcg_at_3
value: 53.691
- type: ndcg_at_5
value: 58.303000000000004
- type: precision_at_1
value: 38.265
- type: precision_at_10
value: 8.919
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.573999999999998
- type: precision_at_5
value: 15.192
- type: recall_at_1
value: 38.265
- type: recall_at_10
value: 89.189
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 64.723
- type: recall_at_5
value: 75.96000000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.287087887491744
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.74244928943812
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.68814324295771
- type: mrr
value: 75.46266983247591
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 90.45240209600391
- type: cos_sim_spearman
value: 87.95079919934645
- type: euclidean_pearson
value: 88.93438602492702
- type: euclidean_spearman
value: 88.28152962682988
- type: manhattan_pearson
value: 88.92193964325268
- type: manhattan_spearman
value: 88.21466063329498
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 15.605427974947808
- type: f1
value: 14.989877233698866
- type: precision
value: 14.77906814441261
- type: recall
value: 15.605427974947808
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 33.38102575390711
- type: f1
value: 32.41704114719127
- type: precision
value: 32.057363829835964
- type: recall
value: 33.38102575390711
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 0.1939729823346034
- type: f1
value: 0.17832215223820772
- type: precision
value: 0.17639155671715423
- type: recall
value: 0.1939729823346034
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 3.0542390731964195
- type: f1
value: 2.762857644374232
- type: precision
value: 2.6505178163945935
- type: recall
value: 3.0542390731964195
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.29545454545453
- type: f1
value: 87.26415991342238
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.035319537839484
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.667313307057285
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.979
- type: map_at_10
value: 46.275
- type: map_at_100
value: 47.975
- type: map_at_1000
value: 48.089
- type: map_at_3
value: 42.507
- type: map_at_5
value: 44.504
- type: mrr_at_1
value: 42.346000000000004
- type: mrr_at_10
value: 53.013
- type: mrr_at_100
value: 53.717000000000006
- type: mrr_at_1000
value: 53.749
- type: mrr_at_3
value: 50.405
- type: mrr_at_5
value: 51.915
- type: ndcg_at_1
value: 42.346000000000004
- type: ndcg_at_10
value: 53.179
- type: ndcg_at_100
value: 58.458
- type: ndcg_at_1000
value: 60.057
- type: ndcg_at_3
value: 48.076
- type: ndcg_at_5
value: 50.283
- type: precision_at_1
value: 42.346000000000004
- type: precision_at_10
value: 10.386
- type: precision_at_100
value: 1.635
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 23.413999999999998
- type: precision_at_5
value: 16.624
- type: recall_at_1
value: 33.979
- type: recall_at_10
value: 65.553
- type: recall_at_100
value: 87.18599999999999
- type: recall_at_1000
value: 97.25200000000001
- type: recall_at_3
value: 50.068999999999996
- type: recall_at_5
value: 56.882
- type: map_at_1
value: 31.529
- type: map_at_10
value: 42.219
- type: map_at_100
value: 43.408
- type: map_at_1000
value: 43.544
- type: map_at_3
value: 39.178000000000004
- type: map_at_5
value: 40.87
- type: mrr_at_1
value: 39.873
- type: mrr_at_10
value: 48.25
- type: mrr_at_100
value: 48.867
- type: mrr_at_1000
value: 48.908
- type: mrr_at_3
value: 46.03
- type: mrr_at_5
value: 47.355000000000004
- type: ndcg_at_1
value: 39.873
- type: ndcg_at_10
value: 47.933
- type: ndcg_at_100
value: 52.156000000000006
- type: ndcg_at_1000
value: 54.238
- type: ndcg_at_3
value: 43.791999999999994
- type: ndcg_at_5
value: 45.678999999999995
- type: precision_at_1
value: 39.873
- type: precision_at_10
value: 9.032
- type: precision_at_100
value: 1.419
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 21.231
- type: precision_at_5
value: 14.981
- type: recall_at_1
value: 31.529
- type: recall_at_10
value: 57.925000000000004
- type: recall_at_100
value: 75.89
- type: recall_at_1000
value: 89.007
- type: recall_at_3
value: 45.363
- type: recall_at_5
value: 50.973
- type: map_at_1
value: 41.289
- type: map_at_10
value: 54.494
- type: map_at_100
value: 55.494
- type: map_at_1000
value: 55.545
- type: map_at_3
value: 51.20099999999999
- type: map_at_5
value: 53.147
- type: mrr_at_1
value: 47.335
- type: mrr_at_10
value: 57.772
- type: mrr_at_100
value: 58.428000000000004
- type: mrr_at_1000
value: 58.453
- type: mrr_at_3
value: 55.434000000000005
- type: mrr_at_5
value: 56.8
- type: ndcg_at_1
value: 47.335
- type: ndcg_at_10
value: 60.382999999999996
- type: ndcg_at_100
value: 64.294
- type: ndcg_at_1000
value: 65.211
- type: ndcg_at_3
value: 55.098
- type: ndcg_at_5
value: 57.776
- type: precision_at_1
value: 47.335
- type: precision_at_10
value: 9.724
- type: precision_at_100
value: 1.26
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 24.786
- type: precision_at_5
value: 16.977999999999998
- type: recall_at_1
value: 41.289
- type: recall_at_10
value: 74.36399999999999
- type: recall_at_100
value: 91.19800000000001
- type: recall_at_1000
value: 97.508
- type: recall_at_3
value: 60.285
- type: recall_at_5
value: 66.814
- type: map_at_1
value: 28.816999999999997
- type: map_at_10
value: 37.856
- type: map_at_100
value: 38.824
- type: map_at_1000
value: 38.902
- type: map_at_3
value: 34.982
- type: map_at_5
value: 36.831
- type: mrr_at_1
value: 31.073
- type: mrr_at_10
value: 39.985
- type: mrr_at_100
value: 40.802
- type: mrr_at_1000
value: 40.861999999999995
- type: mrr_at_3
value: 37.419999999999995
- type: mrr_at_5
value: 39.104
- type: ndcg_at_1
value: 31.073
- type: ndcg_at_10
value: 42.958
- type: ndcg_at_100
value: 47.671
- type: ndcg_at_1000
value: 49.633
- type: ndcg_at_3
value: 37.602000000000004
- type: ndcg_at_5
value: 40.688
- type: precision_at_1
value: 31.073
- type: precision_at_10
value: 6.531000000000001
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 15.857
- type: precision_at_5
value: 11.209
- type: recall_at_1
value: 28.816999999999997
- type: recall_at_10
value: 56.538999999999994
- type: recall_at_100
value: 78.17699999999999
- type: recall_at_1000
value: 92.92200000000001
- type: recall_at_3
value: 42.294
- type: recall_at_5
value: 49.842999999999996
- type: map_at_1
value: 18.397
- type: map_at_10
value: 27.256999999999998
- type: map_at_100
value: 28.541
- type: map_at_1000
value: 28.658
- type: map_at_3
value: 24.565
- type: map_at_5
value: 26.211000000000002
- type: mrr_at_1
value: 22.761
- type: mrr_at_10
value: 32.248
- type: mrr_at_100
value: 33.171
- type: mrr_at_1000
value: 33.227000000000004
- type: mrr_at_3
value: 29.498
- type: mrr_at_5
value: 31.246000000000002
- type: ndcg_at_1
value: 22.761
- type: ndcg_at_10
value: 32.879999999999995
- type: ndcg_at_100
value: 38.913
- type: ndcg_at_1000
value: 41.504999999999995
- type: ndcg_at_3
value: 27.988000000000003
- type: ndcg_at_5
value: 30.548
- type: precision_at_1
value: 22.761
- type: precision_at_10
value: 6.045
- type: precision_at_100
value: 1.044
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 13.433
- type: precision_at_5
value: 9.925
- type: recall_at_1
value: 18.397
- type: recall_at_10
value: 45.14
- type: recall_at_100
value: 71.758
- type: recall_at_1000
value: 89.854
- type: recall_at_3
value: 31.942999999999998
- type: recall_at_5
value: 38.249
- type: map_at_1
value: 30.604
- type: map_at_10
value: 42.132
- type: map_at_100
value: 43.419000000000004
- type: map_at_1000
value: 43.527
- type: map_at_3
value: 38.614
- type: map_at_5
value: 40.705000000000005
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.696
- type: mrr_at_100
value: 48.483
- type: mrr_at_1000
value: 48.53
- type: mrr_at_3
value: 45.123999999999995
- type: mrr_at_5
value: 46.635
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 48.421
- type: ndcg_at_100
value: 53.568000000000005
- type: ndcg_at_1000
value: 55.574999999999996
- type: ndcg_at_3
value: 42.89
- type: ndcg_at_5
value: 45.683
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.758000000000001
- type: precision_at_100
value: 1.319
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 20.244
- type: precision_at_5
value: 14.533
- type: recall_at_1
value: 30.604
- type: recall_at_10
value: 61.605
- type: recall_at_100
value: 82.787
- type: recall_at_1000
value: 95.78
- type: recall_at_3
value: 46.303
- type: recall_at_5
value: 53.351000000000006
- type: map_at_1
value: 26.262999999999998
- type: map_at_10
value: 36.858999999999995
- type: map_at_100
value: 38.241
- type: map_at_1000
value: 38.346999999999994
- type: map_at_3
value: 33.171
- type: map_at_5
value: 35.371
- type: mrr_at_1
value: 32.42
- type: mrr_at_10
value: 42.361
- type: mrr_at_100
value: 43.219
- type: mrr_at_1000
value: 43.271
- type: mrr_at_3
value: 39.593
- type: mrr_at_5
value: 41.248000000000005
- type: ndcg_at_1
value: 32.42
- type: ndcg_at_10
value: 43.081
- type: ndcg_at_100
value: 48.837
- type: ndcg_at_1000
value: 50.954
- type: ndcg_at_3
value: 37.413000000000004
- type: ndcg_at_5
value: 40.239000000000004
- type: precision_at_1
value: 32.42
- type: precision_at_10
value: 8.071
- type: precision_at_100
value: 1.272
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 17.922
- type: precision_at_5
value: 13.311
- type: recall_at_1
value: 26.262999999999998
- type: recall_at_10
value: 56.062999999999995
- type: recall_at_100
value: 80.636
- type: recall_at_1000
value: 94.707
- type: recall_at_3
value: 40.425
- type: recall_at_5
value: 47.663
- type: map_at_1
value: 27.86616666666667
- type: map_at_10
value: 37.584999999999994
- type: map_at_100
value: 38.80291666666667
- type: map_at_1000
value: 38.91358333333333
- type: map_at_3
value: 34.498
- type: map_at_5
value: 36.269999999999996
- type: mrr_at_1
value: 33.07566666666667
- type: mrr_at_10
value: 41.92366666666666
- type: mrr_at_100
value: 42.73516666666667
- type: mrr_at_1000
value: 42.785666666666664
- type: mrr_at_3
value: 39.39075
- type: mrr_at_5
value: 40.89133333333334
- type: ndcg_at_1
value: 33.07566666666667
- type: ndcg_at_10
value: 43.19875
- type: ndcg_at_100
value: 48.32083333333334
- type: ndcg_at_1000
value: 50.418000000000006
- type: ndcg_at_3
value: 38.10308333333333
- type: ndcg_at_5
value: 40.5985
- type: precision_at_1
value: 33.07566666666667
- type: precision_at_10
value: 7.581916666666666
- type: precision_at_100
value: 1.1975
- type: precision_at_1000
value: 0.15699999999999997
- type: precision_at_3
value: 17.49075
- type: precision_at_5
value: 12.5135
- type: recall_at_1
value: 27.86616666666667
- type: recall_at_10
value: 55.449749999999995
- type: recall_at_100
value: 77.92516666666666
- type: recall_at_1000
value: 92.31358333333333
- type: recall_at_3
value: 41.324416666666664
- type: recall_at_5
value: 47.72533333333333
- type: map_at_1
value: 26.648
- type: map_at_10
value: 33.155
- type: map_at_100
value: 34.149
- type: map_at_1000
value: 34.239000000000004
- type: map_at_3
value: 30.959999999999997
- type: map_at_5
value: 32.172
- type: mrr_at_1
value: 30.061
- type: mrr_at_10
value: 36.229
- type: mrr_at_100
value: 37.088
- type: mrr_at_1000
value: 37.15
- type: mrr_at_3
value: 34.254
- type: mrr_at_5
value: 35.297
- type: ndcg_at_1
value: 30.061
- type: ndcg_at_10
value: 37.247
- type: ndcg_at_100
value: 42.093
- type: ndcg_at_1000
value: 44.45
- type: ndcg_at_3
value: 33.211
- type: ndcg_at_5
value: 35.083999999999996
- type: precision_at_1
value: 30.061
- type: precision_at_10
value: 5.7059999999999995
- type: precision_at_100
value: 0.8880000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 13.957
- type: precision_at_5
value: 9.663
- type: recall_at_1
value: 26.648
- type: recall_at_10
value: 46.85
- type: recall_at_100
value: 68.87
- type: recall_at_1000
value: 86.508
- type: recall_at_3
value: 35.756
- type: recall_at_5
value: 40.376
- type: map_at_1
value: 19.058
- type: map_at_10
value: 26.722
- type: map_at_100
value: 27.863
- type: map_at_1000
value: 27.988000000000003
- type: map_at_3
value: 24.258
- type: map_at_5
value: 25.531
- type: mrr_at_1
value: 23.09
- type: mrr_at_10
value: 30.711
- type: mrr_at_100
value: 31.628
- type: mrr_at_1000
value: 31.702
- type: mrr_at_3
value: 28.418
- type: mrr_at_5
value: 29.685
- type: ndcg_at_1
value: 23.09
- type: ndcg_at_10
value: 31.643
- type: ndcg_at_100
value: 37.047999999999995
- type: ndcg_at_1000
value: 39.896
- type: ndcg_at_3
value: 27.189999999999998
- type: ndcg_at_5
value: 29.112
- type: precision_at_1
value: 23.09
- type: precision_at_10
value: 5.743
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.790000000000001
- type: precision_at_5
value: 9.195
- type: recall_at_1
value: 19.058
- type: recall_at_10
value: 42.527
- type: recall_at_100
value: 66.833
- type: recall_at_1000
value: 87.008
- type: recall_at_3
value: 29.876
- type: recall_at_5
value: 34.922
- type: map_at_1
value: 28.066999999999997
- type: map_at_10
value: 37.543
- type: map_at_100
value: 38.725
- type: map_at_1000
value: 38.815
- type: map_at_3
value: 34.488
- type: map_at_5
value: 36.222
- type: mrr_at_1
value: 33.116
- type: mrr_at_10
value: 41.743
- type: mrr_at_100
value: 42.628
- type: mrr_at_1000
value: 42.675999999999995
- type: mrr_at_3
value: 39.241
- type: mrr_at_5
value: 40.622
- type: ndcg_at_1
value: 33.116
- type: ndcg_at_10
value: 43.089
- type: ndcg_at_100
value: 48.61
- type: ndcg_at_1000
value: 50.585
- type: ndcg_at_3
value: 37.816
- type: ndcg_at_5
value: 40.256
- type: precision_at_1
value: 33.116
- type: precision_at_10
value: 7.313
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.102
- type: precision_at_5
value: 12.09
- type: recall_at_1
value: 28.066999999999997
- type: recall_at_10
value: 55.684
- type: recall_at_100
value: 80.092
- type: recall_at_1000
value: 93.605
- type: recall_at_3
value: 41.277
- type: recall_at_5
value: 47.46
- type: map_at_1
value: 27.094
- type: map_at_10
value: 35.939
- type: map_at_100
value: 37.552
- type: map_at_1000
value: 37.771
- type: map_at_3
value: 32.414
- type: map_at_5
value: 34.505
- type: mrr_at_1
value: 32.609
- type: mrr_at_10
value: 40.521
- type: mrr_at_100
value: 41.479
- type: mrr_at_1000
value: 41.524
- type: mrr_at_3
value: 37.451
- type: mrr_at_5
value: 39.387
- type: ndcg_at_1
value: 32.609
- type: ndcg_at_10
value: 41.83
- type: ndcg_at_100
value: 47.763
- type: ndcg_at_1000
value: 50.102999999999994
- type: ndcg_at_3
value: 36.14
- type: ndcg_at_5
value: 39.153999999999996
- type: precision_at_1
value: 32.609
- type: precision_at_10
value: 7.925
- type: precision_at_100
value: 1.591
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 12.411
- type: recall_at_1
value: 27.094
- type: recall_at_10
value: 53.32900000000001
- type: recall_at_100
value: 79.52
- type: recall_at_1000
value: 93.958
- type: recall_at_3
value: 37.773
- type: recall_at_5
value: 45.321
- type: map_at_1
value: 22.649
- type: map_at_10
value: 30.569000000000003
- type: map_at_100
value: 31.444
- type: map_at_1000
value: 31.538
- type: map_at_3
value: 27.638
- type: map_at_5
value: 29.171000000000003
- type: mrr_at_1
value: 24.399
- type: mrr_at_10
value: 32.555
- type: mrr_at_100
value: 33.312000000000005
- type: mrr_at_1000
value: 33.376
- type: mrr_at_3
value: 29.820999999999998
- type: mrr_at_5
value: 31.402
- type: ndcg_at_1
value: 24.399
- type: ndcg_at_10
value: 35.741
- type: ndcg_at_100
value: 40.439
- type: ndcg_at_1000
value: 42.809000000000005
- type: ndcg_at_3
value: 30.020999999999997
- type: ndcg_at_5
value: 32.68
- type: precision_at_1
value: 24.399
- type: precision_at_10
value: 5.749
- type: precision_at_100
value: 0.878
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.815999999999999
- type: precision_at_5
value: 9.242
- type: recall_at_1
value: 22.649
- type: recall_at_10
value: 49.818
- type: recall_at_100
value: 72.155
- type: recall_at_1000
value: 89.654
- type: recall_at_3
value: 34.528999999999996
- type: recall_at_5
value: 40.849999999999994
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.587
- type: map_at_10
value: 23.021
- type: map_at_100
value: 25.095
- type: map_at_1000
value: 25.295
- type: map_at_3
value: 19.463
- type: map_at_5
value: 21.389
- type: mrr_at_1
value: 29.576999999999998
- type: mrr_at_10
value: 41.44
- type: mrr_at_100
value: 42.497
- type: mrr_at_1000
value: 42.529
- type: mrr_at_3
value: 38.284
- type: mrr_at_5
value: 40.249
- type: ndcg_at_1
value: 29.576999999999998
- type: ndcg_at_10
value: 31.491000000000003
- type: ndcg_at_100
value: 39.352
- type: ndcg_at_1000
value: 42.703
- type: ndcg_at_3
value: 26.284999999999997
- type: ndcg_at_5
value: 28.218
- type: precision_at_1
value: 29.576999999999998
- type: precision_at_10
value: 9.713
- type: precision_at_100
value: 1.8079999999999998
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 19.608999999999998
- type: precision_at_5
value: 14.957999999999998
- type: recall_at_1
value: 13.587
- type: recall_at_10
value: 37.001
- type: recall_at_100
value: 63.617999999999995
- type: recall_at_1000
value: 82.207
- type: recall_at_3
value: 24.273
- type: recall_at_5
value: 29.813000000000002
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.98
- type: map_at_10
value: 20.447000000000003
- type: map_at_100
value: 29.032999999999998
- type: map_at_1000
value: 30.8
- type: map_at_3
value: 15.126999999999999
- type: map_at_5
value: 17.327
- type: mrr_at_1
value: 71.25
- type: mrr_at_10
value: 78.014
- type: mrr_at_100
value: 78.303
- type: mrr_at_1000
value: 78.309
- type: mrr_at_3
value: 76.375
- type: mrr_at_5
value: 77.58699999999999
- type: ndcg_at_1
value: 57.99999999999999
- type: ndcg_at_10
value: 41.705
- type: ndcg_at_100
value: 47.466
- type: ndcg_at_1000
value: 55.186
- type: ndcg_at_3
value: 47.089999999999996
- type: ndcg_at_5
value: 43.974000000000004
- type: precision_at_1
value: 71.25
- type: precision_at_10
value: 32.65
- type: precision_at_100
value: 10.89
- type: precision_at_1000
value: 2.197
- type: precision_at_3
value: 50.5
- type: precision_at_5
value: 42.199999999999996
- type: recall_at_1
value: 9.98
- type: recall_at_10
value: 25.144
- type: recall_at_100
value: 53.754999999999995
- type: recall_at_1000
value: 78.56400000000001
- type: recall_at_3
value: 15.964
- type: recall_at_5
value: 19.186
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 54.67999999999999
- type: f1
value: 49.48247525503583
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.798
- type: map_at_10
value: 82.933
- type: map_at_100
value: 83.157
- type: map_at_1000
value: 83.173
- type: map_at_3
value: 81.80199999999999
- type: map_at_5
value: 82.55
- type: mrr_at_1
value: 80.573
- type: mrr_at_10
value: 87.615
- type: mrr_at_100
value: 87.69
- type: mrr_at_1000
value: 87.69200000000001
- type: mrr_at_3
value: 86.86399999999999
- type: mrr_at_5
value: 87.386
- type: ndcg_at_1
value: 80.573
- type: ndcg_at_10
value: 86.64500000000001
- type: ndcg_at_100
value: 87.407
- type: ndcg_at_1000
value: 87.68299999999999
- type: ndcg_at_3
value: 84.879
- type: ndcg_at_5
value: 85.921
- type: precision_at_1
value: 80.573
- type: precision_at_10
value: 10.348
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 32.268
- type: precision_at_5
value: 20.084
- type: recall_at_1
value: 74.798
- type: recall_at_10
value: 93.45400000000001
- type: recall_at_100
value: 96.42500000000001
- type: recall_at_1000
value: 98.158
- type: recall_at_3
value: 88.634
- type: recall_at_5
value: 91.295
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.567
- type: map_at_10
value: 32.967999999999996
- type: map_at_100
value: 35.108
- type: map_at_1000
value: 35.272999999999996
- type: map_at_3
value: 28.701999999999998
- type: map_at_5
value: 31.114000000000004
- type: mrr_at_1
value: 40.432
- type: mrr_at_10
value: 48.956
- type: mrr_at_100
value: 49.832
- type: mrr_at_1000
value: 49.87
- type: mrr_at_3
value: 46.759
- type: mrr_at_5
value: 47.886
- type: ndcg_at_1
value: 40.432
- type: ndcg_at_10
value: 40.644000000000005
- type: ndcg_at_100
value: 48.252
- type: ndcg_at_1000
value: 51.099000000000004
- type: ndcg_at_3
value: 36.992000000000004
- type: ndcg_at_5
value: 38.077
- type: precision_at_1
value: 40.432
- type: precision_at_10
value: 11.296000000000001
- type: precision_at_100
value: 1.9009999999999998
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 24.537
- type: precision_at_5
value: 17.963
- type: recall_at_1
value: 20.567
- type: recall_at_10
value: 47.052
- type: recall_at_100
value: 75.21600000000001
- type: recall_at_1000
value: 92.285
- type: recall_at_3
value: 33.488
- type: recall_at_5
value: 39.334
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.196999999999996
- type: map_at_10
value: 60.697
- type: map_at_100
value: 61.624
- type: map_at_1000
value: 61.692
- type: map_at_3
value: 57.421
- type: map_at_5
value: 59.455000000000005
- type: mrr_at_1
value: 76.39399999999999
- type: mrr_at_10
value: 82.504
- type: mrr_at_100
value: 82.71300000000001
- type: mrr_at_1000
value: 82.721
- type: mrr_at_3
value: 81.494
- type: mrr_at_5
value: 82.137
- type: ndcg_at_1
value: 76.39399999999999
- type: ndcg_at_10
value: 68.92200000000001
- type: ndcg_at_100
value: 72.13199999999999
- type: ndcg_at_1000
value: 73.392
- type: ndcg_at_3
value: 64.226
- type: ndcg_at_5
value: 66.815
- type: precision_at_1
value: 76.39399999999999
- type: precision_at_10
value: 14.442
- type: precision_at_100
value: 1.694
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 41.211
- type: precision_at_5
value: 26.766000000000002
- type: recall_at_1
value: 38.196999999999996
- type: recall_at_10
value: 72.208
- type: recall_at_100
value: 84.71300000000001
- type: recall_at_1000
value: 92.971
- type: recall_at_3
value: 61.816
- type: recall_at_5
value: 66.914
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 89.6556
- type: ap
value: 85.27600392682054
- type: f1
value: 89.63353655386406
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.482
- type: map_at_10
value: 33.701
- type: map_at_100
value: 34.861
- type: map_at_1000
value: 34.914
- type: map_at_3
value: 29.793999999999997
- type: map_at_5
value: 32.072
- type: mrr_at_1
value: 22.163
- type: mrr_at_10
value: 34.371
- type: mrr_at_100
value: 35.471000000000004
- type: mrr_at_1000
value: 35.518
- type: mrr_at_3
value: 30.554
- type: mrr_at_5
value: 32.799
- type: ndcg_at_1
value: 22.163
- type: ndcg_at_10
value: 40.643
- type: ndcg_at_100
value: 46.239999999999995
- type: ndcg_at_1000
value: 47.526
- type: ndcg_at_3
value: 32.714999999999996
- type: ndcg_at_5
value: 36.791000000000004
- type: precision_at_1
value: 22.163
- type: precision_at_10
value: 6.4799999999999995
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.002
- type: precision_at_5
value: 10.453
- type: recall_at_1
value: 21.482
- type: recall_at_10
value: 61.953
- type: recall_at_100
value: 87.86500000000001
- type: recall_at_1000
value: 97.636
- type: recall_at_3
value: 40.441
- type: recall_at_5
value: 50.27
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.3032375740994
- type: f1
value: 95.01515022686607
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 78.10077519379846
- type: f1
value: 58.240739725625644
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.0053799596503
- type: f1
value: 74.11733965804146
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.64021519838602
- type: f1
value: 79.8513960091438
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.92425767945184
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.249612382060754
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.35584955492918
- type: mrr
value: 33.545865224584674
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.978
- type: map_at_10
value: 14.749
- type: map_at_100
value: 19.192
- type: map_at_1000
value: 20.815
- type: map_at_3
value: 10.927000000000001
- type: map_at_5
value: 12.726
- type: mrr_at_1
value: 49.536
- type: mrr_at_10
value: 57.806999999999995
- type: mrr_at_100
value: 58.373
- type: mrr_at_1000
value: 58.407
- type: mrr_at_3
value: 55.779
- type: mrr_at_5
value: 57.095
- type: ndcg_at_1
value: 46.749
- type: ndcg_at_10
value: 37.644
- type: ndcg_at_100
value: 35.559000000000005
- type: ndcg_at_1000
value: 44.375
- type: ndcg_at_3
value: 43.354
- type: ndcg_at_5
value: 41.022999999999996
- type: precision_at_1
value: 48.607
- type: precision_at_10
value: 28.08
- type: precision_at_100
value: 9.155000000000001
- type: precision_at_1000
value: 2.2270000000000003
- type: precision_at_3
value: 40.764
- type: precision_at_5
value: 35.728
- type: recall_at_1
value: 6.978
- type: recall_at_10
value: 17.828
- type: recall_at_100
value: 36.010999999999996
- type: recall_at_1000
value: 68.34700000000001
- type: recall_at_3
value: 11.645999999999999
- type: recall_at_5
value: 14.427000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.219
- type: map_at_10
value: 45.633
- type: map_at_100
value: 46.752
- type: map_at_1000
value: 46.778999999999996
- type: map_at_3
value: 41.392
- type: map_at_5
value: 43.778
- type: mrr_at_1
value: 34.327999999999996
- type: mrr_at_10
value: 48.256
- type: mrr_at_100
value: 49.076
- type: mrr_at_1000
value: 49.092999999999996
- type: mrr_at_3
value: 44.786
- type: mrr_at_5
value: 46.766000000000005
- type: ndcg_at_1
value: 34.299
- type: ndcg_at_10
value: 53.434000000000005
- type: ndcg_at_100
value: 58.03
- type: ndcg_at_1000
value: 58.633
- type: ndcg_at_3
value: 45.433
- type: ndcg_at_5
value: 49.379
- type: precision_at_1
value: 34.299
- type: precision_at_10
value: 8.911
- type: precision_at_100
value: 1.145
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.896
- type: precision_at_5
value: 14.832
- type: recall_at_1
value: 30.219
- type: recall_at_10
value: 74.59400000000001
- type: recall_at_100
value: 94.392
- type: recall_at_1000
value: 98.832
- type: recall_at_3
value: 53.754000000000005
- type: recall_at_5
value: 62.833000000000006
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.139
- type: map_at_10
value: 85.141
- type: map_at_100
value: 85.78099999999999
- type: map_at_1000
value: 85.795
- type: map_at_3
value: 82.139
- type: map_at_5
value: 84.075
- type: mrr_at_1
value: 81.98
- type: mrr_at_10
value: 88.056
- type: mrr_at_100
value: 88.152
- type: mrr_at_1000
value: 88.152
- type: mrr_at_3
value: 87.117
- type: mrr_at_5
value: 87.78099999999999
- type: ndcg_at_1
value: 82.02000000000001
- type: ndcg_at_10
value: 88.807
- type: ndcg_at_100
value: 89.99000000000001
- type: ndcg_at_1000
value: 90.068
- type: ndcg_at_3
value: 85.989
- type: ndcg_at_5
value: 87.627
- type: precision_at_1
value: 82.02000000000001
- type: precision_at_10
value: 13.472999999999999
- type: precision_at_100
value: 1.534
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.788
- type: recall_at_1
value: 71.139
- type: recall_at_10
value: 95.707
- type: recall_at_100
value: 99.666
- type: recall_at_1000
value: 99.983
- type: recall_at_3
value: 87.64699999999999
- type: recall_at_5
value: 92.221
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.11035509193503
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.44241881422526
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.122999999999999
- type: map_at_10
value: 14.45
- type: map_at_100
value: 17.108999999999998
- type: map_at_1000
value: 17.517
- type: map_at_3
value: 10.213999999999999
- type: map_at_5
value: 12.278
- type: mrr_at_1
value: 25.3
- type: mrr_at_10
value: 37.791999999999994
- type: mrr_at_100
value: 39.086
- type: mrr_at_1000
value: 39.121
- type: mrr_at_3
value: 34.666999999999994
- type: mrr_at_5
value: 36.472
- type: ndcg_at_1
value: 25.3
- type: ndcg_at_10
value: 23.469
- type: ndcg_at_100
value: 33.324
- type: ndcg_at_1000
value: 39.357
- type: ndcg_at_3
value: 22.478
- type: ndcg_at_5
value: 19.539
- type: precision_at_1
value: 25.3
- type: precision_at_10
value: 12.3
- type: precision_at_100
value: 2.654
- type: precision_at_1000
value: 0.40800000000000003
- type: precision_at_3
value: 21.667
- type: precision_at_5
value: 17.5
- type: recall_at_1
value: 5.122999999999999
- type: recall_at_10
value: 24.937
- type: recall_at_100
value: 53.833
- type: recall_at_1000
value: 82.85
- type: recall_at_3
value: 13.178
- type: recall_at_5
value: 17.747
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.76549431206278
- type: cos_sim_spearman
value: 81.28563534883214
- type: euclidean_pearson
value: 84.17180713818567
- type: euclidean_spearman
value: 81.1684082302606
- type: manhattan_pearson
value: 84.12189753972959
- type: manhattan_spearman
value: 81.1134998997958
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.75137587182017
- type: cos_sim_spearman
value: 76.155337187325
- type: euclidean_pearson
value: 83.54551546726665
- type: euclidean_spearman
value: 76.30324990565346
- type: manhattan_pearson
value: 83.52192617483797
- type: manhattan_spearman
value: 76.30017227216015
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 87.13890050398628
- type: cos_sim_spearman
value: 87.84898360302155
- type: euclidean_pearson
value: 86.89491809082031
- type: euclidean_spearman
value: 87.99935689905651
- type: manhattan_pearson
value: 86.86526424376366
- type: manhattan_spearman
value: 87.96850732980495
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.01978753231558
- type: cos_sim_spearman
value: 83.38989083933329
- type: euclidean_pearson
value: 85.28405032045376
- type: euclidean_spearman
value: 83.51703914276501
- type: manhattan_pearson
value: 85.25775133078966
- type: manhattan_spearman
value: 83.52815667821727
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.28482294437876
- type: cos_sim_spearman
value: 89.42976214499576
- type: euclidean_pearson
value: 88.72677957272468
- type: euclidean_spearman
value: 89.30001736116229
- type: manhattan_pearson
value: 88.64119331622562
- type: manhattan_spearman
value: 89.21771022634893
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.79810159351987
- type: cos_sim_spearman
value: 85.34918402034273
- type: euclidean_pearson
value: 84.76058606229002
- type: euclidean_spearman
value: 85.45159829941214
- type: manhattan_pearson
value: 84.73926491888156
- type: manhattan_spearman
value: 85.42568221985898
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.92796712570272
- type: cos_sim_spearman
value: 88.58925922945812
- type: euclidean_pearson
value: 88.97231215531797
- type: euclidean_spearman
value: 88.27036385068719
- type: manhattan_pearson
value: 88.95761469412228
- type: manhattan_spearman
value: 88.23980432487681
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.85679810182282
- type: cos_sim_spearman
value: 67.80696709003128
- type: euclidean_pearson
value: 68.77524185947989
- type: euclidean_spearman
value: 68.032438075422
- type: manhattan_pearson
value: 68.60489100404182
- type: manhattan_spearman
value: 67.75418889226138
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.33287880999367
- type: cos_sim_spearman
value: 87.32401087204754
- type: euclidean_pearson
value: 87.27961069148029
- type: euclidean_spearman
value: 87.3547683085868
- type: manhattan_pearson
value: 87.24405442789622
- type: manhattan_spearman
value: 87.32896271166672
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.71553665286558
- type: mrr
value: 96.42436176749902
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 61.094
- type: map_at_10
value: 71.066
- type: map_at_100
value: 71.608
- type: map_at_1000
value: 71.629
- type: map_at_3
value: 68.356
- type: map_at_5
value: 70.15
- type: mrr_at_1
value: 64
- type: mrr_at_10
value: 71.82300000000001
- type: mrr_at_100
value: 72.251
- type: mrr_at_1000
value: 72.269
- type: mrr_at_3
value: 69.833
- type: mrr_at_5
value: 71.11699999999999
- type: ndcg_at_1
value: 64
- type: ndcg_at_10
value: 75.286
- type: ndcg_at_100
value: 77.40700000000001
- type: ndcg_at_1000
value: 77.806
- type: ndcg_at_3
value: 70.903
- type: ndcg_at_5
value: 73.36399999999999
- type: precision_at_1
value: 64
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 18.333
- type: recall_at_1
value: 61.094
- type: recall_at_10
value: 87.256
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 75.6
- type: recall_at_5
value: 81.789
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82871287128712
- type: cos_sim_ap
value: 95.9325677692287
- type: cos_sim_f1
value: 91.13924050632912
- type: cos_sim_precision
value: 92.3076923076923
- type: cos_sim_recall
value: 90
- type: dot_accuracy
value: 99.7980198019802
- type: dot_ap
value: 94.56107207796
- type: dot_f1
value: 89.41908713692946
- type: dot_precision
value: 92.88793103448276
- type: dot_recall
value: 86.2
- type: euclidean_accuracy
value: 99.82871287128712
- type: euclidean_ap
value: 95.94390332507025
- type: euclidean_f1
value: 91.17797042325346
- type: euclidean_precision
value: 93.02809573361083
- type: euclidean_recall
value: 89.4
- type: manhattan_accuracy
value: 99.82871287128712
- type: manhattan_ap
value: 95.97587114452257
- type: manhattan_f1
value: 91.25821121778675
- type: manhattan_precision
value: 92.23697650663942
- type: manhattan_recall
value: 90.3
- type: max_accuracy
value: 99.82871287128712
- type: max_ap
value: 95.97587114452257
- type: max_f1
value: 91.25821121778675
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.13974351708839
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.594544722932234
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.718738983377726
- type: mrr
value: 55.61655154486037
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.37028359646597
- type: cos_sim_spearman
value: 30.866534307244443
- type: dot_pearson
value: 29.89037691541816
- type: dot_spearman
value: 29.941267567971718
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.7340000000000002
- type: map_at_100
value: 9.966
- type: map_at_1000
value: 25.119000000000003
- type: map_at_3
value: 0.596
- type: map_at_5
value: 0.941
- type: mrr_at_1
value: 76
- type: mrr_at_10
value: 85.85199999999999
- type: mrr_at_100
value: 85.85199999999999
- type: mrr_at_1000
value: 85.85199999999999
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.56700000000001
- type: ndcg_at_1
value: 71
- type: ndcg_at_10
value: 69.60300000000001
- type: ndcg_at_100
value: 54.166000000000004
- type: ndcg_at_1000
value: 51.085
- type: ndcg_at_3
value: 71.95
- type: ndcg_at_5
value: 71.17599999999999
- type: precision_at_1
value: 76
- type: precision_at_10
value: 74.2
- type: precision_at_100
value: 55.96
- type: precision_at_1000
value: 22.584
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 75.6
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.992
- type: recall_at_100
value: 13.706999999999999
- type: recall_at_1000
value: 48.732
- type: recall_at_3
value: 0.635
- type: recall_at_5
value: 1.034
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8
- type: f1
value: 6.298401229470593
- type: precision
value: 5.916991709050532
- type: recall
value: 8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.341040462427745
- type: f1
value: 14.621650026274303
- type: precision
value: 13.9250609139035
- type: recall
value: 17.341040462427745
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.536585365853659
- type: f1
value: 6.30972482801751
- type: precision
value: 5.796517326875398
- type: recall
value: 8.536585365853659
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.4
- type: f1
value: 4.221126743626743
- type: precision
value: 3.822815143403898
- type: recall
value: 6.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 19.8
- type: f1
value: 18.13768093781855
- type: precision
value: 17.54646004378763
- type: recall
value: 19.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 13.700000000000001
- type: f1
value: 12.367662337662336
- type: precision
value: 11.934237966189185
- type: recall
value: 13.700000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.299999999999999
- type: f1
value: 10.942180289268338
- type: precision
value: 10.153968847262192
- type: recall
value: 14.299999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22.388059701492537
- type: f1
value: 17.00157733660433
- type: precision
value: 15.650551589876702
- type: recall
value: 22.388059701492537
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22
- type: f1
value: 17.4576947358322
- type: precision
value: 16.261363669827777
- type: recall
value: 22
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.292682926829269
- type: f1
value: 5.544048456005624
- type: precision
value: 5.009506603002538
- type: recall
value: 8.292682926829269
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.4
- type: f1
value: 4.148897174789229
- type: precision
value: 3.862217259449564
- type: recall
value: 5.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.5893074119076545
- type: f1
value: 4.375041810373159
- type: precision
value: 4.181207113088141
- type: recall
value: 5.5893074119076545
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 8.17391304347826
- type: f1
value: 6.448011891490153
- type: precision
value: 5.9719116632160105
- type: recall
value: 8.17391304347826
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.8695652173913043
- type: f1
value: 0.582815734989648
- type: precision
value: 0.5580885233059146
- type: recall
value: 0.8695652173913043
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.1
- type: f1
value: 3.5000615825615826
- type: precision
value: 3.2073523577994707
- type: recall
value: 5.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.3
- type: f1
value: 0.10109884927372195
- type: precision
value: 0.10055127118392897
- type: recall
value: 0.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.8600723763570564
- type: f1
value: 2.8177402725050493
- type: precision
value: 2.5662687819699213
- type: recall
value: 3.8600723763570564
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0
- type: f1
value: 0
- type: precision
value: 0
- type: recall
value: 0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 15.299999999999999
- type: f1
value: 11.377964359824292
- type: precision
value: 10.361140908892764
- type: recall
value: 15.299999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.3
- type: f1
value: 0.9600820232399179
- type: precision
value: 0.9151648856810397
- type: recall
value: 1.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 14.095238095238095
- type: f1
value: 11.40081541819044
- type: precision
value: 10.645867976820359
- type: recall
value: 14.095238095238095
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 4
- type: f1
value: 2.3800704501963432
- type: precision
value: 2.0919368034607455
- type: recall
value: 4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.3
- type: f1
value: 0.2002053388090349
- type: precision
value: 0.2001027749229188
- type: recall
value: 0.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 11.700000000000001
- type: f1
value: 10.29755634495992
- type: precision
value: 9.876637220292393
- type: recall
value: 11.700000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.7000000000000002
- type: f1
value: 0.985815849620051
- type: precision
value: 0.8884689922480621
- type: recall
value: 1.7000000000000002
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.599999999999998
- type: f1
value: 14.086312656126182
- type: precision
value: 13.192360560816125
- type: recall
value: 17.599999999999998
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.1
- type: f1
value: 4.683795729173087
- type: precision
value: 4.31687579027912
- type: recall
value: 6.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.4
- type: f1
value: 0.20966666666666667
- type: precision
value: 0.20500700280112047
- type: recall
value: 0.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.6
- type: f1
value: 0.2454665118079752
- type: precision
value: 0.2255125167991618
- type: recall
value: 0.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21
- type: f1
value: 18.965901242066018
- type: precision
value: 18.381437375171
- type: recall
value: 21
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.5390835579514826
- type: f1
value: 0.4048898457205192
- type: precision
value: 0.4046018763809678
- type: recall
value: 0.5390835579514826
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.282051282051282
- type: f1
value: 0.5098554872310529
- type: precision
value: 0.4715099715099715
- type: recall
value: 1.282051282051282
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 10.7
- type: f1
value: 8.045120643200706
- type: precision
value: 7.387598023074453
- type: recall
value: 10.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 2.272727272727273
- type: f1
value: 1.44184724004356
- type: precision
value: 1.4082306862044767
- type: recall
value: 2.272727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.20964360587002098
- type: f1
value: 0.001335309591528796
- type: precision
value: 0.0006697878781789807
- type: recall
value: 0.20964360587002098
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.1
- type: f1
value: 5.522254020507502
- type: precision
value: 5.081849426723903
- type: recall
value: 7.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 36.57587548638132
- type: f1
value: 30.325515383881147
- type: precision
value: 28.59255854392041
- type: recall
value: 36.57587548638132
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 16.23931623931624
- type: f1
value: 13.548783761549718
- type: precision
value: 13.0472896359184
- type: recall
value: 16.23931623931624
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 16.3
- type: f1
value: 13.3418584934734
- type: precision
value: 12.506853047473756
- type: recall
value: 16.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1
- type: f1
value: 0.7764001197963462
- type: precision
value: 0.7551049317943337
- type: recall
value: 1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.9719626168224296
- type: f1
value: 3.190729401654313
- type: precision
value: 3.001159168296747
- type: recall
value: 3.9719626168224296
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.4000000000000004
- type: f1
value: 2.4847456001574653
- type: precision
value: 2.308739271803959
- type: recall
value: 3.4000000000000004
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 36.9
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metrics:
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metrics:
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metrics:
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dataset:
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metrics:
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dataset:
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config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.487179487179487
- type: f1
value: 6.902767160316073
- type: precision
value: 6.450346503818517
- type: recall
value: 9.487179487179487
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.1
- type: f1
value: 0.0002042900919305414
- type: precision
value: 0.00010224948875255625
- type: recall
value: 0.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 5.010438413361169
- type: f1
value: 3.8116647214505277
- type: precision
value: 3.5454644309619634
- type: recall
value: 5.010438413361169
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.2
- type: f1
value: 5.213158915433869
- type: precision
value: 5.080398110661268
- type: recall
value: 6.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.9771986970684038
- type: f1
value: 0.5061388123277374
- type: precision
value: 0.43431053203040165
- type: recall
value: 0.9771986970684038
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 7.3
- type: f1
value: 5.6313180921027755
- type: precision
value: 5.303887400540395
- type: recall
value: 7.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.5999999999999996
- type: f1
value: 3.2180089485458607
- type: precision
value: 3.1006756756756753
- type: recall
value: 3.5999999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 22.04724409448819
- type: f1
value: 17.92525934258218
- type: precision
value: 16.48251629836593
- type: recall
value: 22.04724409448819
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.5
- type: f1
value: 0.1543743186232414
- type: precision
value: 0.13554933572174951
- type: recall
value: 0.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.8310249307479225
- type: f1
value: 0.5102255597841558
- type: precision
value: 0.4859595744731704
- type: recall
value: 0.8310249307479225
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 6.9
- type: f1
value: 4.7258390633390635
- type: precision
value: 4.288366570275279
- type: recall
value: 6.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 17.307692307692307
- type: f1
value: 14.763313609467454
- type: precision
value: 14.129273504273504
- type: recall
value: 17.307692307692307
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.3
- type: f1
value: 0.0022196828248667185
- type: precision
value: 0.0011148527298850575
- type: recall
value: 0.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.3
- type: f1
value: 0.3
- type: precision
value: 0.3
- type: recall
value: 0.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.6
- type: f1
value: 0.500206611570248
- type: precision
value: 0.5001034126163392
- type: recall
value: 0.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 0.4716981132075472
- type: f1
value: 0.2953377695417789
- type: precision
value: 0.2754210459668228
- type: recall
value: 0.4716981132075472
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 4.3999999999999995
- type: f1
value: 3.6228414442700156
- type: precision
value: 3.4318238993710692
- type: recall
value: 4.3999999999999995
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 1.2773722627737227
- type: f1
value: 1.0043318098096732
- type: precision
value: 0.9735777358593729
- type: recall
value: 1.2773722627737227
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 3.9
- type: f1
value: 2.6164533097276226
- type: precision
value: 2.3558186153594085
- type: recall
value: 3.9
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.5779999999999998
- type: map_at_10
value: 8.339
- type: map_at_100
value: 14.601
- type: map_at_1000
value: 16.104
- type: map_at_3
value: 4.06
- type: map_at_5
value: 6.049
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 35.178
- type: mrr_at_100
value: 36.464999999999996
- type: mrr_at_1000
value: 36.464999999999996
- type: mrr_at_3
value: 29.932
- type: mrr_at_5
value: 34.32
- type: ndcg_at_1
value: 16.326999999999998
- type: ndcg_at_10
value: 20.578
- type: ndcg_at_100
value: 34.285
- type: ndcg_at_1000
value: 45.853
- type: ndcg_at_3
value: 19.869999999999997
- type: ndcg_at_5
value: 22.081999999999997
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.714
- type: precision_at_1000
value: 1.547
- type: precision_at_3
value: 23.128999999999998
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 1.5779999999999998
- type: recall_at_10
value: 14.801
- type: recall_at_100
value: 48.516999999999996
- type: recall_at_1000
value: 83.30300000000001
- type: recall_at_3
value: 5.267
- type: recall_at_5
value: 9.415999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.4186
- type: ap
value: 14.536282543597242
- type: f1
value: 55.47661372005608
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.318053197509904
- type: f1
value: 59.68272481532353
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.155753554312
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.99409906419503
- type: cos_sim_ap
value: 76.91824322304332
- type: cos_sim_f1
value: 70.97865694950546
- type: cos_sim_precision
value: 70.03081664098613
- type: cos_sim_recall
value: 71.95250659630607
- type: dot_accuracy
value: 85.37879239434942
- type: dot_ap
value: 71.86454698478344
- type: dot_f1
value: 66.48115355426259
- type: dot_precision
value: 63.84839650145773
- type: dot_recall
value: 69.34036939313984
- type: euclidean_accuracy
value: 87.00005960541218
- type: euclidean_ap
value: 76.9165913835565
- type: euclidean_f1
value: 71.23741557283039
- type: euclidean_precision
value: 68.89327088982007
- type: euclidean_recall
value: 73.7467018469657
- type: manhattan_accuracy
value: 87.06562555880075
- type: manhattan_ap
value: 76.85445703747546
- type: manhattan_f1
value: 70.95560571858539
- type: manhattan_precision
value: 67.61472275334609
- type: manhattan_recall
value: 74.64379947229551
- type: max_accuracy
value: 87.06562555880075
- type: max_ap
value: 76.91824322304332
- type: max_f1
value: 71.23741557283039
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.93934101758063
- type: cos_sim_ap
value: 86.1071528049007
- type: cos_sim_f1
value: 78.21588263552714
- type: cos_sim_precision
value: 75.20073900376609
- type: cos_sim_recall
value: 81.48290729904527
- type: dot_accuracy
value: 88.2504754142896
- type: dot_ap
value: 84.19709379723844
- type: dot_f1
value: 76.92307692307693
- type: dot_precision
value: 71.81969949916528
- type: dot_recall
value: 82.80720665229443
- type: euclidean_accuracy
value: 88.97232894787906
- type: euclidean_ap
value: 86.02763993294909
- type: euclidean_f1
value: 78.18372741427383
- type: euclidean_precision
value: 73.79861918107868
- type: euclidean_recall
value: 83.12288266091777
- type: manhattan_accuracy
value: 88.86948422400745
- type: manhattan_ap
value: 86.0009157821563
- type: manhattan_f1
value: 78.10668017659404
- type: manhattan_precision
value: 73.68564795848695
- type: manhattan_recall
value: 83.09208500153989
- type: max_accuracy
value: 88.97232894787906
- type: max_ap
value: 86.1071528049007
- type: max_f1
value: 78.21588263552714
---
<h1 align="center">GIST Embedding v0</h1>
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
The model is fine-tuned on top of the [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
# Data
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
# Usage
The model can be easily loaded using the Sentence Transformers library.
```Python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-Embedding-v0", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
```
# Training Parameters
Below are the training parameters used to fine-tune the model:
```
Epochs = 80
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 32
Checkpoint step = 103500
Contrastive loss temperature = 0.01
```
# Evaluation
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
# Citation
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
```
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gte-Qwen2-1.5B-instruct | Alibaba-NLP | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"custom_code",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-29T08:02:40 | 2025-01-11T07:09:40 | 533,442 | 190 | ---
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 83.98507462686567
- type: ap
value: 50.93015252587014
- type: f1
value: 78.50416599051215
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.61065
- type: ap
value: 94.89174052954196
- type: f1
value: 96.60942596940565
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.614000000000004
- type: f1
value: 54.90553480294904
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 45.164
- type: map_at_10
value: 61.519
- type: map_at_100
value: 61.769
- type: map_at_1000
value: 61.769
- type: map_at_3
value: 57.443999999999996
- type: map_at_5
value: 60.058
- type: mrr_at_1
value: 46.088
- type: mrr_at_10
value: 61.861
- type: mrr_at_100
value: 62.117999999999995
- type: mrr_at_1000
value: 62.117999999999995
- type: mrr_at_3
value: 57.729
- type: mrr_at_5
value: 60.392
- type: ndcg_at_1
value: 45.164
- type: ndcg_at_10
value: 69.72
- type: ndcg_at_100
value: 70.719
- type: ndcg_at_1000
value: 70.719
- type: ndcg_at_3
value: 61.517999999999994
- type: ndcg_at_5
value: 66.247
- type: precision_at_1
value: 45.164
- type: precision_at_10
value: 9.545
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.443
- type: precision_at_5
value: 16.97
- type: recall_at_1
value: 45.164
- type: recall_at_10
value: 95.448
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 73.329
- type: recall_at_5
value: 84.851
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 50.511868162026175
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 45.007803189284004
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.55292107723382
- type: mrr
value: 77.66158818097877
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.65459047085452
- type: cos_sim_spearman
value: 82.10729255710761
- type: euclidean_pearson
value: 82.78079159312476
- type: euclidean_spearman
value: 80.50002701880933
- type: manhattan_pearson
value: 82.41372641383016
- type: manhattan_spearman
value: 80.57412509272639
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.30844155844156
- type: f1
value: 87.25307322443255
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 43.20754608934859
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 38.818037697335505
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 35.423
- type: map_at_10
value: 47.198
- type: map_at_100
value: 48.899
- type: map_at_1000
value: 49.004
- type: map_at_3
value: 43.114999999999995
- type: map_at_5
value: 45.491
- type: mrr_at_1
value: 42.918
- type: mrr_at_10
value: 53.299
- type: mrr_at_100
value: 54.032000000000004
- type: mrr_at_1000
value: 54.055
- type: mrr_at_3
value: 50.453
- type: mrr_at_5
value: 52.205999999999996
- type: ndcg_at_1
value: 42.918
- type: ndcg_at_10
value: 53.98
- type: ndcg_at_100
value: 59.57
- type: ndcg_at_1000
value: 60.879000000000005
- type: ndcg_at_3
value: 48.224000000000004
- type: ndcg_at_5
value: 50.998
- type: precision_at_1
value: 42.918
- type: precision_at_10
value: 10.299999999999999
- type: precision_at_100
value: 1.687
- type: precision_at_1000
value: 0.211
- type: precision_at_3
value: 22.842000000000002
- type: precision_at_5
value: 16.681
- type: recall_at_1
value: 35.423
- type: recall_at_10
value: 66.824
- type: recall_at_100
value: 89.564
- type: recall_at_1000
value: 97.501
- type: recall_at_3
value: 50.365
- type: recall_at_5
value: 57.921
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 33.205
- type: map_at_10
value: 44.859
- type: map_at_100
value: 46.135
- type: map_at_1000
value: 46.259
- type: map_at_3
value: 41.839
- type: map_at_5
value: 43.662
- type: mrr_at_1
value: 41.146
- type: mrr_at_10
value: 50.621
- type: mrr_at_100
value: 51.207
- type: mrr_at_1000
value: 51.246
- type: mrr_at_3
value: 48.535000000000004
- type: mrr_at_5
value: 49.818
- type: ndcg_at_1
value: 41.146
- type: ndcg_at_10
value: 50.683
- type: ndcg_at_100
value: 54.82
- type: ndcg_at_1000
value: 56.69
- type: ndcg_at_3
value: 46.611000000000004
- type: ndcg_at_5
value: 48.66
- type: precision_at_1
value: 41.146
- type: precision_at_10
value: 9.439
- type: precision_at_100
value: 1.465
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 22.59
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 33.205
- type: recall_at_10
value: 61.028999999999996
- type: recall_at_100
value: 78.152
- type: recall_at_1000
value: 89.59700000000001
- type: recall_at_3
value: 49.05
- type: recall_at_5
value: 54.836
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 41.637
- type: map_at_10
value: 55.162
- type: map_at_100
value: 56.142
- type: map_at_1000
value: 56.188
- type: map_at_3
value: 51.564
- type: map_at_5
value: 53.696
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 58.243
- type: mrr_at_100
value: 58.879999999999995
- type: mrr_at_1000
value: 58.9
- type: mrr_at_3
value: 55.69499999999999
- type: mrr_at_5
value: 57.284
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 61.305
- type: ndcg_at_100
value: 65.077
- type: ndcg_at_1000
value: 65.941
- type: ndcg_at_3
value: 55.422000000000004
- type: ndcg_at_5
value: 58.516
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 9.918000000000001
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.765
- type: precision_at_5
value: 17.204
- type: recall_at_1
value: 41.637
- type: recall_at_10
value: 76.185
- type: recall_at_100
value: 92.149
- type: recall_at_1000
value: 98.199
- type: recall_at_3
value: 60.856
- type: recall_at_5
value: 68.25099999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.27
- type: map_at_10
value: 37.463
- type: map_at_100
value: 38.434000000000005
- type: map_at_1000
value: 38.509
- type: map_at_3
value: 34.226
- type: map_at_5
value: 36.161
- type: mrr_at_1
value: 28.588
- type: mrr_at_10
value: 39.383
- type: mrr_at_100
value: 40.23
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 36.422
- type: mrr_at_5
value: 38.252
- type: ndcg_at_1
value: 28.588
- type: ndcg_at_10
value: 43.511
- type: ndcg_at_100
value: 48.274
- type: ndcg_at_1000
value: 49.975
- type: ndcg_at_3
value: 37.319
- type: ndcg_at_5
value: 40.568
- type: precision_at_1
value: 28.588
- type: precision_at_10
value: 6.893000000000001
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 16.347
- type: precision_at_5
value: 11.661000000000001
- type: recall_at_1
value: 26.27
- type: recall_at_10
value: 60.284000000000006
- type: recall_at_100
value: 81.902
- type: recall_at_1000
value: 94.43
- type: recall_at_3
value: 43.537
- type: recall_at_5
value: 51.475
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 18.168
- type: map_at_10
value: 28.410000000000004
- type: map_at_100
value: 29.78
- type: map_at_1000
value: 29.892999999999997
- type: map_at_3
value: 25.238
- type: map_at_5
value: 26.96
- type: mrr_at_1
value: 23.507
- type: mrr_at_10
value: 33.382
- type: mrr_at_100
value: 34.404
- type: mrr_at_1000
value: 34.467999999999996
- type: mrr_at_3
value: 30.637999999999998
- type: mrr_at_5
value: 32.199
- type: ndcg_at_1
value: 23.507
- type: ndcg_at_10
value: 34.571000000000005
- type: ndcg_at_100
value: 40.663
- type: ndcg_at_1000
value: 43.236000000000004
- type: ndcg_at_3
value: 29.053
- type: ndcg_at_5
value: 31.563999999999997
- type: precision_at_1
value: 23.507
- type: precision_at_10
value: 6.654
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 14.427999999999999
- type: precision_at_5
value: 10.498000000000001
- type: recall_at_1
value: 18.168
- type: recall_at_10
value: 48.443000000000005
- type: recall_at_100
value: 74.47
- type: recall_at_1000
value: 92.494
- type: recall_at_3
value: 33.379999999999995
- type: recall_at_5
value: 39.76
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 32.39
- type: map_at_10
value: 44.479
- type: map_at_100
value: 45.977000000000004
- type: map_at_1000
value: 46.087
- type: map_at_3
value: 40.976
- type: map_at_5
value: 43.038
- type: mrr_at_1
value: 40.135
- type: mrr_at_10
value: 50.160000000000004
- type: mrr_at_100
value: 51.052
- type: mrr_at_1000
value: 51.087
- type: mrr_at_3
value: 47.818
- type: mrr_at_5
value: 49.171
- type: ndcg_at_1
value: 40.135
- type: ndcg_at_10
value: 50.731
- type: ndcg_at_100
value: 56.452000000000005
- type: ndcg_at_1000
value: 58.123000000000005
- type: ndcg_at_3
value: 45.507
- type: ndcg_at_5
value: 48.11
- type: precision_at_1
value: 40.135
- type: precision_at_10
value: 9.192
- type: precision_at_100
value: 1.397
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 21.816
- type: precision_at_5
value: 15.476
- type: recall_at_1
value: 32.39
- type: recall_at_10
value: 63.597
- type: recall_at_100
value: 86.737
- type: recall_at_1000
value: 97.039
- type: recall_at_3
value: 48.906
- type: recall_at_5
value: 55.659000000000006
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.397
- type: map_at_10
value: 39.871
- type: map_at_100
value: 41.309000000000005
- type: map_at_1000
value: 41.409
- type: map_at_3
value: 36.047000000000004
- type: map_at_5
value: 38.104
- type: mrr_at_1
value: 34.703
- type: mrr_at_10
value: 44.773
- type: mrr_at_100
value: 45.64
- type: mrr_at_1000
value: 45.678999999999995
- type: mrr_at_3
value: 41.705
- type: mrr_at_5
value: 43.406
- type: ndcg_at_1
value: 34.703
- type: ndcg_at_10
value: 46.271
- type: ndcg_at_100
value: 52.037
- type: ndcg_at_1000
value: 53.81700000000001
- type: ndcg_at_3
value: 39.966
- type: ndcg_at_5
value: 42.801
- type: precision_at_1
value: 34.703
- type: precision_at_10
value: 8.744
- type: precision_at_100
value: 1.348
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 19.102
- type: precision_at_5
value: 13.836
- type: recall_at_1
value: 28.397
- type: recall_at_10
value: 60.299
- type: recall_at_100
value: 84.595
- type: recall_at_1000
value: 96.155
- type: recall_at_3
value: 43.065
- type: recall_at_5
value: 50.371
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.044333333333338
- type: map_at_10
value: 38.78691666666666
- type: map_at_100
value: 40.113
- type: map_at_1000
value: 40.22125
- type: map_at_3
value: 35.52966666666667
- type: map_at_5
value: 37.372749999999996
- type: mrr_at_1
value: 33.159083333333335
- type: mrr_at_10
value: 42.913583333333335
- type: mrr_at_100
value: 43.7845
- type: mrr_at_1000
value: 43.830333333333336
- type: mrr_at_3
value: 40.29816666666667
- type: mrr_at_5
value: 41.81366666666667
- type: ndcg_at_1
value: 33.159083333333335
- type: ndcg_at_10
value: 44.75750000000001
- type: ndcg_at_100
value: 50.13658333333334
- type: ndcg_at_1000
value: 52.037
- type: ndcg_at_3
value: 39.34258333333334
- type: ndcg_at_5
value: 41.93708333333333
- type: precision_at_1
value: 33.159083333333335
- type: precision_at_10
value: 7.952416666666667
- type: precision_at_100
value: 1.2571666666666668
- type: precision_at_1000
value: 0.16099999999999998
- type: precision_at_3
value: 18.303833333333337
- type: precision_at_5
value: 13.057083333333333
- type: recall_at_1
value: 28.044333333333338
- type: recall_at_10
value: 58.237249999999996
- type: recall_at_100
value: 81.35391666666666
- type: recall_at_1000
value: 94.21283333333334
- type: recall_at_3
value: 43.32341666666667
- type: recall_at_5
value: 49.94908333333333
- type: map_at_1
value: 18.398
- type: map_at_10
value: 27.929
- type: map_at_100
value: 29.032999999999998
- type: map_at_1000
value: 29.126
- type: map_at_3
value: 25.070999999999998
- type: map_at_5
value: 26.583000000000002
- type: mrr_at_1
value: 19.963
- type: mrr_at_10
value: 29.997
- type: mrr_at_100
value: 30.9
- type: mrr_at_1000
value: 30.972
- type: mrr_at_3
value: 27.264
- type: mrr_at_5
value: 28.826
- type: ndcg_at_1
value: 19.963
- type: ndcg_at_10
value: 33.678999999999995
- type: ndcg_at_100
value: 38.931
- type: ndcg_at_1000
value: 41.379
- type: ndcg_at_3
value: 28.000000000000004
- type: ndcg_at_5
value: 30.637999999999998
- type: precision_at_1
value: 19.963
- type: precision_at_10
value: 5.7299999999999995
- type: precision_at_100
value: 0.902
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 12.631
- type: precision_at_5
value: 9.057
- type: recall_at_1
value: 18.398
- type: recall_at_10
value: 49.254
- type: recall_at_100
value: 73.182
- type: recall_at_1000
value: 91.637
- type: recall_at_3
value: 34.06
- type: recall_at_5
value: 40.416000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 27.838
- type: map_at_10
value: 36.04
- type: map_at_100
value: 37.113
- type: map_at_1000
value: 37.204
- type: map_at_3
value: 33.585
- type: map_at_5
value: 34.845
- type: mrr_at_1
value: 30.982
- type: mrr_at_10
value: 39.105000000000004
- type: mrr_at_100
value: 39.98
- type: mrr_at_1000
value: 40.042
- type: mrr_at_3
value: 36.912
- type: mrr_at_5
value: 38.062000000000005
- type: ndcg_at_1
value: 30.982
- type: ndcg_at_10
value: 40.982
- type: ndcg_at_100
value: 46.092
- type: ndcg_at_1000
value: 48.25
- type: ndcg_at_3
value: 36.41
- type: ndcg_at_5
value: 38.379999999999995
- type: precision_at_1
value: 30.982
- type: precision_at_10
value: 6.534
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.124
- type: precision_at_3
value: 15.745999999999999
- type: precision_at_5
value: 10.828
- type: recall_at_1
value: 27.838
- type: recall_at_10
value: 52.971000000000004
- type: recall_at_100
value: 76.357
- type: recall_at_1000
value: 91.973
- type: recall_at_3
value: 40.157
- type: recall_at_5
value: 45.147999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 19.059
- type: map_at_10
value: 27.454
- type: map_at_100
value: 28.736
- type: map_at_1000
value: 28.865000000000002
- type: map_at_3
value: 24.773999999999997
- type: map_at_5
value: 26.266000000000002
- type: mrr_at_1
value: 23.125
- type: mrr_at_10
value: 31.267
- type: mrr_at_100
value: 32.32
- type: mrr_at_1000
value: 32.394
- type: mrr_at_3
value: 28.894
- type: mrr_at_5
value: 30.281000000000002
- type: ndcg_at_1
value: 23.125
- type: ndcg_at_10
value: 32.588
- type: ndcg_at_100
value: 38.432
- type: ndcg_at_1000
value: 41.214
- type: ndcg_at_3
value: 27.938000000000002
- type: ndcg_at_5
value: 30.127
- type: precision_at_1
value: 23.125
- type: precision_at_10
value: 5.9639999999999995
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.148
- type: precision_at_3
value: 13.294
- type: precision_at_5
value: 9.628
- type: recall_at_1
value: 19.059
- type: recall_at_10
value: 44.25
- type: recall_at_100
value: 69.948
- type: recall_at_1000
value: 89.35300000000001
- type: recall_at_3
value: 31.114000000000004
- type: recall_at_5
value: 36.846000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 28.355999999999998
- type: map_at_10
value: 39.055
- type: map_at_100
value: 40.486
- type: map_at_1000
value: 40.571
- type: map_at_3
value: 35.69
- type: map_at_5
value: 37.605
- type: mrr_at_1
value: 33.302
- type: mrr_at_10
value: 42.986000000000004
- type: mrr_at_100
value: 43.957
- type: mrr_at_1000
value: 43.996
- type: mrr_at_3
value: 40.111999999999995
- type: mrr_at_5
value: 41.735
- type: ndcg_at_1
value: 33.302
- type: ndcg_at_10
value: 44.962999999999994
- type: ndcg_at_100
value: 50.917
- type: ndcg_at_1000
value: 52.622
- type: ndcg_at_3
value: 39.182
- type: ndcg_at_5
value: 41.939
- type: precision_at_1
value: 33.302
- type: precision_at_10
value: 7.779999999999999
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.035
- type: precision_at_5
value: 12.873000000000001
- type: recall_at_1
value: 28.355999999999998
- type: recall_at_10
value: 58.782000000000004
- type: recall_at_100
value: 84.02199999999999
- type: recall_at_1000
value: 95.511
- type: recall_at_3
value: 43.126999999999995
- type: recall_at_5
value: 50.14999999999999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.391
- type: map_at_10
value: 37.523
- type: map_at_100
value: 39.312000000000005
- type: map_at_1000
value: 39.54
- type: map_at_3
value: 34.231
- type: map_at_5
value: 36.062
- type: mrr_at_1
value: 32.016
- type: mrr_at_10
value: 41.747
- type: mrr_at_100
value: 42.812
- type: mrr_at_1000
value: 42.844
- type: mrr_at_3
value: 39.129999999999995
- type: mrr_at_5
value: 40.524
- type: ndcg_at_1
value: 32.016
- type: ndcg_at_10
value: 43.826
- type: ndcg_at_100
value: 50.373999999999995
- type: ndcg_at_1000
value: 52.318
- type: ndcg_at_3
value: 38.479
- type: ndcg_at_5
value: 40.944
- type: precision_at_1
value: 32.016
- type: precision_at_10
value: 8.280999999999999
- type: precision_at_100
value: 1.6760000000000002
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 18.05
- type: precision_at_5
value: 13.083
- type: recall_at_1
value: 27.391
- type: recall_at_10
value: 56.928999999999995
- type: recall_at_100
value: 85.169
- type: recall_at_1000
value: 96.665
- type: recall_at_3
value: 42.264
- type: recall_at_5
value: 48.556
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 19.681
- type: map_at_10
value: 32.741
- type: map_at_100
value: 34.811
- type: map_at_1000
value: 35.003
- type: map_at_3
value: 27.697
- type: map_at_5
value: 30.372
- type: mrr_at_1
value: 44.951
- type: mrr_at_10
value: 56.34400000000001
- type: mrr_at_100
value: 56.961
- type: mrr_at_1000
value: 56.987
- type: mrr_at_3
value: 53.681
- type: mrr_at_5
value: 55.407
- type: ndcg_at_1
value: 44.951
- type: ndcg_at_10
value: 42.905
- type: ndcg_at_100
value: 49.95
- type: ndcg_at_1000
value: 52.917
- type: ndcg_at_3
value: 36.815
- type: ndcg_at_5
value: 38.817
- type: precision_at_1
value: 44.951
- type: precision_at_10
value: 12.989999999999998
- type: precision_at_100
value: 2.068
- type: precision_at_1000
value: 0.263
- type: precision_at_3
value: 27.275
- type: precision_at_5
value: 20.365
- type: recall_at_1
value: 19.681
- type: recall_at_10
value: 48.272999999999996
- type: recall_at_100
value: 71.87400000000001
- type: recall_at_1000
value: 87.929
- type: recall_at_3
value: 32.653999999999996
- type: recall_at_5
value: 39.364
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 10.231
- type: map_at_10
value: 22.338
- type: map_at_100
value: 31.927
- type: map_at_1000
value: 33.87
- type: map_at_3
value: 15.559999999999999
- type: map_at_5
value: 18.239
- type: mrr_at_1
value: 75.0
- type: mrr_at_10
value: 81.303
- type: mrr_at_100
value: 81.523
- type: mrr_at_1000
value: 81.53
- type: mrr_at_3
value: 80.083
- type: mrr_at_5
value: 80.758
- type: ndcg_at_1
value: 64.625
- type: ndcg_at_10
value: 48.687000000000005
- type: ndcg_at_100
value: 52.791
- type: ndcg_at_1000
value: 60.041999999999994
- type: ndcg_at_3
value: 53.757999999999996
- type: ndcg_at_5
value: 50.76500000000001
- type: precision_at_1
value: 75.0
- type: precision_at_10
value: 38.3
- type: precision_at_100
value: 12.025
- type: precision_at_1000
value: 2.3970000000000002
- type: precision_at_3
value: 55.417
- type: precision_at_5
value: 47.5
- type: recall_at_1
value: 10.231
- type: recall_at_10
value: 27.697
- type: recall_at_100
value: 57.409
- type: recall_at_1000
value: 80.547
- type: recall_at_3
value: 16.668
- type: recall_at_5
value: 20.552
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 61.365
- type: f1
value: 56.7540827912991
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 83.479
- type: map_at_10
value: 88.898
- type: map_at_100
value: 89.11
- type: map_at_1000
value: 89.12400000000001
- type: map_at_3
value: 88.103
- type: map_at_5
value: 88.629
- type: mrr_at_1
value: 89.934
- type: mrr_at_10
value: 93.91000000000001
- type: mrr_at_100
value: 93.937
- type: mrr_at_1000
value: 93.938
- type: mrr_at_3
value: 93.62700000000001
- type: mrr_at_5
value: 93.84599999999999
- type: ndcg_at_1
value: 89.934
- type: ndcg_at_10
value: 91.574
- type: ndcg_at_100
value: 92.238
- type: ndcg_at_1000
value: 92.45
- type: ndcg_at_3
value: 90.586
- type: ndcg_at_5
value: 91.16300000000001
- type: precision_at_1
value: 89.934
- type: precision_at_10
value: 10.555
- type: precision_at_100
value: 1.1159999999999999
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.588
- type: precision_at_5
value: 20.642
- type: recall_at_1
value: 83.479
- type: recall_at_10
value: 94.971
- type: recall_at_100
value: 97.397
- type: recall_at_1000
value: 98.666
- type: recall_at_3
value: 92.24799999999999
- type: recall_at_5
value: 93.797
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 27.16
- type: map_at_10
value: 45.593
- type: map_at_100
value: 47.762
- type: map_at_1000
value: 47.899
- type: map_at_3
value: 39.237
- type: map_at_5
value: 42.970000000000006
- type: mrr_at_1
value: 52.623
- type: mrr_at_10
value: 62.637
- type: mrr_at_100
value: 63.169
- type: mrr_at_1000
value: 63.185
- type: mrr_at_3
value: 59.928000000000004
- type: mrr_at_5
value: 61.702999999999996
- type: ndcg_at_1
value: 52.623
- type: ndcg_at_10
value: 54.701
- type: ndcg_at_100
value: 61.263
- type: ndcg_at_1000
value: 63.134
- type: ndcg_at_3
value: 49.265
- type: ndcg_at_5
value: 51.665000000000006
- type: precision_at_1
value: 52.623
- type: precision_at_10
value: 15.185
- type: precision_at_100
value: 2.202
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 32.767
- type: precision_at_5
value: 24.722
- type: recall_at_1
value: 27.16
- type: recall_at_10
value: 63.309000000000005
- type: recall_at_100
value: 86.722
- type: recall_at_1000
value: 97.505
- type: recall_at_3
value: 45.045
- type: recall_at_5
value: 54.02400000000001
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.573
- type: map_at_10
value: 59.373
- type: map_at_100
value: 60.292
- type: map_at_1000
value: 60.358999999999995
- type: map_at_3
value: 56.159000000000006
- type: map_at_5
value: 58.123999999999995
- type: mrr_at_1
value: 85.14500000000001
- type: mrr_at_10
value: 89.25999999999999
- type: mrr_at_100
value: 89.373
- type: mrr_at_1000
value: 89.377
- type: mrr_at_3
value: 88.618
- type: mrr_at_5
value: 89.036
- type: ndcg_at_1
value: 85.14500000000001
- type: ndcg_at_10
value: 68.95
- type: ndcg_at_100
value: 71.95
- type: ndcg_at_1000
value: 73.232
- type: ndcg_at_3
value: 64.546
- type: ndcg_at_5
value: 66.945
- type: precision_at_1
value: 85.14500000000001
- type: precision_at_10
value: 13.865
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 39.703
- type: precision_at_5
value: 25.718000000000004
- type: recall_at_1
value: 42.573
- type: recall_at_10
value: 69.325
- type: recall_at_100
value: 80.932
- type: recall_at_1000
value: 89.446
- type: recall_at_3
value: 59.553999999999995
- type: recall_at_5
value: 64.294
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 95.8336
- type: ap
value: 93.78862962194073
- type: f1
value: 95.83192650728371
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 23.075000000000003
- type: map_at_10
value: 36.102000000000004
- type: map_at_100
value: 37.257
- type: map_at_1000
value: 37.3
- type: map_at_3
value: 32.144
- type: map_at_5
value: 34.359
- type: mrr_at_1
value: 23.711
- type: mrr_at_10
value: 36.671
- type: mrr_at_100
value: 37.763999999999996
- type: mrr_at_1000
value: 37.801
- type: mrr_at_3
value: 32.775
- type: mrr_at_5
value: 34.977000000000004
- type: ndcg_at_1
value: 23.711
- type: ndcg_at_10
value: 43.361
- type: ndcg_at_100
value: 48.839
- type: ndcg_at_1000
value: 49.88
- type: ndcg_at_3
value: 35.269
- type: ndcg_at_5
value: 39.224
- type: precision_at_1
value: 23.711
- type: precision_at_10
value: 6.866999999999999
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.083
- type: recall_at_1
value: 23.075000000000003
- type: recall_at_10
value: 65.756
- type: recall_at_100
value: 90.88199999999999
- type: recall_at_1000
value: 98.739
- type: recall_at_3
value: 43.691
- type: recall_at_5
value: 53.15800000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.69493844049248
- type: f1
value: 97.55048089616261
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 88.75968992248062
- type: f1
value: 72.26321223399123
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 82.40080699394754
- type: f1
value: 79.62590029057968
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 84.49562878278414
- type: f1
value: 84.0040193313333
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 39.386760057101945
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 37.89687154075537
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.94151656057482
- type: mrr
value: 35.32684700746953
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.239999999999999
- type: map_at_10
value: 14.862
- type: map_at_100
value: 18.955
- type: map_at_1000
value: 20.694000000000003
- type: map_at_3
value: 10.683
- type: map_at_5
value: 12.674
- type: mrr_at_1
value: 50.15500000000001
- type: mrr_at_10
value: 59.697
- type: mrr_at_100
value: 60.095
- type: mrr_at_1000
value: 60.129999999999995
- type: mrr_at_3
value: 58.35900000000001
- type: mrr_at_5
value: 58.839
- type: ndcg_at_1
value: 48.452
- type: ndcg_at_10
value: 39.341
- type: ndcg_at_100
value: 35.866
- type: ndcg_at_1000
value: 45.111000000000004
- type: ndcg_at_3
value: 44.527
- type: ndcg_at_5
value: 42.946
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 29.536
- type: precision_at_100
value: 9.142
- type: precision_at_1000
value: 2.2849999999999997
- type: precision_at_3
value: 41.899
- type: precision_at_5
value: 37.647000000000006
- type: recall_at_1
value: 6.239999999999999
- type: recall_at_10
value: 19.278000000000002
- type: recall_at_100
value: 36.074
- type: recall_at_1000
value: 70.017
- type: recall_at_3
value: 12.066
- type: recall_at_5
value: 15.254000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 39.75
- type: map_at_10
value: 56.443
- type: map_at_100
value: 57.233999999999995
- type: map_at_1000
value: 57.249
- type: map_at_3
value: 52.032999999999994
- type: map_at_5
value: 54.937999999999995
- type: mrr_at_1
value: 44.728
- type: mrr_at_10
value: 58.939
- type: mrr_at_100
value: 59.489000000000004
- type: mrr_at_1000
value: 59.499
- type: mrr_at_3
value: 55.711999999999996
- type: mrr_at_5
value: 57.89
- type: ndcg_at_1
value: 44.728
- type: ndcg_at_10
value: 63.998999999999995
- type: ndcg_at_100
value: 67.077
- type: ndcg_at_1000
value: 67.40899999999999
- type: ndcg_at_3
value: 56.266000000000005
- type: ndcg_at_5
value: 60.88
- type: precision_at_1
value: 44.728
- type: precision_at_10
value: 10.09
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.145
- type: precision_at_5
value: 17.822
- type: recall_at_1
value: 39.75
- type: recall_at_10
value: 84.234
- type: recall_at_100
value: 97.055
- type: recall_at_1000
value: 99.517
- type: recall_at_3
value: 64.851
- type: recall_at_5
value: 75.343
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 72.085
- type: map_at_10
value: 86.107
- type: map_at_100
value: 86.727
- type: map_at_1000
value: 86.74
- type: map_at_3
value: 83.21
- type: map_at_5
value: 85.06
- type: mrr_at_1
value: 82.94
- type: mrr_at_10
value: 88.845
- type: mrr_at_100
value: 88.926
- type: mrr_at_1000
value: 88.927
- type: mrr_at_3
value: 87.993
- type: mrr_at_5
value: 88.62299999999999
- type: ndcg_at_1
value: 82.97
- type: ndcg_at_10
value: 89.645
- type: ndcg_at_100
value: 90.717
- type: ndcg_at_1000
value: 90.78
- type: ndcg_at_3
value: 86.99900000000001
- type: ndcg_at_5
value: 88.52600000000001
- type: precision_at_1
value: 82.97
- type: precision_at_10
value: 13.569
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.043
- type: precision_at_5
value: 24.992
- type: recall_at_1
value: 72.085
- type: recall_at_10
value: 96.262
- type: recall_at_100
value: 99.77000000000001
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 88.652
- type: recall_at_5
value: 93.01899999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.82153952668092
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.094465801879295
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.688
- type: map_at_10
value: 15.201999999999998
- type: map_at_100
value: 18.096
- type: map_at_1000
value: 18.481
- type: map_at_3
value: 10.734
- type: map_at_5
value: 12.94
- type: mrr_at_1
value: 28.000000000000004
- type: mrr_at_10
value: 41.101
- type: mrr_at_100
value: 42.202
- type: mrr_at_1000
value: 42.228
- type: mrr_at_3
value: 37.683
- type: mrr_at_5
value: 39.708
- type: ndcg_at_1
value: 28.000000000000004
- type: ndcg_at_10
value: 24.976000000000003
- type: ndcg_at_100
value: 35.129
- type: ndcg_at_1000
value: 40.77
- type: ndcg_at_3
value: 23.787
- type: ndcg_at_5
value: 20.816000000000003
- type: precision_at_1
value: 28.000000000000004
- type: precision_at_10
value: 13.04
- type: precision_at_100
value: 2.761
- type: precision_at_1000
value: 0.41000000000000003
- type: precision_at_3
value: 22.6
- type: precision_at_5
value: 18.52
- type: recall_at_1
value: 5.688
- type: recall_at_10
value: 26.43
- type: recall_at_100
value: 56.02
- type: recall_at_1000
value: 83.21
- type: recall_at_3
value: 13.752
- type: recall_at_5
value: 18.777
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.15084859283178
- type: cos_sim_spearman
value: 80.49030614009419
- type: euclidean_pearson
value: 81.84574978672468
- type: euclidean_spearman
value: 79.89787150656818
- type: manhattan_pearson
value: 81.63076538567131
- type: manhattan_spearman
value: 79.69867352121841
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.64097921490992
- type: cos_sim_spearman
value: 77.25370084896514
- type: euclidean_pearson
value: 82.71210826468788
- type: euclidean_spearman
value: 78.50445584994826
- type: manhattan_pearson
value: 82.92580164330298
- type: manhattan_spearman
value: 78.69686891301019
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 87.24596417308994
- type: cos_sim_spearman
value: 87.79454220555091
- type: euclidean_pearson
value: 87.40242561671164
- type: euclidean_spearman
value: 88.25955597373556
- type: manhattan_pearson
value: 87.25160240485849
- type: manhattan_spearman
value: 88.155794979818
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.44914233422564
- type: cos_sim_spearman
value: 82.91015471820322
- type: euclidean_pearson
value: 84.7206656630327
- type: euclidean_spearman
value: 83.86408872059216
- type: manhattan_pearson
value: 84.72816725158454
- type: manhattan_spearman
value: 84.01603388572788
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.6168026237477
- type: cos_sim_spearman
value: 88.45414278092397
- type: euclidean_pearson
value: 88.57023240882022
- type: euclidean_spearman
value: 89.04102190922094
- type: manhattan_pearson
value: 88.66695535796354
- type: manhattan_spearman
value: 89.19898476680969
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.27925826089424
- type: cos_sim_spearman
value: 85.45291099550461
- type: euclidean_pearson
value: 83.63853036580834
- type: euclidean_spearman
value: 84.33468035821484
- type: manhattan_pearson
value: 83.72778773251596
- type: manhattan_spearman
value: 84.51583132445376
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.67375185692552
- type: cos_sim_spearman
value: 90.32542469203855
- type: euclidean_pearson
value: 89.63513717951847
- type: euclidean_spearman
value: 89.87760271003745
- type: manhattan_pearson
value: 89.28381452982924
- type: manhattan_spearman
value: 89.53568197785721
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 66.24644693819846
- type: cos_sim_spearman
value: 66.09889420525377
- type: euclidean_pearson
value: 63.72551583520747
- type: euclidean_spearman
value: 63.01385470780679
- type: manhattan_pearson
value: 64.09258157214097
- type: manhattan_spearman
value: 63.080517752822594
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.27321463839989
- type: cos_sim_spearman
value: 86.37572865993327
- type: euclidean_pearson
value: 86.36268020198149
- type: euclidean_spearman
value: 86.31089339478922
- type: manhattan_pearson
value: 86.4260445761947
- type: manhattan_spearman
value: 86.45885895320457
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.52456702387798
- type: mrr
value: 96.34556529164372
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.99400000000001
- type: map_at_10
value: 73.38799999999999
- type: map_at_100
value: 73.747
- type: map_at_1000
value: 73.75
- type: map_at_3
value: 70.04599999999999
- type: map_at_5
value: 72.095
- type: mrr_at_1
value: 65.0
- type: mrr_at_10
value: 74.42800000000001
- type: mrr_at_100
value: 74.722
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.056
- type: mrr_at_5
value: 73.60600000000001
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 78.435
- type: ndcg_at_100
value: 79.922
- type: ndcg_at_1000
value: 80.00500000000001
- type: ndcg_at_3
value: 73.05199999999999
- type: ndcg_at_5
value: 75.98
- type: precision_at_1
value: 65.0
- type: precision_at_10
value: 10.5
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.555999999999997
- type: precision_at_5
value: 19.0
- type: recall_at_1
value: 61.99400000000001
- type: recall_at_10
value: 92.72200000000001
- type: recall_at_100
value: 99.333
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 78.739
- type: recall_at_5
value: 85.828
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 95.3203137438653
- type: cos_sim_f1
value: 89.12386706948641
- type: cos_sim_precision
value: 89.75659229208925
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.67821782178218
- type: dot_ap
value: 89.94069840000675
- type: dot_f1
value: 83.45902463549521
- type: dot_precision
value: 83.9231547017189
- type: dot_recall
value: 83.0
- type: euclidean_accuracy
value: 99.78613861386138
- type: euclidean_ap
value: 95.10648259135526
- type: euclidean_f1
value: 88.77338877338877
- type: euclidean_precision
value: 92.42424242424242
- type: euclidean_recall
value: 85.39999999999999
- type: manhattan_accuracy
value: 99.7950495049505
- type: manhattan_ap
value: 95.29987661320946
- type: manhattan_f1
value: 89.21313183949972
- type: manhattan_precision
value: 93.14472252448314
- type: manhattan_recall
value: 85.6
- type: max_accuracy
value: 99.7950495049505
- type: max_ap
value: 95.3203137438653
- type: max_f1
value: 89.21313183949972
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 67.65446577183913
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 46.30749237193961
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.91481849959949
- type: mrr
value: 55.853506175197346
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.08196549170419
- type: cos_sim_spearman
value: 31.16661390597077
- type: dot_pearson
value: 29.892258410943466
- type: dot_spearman
value: 30.51328811965085
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.23900000000000002
- type: map_at_10
value: 2.173
- type: map_at_100
value: 14.24
- type: map_at_1000
value: 35.309000000000005
- type: map_at_3
value: 0.7100000000000001
- type: map_at_5
value: 1.163
- type: mrr_at_1
value: 92.0
- type: mrr_at_10
value: 96.0
- type: mrr_at_100
value: 96.0
- type: mrr_at_1000
value: 96.0
- type: mrr_at_3
value: 96.0
- type: mrr_at_5
value: 96.0
- type: ndcg_at_1
value: 90.0
- type: ndcg_at_10
value: 85.382
- type: ndcg_at_100
value: 68.03
- type: ndcg_at_1000
value: 61.021
- type: ndcg_at_3
value: 89.765
- type: ndcg_at_5
value: 88.444
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 88.0
- type: precision_at_100
value: 70.02000000000001
- type: precision_at_1000
value: 26.984
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 92.80000000000001
- type: recall_at_1
value: 0.23900000000000002
- type: recall_at_10
value: 2.313
- type: recall_at_100
value: 17.049
- type: recall_at_1000
value: 57.489999999999995
- type: recall_at_3
value: 0.737
- type: recall_at_5
value: 1.221
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.75
- type: map_at_10
value: 11.29
- type: map_at_100
value: 18.032999999999998
- type: map_at_1000
value: 19.746
- type: map_at_3
value: 6.555
- type: map_at_5
value: 8.706999999999999
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 50.55
- type: mrr_at_100
value: 51.659
- type: mrr_at_1000
value: 51.659
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 49.728
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 27.894000000000002
- type: ndcg_at_100
value: 39.769
- type: ndcg_at_1000
value: 51.495999999999995
- type: ndcg_at_3
value: 32.954
- type: ndcg_at_5
value: 31.502999999999997
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.265
- type: precision_at_100
value: 7.898
- type: precision_at_1000
value: 1.58
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.429000000000002
- type: recall_at_1
value: 2.75
- type: recall_at_10
value: 16.953
- type: recall_at_100
value: 48.68
- type: recall_at_1000
value: 85.18599999999999
- type: recall_at_3
value: 7.710999999999999
- type: recall_at_5
value: 11.484
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 82.66099999999999
- type: ap
value: 25.555698090238337
- type: f1
value: 66.48402012461622
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.94567062818335
- type: f1
value: 73.28139189595674
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.581627240203474
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.78089050485785
- type: cos_sim_ap
value: 79.64487116574168
- type: cos_sim_f1
value: 72.46563021970964
- type: cos_sim_precision
value: 70.62359128474831
- type: cos_sim_recall
value: 74.40633245382587
- type: dot_accuracy
value: 86.2609524944865
- type: dot_ap
value: 75.513046857613
- type: dot_f1
value: 68.58213616489695
- type: dot_precision
value: 65.12455516014235
- type: dot_recall
value: 72.42744063324538
- type: euclidean_accuracy
value: 87.6080348095607
- type: euclidean_ap
value: 79.00204933649795
- type: euclidean_f1
value: 72.14495342605589
- type: euclidean_precision
value: 69.85421299728193
- type: euclidean_recall
value: 74.5910290237467
- type: manhattan_accuracy
value: 87.59611372712642
- type: manhattan_ap
value: 78.78523756706264
- type: manhattan_f1
value: 71.86499137718648
- type: manhattan_precision
value: 67.39833641404806
- type: manhattan_recall
value: 76.96569920844327
- type: max_accuracy
value: 87.78089050485785
- type: max_ap
value: 79.64487116574168
- type: max_f1
value: 72.46563021970964
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.98719292117825
- type: cos_sim_ap
value: 87.58146137353202
- type: cos_sim_f1
value: 80.28543232369239
- type: cos_sim_precision
value: 79.1735289714029
- type: cos_sim_recall
value: 81.42901139513397
- type: dot_accuracy
value: 88.9199363526992
- type: dot_ap
value: 84.98499998630417
- type: dot_f1
value: 78.21951400757969
- type: dot_precision
value: 75.58523624874336
- type: dot_recall
value: 81.04404065291038
- type: euclidean_accuracy
value: 89.77374160748244
- type: euclidean_ap
value: 87.35151562835209
- type: euclidean_f1
value: 79.92160922940393
- type: euclidean_precision
value: 76.88531587933979
- type: euclidean_recall
value: 83.20757622420696
- type: manhattan_accuracy
value: 89.72717041176699
- type: manhattan_ap
value: 87.34065592142515
- type: manhattan_f1
value: 79.85603419187943
- type: manhattan_precision
value: 77.82243332115455
- type: manhattan_recall
value: 81.99876809362489
- type: max_accuracy
value: 89.98719292117825
- type: max_ap
value: 87.58146137353202
- type: max_f1
value: 80.28543232369239
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 53.45954203592337
- type: cos_sim_spearman
value: 58.42154680418638
- type: euclidean_pearson
value: 56.41543791722753
- type: euclidean_spearman
value: 58.39328016640146
- type: manhattan_pearson
value: 56.318510356833876
- type: manhattan_spearman
value: 58.28423447818184
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 50.78356460675945
- type: cos_sim_spearman
value: 55.6530411663269
- type: euclidean_pearson
value: 56.50763660417816
- type: euclidean_spearman
value: 55.733823335669065
- type: manhattan_pearson
value: 56.45323093512866
- type: manhattan_spearman
value: 55.63248619032702
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.209999999999994
- type: f1
value: 46.08892432018655
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 70.25573992001478
- type: cos_sim_spearman
value: 73.85247134951433
- type: euclidean_pearson
value: 72.60033082168442
- type: euclidean_spearman
value: 73.72445893756499
- type: manhattan_pearson
value: 72.59932284620231
- type: manhattan_spearman
value: 73.68002490614583
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 45.21317724305628
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 42.49825170976724
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.15661686810597
- type: mrr
value: 90.11222222222223
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.1204726064383
- type: mrr
value: 90.20142857142858
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 27.224999999999998
- type: map_at_10
value: 40.169
- type: map_at_100
value: 42.0
- type: map_at_1000
value: 42.109
- type: map_at_3
value: 35.76
- type: map_at_5
value: 38.221
- type: mrr_at_1
value: 40.56
- type: mrr_at_10
value: 49.118
- type: mrr_at_100
value: 50.092999999999996
- type: mrr_at_1000
value: 50.133
- type: mrr_at_3
value: 46.507
- type: mrr_at_5
value: 47.973
- type: ndcg_at_1
value: 40.56
- type: ndcg_at_10
value: 46.972
- type: ndcg_at_100
value: 54.04
- type: ndcg_at_1000
value: 55.862
- type: ndcg_at_3
value: 41.36
- type: ndcg_at_5
value: 43.704
- type: precision_at_1
value: 40.56
- type: precision_at_10
value: 10.302999999999999
- type: precision_at_100
value: 1.606
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 23.064
- type: precision_at_5
value: 16.764000000000003
- type: recall_at_1
value: 27.224999999999998
- type: recall_at_10
value: 58.05200000000001
- type: recall_at_100
value: 87.092
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 41.373
- type: recall_at_5
value: 48.453
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 77.40228502705953
- type: cos_sim_ap
value: 86.22359172956327
- type: cos_sim_f1
value: 78.96328293736501
- type: cos_sim_precision
value: 73.36945615091311
- type: cos_sim_recall
value: 85.48047696983868
- type: dot_accuracy
value: 75.53818400481059
- type: dot_ap
value: 83.70164011305312
- type: dot_f1
value: 77.67298719348754
- type: dot_precision
value: 67.49482401656314
- type: dot_recall
value: 91.46598082768296
- type: euclidean_accuracy
value: 77.94347564642213
- type: euclidean_ap
value: 86.4652108728609
- type: euclidean_f1
value: 79.15555555555555
- type: euclidean_precision
value: 75.41816641964853
- type: euclidean_recall
value: 83.28267477203647
- type: manhattan_accuracy
value: 77.45039085989175
- type: manhattan_ap
value: 86.09986583900665
- type: manhattan_f1
value: 78.93669264438988
- type: manhattan_precision
value: 72.63261296660117
- type: manhattan_recall
value: 86.43909282207154
- type: max_accuracy
value: 77.94347564642213
- type: max_ap
value: 86.4652108728609
- type: max_f1
value: 79.15555555555555
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 69.336
- type: map_at_10
value: 77.16
- type: map_at_100
value: 77.47500000000001
- type: map_at_1000
value: 77.482
- type: map_at_3
value: 75.42999999999999
- type: map_at_5
value: 76.468
- type: mrr_at_1
value: 69.44200000000001
- type: mrr_at_10
value: 77.132
- type: mrr_at_100
value: 77.43299999999999
- type: mrr_at_1000
value: 77.44
- type: mrr_at_3
value: 75.395
- type: mrr_at_5
value: 76.459
- type: ndcg_at_1
value: 69.547
- type: ndcg_at_10
value: 80.794
- type: ndcg_at_100
value: 82.245
- type: ndcg_at_1000
value: 82.40899999999999
- type: ndcg_at_3
value: 77.303
- type: ndcg_at_5
value: 79.168
- type: precision_at_1
value: 69.547
- type: precision_at_10
value: 9.305
- type: precision_at_100
value: 0.9979999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.749000000000002
- type: precision_at_5
value: 17.576
- type: recall_at_1
value: 69.336
- type: recall_at_10
value: 92.097
- type: recall_at_100
value: 98.736
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 82.64
- type: recall_at_5
value: 87.144
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 26.817999999999998
- type: map_at_10
value: 82.67
- type: map_at_100
value: 85.304
- type: map_at_1000
value: 85.334
- type: map_at_3
value: 57.336
- type: map_at_5
value: 72.474
- type: mrr_at_1
value: 91.45
- type: mrr_at_10
value: 94.272
- type: mrr_at_100
value: 94.318
- type: mrr_at_1000
value: 94.32000000000001
- type: mrr_at_3
value: 94.0
- type: mrr_at_5
value: 94.17699999999999
- type: ndcg_at_1
value: 91.45
- type: ndcg_at_10
value: 89.404
- type: ndcg_at_100
value: 91.724
- type: ndcg_at_1000
value: 91.973
- type: ndcg_at_3
value: 88.104
- type: ndcg_at_5
value: 87.25699999999999
- type: precision_at_1
value: 91.45
- type: precision_at_10
value: 42.585
- type: precision_at_100
value: 4.838
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 78.8
- type: precision_at_5
value: 66.66
- type: recall_at_1
value: 26.817999999999998
- type: recall_at_10
value: 90.67
- type: recall_at_100
value: 98.36200000000001
- type: recall_at_1000
value: 99.583
- type: recall_at_3
value: 59.614999999999995
- type: recall_at_5
value: 77.05199999999999
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 47.699999999999996
- type: map_at_10
value: 57.589999999999996
- type: map_at_100
value: 58.226
- type: map_at_1000
value: 58.251
- type: map_at_3
value: 55.233
- type: map_at_5
value: 56.633
- type: mrr_at_1
value: 47.699999999999996
- type: mrr_at_10
value: 57.589999999999996
- type: mrr_at_100
value: 58.226
- type: mrr_at_1000
value: 58.251
- type: mrr_at_3
value: 55.233
- type: mrr_at_5
value: 56.633
- type: ndcg_at_1
value: 47.699999999999996
- type: ndcg_at_10
value: 62.505
- type: ndcg_at_100
value: 65.517
- type: ndcg_at_1000
value: 66.19800000000001
- type: ndcg_at_3
value: 57.643
- type: ndcg_at_5
value: 60.181
- type: precision_at_1
value: 47.699999999999996
- type: precision_at_10
value: 7.8
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.532999999999998
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 47.699999999999996
- type: recall_at_10
value: 78.0
- type: recall_at_100
value: 91.9
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 64.60000000000001
- type: recall_at_5
value: 70.8
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 44.84801846864178
- type: f1
value: 37.47347897956339
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.68244615477374
- type: f1
value: 80.0445640948843
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 69.57786502217138
- type: cos_sim_spearman
value: 75.39106054489906
- type: euclidean_pearson
value: 73.72082954602402
- type: euclidean_spearman
value: 75.14421475913619
- type: manhattan_pearson
value: 73.62463076633642
- type: manhattan_spearman
value: 75.01301565104112
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 29.143797057999134
- type: mrr
value: 28.08174603174603
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 70.492
- type: map_at_10
value: 79.501
- type: map_at_100
value: 79.728
- type: map_at_1000
value: 79.735
- type: map_at_3
value: 77.77
- type: map_at_5
value: 78.851
- type: mrr_at_1
value: 72.822
- type: mrr_at_10
value: 80.001
- type: mrr_at_100
value: 80.19
- type: mrr_at_1000
value: 80.197
- type: mrr_at_3
value: 78.484
- type: mrr_at_5
value: 79.42099999999999
- type: ndcg_at_1
value: 72.822
- type: ndcg_at_10
value: 83.013
- type: ndcg_at_100
value: 84.013
- type: ndcg_at_1000
value: 84.20400000000001
- type: ndcg_at_3
value: 79.728
- type: ndcg_at_5
value: 81.542
- type: precision_at_1
value: 72.822
- type: precision_at_10
value: 9.917
- type: precision_at_100
value: 1.042
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.847
- type: precision_at_5
value: 18.871
- type: recall_at_1
value: 70.492
- type: recall_at_10
value: 93.325
- type: recall_at_100
value: 97.822
- type: recall_at_1000
value: 99.319
- type: recall_at_3
value: 84.636
- type: recall_at_5
value: 88.93100000000001
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.88298587760592
- type: f1
value: 73.89001762017176
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.76328177538669
- type: f1
value: 80.24718532423358
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 49.6
- type: map_at_10
value: 55.620999999999995
- type: map_at_100
value: 56.204
- type: map_at_1000
value: 56.251
- type: map_at_3
value: 54.132999999999996
- type: map_at_5
value: 54.933
- type: mrr_at_1
value: 49.7
- type: mrr_at_10
value: 55.67100000000001
- type: mrr_at_100
value: 56.254000000000005
- type: mrr_at_1000
value: 56.301
- type: mrr_at_3
value: 54.18300000000001
- type: mrr_at_5
value: 54.983000000000004
- type: ndcg_at_1
value: 49.6
- type: ndcg_at_10
value: 58.645
- type: ndcg_at_100
value: 61.789
- type: ndcg_at_1000
value: 63.219
- type: ndcg_at_3
value: 55.567
- type: ndcg_at_5
value: 57.008
- type: precision_at_1
value: 49.6
- type: precision_at_10
value: 6.819999999999999
- type: precision_at_100
value: 0.836
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 19.900000000000002
- type: precision_at_5
value: 12.64
- type: recall_at_1
value: 49.6
- type: recall_at_10
value: 68.2
- type: recall_at_100
value: 83.6
- type: recall_at_1000
value: 95.3
- type: recall_at_3
value: 59.699999999999996
- type: recall_at_5
value: 63.2
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 74.45666666666666
- type: f1
value: 74.32582402190089
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 80.67135896047645
- type: cos_sim_ap
value: 87.60421240712051
- type: cos_sim_f1
value: 82.1304131408661
- type: cos_sim_precision
value: 77.68361581920904
- type: cos_sim_recall
value: 87.11721224920802
- type: dot_accuracy
value: 79.04710341093666
- type: dot_ap
value: 85.6370059719336
- type: dot_f1
value: 80.763723150358
- type: dot_precision
value: 73.69337979094077
- type: dot_recall
value: 89.33474128827878
- type: euclidean_accuracy
value: 81.05035192203573
- type: euclidean_ap
value: 87.7880240053663
- type: euclidean_f1
value: 82.50244379276637
- type: euclidean_precision
value: 76.7970882620564
- type: euclidean_recall
value: 89.1235480464625
- type: manhattan_accuracy
value: 80.61721710882512
- type: manhattan_ap
value: 87.43568120591175
- type: manhattan_f1
value: 81.89526184538653
- type: manhattan_precision
value: 77.5992438563327
- type: manhattan_recall
value: 86.6948257655755
- type: max_accuracy
value: 81.05035192203573
- type: max_ap
value: 87.7880240053663
- type: max_f1
value: 82.50244379276637
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 93.5
- type: ap
value: 91.31357903446782
- type: f1
value: 93.48088994006616
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 36.93293453538077
- type: cos_sim_spearman
value: 42.45972506308574
- type: euclidean_pearson
value: 42.34945133152159
- type: euclidean_spearman
value: 42.331610303674644
- type: manhattan_pearson
value: 42.31455070249498
- type: manhattan_spearman
value: 42.19887982891834
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 33.683290790043785
- type: cos_sim_spearman
value: 35.149171171202994
- type: euclidean_pearson
value: 32.33806561267862
- type: euclidean_spearman
value: 34.483576387347966
- type: manhattan_pearson
value: 32.47629754599608
- type: manhattan_spearman
value: 34.66434471867615
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 66.46322760516104
- type: cos_sim_spearman
value: 67.398478319726
- type: euclidean_pearson
value: 64.7223480293625
- type: euclidean_spearman
value: 66.83118568812951
- type: manhattan_pearson
value: 64.88440039828305
- type: manhattan_spearman
value: 66.80429458952257
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 79.08991383232105
- type: cos_sim_spearman
value: 79.39715677296854
- type: euclidean_pearson
value: 78.63201279320496
- type: euclidean_spearman
value: 79.40262660785731
- type: manhattan_pearson
value: 78.98138363146906
- type: manhattan_spearman
value: 79.79968413014194
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.43289278789972
- type: mrr
value: 77.53012460908535
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 27.733999999999998
- type: map_at_10
value: 78.24799999999999
- type: map_at_100
value: 81.765
- type: map_at_1000
value: 81.824
- type: map_at_3
value: 54.92
- type: map_at_5
value: 67.61399999999999
- type: mrr_at_1
value: 90.527
- type: mrr_at_10
value: 92.843
- type: mrr_at_100
value: 92.927
- type: mrr_at_1000
value: 92.93
- type: mrr_at_3
value: 92.45100000000001
- type: mrr_at_5
value: 92.693
- type: ndcg_at_1
value: 90.527
- type: ndcg_at_10
value: 85.466
- type: ndcg_at_100
value: 88.846
- type: ndcg_at_1000
value: 89.415
- type: ndcg_at_3
value: 86.768
- type: ndcg_at_5
value: 85.46000000000001
- type: precision_at_1
value: 90.527
- type: precision_at_10
value: 42.488
- type: precision_at_100
value: 5.024
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 75.907
- type: precision_at_5
value: 63.727000000000004
- type: recall_at_1
value: 27.733999999999998
- type: recall_at_10
value: 84.346
- type: recall_at_100
value: 95.536
- type: recall_at_1000
value: 98.42999999999999
- type: recall_at_3
value: 56.455
- type: recall_at_5
value: 70.755
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 49.952000000000005
- type: f1
value: 48.264617195258054
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 68.23769904483508
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 62.50294403136556
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 54.0
- type: map_at_10
value: 63.668
- type: map_at_100
value: 64.217
- type: map_at_1000
value: 64.23100000000001
- type: map_at_3
value: 61.7
- type: map_at_5
value: 62.870000000000005
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 63.668
- type: mrr_at_100
value: 64.217
- type: mrr_at_1000
value: 64.23100000000001
- type: mrr_at_3
value: 61.7
- type: mrr_at_5
value: 62.870000000000005
- type: ndcg_at_1
value: 54.0
- type: ndcg_at_10
value: 68.11399999999999
- type: ndcg_at_100
value: 70.723
- type: ndcg_at_1000
value: 71.123
- type: ndcg_at_3
value: 64.074
- type: ndcg_at_5
value: 66.178
- type: precision_at_1
value: 54.0
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.2
- type: recall_at_1
value: 54.0
- type: recall_at_10
value: 82.0
- type: recall_at_100
value: 94.1
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.0
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 86.63000000000001
- type: ap
value: 69.99457882599567
- type: f1
value: 85.07735617998541
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 44.594104491193555
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 63.97614314115309
- type: f1
value: 52.15634261679283
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
- type: map_at_1
value: 32.646
- type: map_at_10
value: 47.963
- type: map_at_100
value: 48.789
- type: map_at_1000
value: 48.797000000000004
- type: map_at_3
value: 43.196
- type: map_at_5
value: 46.016
- type: mrr_at_1
value: 33.073
- type: mrr_at_10
value: 48.126000000000005
- type: mrr_at_100
value: 48.946
- type: mrr_at_1000
value: 48.953
- type: mrr_at_3
value: 43.374
- type: mrr_at_5
value: 46.147
- type: ndcg_at_1
value: 32.646
- type: ndcg_at_10
value: 56.481
- type: ndcg_at_100
value: 59.922
- type: ndcg_at_1000
value: 60.07
- type: ndcg_at_3
value: 46.675
- type: ndcg_at_5
value: 51.76500000000001
- type: precision_at_1
value: 32.646
- type: precision_at_10
value: 8.371
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.919
- type: precision_at_5
value: 13.825999999999999
- type: recall_at_1
value: 32.646
- type: recall_at_10
value: 83.71300000000001
- type: recall_at_100
value: 98.578
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.757000000000005
- type: recall_at_5
value: 69.132
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 68.56
- type: ap
value: 23.310493680488513
- type: f1
value: 58.85369533105693
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 88.5
- type: cos_sim_ap
value: 72.42140924378361
- type: cos_sim_f1
value: 66.0919540229885
- type: cos_sim_precision
value: 72.78481012658227
- type: cos_sim_recall
value: 60.526315789473685
- type: dot_accuracy
value: 88.5
- type: dot_ap
value: 72.42140924378361
- type: dot_f1
value: 66.0919540229885
- type: dot_precision
value: 72.78481012658227
- type: dot_recall
value: 60.526315789473685
- type: euclidean_accuracy
value: 88.5
- type: euclidean_ap
value: 72.42140924378361
- type: euclidean_f1
value: 66.0919540229885
- type: euclidean_precision
value: 72.78481012658227
- type: euclidean_recall
value: 60.526315789473685
- type: manhattan_accuracy
value: 88.5
- type: manhattan_ap
value: 72.49745515311696
- type: manhattan_f1
value: 66.0968660968661
- type: manhattan_precision
value: 72.04968944099379
- type: manhattan_recall
value: 61.05263157894737
- type: max_accuracy
value: 88.5
- type: max_ap
value: 72.49745515311696
- type: max_f1
value: 66.0968660968661
- task:
type: STS
dataset:
name: MTEB CDSC-R
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 90.32269765590145
- type: cos_sim_spearman
value: 89.73666311491672
- type: euclidean_pearson
value: 88.2933868516544
- type: euclidean_spearman
value: 89.73666311491672
- type: manhattan_pearson
value: 88.33474590219448
- type: manhattan_spearman
value: 89.8548364866583
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: clarin-knext/dbpedia-pl
config: default
split: test
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
metrics:
- type: map_at_1
value: 7.632999999999999
- type: map_at_10
value: 16.426
- type: map_at_100
value: 22.651
- type: map_at_1000
value: 24.372
- type: map_at_3
value: 11.706
- type: map_at_5
value: 13.529
- type: mrr_at_1
value: 60.75000000000001
- type: mrr_at_10
value: 68.613
- type: mrr_at_100
value: 69.001
- type: mrr_at_1000
value: 69.021
- type: mrr_at_3
value: 67.0
- type: mrr_at_5
value: 67.925
- type: ndcg_at_1
value: 49.875
- type: ndcg_at_10
value: 36.978
- type: ndcg_at_100
value: 40.031
- type: ndcg_at_1000
value: 47.566
- type: ndcg_at_3
value: 41.148
- type: ndcg_at_5
value: 38.702
- type: precision_at_1
value: 60.75000000000001
- type: precision_at_10
value: 29.7
- type: precision_at_100
value: 9.278
- type: precision_at_1000
value: 2.099
- type: precision_at_3
value: 44.0
- type: precision_at_5
value: 37.6
- type: recall_at_1
value: 7.632999999999999
- type: recall_at_10
value: 22.040000000000003
- type: recall_at_100
value: 44.024
- type: recall_at_1000
value: 67.848
- type: recall_at_3
value: 13.093
- type: recall_at_5
value: 15.973
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: clarin-knext/fiqa-pl
config: default
split: test
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
metrics:
- type: map_at_1
value: 15.473
- type: map_at_10
value: 24.579
- type: map_at_100
value: 26.387
- type: map_at_1000
value: 26.57
- type: map_at_3
value: 21.278
- type: map_at_5
value: 23.179
- type: mrr_at_1
value: 30.709999999999997
- type: mrr_at_10
value: 38.994
- type: mrr_at_100
value: 39.993
- type: mrr_at_1000
value: 40.044999999999995
- type: mrr_at_3
value: 36.342999999999996
- type: mrr_at_5
value: 37.846999999999994
- type: ndcg_at_1
value: 30.709999999999997
- type: ndcg_at_10
value: 31.608999999999998
- type: ndcg_at_100
value: 38.807
- type: ndcg_at_1000
value: 42.208
- type: ndcg_at_3
value: 28.086
- type: ndcg_at_5
value: 29.323
- type: precision_at_1
value: 30.709999999999997
- type: precision_at_10
value: 8.688
- type: precision_at_100
value: 1.608
- type: precision_at_1000
value: 0.22100000000000003
- type: precision_at_3
value: 18.724
- type: precision_at_5
value: 13.950999999999999
- type: recall_at_1
value: 15.473
- type: recall_at_10
value: 38.361000000000004
- type: recall_at_100
value: 65.2
- type: recall_at_1000
value: 85.789
- type: recall_at_3
value: 25.401
- type: recall_at_5
value: 30.875999999999998
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: clarin-knext/hotpotqa-pl
config: default
split: test
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
metrics:
- type: map_at_1
value: 38.096000000000004
- type: map_at_10
value: 51.44499999999999
- type: map_at_100
value: 52.325
- type: map_at_1000
value: 52.397000000000006
- type: map_at_3
value: 48.626999999999995
- type: map_at_5
value: 50.342
- type: mrr_at_1
value: 76.19200000000001
- type: mrr_at_10
value: 81.191
- type: mrr_at_100
value: 81.431
- type: mrr_at_1000
value: 81.443
- type: mrr_at_3
value: 80.30199999999999
- type: mrr_at_5
value: 80.85900000000001
- type: ndcg_at_1
value: 76.19200000000001
- type: ndcg_at_10
value: 60.9
- type: ndcg_at_100
value: 64.14699999999999
- type: ndcg_at_1000
value: 65.647
- type: ndcg_at_3
value: 56.818000000000005
- type: ndcg_at_5
value: 59.019999999999996
- type: precision_at_1
value: 76.19200000000001
- type: precision_at_10
value: 12.203
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 34.616
- type: precision_at_5
value: 22.515
- type: recall_at_1
value: 38.096000000000004
- type: recall_at_10
value: 61.013
- type: recall_at_100
value: 73.90299999999999
- type: recall_at_1000
value: 83.91
- type: recall_at_3
value: 51.92400000000001
- type: recall_at_5
value: 56.286
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: clarin-knext/msmarco-pl
config: default
split: test
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
metrics:
- type: map_at_1
value: 1.548
- type: map_at_10
value: 11.049000000000001
- type: map_at_100
value: 28.874
- type: map_at_1000
value: 34.931
- type: map_at_3
value: 4.162
- type: map_at_5
value: 6.396
- type: mrr_at_1
value: 90.69800000000001
- type: mrr_at_10
value: 92.093
- type: mrr_at_100
value: 92.345
- type: mrr_at_1000
value: 92.345
- type: mrr_at_3
value: 91.86
- type: mrr_at_5
value: 91.86
- type: ndcg_at_1
value: 74.031
- type: ndcg_at_10
value: 63.978
- type: ndcg_at_100
value: 53.101
- type: ndcg_at_1000
value: 60.675999999999995
- type: ndcg_at_3
value: 71.421
- type: ndcg_at_5
value: 68.098
- type: precision_at_1
value: 90.69800000000001
- type: precision_at_10
value: 71.86
- type: precision_at_100
value: 31.395
- type: precision_at_1000
value: 5.981
- type: precision_at_3
value: 84.49600000000001
- type: precision_at_5
value: 79.07
- type: recall_at_1
value: 1.548
- type: recall_at_10
value: 12.149000000000001
- type: recall_at_100
value: 40.794999999999995
- type: recall_at_1000
value: 67.974
- type: recall_at_3
value: 4.244
- type: recall_at_5
value: 6.608
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.55413584398119
- type: f1
value: 69.65610882318181
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.37188971082716
- type: f1
value: 75.64847309941361
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: clarin-knext/nfcorpus-pl
config: default
split: test
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
metrics:
- type: map_at_1
value: 4.919
- type: map_at_10
value: 10.834000000000001
- type: map_at_100
value: 13.38
- type: map_at_1000
value: 14.581
- type: map_at_3
value: 8.198
- type: map_at_5
value: 9.428
- type: mrr_at_1
value: 41.176
- type: mrr_at_10
value: 50.083
- type: mrr_at_100
value: 50.559
- type: mrr_at_1000
value: 50.604000000000006
- type: mrr_at_3
value: 47.936
- type: mrr_at_5
value: 49.407000000000004
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 30.098000000000003
- type: ndcg_at_100
value: 27.061
- type: ndcg_at_1000
value: 35.94
- type: ndcg_at_3
value: 35.135
- type: ndcg_at_5
value: 33.335
- type: precision_at_1
value: 41.176
- type: precision_at_10
value: 22.259999999999998
- type: precision_at_100
value: 6.712
- type: precision_at_1000
value: 1.9060000000000001
- type: precision_at_3
value: 33.23
- type: precision_at_5
value: 29.04
- type: recall_at_1
value: 4.919
- type: recall_at_10
value: 14.196
- type: recall_at_100
value: 26.948
- type: recall_at_1000
value: 59.211000000000006
- type: recall_at_3
value: 9.44
- type: recall_at_5
value: 11.569
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
- type: map_at_1
value: 25.35
- type: map_at_10
value: 37.884
- type: map_at_100
value: 38.955
- type: map_at_1000
value: 39.007999999999996
- type: map_at_3
value: 34.239999999999995
- type: map_at_5
value: 36.398
- type: mrr_at_1
value: 28.737000000000002
- type: mrr_at_10
value: 39.973
- type: mrr_at_100
value: 40.844
- type: mrr_at_1000
value: 40.885
- type: mrr_at_3
value: 36.901
- type: mrr_at_5
value: 38.721
- type: ndcg_at_1
value: 28.708
- type: ndcg_at_10
value: 44.204
- type: ndcg_at_100
value: 48.978
- type: ndcg_at_1000
value: 50.33
- type: ndcg_at_3
value: 37.36
- type: ndcg_at_5
value: 40.912
- type: precision_at_1
value: 28.708
- type: precision_at_10
value: 7.367
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 17.034
- type: precision_at_5
value: 12.293999999999999
- type: recall_at_1
value: 25.35
- type: recall_at_10
value: 61.411
- type: recall_at_100
value: 82.599
- type: recall_at_1000
value: 92.903
- type: recall_at_3
value: 43.728
- type: recall_at_5
value: 51.854
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.49422763833996
- type: f1
value: 66.73472657783407
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 81.0
- type: cos_sim_ap
value: 91.47194213011349
- type: cos_sim_f1
value: 84.73767885532592
- type: cos_sim_precision
value: 81.49847094801224
- type: cos_sim_recall
value: 88.24503311258279
- type: dot_accuracy
value: 81.0
- type: dot_ap
value: 91.47194213011349
- type: dot_f1
value: 84.73767885532592
- type: dot_precision
value: 81.49847094801224
- type: dot_recall
value: 88.24503311258279
- type: euclidean_accuracy
value: 81.0
- type: euclidean_ap
value: 91.47194213011349
- type: euclidean_f1
value: 84.73767885532592
- type: euclidean_precision
value: 81.49847094801224
- type: euclidean_recall
value: 88.24503311258279
- type: manhattan_accuracy
value: 81.0
- type: manhattan_ap
value: 91.46464475050571
- type: manhattan_f1
value: 84.48687350835321
- type: manhattan_precision
value: 81.31699846860643
- type: manhattan_recall
value: 87.91390728476821
- type: max_accuracy
value: 81.0
- type: max_ap
value: 91.47194213011349
- type: max_f1
value: 84.73767885532592
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 97.6808905380334
- type: cos_sim_ap
value: 99.27948611836348
- type: cos_sim_f1
value: 96.15975422427034
- type: cos_sim_precision
value: 96.90402476780186
- type: cos_sim_recall
value: 95.42682926829268
- type: dot_accuracy
value: 97.6808905380334
- type: dot_ap
value: 99.2794861183635
- type: dot_f1
value: 96.15975422427034
- type: dot_precision
value: 96.90402476780186
- type: dot_recall
value: 95.42682926829268
- type: euclidean_accuracy
value: 97.6808905380334
- type: euclidean_ap
value: 99.2794861183635
- type: euclidean_f1
value: 96.15975422427034
- type: euclidean_precision
value: 96.90402476780186
- type: euclidean_recall
value: 95.42682926829268
- type: manhattan_accuracy
value: 97.6808905380334
- type: manhattan_ap
value: 99.28715055268721
- type: manhattan_f1
value: 96.14791987673343
- type: manhattan_precision
value: 97.19626168224299
- type: manhattan_recall
value: 95.1219512195122
- type: max_accuracy
value: 97.6808905380334
- type: max_ap
value: 99.28715055268721
- type: max_f1
value: 96.15975422427034
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.16343490304708
- type: f1
value: 83.3442579486744
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 68.40080971659918
- type: f1
value: 53.13720751142237
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
- type: map_at_1
value: 63.322
- type: map_at_10
value: 76.847
- type: map_at_100
value: 77.616
- type: map_at_1000
value: 77.644
- type: map_at_3
value: 73.624
- type: map_at_5
value: 75.603
- type: mrr_at_1
value: 72.88
- type: mrr_at_10
value: 80.376
- type: mrr_at_100
value: 80.604
- type: mrr_at_1000
value: 80.61
- type: mrr_at_3
value: 78.92
- type: mrr_at_5
value: 79.869
- type: ndcg_at_1
value: 72.89999999999999
- type: ndcg_at_10
value: 81.43
- type: ndcg_at_100
value: 83.394
- type: ndcg_at_1000
value: 83.685
- type: ndcg_at_3
value: 77.62599999999999
- type: ndcg_at_5
value: 79.656
- type: precision_at_1
value: 72.89999999999999
- type: precision_at_10
value: 12.548
- type: precision_at_100
value: 1.4869999999999999
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 34.027
- type: precision_at_5
value: 22.654
- type: recall_at_1
value: 63.322
- type: recall_at_10
value: 90.664
- type: recall_at_100
value: 97.974
- type: recall_at_1000
value: 99.636
- type: recall_at_3
value: 80.067
- type: recall_at_5
value: 85.526
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
metrics:
- type: map_at_1
value: 3.95
- type: map_at_10
value: 9.658999999999999
- type: map_at_100
value: 11.384
- type: map_at_1000
value: 11.677
- type: map_at_3
value: 7.055
- type: map_at_5
value: 8.244
- type: mrr_at_1
value: 19.5
- type: mrr_at_10
value: 28.777
- type: mrr_at_100
value: 29.936
- type: mrr_at_1000
value: 30.009999999999998
- type: mrr_at_3
value: 25.55
- type: mrr_at_5
value: 27.284999999999997
- type: ndcg_at_1
value: 19.5
- type: ndcg_at_10
value: 16.589000000000002
- type: ndcg_at_100
value: 23.879
- type: ndcg_at_1000
value: 29.279
- type: ndcg_at_3
value: 15.719
- type: ndcg_at_5
value: 13.572000000000001
- type: precision_at_1
value: 19.5
- type: precision_at_10
value: 8.62
- type: precision_at_100
value: 1.924
- type: precision_at_1000
value: 0.322
- type: precision_at_3
value: 14.6
- type: precision_at_5
value: 11.78
- type: recall_at_1
value: 3.95
- type: recall_at_10
value: 17.477999999999998
- type: recall_at_100
value: 38.99
- type: recall_at_1000
value: 65.417
- type: recall_at_3
value: 8.883000000000001
- type: recall_at_5
value: 11.933
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.48960456583775
- type: cos_sim_ap
value: 76.31522115825375
- type: cos_sim_f1
value: 70.35573122529645
- type: cos_sim_precision
value: 70.9934735315446
- type: cos_sim_recall
value: 69.72934472934473
- type: dot_accuracy
value: 83.48960456583775
- type: dot_ap
value: 76.31522115825373
- type: dot_f1
value: 70.35573122529645
- type: dot_precision
value: 70.9934735315446
- type: dot_recall
value: 69.72934472934473
- type: euclidean_accuracy
value: 83.48960456583775
- type: euclidean_ap
value: 76.31522115825373
- type: euclidean_f1
value: 70.35573122529645
- type: euclidean_precision
value: 70.9934735315446
- type: euclidean_recall
value: 69.72934472934473
- type: manhattan_accuracy
value: 83.46922136159804
- type: manhattan_ap
value: 76.18474601388084
- type: manhattan_f1
value: 70.34779490856937
- type: manhattan_precision
value: 70.83032490974729
- type: manhattan_recall
value: 69.87179487179486
- type: max_accuracy
value: 83.48960456583775
- type: max_ap
value: 76.31522115825375
- type: max_f1
value: 70.35573122529645
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 77.95374883876302
- type: cos_sim_spearman
value: 73.77630219171942
- type: euclidean_pearson
value: 75.81927069594934
- type: euclidean_spearman
value: 73.7763211303831
- type: manhattan_pearson
value: 76.03126859057528
- type: manhattan_spearman
value: 73.96528138013369
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 37.388282764841826
- type: cos_sim_spearman
value: 40.83477184710897
- type: euclidean_pearson
value: 26.754737044177805
- type: euclidean_spearman
value: 40.83477184710897
- type: manhattan_pearson
value: 26.760453110872458
- type: manhattan_spearman
value: 41.034477441383856
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
- type: map_at_1
value: 49.15
- type: map_at_10
value: 61.690999999999995
- type: map_at_100
value: 62.348000000000006
- type: map_at_1000
value: 62.38
- type: map_at_3
value: 58.824
- type: map_at_5
value: 60.662000000000006
- type: mrr_at_1
value: 51.333
- type: mrr_at_10
value: 62.731
- type: mrr_at_100
value: 63.245
- type: mrr_at_1000
value: 63.275000000000006
- type: mrr_at_3
value: 60.667
- type: mrr_at_5
value: 61.93300000000001
- type: ndcg_at_1
value: 51.333
- type: ndcg_at_10
value: 67.168
- type: ndcg_at_100
value: 69.833
- type: ndcg_at_1000
value: 70.56700000000001
- type: ndcg_at_3
value: 62.40599999999999
- type: ndcg_at_5
value: 65.029
- type: precision_at_1
value: 51.333
- type: precision_at_10
value: 9.333
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.333
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 49.15
- type: recall_at_10
value: 82.533
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 69.917
- type: recall_at_5
value: 76.356
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: clarin-knext/trec-covid-pl
config: default
split: test
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
metrics:
- type: map_at_1
value: 0.261
- type: map_at_10
value: 2.1260000000000003
- type: map_at_100
value: 12.171999999999999
- type: map_at_1000
value: 26.884999999999998
- type: map_at_3
value: 0.695
- type: map_at_5
value: 1.134
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 96.952
- type: mrr_at_100
value: 96.952
- type: mrr_at_1000
value: 96.952
- type: mrr_at_3
value: 96.667
- type: mrr_at_5
value: 96.667
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 81.193
- type: ndcg_at_100
value: 61.129
- type: ndcg_at_1000
value: 51.157
- type: ndcg_at_3
value: 85.693
- type: ndcg_at_5
value: 84.129
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 85.39999999999999
- type: precision_at_100
value: 62.03999999999999
- type: precision_at_1000
value: 22.224
- type: precision_at_3
value: 88.0
- type: precision_at_5
value: 88.0
- type: recall_at_1
value: 0.261
- type: recall_at_10
value: 2.262
- type: recall_at_100
value: 14.981
- type: recall_at_1000
value: 46.837
- type: recall_at_3
value: 0.703
- type: recall_at_5
value: 1.172
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 70.55290063940157
- type: v_measure
value: 55.41500719337263
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
metrics:
- type: map
value: 73.48697375332002
- type: mrr
value: 75.01836585523822
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: map_at_1
value: 38.454
- type: map_at_10
value: 51.605000000000004
- type: map_at_100
value: 52.653000000000006
- type: map_at_1000
value: 52.697
- type: map_at_3
value: 48.304
- type: map_at_5
value: 50.073
- type: mrr_at_1
value: 43.307
- type: mrr_at_10
value: 54.400000000000006
- type: mrr_at_100
value: 55.147999999999996
- type: mrr_at_1000
value: 55.174
- type: mrr_at_3
value: 51.77
- type: mrr_at_5
value: 53.166999999999994
- type: ndcg_at_1
value: 43.307
- type: ndcg_at_10
value: 57.891000000000005
- type: ndcg_at_100
value: 62.161
- type: ndcg_at_1000
value: 63.083
- type: ndcg_at_3
value: 51.851
- type: ndcg_at_5
value: 54.605000000000004
- type: precision_at_1
value: 43.307
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.172
- type: precision_at_1000
value: 0.127
- type: precision_at_3
value: 22.798
- type: precision_at_5
value: 15.492
- type: recall_at_1
value: 38.454
- type: recall_at_10
value: 74.166
- type: recall_at_100
value: 92.43599999999999
- type: recall_at_1000
value: 99.071
- type: recall_at_3
value: 58.087
- type: recall_at_5
value: 64.568
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.474
- type: f1
value: 50.38275392350236
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 2.252
- type: map_at_10
value: 4.661
- type: map_at_100
value: 5.271
- type: map_at_1000
value: 5.3629999999999995
- type: map_at_3
value: 3.604
- type: map_at_5
value: 4.3020000000000005
- type: mrr_at_1
value: 2.252
- type: mrr_at_10
value: 4.661
- type: mrr_at_100
value: 5.271
- type: mrr_at_1000
value: 5.3629999999999995
- type: mrr_at_3
value: 3.604
- type: mrr_at_5
value: 4.3020000000000005
- type: ndcg_at_1
value: 2.252
- type: ndcg_at_10
value: 6.3020000000000005
- type: ndcg_at_100
value: 10.342
- type: ndcg_at_1000
value: 13.475999999999999
- type: ndcg_at_3
value: 4.0649999999999995
- type: ndcg_at_5
value: 5.344
- type: precision_at_1
value: 2.252
- type: precision_at_10
value: 1.171
- type: precision_at_100
value: 0.333
- type: precision_at_1000
value: 0.059000000000000004
- type: precision_at_3
value: 1.802
- type: precision_at_5
value: 1.712
- type: recall_at_1
value: 2.252
- type: recall_at_10
value: 11.712
- type: recall_at_100
value: 33.333
- type: recall_at_1000
value: 59.458999999999996
- type: recall_at_3
value: 5.405
- type: recall_at_5
value: 8.559
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 28.301882091023288
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: mlsum
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 45.26992995191701
- type: v_measure
value: 42.773174876871145
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.47635452552458
- type: f1
value: 93.19922617577213
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 80.2317569683683
- type: f1
value: 56.18060418621901
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 85.18957345971565
- type: f1
value: 80.829981537394
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 71.04138999801822
- type: v_measure
value: 71.7056263158008
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.65097511768661
- type: f1
value: 73.82441070598712
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.09885675857431
- type: f1
value: 78.28407777434224
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 25.307000000000002
- type: map_at_10
value: 36.723
- type: map_at_100
value: 37.713
- type: map_at_1000
value: 37.769000000000005
- type: map_at_3
value: 33.77
- type: map_at_5
value: 35.463
- type: mrr_at_1
value: 25.307000000000002
- type: mrr_at_10
value: 36.723
- type: mrr_at_100
value: 37.713
- type: mrr_at_1000
value: 37.769000000000005
- type: mrr_at_3
value: 33.77
- type: mrr_at_5
value: 35.463
- type: ndcg_at_1
value: 25.307000000000002
- type: ndcg_at_10
value: 42.559999999999995
- type: ndcg_at_100
value: 47.457
- type: ndcg_at_1000
value: 49.162
- type: ndcg_at_3
value: 36.461
- type: ndcg_at_5
value: 39.504
- type: precision_at_1
value: 25.307000000000002
- type: precision_at_10
value: 6.106
- type: precision_at_100
value: 0.8420000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 14.741999999999999
- type: precision_at_5
value: 10.319
- type: recall_at_1
value: 25.307000000000002
- type: recall_at_10
value: 61.056999999999995
- type: recall_at_100
value: 84.152
- type: recall_at_1000
value: 98.03399999999999
- type: recall_at_3
value: 44.226
- type: recall_at_5
value: 51.597
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.90069513406156
- type: cos_sim_ap
value: 100.0
- type: cos_sim_f1
value: 99.95032290114257
- type: cos_sim_precision
value: 100.0
- type: cos_sim_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_accuracy
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 70.8
- type: cos_sim_ap
value: 73.7671529695957
- type: cos_sim_f1
value: 68.80964339527875
- type: cos_sim_precision
value: 62.95955882352941
- type: cos_sim_recall
value: 75.85825027685493
- type: dot_accuracy
value: 70.8
- type: dot_ap
value: 73.78345265366947
- type: dot_f1
value: 68.80964339527875
- type: dot_precision
value: 62.95955882352941
- type: dot_recall
value: 75.85825027685493
- type: euclidean_accuracy
value: 70.8
- type: euclidean_ap
value: 73.7671529695957
- type: euclidean_f1
value: 68.80964339527875
- type: euclidean_precision
value: 62.95955882352941
- type: euclidean_recall
value: 75.85825027685493
- type: manhattan_accuracy
value: 70.75
- type: manhattan_ap
value: 73.78996383615953
- type: manhattan_f1
value: 68.79432624113475
- type: manhattan_precision
value: 63.39869281045751
- type: manhattan_recall
value: 75.1937984496124
- type: max_accuracy
value: 70.8
- type: max_ap
value: 73.78996383615953
- type: max_f1
value: 68.80964339527875
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 84.03253762760392
- type: cos_sim_spearman
value: 79.68280105762004
- type: euclidean_pearson
value: 80.98265050044444
- type: euclidean_spearman
value: 79.68233242682867
- type: manhattan_pearson
value: 80.9678911810704
- type: manhattan_spearman
value: 79.70264097683109
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 80.56896987572884
- type: cos_sim_spearman
value: 81.84352499523287
- type: euclidean_pearson
value: 80.40831759421305
- type: euclidean_spearman
value: 81.84352499523287
- type: manhattan_pearson
value: 80.74333857561238
- type: manhattan_spearman
value: 82.41503246733892
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: stsb_multi_mt
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 82.71826762276979
- type: cos_sim_spearman
value: 82.25433354916042
- type: euclidean_pearson
value: 81.87115571724316
- type: euclidean_spearman
value: 82.25322342890107
- type: manhattan_pearson
value: 82.11174867527224
- type: manhattan_spearman
value: 82.55905365203084
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 30.659441623392887
- type: cos_sim_spearman
value: 30.501134097353315
- type: dot_pearson
value: 30.659444768851056
- type: dot_spearman
value: 30.501134097353315
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
- type: map
value: 94.03333333333333
- type: mrr
value: 94.03333333333333
- task:
type: Retrieval
dataset:
name: MTEB SyntecRetrieval
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
metrics:
- type: map_at_1
value: 79.0
- type: map_at_10
value: 87.61
- type: map_at_100
value: 87.655
- type: map_at_1000
value: 87.655
- type: map_at_3
value: 87.167
- type: map_at_5
value: 87.36699999999999
- type: mrr_at_1
value: 79.0
- type: mrr_at_10
value: 87.61
- type: mrr_at_100
value: 87.655
- type: mrr_at_1000
value: 87.655
- type: mrr_at_3
value: 87.167
- type: mrr_at_5
value: 87.36699999999999
- type: ndcg_at_1
value: 79.0
- type: ndcg_at_10
value: 90.473
- type: ndcg_at_100
value: 90.694
- type: ndcg_at_1000
value: 90.694
- type: ndcg_at_3
value: 89.464
- type: ndcg_at_5
value: 89.851
- type: precision_at_1
value: 79.0
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 32.0
- type: precision_at_5
value: 19.400000000000002
- type: recall_at_1
value: 79.0
- type: recall_at_10
value: 99.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 96.0
- type: recall_at_5
value: 97.0
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 39.395
- type: map_at_10
value: 59.123999999999995
- type: map_at_100
value: 60.704
- type: map_at_1000
value: 60.760000000000005
- type: map_at_3
value: 53.187
- type: map_at_5
value: 56.863
- type: mrr_at_1
value: 62.083
- type: mrr_at_10
value: 68.87299999999999
- type: mrr_at_100
value: 69.46900000000001
- type: mrr_at_1000
value: 69.48299999999999
- type: mrr_at_3
value: 66.8
- type: mrr_at_5
value: 67.928
- type: ndcg_at_1
value: 62.083
- type: ndcg_at_10
value: 65.583
- type: ndcg_at_100
value: 70.918
- type: ndcg_at_1000
value: 71.72800000000001
- type: ndcg_at_3
value: 60.428000000000004
- type: ndcg_at_5
value: 61.853
- type: precision_at_1
value: 62.083
- type: precision_at_10
value: 15.033
- type: precision_at_100
value: 1.9529999999999998
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 36.315
- type: precision_at_5
value: 25.955000000000002
- type: recall_at_1
value: 39.395
- type: recall_at_10
value: 74.332
- type: recall_at_100
value: 94.729
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 57.679
- type: recall_at_5
value: 65.036
---
## gte-Qwen2-1.5B-instruct
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on [Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) LLM model and use the same training data and strategies as the [gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) model.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
## Model Information
- Model Size: 1.5B
- Embedding Dimension: 1536
- Max Input Tokens: 32k
## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
```
Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
### infinity_emb
Usage via [infinity, MIT Licensed](https://github.com/michaelfeil/infinity).
```bash
docker run \
--gpus "0" -p "7997":"7997" \
michaelf34/infinity:0.0.68-trt-onnx \
v2 --model-id Alibaba-NLP/gte-Qwen2-1.5B-instruct --revision "refs/pr/20" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer
```
## Evaluation
### MTEB & C-MTEB
You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-1.5B-instruct** on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|:----:|:---------:|:----------:|:----------:|:----------:|
| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - |
| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - |
| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - |
| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - |
| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - |
| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - |
| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - |
| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** |
| [**gte-Qwen2-1.5B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | **67.16** | **67.65** | **66.60** | **64.04** |
### GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|
| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |
| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |
| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |
| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |
| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |
| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |
| [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB |
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Community support
### Fine-tuning
GTE models can be fine-tuned with a third party framework SWIFT.
```shell
pip install ms-swift -U
```
```shell
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
NPROC_PER_NODE=$nproc_per_node \
USE_HF=1 \
swift sft \
--model Alibaba-NLP/gte-Qwen2-1.5B-instruct \
--train_type lora \
--dataset 'sentence-transformers/stsb' \
--torch_dtype bfloat16 \
--num_train_epochs 10 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--use_chat_template false \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type cosine_similarity \
--dataloader_drop_last true
```
## Citation
If you find our paper or models helpful, please consider cite:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-large-en | BAAI | feature-extraction | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-transfomres",
"en",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-02T07:11:51 | 2023-10-12T03:35:38 | 516,687 | 206 | ---
language:
- en
license: mit
tags:
- mteb
- sentence-transfomres
- transformers
model-index:
- name: bge-large-en
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.94029850746269
- type: ap
value: 40.00228964744091
- type: f1
value: 70.86088267934595
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.93745
- type: ap
value: 88.24758534667426
- type: f1
value: 91.91033034217591
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.158
- type: f1
value: 45.78935185074774
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.972
- type: map_at_10
value: 54.874
- type: map_at_100
value: 55.53399999999999
- type: map_at_1000
value: 55.539
- type: map_at_3
value: 51.031000000000006
- type: map_at_5
value: 53.342999999999996
- type: mrr_at_1
value: 40.541
- type: mrr_at_10
value: 55.096000000000004
- type: mrr_at_100
value: 55.75599999999999
- type: mrr_at_1000
value: 55.761
- type: mrr_at_3
value: 51.221000000000004
- type: mrr_at_5
value: 53.568000000000005
- type: ndcg_at_1
value: 39.972
- type: ndcg_at_10
value: 62.456999999999994
- type: ndcg_at_100
value: 65.262
- type: ndcg_at_1000
value: 65.389
- type: ndcg_at_3
value: 54.673
- type: ndcg_at_5
value: 58.80499999999999
- type: precision_at_1
value: 39.972
- type: precision_at_10
value: 8.634
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.740000000000002
- type: precision_at_5
value: 15.036
- type: recall_at_1
value: 39.972
- type: recall_at_10
value: 86.344
- type: recall_at_100
value: 98.578
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 65.22
- type: recall_at_5
value: 75.178
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.94652870403906
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.17257160340209
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.97867370559182
- type: mrr
value: 77.00820032537484
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 80.00986015960616
- type: cos_sim_spearman
value: 80.36387933827882
- type: euclidean_pearson
value: 80.32305287257296
- type: euclidean_spearman
value: 82.0524720308763
- type: manhattan_pearson
value: 80.19847473906454
- type: manhattan_spearman
value: 81.87957652506985
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.00000000000001
- type: f1
value: 87.99039027511853
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 41.36932844640705
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 38.34983239611985
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.257999999999996
- type: map_at_10
value: 42.937
- type: map_at_100
value: 44.406
- type: map_at_1000
value: 44.536
- type: map_at_3
value: 39.22
- type: map_at_5
value: 41.458
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 48.701
- type: mrr_at_100
value: 49.431000000000004
- type: mrr_at_1000
value: 49.476
- type: mrr_at_3
value: 45.875
- type: mrr_at_5
value: 47.67
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 49.35
- type: ndcg_at_100
value: 54.618
- type: ndcg_at_1000
value: 56.655
- type: ndcg_at_3
value: 43.826
- type: ndcg_at_5
value: 46.72
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 9.328
- type: precision_at_100
value: 1.484
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.649
- type: precision_at_5
value: 15.25
- type: recall_at_1
value: 32.257999999999996
- type: recall_at_10
value: 61.849
- type: recall_at_100
value: 83.70400000000001
- type: recall_at_1000
value: 96.344
- type: recall_at_3
value: 46.037
- type: recall_at_5
value: 53.724000000000004
- type: map_at_1
value: 32.979
- type: map_at_10
value: 43.376999999999995
- type: map_at_100
value: 44.667
- type: map_at_1000
value: 44.794
- type: map_at_3
value: 40.461999999999996
- type: map_at_5
value: 42.138
- type: mrr_at_1
value: 41.146
- type: mrr_at_10
value: 49.575
- type: mrr_at_100
value: 50.187000000000005
- type: mrr_at_1000
value: 50.231
- type: mrr_at_3
value: 47.601
- type: mrr_at_5
value: 48.786
- type: ndcg_at_1
value: 41.146
- type: ndcg_at_10
value: 48.957
- type: ndcg_at_100
value: 53.296
- type: ndcg_at_1000
value: 55.254000000000005
- type: ndcg_at_3
value: 45.235
- type: ndcg_at_5
value: 47.014
- type: precision_at_1
value: 41.146
- type: precision_at_10
value: 9.107999999999999
- type: precision_at_100
value: 1.481
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 21.783
- type: precision_at_5
value: 15.274
- type: recall_at_1
value: 32.979
- type: recall_at_10
value: 58.167
- type: recall_at_100
value: 76.374
- type: recall_at_1000
value: 88.836
- type: recall_at_3
value: 46.838
- type: recall_at_5
value: 52.006
- type: map_at_1
value: 40.326
- type: map_at_10
value: 53.468
- type: map_at_100
value: 54.454
- type: map_at_1000
value: 54.508
- type: map_at_3
value: 50.12799999999999
- type: map_at_5
value: 51.991
- type: mrr_at_1
value: 46.394999999999996
- type: mrr_at_10
value: 57.016999999999996
- type: mrr_at_100
value: 57.67099999999999
- type: mrr_at_1000
value: 57.699999999999996
- type: mrr_at_3
value: 54.65
- type: mrr_at_5
value: 56.101
- type: ndcg_at_1
value: 46.394999999999996
- type: ndcg_at_10
value: 59.507
- type: ndcg_at_100
value: 63.31099999999999
- type: ndcg_at_1000
value: 64.388
- type: ndcg_at_3
value: 54.04600000000001
- type: ndcg_at_5
value: 56.723
- type: precision_at_1
value: 46.394999999999996
- type: precision_at_10
value: 9.567
- type: precision_at_100
value: 1.234
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 24.117
- type: precision_at_5
value: 16.426
- type: recall_at_1
value: 40.326
- type: recall_at_10
value: 73.763
- type: recall_at_100
value: 89.927
- type: recall_at_1000
value: 97.509
- type: recall_at_3
value: 59.34
- type: recall_at_5
value: 65.915
- type: map_at_1
value: 26.661
- type: map_at_10
value: 35.522
- type: map_at_100
value: 36.619
- type: map_at_1000
value: 36.693999999999996
- type: map_at_3
value: 33.154
- type: map_at_5
value: 34.353
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 37.403999999999996
- type: mrr_at_100
value: 38.374
- type: mrr_at_1000
value: 38.428000000000004
- type: mrr_at_3
value: 35.235
- type: mrr_at_5
value: 36.269
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 40.431
- type: ndcg_at_100
value: 45.745999999999995
- type: ndcg_at_1000
value: 47.493
- type: ndcg_at_3
value: 35.733
- type: ndcg_at_5
value: 37.722
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.922
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 15.140999999999998
- type: precision_at_5
value: 10.305
- type: recall_at_1
value: 26.661
- type: recall_at_10
value: 53.675
- type: recall_at_100
value: 77.891
- type: recall_at_1000
value: 90.72
- type: recall_at_3
value: 40.751
- type: recall_at_5
value: 45.517
- type: map_at_1
value: 18.886
- type: map_at_10
value: 27.288
- type: map_at_100
value: 28.327999999999996
- type: map_at_1000
value: 28.438999999999997
- type: map_at_3
value: 24.453
- type: map_at_5
value: 25.959
- type: mrr_at_1
value: 23.134
- type: mrr_at_10
value: 32.004
- type: mrr_at_100
value: 32.789
- type: mrr_at_1000
value: 32.857
- type: mrr_at_3
value: 29.084
- type: mrr_at_5
value: 30.614
- type: ndcg_at_1
value: 23.134
- type: ndcg_at_10
value: 32.852
- type: ndcg_at_100
value: 37.972
- type: ndcg_at_1000
value: 40.656
- type: ndcg_at_3
value: 27.435
- type: ndcg_at_5
value: 29.823
- type: precision_at_1
value: 23.134
- type: precision_at_10
value: 6.032
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 13.017999999999999
- type: precision_at_5
value: 9.501999999999999
- type: recall_at_1
value: 18.886
- type: recall_at_10
value: 45.34
- type: recall_at_100
value: 67.947
- type: recall_at_1000
value: 86.924
- type: recall_at_3
value: 30.535
- type: recall_at_5
value: 36.451
- type: map_at_1
value: 28.994999999999997
- type: map_at_10
value: 40.04
- type: map_at_100
value: 41.435
- type: map_at_1000
value: 41.537
- type: map_at_3
value: 37.091
- type: map_at_5
value: 38.802
- type: mrr_at_1
value: 35.034
- type: mrr_at_10
value: 45.411
- type: mrr_at_100
value: 46.226
- type: mrr_at_1000
value: 46.27
- type: mrr_at_3
value: 43.086
- type: mrr_at_5
value: 44.452999999999996
- type: ndcg_at_1
value: 35.034
- type: ndcg_at_10
value: 46.076
- type: ndcg_at_100
value: 51.483000000000004
- type: ndcg_at_1000
value: 53.433
- type: ndcg_at_3
value: 41.304
- type: ndcg_at_5
value: 43.641999999999996
- type: precision_at_1
value: 35.034
- type: precision_at_10
value: 8.258000000000001
- type: precision_at_100
value: 1.268
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 19.57
- type: precision_at_5
value: 13.782
- type: recall_at_1
value: 28.994999999999997
- type: recall_at_10
value: 58.538000000000004
- type: recall_at_100
value: 80.72399999999999
- type: recall_at_1000
value: 93.462
- type: recall_at_3
value: 45.199
- type: recall_at_5
value: 51.237
- type: map_at_1
value: 24.795
- type: map_at_10
value: 34.935
- type: map_at_100
value: 36.306
- type: map_at_1000
value: 36.417
- type: map_at_3
value: 31.831
- type: map_at_5
value: 33.626
- type: mrr_at_1
value: 30.479
- type: mrr_at_10
value: 40.225
- type: mrr_at_100
value: 41.055
- type: mrr_at_1000
value: 41.114
- type: mrr_at_3
value: 37.538
- type: mrr_at_5
value: 39.073
- type: ndcg_at_1
value: 30.479
- type: ndcg_at_10
value: 40.949999999999996
- type: ndcg_at_100
value: 46.525
- type: ndcg_at_1000
value: 48.892
- type: ndcg_at_3
value: 35.79
- type: ndcg_at_5
value: 38.237
- type: precision_at_1
value: 30.479
- type: precision_at_10
value: 7.6259999999999994
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 17.199
- type: precision_at_5
value: 12.466000000000001
- type: recall_at_1
value: 24.795
- type: recall_at_10
value: 53.421
- type: recall_at_100
value: 77.189
- type: recall_at_1000
value: 93.407
- type: recall_at_3
value: 39.051
- type: recall_at_5
value: 45.462
- type: map_at_1
value: 26.853499999999997
- type: map_at_10
value: 36.20433333333333
- type: map_at_100
value: 37.40391666666667
- type: map_at_1000
value: 37.515
- type: map_at_3
value: 33.39975
- type: map_at_5
value: 34.9665
- type: mrr_at_1
value: 31.62666666666667
- type: mrr_at_10
value: 40.436749999999996
- type: mrr_at_100
value: 41.260333333333335
- type: mrr_at_1000
value: 41.31525
- type: mrr_at_3
value: 38.06733333333332
- type: mrr_at_5
value: 39.41541666666667
- type: ndcg_at_1
value: 31.62666666666667
- type: ndcg_at_10
value: 41.63341666666667
- type: ndcg_at_100
value: 46.704166666666666
- type: ndcg_at_1000
value: 48.88483333333335
- type: ndcg_at_3
value: 36.896
- type: ndcg_at_5
value: 39.11891666666667
- type: precision_at_1
value: 31.62666666666667
- type: precision_at_10
value: 7.241083333333333
- type: precision_at_100
value: 1.1488333333333334
- type: precision_at_1000
value: 0.15250000000000002
- type: precision_at_3
value: 16.908333333333335
- type: precision_at_5
value: 11.942833333333333
- type: recall_at_1
value: 26.853499999999997
- type: recall_at_10
value: 53.461333333333336
- type: recall_at_100
value: 75.63633333333333
- type: recall_at_1000
value: 90.67016666666666
- type: recall_at_3
value: 40.24241666666667
- type: recall_at_5
value: 45.98608333333333
- type: map_at_1
value: 25.241999999999997
- type: map_at_10
value: 31.863999999999997
- type: map_at_100
value: 32.835
- type: map_at_1000
value: 32.928000000000004
- type: map_at_3
value: 29.694
- type: map_at_5
value: 30.978
- type: mrr_at_1
value: 28.374
- type: mrr_at_10
value: 34.814
- type: mrr_at_100
value: 35.596
- type: mrr_at_1000
value: 35.666
- type: mrr_at_3
value: 32.745000000000005
- type: mrr_at_5
value: 34.049
- type: ndcg_at_1
value: 28.374
- type: ndcg_at_10
value: 35.969
- type: ndcg_at_100
value: 40.708
- type: ndcg_at_1000
value: 43.08
- type: ndcg_at_3
value: 31.968999999999998
- type: ndcg_at_5
value: 34.069
- type: precision_at_1
value: 28.374
- type: precision_at_10
value: 5.583
- type: precision_at_100
value: 0.8630000000000001
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 13.547999999999998
- type: precision_at_5
value: 9.447999999999999
- type: recall_at_1
value: 25.241999999999997
- type: recall_at_10
value: 45.711
- type: recall_at_100
value: 67.482
- type: recall_at_1000
value: 85.13300000000001
- type: recall_at_3
value: 34.622
- type: recall_at_5
value: 40.043
- type: map_at_1
value: 17.488999999999997
- type: map_at_10
value: 25.142999999999997
- type: map_at_100
value: 26.244
- type: map_at_1000
value: 26.363999999999997
- type: map_at_3
value: 22.654
- type: map_at_5
value: 24.017
- type: mrr_at_1
value: 21.198
- type: mrr_at_10
value: 28.903000000000002
- type: mrr_at_100
value: 29.860999999999997
- type: mrr_at_1000
value: 29.934
- type: mrr_at_3
value: 26.634999999999998
- type: mrr_at_5
value: 27.903
- type: ndcg_at_1
value: 21.198
- type: ndcg_at_10
value: 29.982999999999997
- type: ndcg_at_100
value: 35.275
- type: ndcg_at_1000
value: 38.074000000000005
- type: ndcg_at_3
value: 25.502999999999997
- type: ndcg_at_5
value: 27.557
- type: precision_at_1
value: 21.198
- type: precision_at_10
value: 5.502
- type: precision_at_100
value: 0.942
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 12.044
- type: precision_at_5
value: 8.782
- type: recall_at_1
value: 17.488999999999997
- type: recall_at_10
value: 40.821000000000005
- type: recall_at_100
value: 64.567
- type: recall_at_1000
value: 84.452
- type: recall_at_3
value: 28.351
- type: recall_at_5
value: 33.645
- type: map_at_1
value: 27.066000000000003
- type: map_at_10
value: 36.134
- type: map_at_100
value: 37.285000000000004
- type: map_at_1000
value: 37.389
- type: map_at_3
value: 33.522999999999996
- type: map_at_5
value: 34.905
- type: mrr_at_1
value: 31.436999999999998
- type: mrr_at_10
value: 40.225
- type: mrr_at_100
value: 41.079
- type: mrr_at_1000
value: 41.138000000000005
- type: mrr_at_3
value: 38.074999999999996
- type: mrr_at_5
value: 39.190000000000005
- type: ndcg_at_1
value: 31.436999999999998
- type: ndcg_at_10
value: 41.494
- type: ndcg_at_100
value: 46.678999999999995
- type: ndcg_at_1000
value: 48.964
- type: ndcg_at_3
value: 36.828
- type: ndcg_at_5
value: 38.789
- type: precision_at_1
value: 31.436999999999998
- type: precision_at_10
value: 6.931
- type: precision_at_100
value: 1.072
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 16.729
- type: precision_at_5
value: 11.567
- type: recall_at_1
value: 27.066000000000003
- type: recall_at_10
value: 53.705000000000005
- type: recall_at_100
value: 75.968
- type: recall_at_1000
value: 91.937
- type: recall_at_3
value: 40.865
- type: recall_at_5
value: 45.739999999999995
- type: map_at_1
value: 24.979000000000003
- type: map_at_10
value: 32.799
- type: map_at_100
value: 34.508
- type: map_at_1000
value: 34.719
- type: map_at_3
value: 29.947000000000003
- type: map_at_5
value: 31.584
- type: mrr_at_1
value: 30.237000000000002
- type: mrr_at_10
value: 37.651
- type: mrr_at_100
value: 38.805
- type: mrr_at_1000
value: 38.851
- type: mrr_at_3
value: 35.046
- type: mrr_at_5
value: 36.548
- type: ndcg_at_1
value: 30.237000000000002
- type: ndcg_at_10
value: 38.356
- type: ndcg_at_100
value: 44.906
- type: ndcg_at_1000
value: 47.299
- type: ndcg_at_3
value: 33.717999999999996
- type: ndcg_at_5
value: 35.946
- type: precision_at_1
value: 30.237000000000002
- type: precision_at_10
value: 7.292
- type: precision_at_100
value: 1.496
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 11.344
- type: recall_at_1
value: 24.979000000000003
- type: recall_at_10
value: 48.624
- type: recall_at_100
value: 77.932
- type: recall_at_1000
value: 92.66499999999999
- type: recall_at_3
value: 35.217
- type: recall_at_5
value: 41.394
- type: map_at_1
value: 22.566
- type: map_at_10
value: 30.945
- type: map_at_100
value: 31.759999999999998
- type: map_at_1000
value: 31.855
- type: map_at_3
value: 28.64
- type: map_at_5
value: 29.787000000000003
- type: mrr_at_1
value: 24.954
- type: mrr_at_10
value: 33.311
- type: mrr_at_100
value: 34.050000000000004
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.238
- type: mrr_at_5
value: 32.329
- type: ndcg_at_1
value: 24.954
- type: ndcg_at_10
value: 35.676
- type: ndcg_at_100
value: 39.931
- type: ndcg_at_1000
value: 42.43
- type: ndcg_at_3
value: 31.365
- type: ndcg_at_5
value: 33.184999999999995
- type: precision_at_1
value: 24.954
- type: precision_at_10
value: 5.564
- type: precision_at_100
value: 0.826
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 13.555
- type: precision_at_5
value: 9.168
- type: recall_at_1
value: 22.566
- type: recall_at_10
value: 47.922
- type: recall_at_100
value: 67.931
- type: recall_at_1000
value: 86.653
- type: recall_at_3
value: 36.103
- type: recall_at_5
value: 40.699000000000005
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.950000000000003
- type: map_at_10
value: 28.612
- type: map_at_100
value: 30.476999999999997
- type: map_at_1000
value: 30.674
- type: map_at_3
value: 24.262
- type: map_at_5
value: 26.554
- type: mrr_at_1
value: 38.241
- type: mrr_at_10
value: 50.43
- type: mrr_at_100
value: 51.059
- type: mrr_at_1000
value: 51.090999999999994
- type: mrr_at_3
value: 47.514
- type: mrr_at_5
value: 49.246
- type: ndcg_at_1
value: 38.241
- type: ndcg_at_10
value: 38.218
- type: ndcg_at_100
value: 45.003
- type: ndcg_at_1000
value: 48.269
- type: ndcg_at_3
value: 32.568000000000005
- type: ndcg_at_5
value: 34.400999999999996
- type: precision_at_1
value: 38.241
- type: precision_at_10
value: 11.674
- type: precision_at_100
value: 1.913
- type: precision_at_1000
value: 0.252
- type: precision_at_3
value: 24.387
- type: precision_at_5
value: 18.163
- type: recall_at_1
value: 16.950000000000003
- type: recall_at_10
value: 43.769000000000005
- type: recall_at_100
value: 66.875
- type: recall_at_1000
value: 84.92699999999999
- type: recall_at_3
value: 29.353
- type: recall_at_5
value: 35.467
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.276
- type: map_at_10
value: 20.848
- type: map_at_100
value: 29.804000000000002
- type: map_at_1000
value: 31.398
- type: map_at_3
value: 14.886
- type: map_at_5
value: 17.516000000000002
- type: mrr_at_1
value: 71
- type: mrr_at_10
value: 78.724
- type: mrr_at_100
value: 78.976
- type: mrr_at_1000
value: 78.986
- type: mrr_at_3
value: 77.333
- type: mrr_at_5
value: 78.021
- type: ndcg_at_1
value: 57.875
- type: ndcg_at_10
value: 43.855
- type: ndcg_at_100
value: 48.99
- type: ndcg_at_1000
value: 56.141
- type: ndcg_at_3
value: 48.914
- type: ndcg_at_5
value: 45.961
- type: precision_at_1
value: 71
- type: precision_at_10
value: 34.575
- type: precision_at_100
value: 11.182
- type: precision_at_1000
value: 2.044
- type: precision_at_3
value: 52.5
- type: precision_at_5
value: 44.2
- type: recall_at_1
value: 9.276
- type: recall_at_10
value: 26.501
- type: recall_at_100
value: 55.72899999999999
- type: recall_at_1000
value: 78.532
- type: recall_at_3
value: 16.365
- type: recall_at_5
value: 20.154
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.71
- type: f1
value: 47.74801556489574
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 73.405
- type: map_at_10
value: 82.822
- type: map_at_100
value: 83.042
- type: map_at_1000
value: 83.055
- type: map_at_3
value: 81.65299999999999
- type: map_at_5
value: 82.431
- type: mrr_at_1
value: 79.178
- type: mrr_at_10
value: 87.02
- type: mrr_at_100
value: 87.095
- type: mrr_at_1000
value: 87.09700000000001
- type: mrr_at_3
value: 86.309
- type: mrr_at_5
value: 86.824
- type: ndcg_at_1
value: 79.178
- type: ndcg_at_10
value: 86.72
- type: ndcg_at_100
value: 87.457
- type: ndcg_at_1000
value: 87.691
- type: ndcg_at_3
value: 84.974
- type: ndcg_at_5
value: 86.032
- type: precision_at_1
value: 79.178
- type: precision_at_10
value: 10.548
- type: precision_at_100
value: 1.113
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 32.848
- type: precision_at_5
value: 20.45
- type: recall_at_1
value: 73.405
- type: recall_at_10
value: 94.39699999999999
- type: recall_at_100
value: 97.219
- type: recall_at_1000
value: 98.675
- type: recall_at_3
value: 89.679
- type: recall_at_5
value: 92.392
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.651
- type: map_at_10
value: 36.886
- type: map_at_100
value: 38.811
- type: map_at_1000
value: 38.981
- type: map_at_3
value: 32.538
- type: map_at_5
value: 34.763
- type: mrr_at_1
value: 44.444
- type: mrr_at_10
value: 53.168000000000006
- type: mrr_at_100
value: 53.839000000000006
- type: mrr_at_1000
value: 53.869
- type: mrr_at_3
value: 50.54
- type: mrr_at_5
value: 52.068000000000005
- type: ndcg_at_1
value: 44.444
- type: ndcg_at_10
value: 44.994
- type: ndcg_at_100
value: 51.599
- type: ndcg_at_1000
value: 54.339999999999996
- type: ndcg_at_3
value: 41.372
- type: ndcg_at_5
value: 42.149
- type: precision_at_1
value: 44.444
- type: precision_at_10
value: 12.407
- type: precision_at_100
value: 1.9269999999999998
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 27.726
- type: precision_at_5
value: 19.814999999999998
- type: recall_at_1
value: 22.651
- type: recall_at_10
value: 52.075
- type: recall_at_100
value: 76.51400000000001
- type: recall_at_1000
value: 92.852
- type: recall_at_3
value: 37.236000000000004
- type: recall_at_5
value: 43.175999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.777
- type: map_at_10
value: 66.79899999999999
- type: map_at_100
value: 67.65299999999999
- type: map_at_1000
value: 67.706
- type: map_at_3
value: 63.352
- type: map_at_5
value: 65.52900000000001
- type: mrr_at_1
value: 81.553
- type: mrr_at_10
value: 86.983
- type: mrr_at_100
value: 87.132
- type: mrr_at_1000
value: 87.136
- type: mrr_at_3
value: 86.156
- type: mrr_at_5
value: 86.726
- type: ndcg_at_1
value: 81.553
- type: ndcg_at_10
value: 74.64
- type: ndcg_at_100
value: 77.459
- type: ndcg_at_1000
value: 78.43
- type: ndcg_at_3
value: 69.878
- type: ndcg_at_5
value: 72.59400000000001
- type: precision_at_1
value: 81.553
- type: precision_at_10
value: 15.654000000000002
- type: precision_at_100
value: 1.783
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 45.199
- type: precision_at_5
value: 29.267
- type: recall_at_1
value: 40.777
- type: recall_at_10
value: 78.271
- type: recall_at_100
value: 89.129
- type: recall_at_1000
value: 95.49
- type: recall_at_3
value: 67.79899999999999
- type: recall_at_5
value: 73.167
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 93.5064
- type: ap
value: 90.25495114444111
- type: f1
value: 93.5012434973381
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.301
- type: map_at_10
value: 35.657
- type: map_at_100
value: 36.797000000000004
- type: map_at_1000
value: 36.844
- type: map_at_3
value: 31.743
- type: map_at_5
value: 34.003
- type: mrr_at_1
value: 23.854
- type: mrr_at_10
value: 36.242999999999995
- type: mrr_at_100
value: 37.32
- type: mrr_at_1000
value: 37.361
- type: mrr_at_3
value: 32.4
- type: mrr_at_5
value: 34.634
- type: ndcg_at_1
value: 23.868000000000002
- type: ndcg_at_10
value: 42.589
- type: ndcg_at_100
value: 48.031
- type: ndcg_at_1000
value: 49.189
- type: ndcg_at_3
value: 34.649
- type: ndcg_at_5
value: 38.676
- type: precision_at_1
value: 23.868000000000002
- type: precision_at_10
value: 6.6850000000000005
- type: precision_at_100
value: 0.9400000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.651
- type: precision_at_5
value: 10.834000000000001
- type: recall_at_1
value: 23.301
- type: recall_at_10
value: 63.88700000000001
- type: recall_at_100
value: 88.947
- type: recall_at_1000
value: 97.783
- type: recall_at_3
value: 42.393
- type: recall_at_5
value: 52.036
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.64888280893753
- type: f1
value: 94.41310774203512
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.72184222526221
- type: f1
value: 61.522034067350106
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 79.60659045057163
- type: f1
value: 77.268649687049
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.83254875588432
- type: f1
value: 81.61520635919082
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 36.31529875009507
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.734233714415073
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.994501713009452
- type: mrr
value: 32.13512850703073
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.603000000000001
- type: map_at_10
value: 13.767999999999999
- type: map_at_100
value: 17.197000000000003
- type: map_at_1000
value: 18.615000000000002
- type: map_at_3
value: 10.567
- type: map_at_5
value: 12.078999999999999
- type: mrr_at_1
value: 44.891999999999996
- type: mrr_at_10
value: 53.75299999999999
- type: mrr_at_100
value: 54.35
- type: mrr_at_1000
value: 54.388000000000005
- type: mrr_at_3
value: 51.495999999999995
- type: mrr_at_5
value: 52.688
- type: ndcg_at_1
value: 43.189
- type: ndcg_at_10
value: 34.567
- type: ndcg_at_100
value: 32.273
- type: ndcg_at_1000
value: 41.321999999999996
- type: ndcg_at_3
value: 40.171
- type: ndcg_at_5
value: 37.502
- type: precision_at_1
value: 44.582
- type: precision_at_10
value: 25.139
- type: precision_at_100
value: 7.739999999999999
- type: precision_at_1000
value: 2.054
- type: precision_at_3
value: 37.152
- type: precision_at_5
value: 31.826999999999998
- type: recall_at_1
value: 6.603000000000001
- type: recall_at_10
value: 17.023
- type: recall_at_100
value: 32.914
- type: recall_at_1000
value: 64.44800000000001
- type: recall_at_3
value: 11.457
- type: recall_at_5
value: 13.816
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.026000000000003
- type: map_at_10
value: 45.429
- type: map_at_100
value: 46.45
- type: map_at_1000
value: 46.478
- type: map_at_3
value: 41.147
- type: map_at_5
value: 43.627
- type: mrr_at_1
value: 33.951
- type: mrr_at_10
value: 47.953
- type: mrr_at_100
value: 48.731
- type: mrr_at_1000
value: 48.751
- type: mrr_at_3
value: 44.39
- type: mrr_at_5
value: 46.533
- type: ndcg_at_1
value: 33.951
- type: ndcg_at_10
value: 53.24100000000001
- type: ndcg_at_100
value: 57.599999999999994
- type: ndcg_at_1000
value: 58.270999999999994
- type: ndcg_at_3
value: 45.190999999999995
- type: ndcg_at_5
value: 49.339
- type: precision_at_1
value: 33.951
- type: precision_at_10
value: 8.856
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.713
- type: precision_at_5
value: 14.838000000000001
- type: recall_at_1
value: 30.026000000000003
- type: recall_at_10
value: 74.512
- type: recall_at_100
value: 93.395
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 53.677
- type: recall_at_5
value: 63.198
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.41300000000001
- type: map_at_10
value: 85.387
- type: map_at_100
value: 86.027
- type: map_at_1000
value: 86.041
- type: map_at_3
value: 82.543
- type: map_at_5
value: 84.304
- type: mrr_at_1
value: 82.35
- type: mrr_at_10
value: 88.248
- type: mrr_at_100
value: 88.348
- type: mrr_at_1000
value: 88.349
- type: mrr_at_3
value: 87.348
- type: mrr_at_5
value: 87.96300000000001
- type: ndcg_at_1
value: 82.37
- type: ndcg_at_10
value: 88.98
- type: ndcg_at_100
value: 90.16499999999999
- type: ndcg_at_1000
value: 90.239
- type: ndcg_at_3
value: 86.34100000000001
- type: ndcg_at_5
value: 87.761
- type: precision_at_1
value: 82.37
- type: precision_at_10
value: 13.471
- type: precision_at_100
value: 1.534
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.827
- type: precision_at_5
value: 24.773999999999997
- type: recall_at_1
value: 71.41300000000001
- type: recall_at_10
value: 95.748
- type: recall_at_100
value: 99.69200000000001
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 87.996
- type: recall_at_5
value: 92.142
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.96878497780007
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 65.31371347128074
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.287
- type: map_at_10
value: 13.530000000000001
- type: map_at_100
value: 15.891
- type: map_at_1000
value: 16.245
- type: map_at_3
value: 9.612
- type: map_at_5
value: 11.672
- type: mrr_at_1
value: 26
- type: mrr_at_10
value: 37.335
- type: mrr_at_100
value: 38.443
- type: mrr_at_1000
value: 38.486
- type: mrr_at_3
value: 33.783
- type: mrr_at_5
value: 36.028
- type: ndcg_at_1
value: 26
- type: ndcg_at_10
value: 22.215
- type: ndcg_at_100
value: 31.101
- type: ndcg_at_1000
value: 36.809
- type: ndcg_at_3
value: 21.104
- type: ndcg_at_5
value: 18.759999999999998
- type: precision_at_1
value: 26
- type: precision_at_10
value: 11.43
- type: precision_at_100
value: 2.424
- type: precision_at_1000
value: 0.379
- type: precision_at_3
value: 19.7
- type: precision_at_5
value: 16.619999999999997
- type: recall_at_1
value: 5.287
- type: recall_at_10
value: 23.18
- type: recall_at_100
value: 49.208
- type: recall_at_1000
value: 76.85300000000001
- type: recall_at_3
value: 11.991999999999999
- type: recall_at_5
value: 16.85
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.87834913790886
- type: cos_sim_spearman
value: 81.04583513112122
- type: euclidean_pearson
value: 81.20484174558065
- type: euclidean_spearman
value: 80.76430832561769
- type: manhattan_pearson
value: 81.21416730978615
- type: manhattan_spearman
value: 80.7797637394211
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.56143998865157
- type: cos_sim_spearman
value: 79.75387012744471
- type: euclidean_pearson
value: 83.7877519997019
- type: euclidean_spearman
value: 79.90489748003296
- type: manhattan_pearson
value: 83.7540590666095
- type: manhattan_spearman
value: 79.86434577931573
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.92102564177941
- type: cos_sim_spearman
value: 84.98234585939103
- type: euclidean_pearson
value: 84.47729567593696
- type: euclidean_spearman
value: 85.09490696194469
- type: manhattan_pearson
value: 84.38622951588229
- type: manhattan_spearman
value: 85.02507171545574
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.1891164763377
- type: cos_sim_spearman
value: 80.7997969966883
- type: euclidean_pearson
value: 80.48572256162396
- type: euclidean_spearman
value: 80.57851903536378
- type: manhattan_pearson
value: 80.4324819433651
- type: manhattan_spearman
value: 80.5074526239062
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.64319975116025
- type: cos_sim_spearman
value: 84.88671197763652
- type: euclidean_pearson
value: 84.74692193293231
- type: euclidean_spearman
value: 85.27151722073653
- type: manhattan_pearson
value: 84.72460516785438
- type: manhattan_spearman
value: 85.26518899786687
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.24687565822381
- type: cos_sim_spearman
value: 85.60418454111263
- type: euclidean_pearson
value: 84.85829740169851
- type: euclidean_spearman
value: 85.66378014138306
- type: manhattan_pearson
value: 84.84672408808835
- type: manhattan_spearman
value: 85.63331924364891
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.87758895415485
- type: cos_sim_spearman
value: 85.8193745617297
- type: euclidean_pearson
value: 85.78719118848134
- type: euclidean_spearman
value: 84.35797575385688
- type: manhattan_pearson
value: 85.97919844815692
- type: manhattan_spearman
value: 84.58334745175151
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.27076035963599
- type: cos_sim_spearman
value: 67.21433656439973
- type: euclidean_pearson
value: 68.07434078679324
- type: euclidean_spearman
value: 66.0249731719049
- type: manhattan_pearson
value: 67.95495198947476
- type: manhattan_spearman
value: 65.99893908331886
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.22437747056817
- type: cos_sim_spearman
value: 85.0995685206174
- type: euclidean_pearson
value: 84.08616925603394
- type: euclidean_spearman
value: 84.89633925691658
- type: manhattan_pearson
value: 84.08332675923133
- type: manhattan_spearman
value: 84.8858228112915
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.6909022589666
- type: mrr
value: 96.43341952165481
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.660999999999994
- type: map_at_10
value: 67.625
- type: map_at_100
value: 68.07600000000001
- type: map_at_1000
value: 68.10199999999999
- type: map_at_3
value: 64.50399999999999
- type: map_at_5
value: 66.281
- type: mrr_at_1
value: 61
- type: mrr_at_10
value: 68.953
- type: mrr_at_100
value: 69.327
- type: mrr_at_1000
value: 69.352
- type: mrr_at_3
value: 66.833
- type: mrr_at_5
value: 68.05
- type: ndcg_at_1
value: 61
- type: ndcg_at_10
value: 72.369
- type: ndcg_at_100
value: 74.237
- type: ndcg_at_1000
value: 74.939
- type: ndcg_at_3
value: 67.284
- type: ndcg_at_5
value: 69.72500000000001
- type: precision_at_1
value: 61
- type: precision_at_10
value: 9.733
- type: precision_at_100
value: 1.0670000000000002
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.222
- type: precision_at_5
value: 17.4
- type: recall_at_1
value: 57.660999999999994
- type: recall_at_10
value: 85.656
- type: recall_at_100
value: 93.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 71.961
- type: recall_at_5
value: 78.094
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.86930693069307
- type: cos_sim_ap
value: 96.76685487950894
- type: cos_sim_f1
value: 93.44587884806354
- type: cos_sim_precision
value: 92.80078895463511
- type: cos_sim_recall
value: 94.1
- type: dot_accuracy
value: 99.54356435643564
- type: dot_ap
value: 81.18659960405607
- type: dot_f1
value: 75.78008915304605
- type: dot_precision
value: 75.07360157016683
- type: dot_recall
value: 76.5
- type: euclidean_accuracy
value: 99.87326732673267
- type: euclidean_ap
value: 96.8102411908941
- type: euclidean_f1
value: 93.6127744510978
- type: euclidean_precision
value: 93.42629482071713
- type: euclidean_recall
value: 93.8
- type: manhattan_accuracy
value: 99.87425742574257
- type: manhattan_ap
value: 96.82857341435529
- type: manhattan_f1
value: 93.62129583124059
- type: manhattan_precision
value: 94.04641775983855
- type: manhattan_recall
value: 93.2
- type: max_accuracy
value: 99.87425742574257
- type: max_ap
value: 96.82857341435529
- type: max_f1
value: 93.62129583124059
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.92560972698926
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.92797240259008
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.244624045597654
- type: mrr
value: 56.185303666921314
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.02491987312937
- type: cos_sim_spearman
value: 32.055592206679734
- type: dot_pearson
value: 24.731627575422557
- type: dot_spearman
value: 24.308029077069733
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.231
- type: map_at_10
value: 1.899
- type: map_at_100
value: 9.498
- type: map_at_1000
value: 20.979999999999997
- type: map_at_3
value: 0.652
- type: map_at_5
value: 1.069
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.4
- type: mrr_at_100
value: 93.4
- type: mrr_at_1000
value: 93.4
- type: mrr_at_3
value: 93
- type: mrr_at_5
value: 93.4
- type: ndcg_at_1
value: 86
- type: ndcg_at_10
value: 75.375
- type: ndcg_at_100
value: 52.891999999999996
- type: ndcg_at_1000
value: 44.952999999999996
- type: ndcg_at_3
value: 81.05
- type: ndcg_at_5
value: 80.175
- type: precision_at_1
value: 88
- type: precision_at_10
value: 79
- type: precision_at_100
value: 53.16
- type: precision_at_1000
value: 19.408
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 84
- type: recall_at_1
value: 0.231
- type: recall_at_10
value: 2.078
- type: recall_at_100
value: 12.601
- type: recall_at_1000
value: 41.296
- type: recall_at_3
value: 0.6779999999999999
- type: recall_at_5
value: 1.1360000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.782
- type: map_at_10
value: 10.204
- type: map_at_100
value: 16.176
- type: map_at_1000
value: 17.456
- type: map_at_3
value: 5.354
- type: map_at_5
value: 7.503
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 54.010000000000005
- type: mrr_at_100
value: 54.49
- type: mrr_at_1000
value: 54.49
- type: mrr_at_3
value: 48.980000000000004
- type: mrr_at_5
value: 51.735
- type: ndcg_at_1
value: 36.735
- type: ndcg_at_10
value: 26.61
- type: ndcg_at_100
value: 36.967
- type: ndcg_at_1000
value: 47.274
- type: ndcg_at_3
value: 30.363
- type: ndcg_at_5
value: 29.448999999999998
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 23.878
- type: precision_at_100
value: 7.693999999999999
- type: precision_at_1000
value: 1.4489999999999998
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.782
- type: recall_at_10
value: 16.485
- type: recall_at_100
value: 46.924
- type: recall_at_1000
value: 79.365
- type: recall_at_3
value: 6.52
- type: recall_at_5
value: 10.48
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.08300000000001
- type: ap
value: 13.91559884590195
- type: f1
value: 53.956838444291364
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.34069043576683
- type: f1
value: 59.662041994618406
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.70780611078653
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.10734934732073
- type: cos_sim_ap
value: 77.58349999516054
- type: cos_sim_f1
value: 70.25391395868965
- type: cos_sim_precision
value: 70.06035161374967
- type: cos_sim_recall
value: 70.44854881266491
- type: dot_accuracy
value: 80.60439887941826
- type: dot_ap
value: 54.52935200483575
- type: dot_f1
value: 54.170444242973716
- type: dot_precision
value: 47.47715534366309
- type: dot_recall
value: 63.06068601583114
- type: euclidean_accuracy
value: 87.26828396018358
- type: euclidean_ap
value: 78.00158454104036
- type: euclidean_f1
value: 70.70292457670601
- type: euclidean_precision
value: 68.79680479281079
- type: euclidean_recall
value: 72.71767810026385
- type: manhattan_accuracy
value: 87.11330988853788
- type: manhattan_ap
value: 77.92527099601855
- type: manhattan_f1
value: 70.76488706365502
- type: manhattan_precision
value: 68.89055472263868
- type: manhattan_recall
value: 72.74406332453826
- type: max_accuracy
value: 87.26828396018358
- type: max_ap
value: 78.00158454104036
- type: max_f1
value: 70.76488706365502
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.80804905499282
- type: cos_sim_ap
value: 83.06187782630936
- type: cos_sim_f1
value: 74.99716435403985
- type: cos_sim_precision
value: 73.67951860931579
- type: cos_sim_recall
value: 76.36279642747151
- type: dot_accuracy
value: 81.83141227151008
- type: dot_ap
value: 67.18241090841795
- type: dot_f1
value: 62.216037571751606
- type: dot_precision
value: 56.749381227391005
- type: dot_recall
value: 68.84816753926701
- type: euclidean_accuracy
value: 87.91671517832887
- type: euclidean_ap
value: 83.56538942001427
- type: euclidean_f1
value: 75.7327253337256
- type: euclidean_precision
value: 72.48856036606828
- type: euclidean_recall
value: 79.28087465352634
- type: manhattan_accuracy
value: 87.86626304963713
- type: manhattan_ap
value: 83.52939841172832
- type: manhattan_f1
value: 75.73635656329888
- type: manhattan_precision
value: 72.99150182103836
- type: manhattan_recall
value: 78.69571912534647
- type: max_accuracy
value: 87.91671517832887
- type: max_ap
value: 83.56538942001427
- type: max_f1
value: 75.73635656329888
---
**Recommend switching to newest [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
thenlper/gte-large | thenlper | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"sentence-similarity",
"Sentence Transformers",
"en",
"arxiv:2308.03281",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-07-27T09:55:39 | 2024-11-15T14:10:31 | 460,453 | 272 | ---
language:
- en
license: mit
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-large
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.62686567164178
- type: ap
value: 34.46944126809772
- type: f1
value: 66.23684353950857
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.51805
- type: ap
value: 89.49842783330848
- type: f1
value: 92.51112169431808
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.074
- type: f1
value: 48.44785682572955
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.077
- type: map_at_10
value: 48.153
- type: map_at_100
value: 48.963
- type: map_at_1000
value: 48.966
- type: map_at_3
value: 43.184
- type: map_at_5
value: 46.072
- type: mrr_at_1
value: 33.073
- type: mrr_at_10
value: 48.54
- type: mrr_at_100
value: 49.335
- type: mrr_at_1000
value: 49.338
- type: mrr_at_3
value: 43.563
- type: mrr_at_5
value: 46.383
- type: ndcg_at_1
value: 32.077
- type: ndcg_at_10
value: 57.158
- type: ndcg_at_100
value: 60.324999999999996
- type: ndcg_at_1000
value: 60.402
- type: ndcg_at_3
value: 46.934
- type: ndcg_at_5
value: 52.158
- type: precision_at_1
value: 32.077
- type: precision_at_10
value: 8.591999999999999
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.275000000000002
- type: precision_at_5
value: 14.111
- type: recall_at_1
value: 32.077
- type: recall_at_10
value: 85.917
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 57.824
- type: recall_at_5
value: 70.555
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.619246083417295
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.3574067664688
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.06359661829253
- type: mrr
value: 76.15596007562766
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 90.25407547368691
- type: cos_sim_spearman
value: 88.65081514968477
- type: euclidean_pearson
value: 88.14857116664494
- type: euclidean_spearman
value: 88.50683596540692
- type: manhattan_pearson
value: 87.9654797992225
- type: manhattan_spearman
value: 88.21164851646908
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.05844155844157
- type: f1
value: 86.01555597681825
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.10510519739522
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.84689960264385
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.800000000000004
- type: map_at_10
value: 44.857
- type: map_at_100
value: 46.512
- type: map_at_1000
value: 46.635
- type: map_at_3
value: 41.062
- type: map_at_5
value: 43.126
- type: mrr_at_1
value: 39.628
- type: mrr_at_10
value: 50.879
- type: mrr_at_100
value: 51.605000000000004
- type: mrr_at_1000
value: 51.641000000000005
- type: mrr_at_3
value: 48.14
- type: mrr_at_5
value: 49.835
- type: ndcg_at_1
value: 39.628
- type: ndcg_at_10
value: 51.819
- type: ndcg_at_100
value: 57.318999999999996
- type: ndcg_at_1000
value: 58.955999999999996
- type: ndcg_at_3
value: 46.409
- type: ndcg_at_5
value: 48.825
- type: precision_at_1
value: 39.628
- type: precision_at_10
value: 10.072000000000001
- type: precision_at_100
value: 1.625
- type: precision_at_1000
value: 0.21
- type: precision_at_3
value: 22.556
- type: precision_at_5
value: 16.309
- type: recall_at_1
value: 32.800000000000004
- type: recall_at_10
value: 65.078
- type: recall_at_100
value: 87.491
- type: recall_at_1000
value: 97.514
- type: recall_at_3
value: 49.561
- type: recall_at_5
value: 56.135999999999996
- type: map_at_1
value: 32.614
- type: map_at_10
value: 43.578
- type: map_at_100
value: 44.897
- type: map_at_1000
value: 45.023
- type: map_at_3
value: 40.282000000000004
- type: map_at_5
value: 42.117
- type: mrr_at_1
value: 40.510000000000005
- type: mrr_at_10
value: 49.428
- type: mrr_at_100
value: 50.068999999999996
- type: mrr_at_1000
value: 50.111000000000004
- type: mrr_at_3
value: 47.176
- type: mrr_at_5
value: 48.583999999999996
- type: ndcg_at_1
value: 40.510000000000005
- type: ndcg_at_10
value: 49.478
- type: ndcg_at_100
value: 53.852
- type: ndcg_at_1000
value: 55.782
- type: ndcg_at_3
value: 45.091
- type: ndcg_at_5
value: 47.19
- type: precision_at_1
value: 40.510000000000005
- type: precision_at_10
value: 9.363000000000001
- type: precision_at_100
value: 1.51
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 21.741
- type: precision_at_5
value: 15.465000000000002
- type: recall_at_1
value: 32.614
- type: recall_at_10
value: 59.782000000000004
- type: recall_at_100
value: 78.012
- type: recall_at_1000
value: 90.319
- type: recall_at_3
value: 46.825
- type: recall_at_5
value: 52.688
- type: map_at_1
value: 40.266000000000005
- type: map_at_10
value: 53.756
- type: map_at_100
value: 54.809
- type: map_at_1000
value: 54.855
- type: map_at_3
value: 50.073
- type: map_at_5
value: 52.293
- type: mrr_at_1
value: 46.332
- type: mrr_at_10
value: 57.116
- type: mrr_at_100
value: 57.767
- type: mrr_at_1000
value: 57.791000000000004
- type: mrr_at_3
value: 54.461999999999996
- type: mrr_at_5
value: 56.092
- type: ndcg_at_1
value: 46.332
- type: ndcg_at_10
value: 60.092
- type: ndcg_at_100
value: 64.034
- type: ndcg_at_1000
value: 64.937
- type: ndcg_at_3
value: 54.071000000000005
- type: ndcg_at_5
value: 57.254000000000005
- type: precision_at_1
value: 46.332
- type: precision_at_10
value: 9.799
- type: precision_at_100
value: 1.278
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.368000000000002
- type: precision_at_5
value: 16.89
- type: recall_at_1
value: 40.266000000000005
- type: recall_at_10
value: 75.41499999999999
- type: recall_at_100
value: 92.01700000000001
- type: recall_at_1000
value: 98.379
- type: recall_at_3
value: 59.476
- type: recall_at_5
value: 67.297
- type: map_at_1
value: 28.589
- type: map_at_10
value: 37.755
- type: map_at_100
value: 38.881
- type: map_at_1000
value: 38.954
- type: map_at_3
value: 34.759
- type: map_at_5
value: 36.544
- type: mrr_at_1
value: 30.734
- type: mrr_at_10
value: 39.742
- type: mrr_at_100
value: 40.774
- type: mrr_at_1000
value: 40.824
- type: mrr_at_3
value: 37.137
- type: mrr_at_5
value: 38.719
- type: ndcg_at_1
value: 30.734
- type: ndcg_at_10
value: 42.978
- type: ndcg_at_100
value: 48.309000000000005
- type: ndcg_at_1000
value: 50.068
- type: ndcg_at_3
value: 37.361
- type: ndcg_at_5
value: 40.268
- type: precision_at_1
value: 30.734
- type: precision_at_10
value: 6.565
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.744
- type: precision_at_5
value: 11.096
- type: recall_at_1
value: 28.589
- type: recall_at_10
value: 57.126999999999995
- type: recall_at_100
value: 81.051
- type: recall_at_1000
value: 94.027
- type: recall_at_3
value: 42.045
- type: recall_at_5
value: 49.019
- type: map_at_1
value: 18.5
- type: map_at_10
value: 27.950999999999997
- type: map_at_100
value: 29.186
- type: map_at_1000
value: 29.298000000000002
- type: map_at_3
value: 25.141000000000002
- type: map_at_5
value: 26.848
- type: mrr_at_1
value: 22.637
- type: mrr_at_10
value: 32.572
- type: mrr_at_100
value: 33.472
- type: mrr_at_1000
value: 33.533
- type: mrr_at_3
value: 29.747
- type: mrr_at_5
value: 31.482
- type: ndcg_at_1
value: 22.637
- type: ndcg_at_10
value: 33.73
- type: ndcg_at_100
value: 39.568
- type: ndcg_at_1000
value: 42.201
- type: ndcg_at_3
value: 28.505999999999997
- type: ndcg_at_5
value: 31.255
- type: precision_at_1
value: 22.637
- type: precision_at_10
value: 6.281000000000001
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 13.847000000000001
- type: precision_at_5
value: 10.224
- type: recall_at_1
value: 18.5
- type: recall_at_10
value: 46.744
- type: recall_at_100
value: 72.072
- type: recall_at_1000
value: 91.03999999999999
- type: recall_at_3
value: 32.551
- type: recall_at_5
value: 39.533
- type: map_at_1
value: 30.602
- type: map_at_10
value: 42.18
- type: map_at_100
value: 43.6
- type: map_at_1000
value: 43.704
- type: map_at_3
value: 38.413000000000004
- type: map_at_5
value: 40.626
- type: mrr_at_1
value: 37.344
- type: mrr_at_10
value: 47.638000000000005
- type: mrr_at_100
value: 48.485
- type: mrr_at_1000
value: 48.52
- type: mrr_at_3
value: 44.867000000000004
- type: mrr_at_5
value: 46.566
- type: ndcg_at_1
value: 37.344
- type: ndcg_at_10
value: 48.632
- type: ndcg_at_100
value: 54.215
- type: ndcg_at_1000
value: 55.981
- type: ndcg_at_3
value: 42.681999999999995
- type: ndcg_at_5
value: 45.732
- type: precision_at_1
value: 37.344
- type: precision_at_10
value: 8.932
- type: precision_at_100
value: 1.376
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 20.276
- type: precision_at_5
value: 14.726
- type: recall_at_1
value: 30.602
- type: recall_at_10
value: 62.273
- type: recall_at_100
value: 85.12100000000001
- type: recall_at_1000
value: 96.439
- type: recall_at_3
value: 45.848
- type: recall_at_5
value: 53.615
- type: map_at_1
value: 23.952
- type: map_at_10
value: 35.177
- type: map_at_100
value: 36.59
- type: map_at_1000
value: 36.703
- type: map_at_3
value: 31.261
- type: map_at_5
value: 33.222
- type: mrr_at_1
value: 29.337999999999997
- type: mrr_at_10
value: 40.152
- type: mrr_at_100
value: 40.963
- type: mrr_at_1000
value: 41.016999999999996
- type: mrr_at_3
value: 36.91
- type: mrr_at_5
value: 38.685
- type: ndcg_at_1
value: 29.337999999999997
- type: ndcg_at_10
value: 41.994
- type: ndcg_at_100
value: 47.587
- type: ndcg_at_1000
value: 49.791000000000004
- type: ndcg_at_3
value: 35.27
- type: ndcg_at_5
value: 38.042
- type: precision_at_1
value: 29.337999999999997
- type: precision_at_10
value: 8.276
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 17.161
- type: precision_at_5
value: 12.671
- type: recall_at_1
value: 23.952
- type: recall_at_10
value: 57.267
- type: recall_at_100
value: 80.886
- type: recall_at_1000
value: 95.611
- type: recall_at_3
value: 38.622
- type: recall_at_5
value: 45.811
- type: map_at_1
value: 27.092083333333335
- type: map_at_10
value: 37.2925
- type: map_at_100
value: 38.57041666666666
- type: map_at_1000
value: 38.68141666666667
- type: map_at_3
value: 34.080000000000005
- type: map_at_5
value: 35.89958333333333
- type: mrr_at_1
value: 31.94758333333333
- type: mrr_at_10
value: 41.51049999999999
- type: mrr_at_100
value: 42.36099999999999
- type: mrr_at_1000
value: 42.4125
- type: mrr_at_3
value: 38.849583333333335
- type: mrr_at_5
value: 40.448249999999994
- type: ndcg_at_1
value: 31.94758333333333
- type: ndcg_at_10
value: 43.17633333333333
- type: ndcg_at_100
value: 48.45241666666668
- type: ndcg_at_1000
value: 50.513999999999996
- type: ndcg_at_3
value: 37.75216666666667
- type: ndcg_at_5
value: 40.393833333333326
- type: precision_at_1
value: 31.94758333333333
- type: precision_at_10
value: 7.688916666666666
- type: precision_at_100
value: 1.2250833333333333
- type: precision_at_1000
value: 0.1595
- type: precision_at_3
value: 17.465999999999998
- type: precision_at_5
value: 12.548083333333333
- type: recall_at_1
value: 27.092083333333335
- type: recall_at_10
value: 56.286583333333326
- type: recall_at_100
value: 79.09033333333333
- type: recall_at_1000
value: 93.27483333333335
- type: recall_at_3
value: 41.35325
- type: recall_at_5
value: 48.072750000000006
- type: map_at_1
value: 25.825
- type: map_at_10
value: 33.723
- type: map_at_100
value: 34.74
- type: map_at_1000
value: 34.824
- type: map_at_3
value: 31.369000000000003
- type: map_at_5
value: 32.533
- type: mrr_at_1
value: 29.293999999999997
- type: mrr_at_10
value: 36.84
- type: mrr_at_100
value: 37.681
- type: mrr_at_1000
value: 37.742
- type: mrr_at_3
value: 34.79
- type: mrr_at_5
value: 35.872
- type: ndcg_at_1
value: 29.293999999999997
- type: ndcg_at_10
value: 38.385999999999996
- type: ndcg_at_100
value: 43.327
- type: ndcg_at_1000
value: 45.53
- type: ndcg_at_3
value: 33.985
- type: ndcg_at_5
value: 35.817
- type: precision_at_1
value: 29.293999999999997
- type: precision_at_10
value: 6.12
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 14.621999999999998
- type: precision_at_5
value: 10.030999999999999
- type: recall_at_1
value: 25.825
- type: recall_at_10
value: 49.647000000000006
- type: recall_at_100
value: 72.32300000000001
- type: recall_at_1000
value: 88.62400000000001
- type: recall_at_3
value: 37.366
- type: recall_at_5
value: 41.957
- type: map_at_1
value: 18.139
- type: map_at_10
value: 26.107000000000003
- type: map_at_100
value: 27.406999999999996
- type: map_at_1000
value: 27.535999999999998
- type: map_at_3
value: 23.445
- type: map_at_5
value: 24.916
- type: mrr_at_1
value: 21.817
- type: mrr_at_10
value: 29.99
- type: mrr_at_100
value: 31.052000000000003
- type: mrr_at_1000
value: 31.128
- type: mrr_at_3
value: 27.627000000000002
- type: mrr_at_5
value: 29.005
- type: ndcg_at_1
value: 21.817
- type: ndcg_at_10
value: 31.135
- type: ndcg_at_100
value: 37.108000000000004
- type: ndcg_at_1000
value: 39.965
- type: ndcg_at_3
value: 26.439
- type: ndcg_at_5
value: 28.655
- type: precision_at_1
value: 21.817
- type: precision_at_10
value: 5.757000000000001
- type: precision_at_100
value: 1.036
- type: precision_at_1000
value: 0.147
- type: precision_at_3
value: 12.537
- type: precision_at_5
value: 9.229
- type: recall_at_1
value: 18.139
- type: recall_at_10
value: 42.272999999999996
- type: recall_at_100
value: 68.657
- type: recall_at_1000
value: 88.93799999999999
- type: recall_at_3
value: 29.266
- type: recall_at_5
value: 34.892
- type: map_at_1
value: 27.755000000000003
- type: map_at_10
value: 37.384
- type: map_at_100
value: 38.56
- type: map_at_1000
value: 38.655
- type: map_at_3
value: 34.214
- type: map_at_5
value: 35.96
- type: mrr_at_1
value: 32.369
- type: mrr_at_10
value: 41.625
- type: mrr_at_100
value: 42.449
- type: mrr_at_1000
value: 42.502
- type: mrr_at_3
value: 38.899
- type: mrr_at_5
value: 40.489999999999995
- type: ndcg_at_1
value: 32.369
- type: ndcg_at_10
value: 43.287
- type: ndcg_at_100
value: 48.504999999999995
- type: ndcg_at_1000
value: 50.552
- type: ndcg_at_3
value: 37.549
- type: ndcg_at_5
value: 40.204
- type: precision_at_1
value: 32.369
- type: precision_at_10
value: 7.425
- type: precision_at_100
value: 1.134
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.102
- type: precision_at_5
value: 12.107999999999999
- type: recall_at_1
value: 27.755000000000003
- type: recall_at_10
value: 57.071000000000005
- type: recall_at_100
value: 79.456
- type: recall_at_1000
value: 93.54299999999999
- type: recall_at_3
value: 41.298
- type: recall_at_5
value: 48.037
- type: map_at_1
value: 24.855
- type: map_at_10
value: 34.53
- type: map_at_100
value: 36.167
- type: map_at_1000
value: 36.394999999999996
- type: map_at_3
value: 31.037
- type: map_at_5
value: 33.119
- type: mrr_at_1
value: 30.631999999999998
- type: mrr_at_10
value: 39.763999999999996
- type: mrr_at_100
value: 40.77
- type: mrr_at_1000
value: 40.826
- type: mrr_at_3
value: 36.495
- type: mrr_at_5
value: 38.561
- type: ndcg_at_1
value: 30.631999999999998
- type: ndcg_at_10
value: 40.942
- type: ndcg_at_100
value: 47.07
- type: ndcg_at_1000
value: 49.363
- type: ndcg_at_3
value: 35.038000000000004
- type: ndcg_at_5
value: 38.161
- type: precision_at_1
value: 30.631999999999998
- type: precision_at_10
value: 7.983999999999999
- type: precision_at_100
value: 1.6070000000000002
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 16.206
- type: precision_at_5
value: 12.253
- type: recall_at_1
value: 24.855
- type: recall_at_10
value: 53.291999999999994
- type: recall_at_100
value: 80.283
- type: recall_at_1000
value: 94.309
- type: recall_at_3
value: 37.257
- type: recall_at_5
value: 45.282
- type: map_at_1
value: 21.208
- type: map_at_10
value: 30.512
- type: map_at_100
value: 31.496000000000002
- type: map_at_1000
value: 31.595000000000002
- type: map_at_3
value: 27.904
- type: map_at_5
value: 29.491
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 32.379999999999995
- type: mrr_at_100
value: 33.245000000000005
- type: mrr_at_1000
value: 33.315
- type: mrr_at_3
value: 29.945
- type: mrr_at_5
value: 31.488
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 35.643
- type: ndcg_at_100
value: 40.535
- type: ndcg_at_1000
value: 43.042
- type: ndcg_at_3
value: 30.625000000000004
- type: ndcg_at_5
value: 33.323
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 5.6930000000000005
- type: precision_at_100
value: 0.889
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 13.431999999999999
- type: precision_at_5
value: 9.575
- type: recall_at_1
value: 21.208
- type: recall_at_10
value: 49.47
- type: recall_at_100
value: 71.71499999999999
- type: recall_at_1000
value: 90.55499999999999
- type: recall_at_3
value: 36.124
- type: recall_at_5
value: 42.606
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.363
- type: map_at_10
value: 20.312
- type: map_at_100
value: 22.225
- type: map_at_1000
value: 22.411
- type: map_at_3
value: 16.68
- type: map_at_5
value: 18.608
- type: mrr_at_1
value: 25.537
- type: mrr_at_10
value: 37.933
- type: mrr_at_100
value: 38.875
- type: mrr_at_1000
value: 38.911
- type: mrr_at_3
value: 34.387
- type: mrr_at_5
value: 36.51
- type: ndcg_at_1
value: 25.537
- type: ndcg_at_10
value: 28.82
- type: ndcg_at_100
value: 36.341
- type: ndcg_at_1000
value: 39.615
- type: ndcg_at_3
value: 23.01
- type: ndcg_at_5
value: 25.269000000000002
- type: precision_at_1
value: 25.537
- type: precision_at_10
value: 9.153
- type: precision_at_100
value: 1.7319999999999998
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 17.22
- type: precision_at_5
value: 13.629
- type: recall_at_1
value: 11.363
- type: recall_at_10
value: 35.382999999999996
- type: recall_at_100
value: 61.367000000000004
- type: recall_at_1000
value: 79.699
- type: recall_at_3
value: 21.495
- type: recall_at_5
value: 27.42
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.65
- type: map_at_10
value: 20.742
- type: map_at_100
value: 29.614
- type: map_at_1000
value: 31.373
- type: map_at_3
value: 14.667
- type: map_at_5
value: 17.186
- type: mrr_at_1
value: 69.75
- type: mrr_at_10
value: 76.762
- type: mrr_at_100
value: 77.171
- type: mrr_at_1000
value: 77.179
- type: mrr_at_3
value: 75.125
- type: mrr_at_5
value: 76.287
- type: ndcg_at_1
value: 57.62500000000001
- type: ndcg_at_10
value: 42.370999999999995
- type: ndcg_at_100
value: 47.897
- type: ndcg_at_1000
value: 55.393
- type: ndcg_at_3
value: 46.317
- type: ndcg_at_5
value: 43.906
- type: precision_at_1
value: 69.75
- type: precision_at_10
value: 33.95
- type: precision_at_100
value: 10.885
- type: precision_at_1000
value: 2.2239999999999998
- type: precision_at_3
value: 49.75
- type: precision_at_5
value: 42.3
- type: recall_at_1
value: 9.65
- type: recall_at_10
value: 26.117
- type: recall_at_100
value: 55.084
- type: recall_at_1000
value: 78.62400000000001
- type: recall_at_3
value: 15.823
- type: recall_at_5
value: 19.652
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.885
- type: f1
value: 42.99567641346983
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.97
- type: map_at_10
value: 80.34599999999999
- type: map_at_100
value: 80.571
- type: map_at_1000
value: 80.584
- type: map_at_3
value: 79.279
- type: map_at_5
value: 79.94
- type: mrr_at_1
value: 76.613
- type: mrr_at_10
value: 85.15700000000001
- type: mrr_at_100
value: 85.249
- type: mrr_at_1000
value: 85.252
- type: mrr_at_3
value: 84.33800000000001
- type: mrr_at_5
value: 84.89
- type: ndcg_at_1
value: 76.613
- type: ndcg_at_10
value: 84.53399999999999
- type: ndcg_at_100
value: 85.359
- type: ndcg_at_1000
value: 85.607
- type: ndcg_at_3
value: 82.76599999999999
- type: ndcg_at_5
value: 83.736
- type: precision_at_1
value: 76.613
- type: precision_at_10
value: 10.206
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 31.913000000000004
- type: precision_at_5
value: 19.769000000000002
- type: recall_at_1
value: 70.97
- type: recall_at_10
value: 92.674
- type: recall_at_100
value: 95.985
- type: recall_at_1000
value: 97.57000000000001
- type: recall_at_3
value: 87.742
- type: recall_at_5
value: 90.28
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.494
- type: map_at_10
value: 36.491
- type: map_at_100
value: 38.550000000000004
- type: map_at_1000
value: 38.726
- type: map_at_3
value: 31.807000000000002
- type: map_at_5
value: 34.299
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.146
- type: mrr_at_100
value: 54.013999999999996
- type: mrr_at_1000
value: 54.044000000000004
- type: mrr_at_3
value: 50.952
- type: mrr_at_5
value: 52.124
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 44.499
- type: ndcg_at_100
value: 51.629000000000005
- type: ndcg_at_1000
value: 54.367
- type: ndcg_at_3
value: 40.900999999999996
- type: ndcg_at_5
value: 41.737
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.346
- type: precision_at_100
value: 1.974
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 27.366
- type: precision_at_5
value: 19.846
- type: recall_at_1
value: 22.494
- type: recall_at_10
value: 51.156
- type: recall_at_100
value: 77.11200000000001
- type: recall_at_1000
value: 93.44
- type: recall_at_3
value: 36.574
- type: recall_at_5
value: 42.361
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.568999999999996
- type: map_at_10
value: 58.485
- type: map_at_100
value: 59.358999999999995
- type: map_at_1000
value: 59.429
- type: map_at_3
value: 55.217000000000006
- type: map_at_5
value: 57.236
- type: mrr_at_1
value: 77.137
- type: mrr_at_10
value: 82.829
- type: mrr_at_100
value: 83.04599999999999
- type: mrr_at_1000
value: 83.05399999999999
- type: mrr_at_3
value: 81.904
- type: mrr_at_5
value: 82.50800000000001
- type: ndcg_at_1
value: 77.137
- type: ndcg_at_10
value: 67.156
- type: ndcg_at_100
value: 70.298
- type: ndcg_at_1000
value: 71.65700000000001
- type: ndcg_at_3
value: 62.535
- type: ndcg_at_5
value: 65.095
- type: precision_at_1
value: 77.137
- type: precision_at_10
value: 13.911999999999999
- type: precision_at_100
value: 1.6389999999999998
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 39.572
- type: precision_at_5
value: 25.766
- type: recall_at_1
value: 38.568999999999996
- type: recall_at_10
value: 69.56099999999999
- type: recall_at_100
value: 81.931
- type: recall_at_1000
value: 90.91799999999999
- type: recall_at_3
value: 59.358999999999995
- type: recall_at_5
value: 64.416
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.45600000000002
- type: ap
value: 84.09725115338568
- type: f1
value: 88.41874909080512
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.404999999999998
- type: map_at_10
value: 33.921
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.164
- type: map_at_3
value: 30.043999999999997
- type: map_at_5
value: 32.327
- type: mrr_at_1
value: 21.977
- type: mrr_at_10
value: 34.505
- type: mrr_at_100
value: 35.638999999999996
- type: mrr_at_1000
value: 35.68
- type: mrr_at_3
value: 30.703999999999997
- type: mrr_at_5
value: 32.96
- type: ndcg_at_1
value: 21.963
- type: ndcg_at_10
value: 40.859
- type: ndcg_at_100
value: 46.614
- type: ndcg_at_1000
value: 47.789
- type: ndcg_at_3
value: 33.007999999999996
- type: ndcg_at_5
value: 37.084
- type: precision_at_1
value: 21.963
- type: precision_at_10
value: 6.493
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.155000000000001
- type: precision_at_5
value: 10.544
- type: recall_at_1
value: 21.404999999999998
- type: recall_at_10
value: 62.175000000000004
- type: recall_at_100
value: 88.786
- type: recall_at_1000
value: 97.738
- type: recall_at_3
value: 40.925
- type: recall_at_5
value: 50.722
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.50661194710442
- type: f1
value: 93.30311193153668
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.24669402644778
- type: f1
value: 54.23122108002977
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.61936785474109
- type: f1
value: 70.52644941025565
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.76529926025555
- type: f1
value: 77.26872729322514
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.39450293021839
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.757796879839294
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.62512146657428
- type: mrr
value: 33.84624322066173
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.462
- type: map_at_10
value: 14.947
- type: map_at_100
value: 19.344
- type: map_at_1000
value: 20.933
- type: map_at_3
value: 10.761999999999999
- type: map_at_5
value: 12.744
- type: mrr_at_1
value: 47.988
- type: mrr_at_10
value: 57.365
- type: mrr_at_100
value: 57.931
- type: mrr_at_1000
value: 57.96
- type: mrr_at_3
value: 54.85
- type: mrr_at_5
value: 56.569
- type: ndcg_at_1
value: 46.129999999999995
- type: ndcg_at_10
value: 38.173
- type: ndcg_at_100
value: 35.983
- type: ndcg_at_1000
value: 44.507000000000005
- type: ndcg_at_3
value: 42.495
- type: ndcg_at_5
value: 41.019
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 28.731
- type: precision_at_100
value: 9.232
- type: precision_at_1000
value: 2.202
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 35.851
- type: recall_at_1
value: 6.462
- type: recall_at_10
value: 18.968
- type: recall_at_100
value: 37.131
- type: recall_at_1000
value: 67.956
- type: recall_at_3
value: 11.905000000000001
- type: recall_at_5
value: 15.097
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.335
- type: map_at_10
value: 46.611999999999995
- type: map_at_100
value: 47.632000000000005
- type: map_at_1000
value: 47.661
- type: map_at_3
value: 41.876999999999995
- type: map_at_5
value: 44.799
- type: mrr_at_1
value: 34.125
- type: mrr_at_10
value: 49.01
- type: mrr_at_100
value: 49.75
- type: mrr_at_1000
value: 49.768
- type: mrr_at_3
value: 45.153
- type: mrr_at_5
value: 47.589999999999996
- type: ndcg_at_1
value: 34.125
- type: ndcg_at_10
value: 54.777
- type: ndcg_at_100
value: 58.914
- type: ndcg_at_1000
value: 59.521
- type: ndcg_at_3
value: 46.015
- type: ndcg_at_5
value: 50.861000000000004
- type: precision_at_1
value: 34.125
- type: precision_at_10
value: 9.166
- type: precision_at_100
value: 1.149
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 21.147
- type: precision_at_5
value: 15.469
- type: recall_at_1
value: 30.335
- type: recall_at_10
value: 77.194
- type: recall_at_100
value: 94.812
- type: recall_at_1000
value: 99.247
- type: recall_at_3
value: 54.681000000000004
- type: recall_at_5
value: 65.86800000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.62
- type: map_at_10
value: 84.536
- type: map_at_100
value: 85.167
- type: map_at_1000
value: 85.184
- type: map_at_3
value: 81.607
- type: map_at_5
value: 83.423
- type: mrr_at_1
value: 81.36
- type: mrr_at_10
value: 87.506
- type: mrr_at_100
value: 87.601
- type: mrr_at_1000
value: 87.601
- type: mrr_at_3
value: 86.503
- type: mrr_at_5
value: 87.179
- type: ndcg_at_1
value: 81.36
- type: ndcg_at_10
value: 88.319
- type: ndcg_at_100
value: 89.517
- type: ndcg_at_1000
value: 89.60900000000001
- type: ndcg_at_3
value: 85.423
- type: ndcg_at_5
value: 86.976
- type: precision_at_1
value: 81.36
- type: precision_at_10
value: 13.415
- type: precision_at_100
value: 1.529
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.342999999999996
- type: precision_at_5
value: 24.534
- type: recall_at_1
value: 70.62
- type: recall_at_10
value: 95.57600000000001
- type: recall_at_100
value: 99.624
- type: recall_at_1000
value: 99.991
- type: recall_at_3
value: 87.22
- type: recall_at_5
value: 91.654
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 60.826438478212744
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.24027467551447
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.997999999999999
- type: map_at_10
value: 14.267
- type: map_at_100
value: 16.843
- type: map_at_1000
value: 17.229
- type: map_at_3
value: 9.834
- type: map_at_5
value: 11.92
- type: mrr_at_1
value: 24.7
- type: mrr_at_10
value: 37.685
- type: mrr_at_100
value: 38.704
- type: mrr_at_1000
value: 38.747
- type: mrr_at_3
value: 34.150000000000006
- type: mrr_at_5
value: 36.075
- type: ndcg_at_1
value: 24.7
- type: ndcg_at_10
value: 23.44
- type: ndcg_at_100
value: 32.617000000000004
- type: ndcg_at_1000
value: 38.628
- type: ndcg_at_3
value: 21.747
- type: ndcg_at_5
value: 19.076
- type: precision_at_1
value: 24.7
- type: precision_at_10
value: 12.47
- type: precision_at_100
value: 2.564
- type: precision_at_1000
value: 0.4
- type: precision_at_3
value: 20.767
- type: precision_at_5
value: 17.06
- type: recall_at_1
value: 4.997999999999999
- type: recall_at_10
value: 25.3
- type: recall_at_100
value: 52.048
- type: recall_at_1000
value: 81.093
- type: recall_at_3
value: 12.642999999999999
- type: recall_at_5
value: 17.312
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.44942006292234
- type: cos_sim_spearman
value: 79.80930790660699
- type: euclidean_pearson
value: 82.93400777494863
- type: euclidean_spearman
value: 80.04664991110705
- type: manhattan_pearson
value: 82.93551681854949
- type: manhattan_spearman
value: 80.03156736837379
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.63574059135726
- type: cos_sim_spearman
value: 76.80552915288186
- type: euclidean_pearson
value: 82.46368529820518
- type: euclidean_spearman
value: 76.60338474719275
- type: manhattan_pearson
value: 82.4558617035968
- type: manhattan_spearman
value: 76.57936082895705
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.24116811084211
- type: cos_sim_spearman
value: 88.10998662068769
- type: euclidean_pearson
value: 87.04961732352689
- type: euclidean_spearman
value: 88.12543945864087
- type: manhattan_pearson
value: 86.9905224528854
- type: manhattan_spearman
value: 88.07827944705546
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.74847296555048
- type: cos_sim_spearman
value: 82.66200957916445
- type: euclidean_pearson
value: 84.48132256004965
- type: euclidean_spearman
value: 82.67915286000596
- type: manhattan_pearson
value: 84.44950477268334
- type: manhattan_spearman
value: 82.63327639173352
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.23056258027053
- type: cos_sim_spearman
value: 88.92791680286955
- type: euclidean_pearson
value: 88.13819235461933
- type: euclidean_spearman
value: 88.87294661361716
- type: manhattan_pearson
value: 88.14212133687899
- type: manhattan_spearman
value: 88.88551854529777
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.64179522732887
- type: cos_sim_spearman
value: 84.25028809903114
- type: euclidean_pearson
value: 83.40175015236979
- type: euclidean_spearman
value: 84.23369296429406
- type: manhattan_pearson
value: 83.43768174261321
- type: manhattan_spearman
value: 84.27855229214734
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.20378955494732
- type: cos_sim_spearman
value: 88.46863559173111
- type: euclidean_pearson
value: 88.8249295811663
- type: euclidean_spearman
value: 88.6312737724905
- type: manhattan_pearson
value: 88.87744466378827
- type: manhattan_spearman
value: 88.82908423767314
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.91342028796086
- type: cos_sim_spearman
value: 69.71495021867864
- type: euclidean_pearson
value: 70.65334330405646
- type: euclidean_spearman
value: 69.4321253472211
- type: manhattan_pearson
value: 70.59743494727465
- type: manhattan_spearman
value: 69.11695509297482
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.42451709766952
- type: cos_sim_spearman
value: 86.07166710670508
- type: euclidean_pearson
value: 86.12711421258899
- type: euclidean_spearman
value: 86.05232086925126
- type: manhattan_pearson
value: 86.15591089932126
- type: manhattan_spearman
value: 86.0890128623439
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.1976344717285
- type: mrr
value: 96.3703145075694
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.511
- type: map_at_10
value: 69.724
- type: map_at_100
value: 70.208
- type: map_at_1000
value: 70.22800000000001
- type: map_at_3
value: 66.986
- type: map_at_5
value: 68.529
- type: mrr_at_1
value: 62.333000000000006
- type: mrr_at_10
value: 70.55
- type: mrr_at_100
value: 70.985
- type: mrr_at_1000
value: 71.004
- type: mrr_at_3
value: 68.611
- type: mrr_at_5
value: 69.728
- type: ndcg_at_1
value: 62.333000000000006
- type: ndcg_at_10
value: 74.265
- type: ndcg_at_100
value: 76.361
- type: ndcg_at_1000
value: 76.82900000000001
- type: ndcg_at_3
value: 69.772
- type: ndcg_at_5
value: 71.94800000000001
- type: precision_at_1
value: 62.333000000000006
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.444000000000003
- type: precision_at_5
value: 18
- type: recall_at_1
value: 59.511
- type: recall_at_10
value: 87.156
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 75.2
- type: recall_at_5
value: 80.661
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81683168316832
- type: cos_sim_ap
value: 95.74716566563774
- type: cos_sim_f1
value: 90.64238745574103
- type: cos_sim_precision
value: 91.7093142272262
- type: cos_sim_recall
value: 89.60000000000001
- type: dot_accuracy
value: 99.69405940594059
- type: dot_ap
value: 91.09013507754594
- type: dot_f1
value: 84.54227113556779
- type: dot_precision
value: 84.58458458458459
- type: dot_recall
value: 84.5
- type: euclidean_accuracy
value: 99.81782178217821
- type: euclidean_ap
value: 95.6324301072609
- type: euclidean_f1
value: 90.58341862845445
- type: euclidean_precision
value: 92.76729559748428
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.81980198019802
- type: manhattan_ap
value: 95.68510494437183
- type: manhattan_f1
value: 90.58945191313342
- type: manhattan_precision
value: 93.79014989293361
- type: manhattan_recall
value: 87.6
- type: max_accuracy
value: 99.81980198019802
- type: max_ap
value: 95.74716566563774
- type: max_f1
value: 90.64238745574103
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 67.63761899427078
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.572473369697235
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.63000245208579
- type: mrr
value: 54.504193722943725
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.300791939416545
- type: cos_sim_spearman
value: 31.662904057924123
- type: dot_pearson
value: 26.21198530758316
- type: dot_spearman
value: 27.006921548904263
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.197
- type: map_at_10
value: 1.752
- type: map_at_100
value: 10.795
- type: map_at_1000
value: 27.18
- type: map_at_3
value: 0.5890000000000001
- type: map_at_5
value: 0.938
- type: mrr_at_1
value: 74
- type: mrr_at_10
value: 85.833
- type: mrr_at_100
value: 85.833
- type: mrr_at_1000
value: 85.833
- type: mrr_at_3
value: 85.333
- type: mrr_at_5
value: 85.833
- type: ndcg_at_1
value: 69
- type: ndcg_at_10
value: 70.22
- type: ndcg_at_100
value: 55.785
- type: ndcg_at_1000
value: 52.93600000000001
- type: ndcg_at_3
value: 72.084
- type: ndcg_at_5
value: 71.184
- type: precision_at_1
value: 74
- type: precision_at_10
value: 75.2
- type: precision_at_100
value: 57.3
- type: precision_at_1000
value: 23.302
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 75.6
- type: recall_at_1
value: 0.197
- type: recall_at_10
value: 2.019
- type: recall_at_100
value: 14.257
- type: recall_at_1000
value: 50.922
- type: recall_at_3
value: 0.642
- type: recall_at_5
value: 1.043
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.803
- type: map_at_10
value: 10.407
- type: map_at_100
value: 16.948
- type: map_at_1000
value: 18.424
- type: map_at_3
value: 5.405
- type: map_at_5
value: 6.908
- type: mrr_at_1
value: 36.735
- type: mrr_at_10
value: 50.221000000000004
- type: mrr_at_100
value: 51.388
- type: mrr_at_1000
value: 51.402
- type: mrr_at_3
value: 47.278999999999996
- type: mrr_at_5
value: 49.626
- type: ndcg_at_1
value: 34.694
- type: ndcg_at_10
value: 25.507
- type: ndcg_at_100
value: 38.296
- type: ndcg_at_1000
value: 49.492000000000004
- type: ndcg_at_3
value: 29.006999999999998
- type: ndcg_at_5
value: 25.979000000000003
- type: precision_at_1
value: 36.735
- type: precision_at_10
value: 22.041
- type: precision_at_100
value: 8.02
- type: precision_at_1000
value: 1.567
- type: precision_at_3
value: 28.571
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.803
- type: recall_at_10
value: 16.378
- type: recall_at_100
value: 50.489
- type: recall_at_1000
value: 85.013
- type: recall_at_3
value: 6.505
- type: recall_at_5
value: 9.243
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.55579999999999
- type: ap
value: 14.206982753316227
- type: f1
value: 54.372142814964285
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.57611771363893
- type: f1
value: 56.924172639063144
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.82304915719759
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.92716218632653
- type: cos_sim_ap
value: 73.73359122546046
- type: cos_sim_f1
value: 68.42559487116262
- type: cos_sim_precision
value: 64.22124508215691
- type: cos_sim_recall
value: 73.21899736147758
- type: dot_accuracy
value: 80.38981939560112
- type: dot_ap
value: 54.61060862444974
- type: dot_f1
value: 53.45710627400769
- type: dot_precision
value: 44.87638839125761
- type: dot_recall
value: 66.09498680738787
- type: euclidean_accuracy
value: 86.02849138701794
- type: euclidean_ap
value: 73.95673761922404
- type: euclidean_f1
value: 68.6783042394015
- type: euclidean_precision
value: 65.1063829787234
- type: euclidean_recall
value: 72.66490765171504
- type: manhattan_accuracy
value: 85.9808070572808
- type: manhattan_ap
value: 73.9050720058029
- type: manhattan_f1
value: 68.57560618983794
- type: manhattan_precision
value: 63.70839936608558
- type: manhattan_recall
value: 74.24802110817942
- type: max_accuracy
value: 86.02849138701794
- type: max_ap
value: 73.95673761922404
- type: max_f1
value: 68.6783042394015
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.72783017037295
- type: cos_sim_ap
value: 85.52705223340233
- type: cos_sim_f1
value: 77.91659078492079
- type: cos_sim_precision
value: 73.93378032764221
- type: cos_sim_recall
value: 82.35294117647058
- type: dot_accuracy
value: 85.41739434159972
- type: dot_ap
value: 77.17734818118443
- type: dot_f1
value: 71.63473589973144
- type: dot_precision
value: 66.96123719622415
- type: dot_recall
value: 77.00954727440714
- type: euclidean_accuracy
value: 88.68125897465751
- type: euclidean_ap
value: 85.47712213906692
- type: euclidean_f1
value: 77.81419950830664
- type: euclidean_precision
value: 75.37162649733006
- type: euclidean_recall
value: 80.42038805050817
- type: manhattan_accuracy
value: 88.67349710870494
- type: manhattan_ap
value: 85.46506475241955
- type: manhattan_f1
value: 77.87259084890393
- type: manhattan_precision
value: 74.54929577464789
- type: manhattan_recall
value: 81.50600554357868
- type: max_accuracy
value: 88.72783017037295
- type: max_ap
value: 85.52705223340233
- type: max_f1
value: 77.91659078492079
---
# gte-large
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large")
model = AutoModel.from_pretrained("thenlper/gte-large")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-large')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
thenlper/gte-base | thenlper | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"sentence-similarity",
"Sentence Transformers",
"en",
"arxiv:2308.03281",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-07-27T03:21:20 | 2024-11-16T08:09:34 | 424,333 | 113 | ---
language:
- en
license: mit
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-base
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.17910447761193
- type: ap
value: 36.827146398068926
- type: f1
value: 68.11292888046363
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.77345000000001
- type: ap
value: 88.33530426691347
- type: f1
value: 91.76549906404642
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.964
- type: f1
value: 48.22995586184998
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.147999999999996
- type: map_at_10
value: 48.253
- type: map_at_100
value: 49.038
- type: map_at_1000
value: 49.042
- type: map_at_3
value: 43.433
- type: map_at_5
value: 46.182
- type: mrr_at_1
value: 32.717
- type: mrr_at_10
value: 48.467
- type: mrr_at_100
value: 49.252
- type: mrr_at_1000
value: 49.254999999999995
- type: mrr_at_3
value: 43.599
- type: mrr_at_5
value: 46.408
- type: ndcg_at_1
value: 32.147999999999996
- type: ndcg_at_10
value: 57.12199999999999
- type: ndcg_at_100
value: 60.316
- type: ndcg_at_1000
value: 60.402
- type: ndcg_at_3
value: 47.178
- type: ndcg_at_5
value: 52.146
- type: precision_at_1
value: 32.147999999999996
- type: precision_at_10
value: 8.542
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.346
- type: precision_at_5
value: 14.026
- type: recall_at_1
value: 32.147999999999996
- type: recall_at_10
value: 85.42
- type: recall_at_100
value: 99.004
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 58.037000000000006
- type: recall_at_5
value: 70.128
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.59706013699614
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.01463593002057
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.80250355752458
- type: mrr
value: 74.79455216989844
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.87448576082345
- type: cos_sim_spearman
value: 87.64235843637468
- type: euclidean_pearson
value: 88.4901825511062
- type: euclidean_spearman
value: 87.74537283182033
- type: manhattan_pearson
value: 88.39040638362911
- type: manhattan_spearman
value: 87.62669542888003
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.06818181818183
- type: f1
value: 85.02524460098233
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.20471092679967
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.58967592147641
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.411
- type: map_at_10
value: 45.162
- type: map_at_100
value: 46.717
- type: map_at_1000
value: 46.836
- type: map_at_3
value: 41.428
- type: map_at_5
value: 43.54
- type: mrr_at_1
value: 39.914
- type: mrr_at_10
value: 51.534
- type: mrr_at_100
value: 52.185
- type: mrr_at_1000
value: 52.22
- type: mrr_at_3
value: 49.046
- type: mrr_at_5
value: 50.548
- type: ndcg_at_1
value: 39.914
- type: ndcg_at_10
value: 52.235
- type: ndcg_at_100
value: 57.4
- type: ndcg_at_1000
value: 58.982
- type: ndcg_at_3
value: 47.332
- type: ndcg_at_5
value: 49.62
- type: precision_at_1
value: 39.914
- type: precision_at_10
value: 10.258000000000001
- type: precision_at_100
value: 1.6219999999999999
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 23.462
- type: precision_at_5
value: 16.71
- type: recall_at_1
value: 32.411
- type: recall_at_10
value: 65.408
- type: recall_at_100
value: 87.248
- type: recall_at_1000
value: 96.951
- type: recall_at_3
value: 50.349999999999994
- type: recall_at_5
value: 57.431
- type: map_at_1
value: 31.911
- type: map_at_10
value: 42.608000000000004
- type: map_at_100
value: 43.948
- type: map_at_1000
value: 44.089
- type: map_at_3
value: 39.652
- type: map_at_5
value: 41.236
- type: mrr_at_1
value: 40.064
- type: mrr_at_10
value: 48.916
- type: mrr_at_100
value: 49.539
- type: mrr_at_1000
value: 49.583
- type: mrr_at_3
value: 46.741
- type: mrr_at_5
value: 48.037
- type: ndcg_at_1
value: 40.064
- type: ndcg_at_10
value: 48.442
- type: ndcg_at_100
value: 52.798
- type: ndcg_at_1000
value: 54.871
- type: ndcg_at_3
value: 44.528
- type: ndcg_at_5
value: 46.211
- type: precision_at_1
value: 40.064
- type: precision_at_10
value: 9.178
- type: precision_at_100
value: 1.452
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 21.614
- type: precision_at_5
value: 15.185
- type: recall_at_1
value: 31.911
- type: recall_at_10
value: 58.155
- type: recall_at_100
value: 76.46300000000001
- type: recall_at_1000
value: 89.622
- type: recall_at_3
value: 46.195
- type: recall_at_5
value: 51.288999999999994
- type: map_at_1
value: 40.597
- type: map_at_10
value: 54.290000000000006
- type: map_at_100
value: 55.340999999999994
- type: map_at_1000
value: 55.388999999999996
- type: map_at_3
value: 50.931000000000004
- type: map_at_5
value: 52.839999999999996
- type: mrr_at_1
value: 46.646
- type: mrr_at_10
value: 57.524
- type: mrr_at_100
value: 58.225
- type: mrr_at_1000
value: 58.245999999999995
- type: mrr_at_3
value: 55.235
- type: mrr_at_5
value: 56.589
- type: ndcg_at_1
value: 46.646
- type: ndcg_at_10
value: 60.324999999999996
- type: ndcg_at_100
value: 64.30900000000001
- type: ndcg_at_1000
value: 65.19
- type: ndcg_at_3
value: 54.983000000000004
- type: ndcg_at_5
value: 57.621
- type: precision_at_1
value: 46.646
- type: precision_at_10
value: 9.774
- type: precision_at_100
value: 1.265
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 24.911
- type: precision_at_5
value: 16.977999999999998
- type: recall_at_1
value: 40.597
- type: recall_at_10
value: 74.773
- type: recall_at_100
value: 91.61200000000001
- type: recall_at_1000
value: 97.726
- type: recall_at_3
value: 60.458
- type: recall_at_5
value: 66.956
- type: map_at_1
value: 27.122
- type: map_at_10
value: 36.711
- type: map_at_100
value: 37.775
- type: map_at_1000
value: 37.842999999999996
- type: map_at_3
value: 33.693
- type: map_at_5
value: 35.607
- type: mrr_at_1
value: 29.153000000000002
- type: mrr_at_10
value: 38.873999999999995
- type: mrr_at_100
value: 39.739000000000004
- type: mrr_at_1000
value: 39.794000000000004
- type: mrr_at_3
value: 36.102000000000004
- type: mrr_at_5
value: 37.876
- type: ndcg_at_1
value: 29.153000000000002
- type: ndcg_at_10
value: 42.048
- type: ndcg_at_100
value: 47.144999999999996
- type: ndcg_at_1000
value: 48.901
- type: ndcg_at_3
value: 36.402
- type: ndcg_at_5
value: 39.562999999999995
- type: precision_at_1
value: 29.153000000000002
- type: precision_at_10
value: 6.4750000000000005
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 15.479999999999999
- type: precision_at_5
value: 11.028
- type: recall_at_1
value: 27.122
- type: recall_at_10
value: 56.279999999999994
- type: recall_at_100
value: 79.597
- type: recall_at_1000
value: 92.804
- type: recall_at_3
value: 41.437000000000005
- type: recall_at_5
value: 49.019
- type: map_at_1
value: 17.757
- type: map_at_10
value: 26.739
- type: map_at_100
value: 28.015
- type: map_at_1000
value: 28.127999999999997
- type: map_at_3
value: 23.986
- type: map_at_5
value: 25.514
- type: mrr_at_1
value: 22.015
- type: mrr_at_10
value: 31.325999999999997
- type: mrr_at_100
value: 32.368
- type: mrr_at_1000
value: 32.426
- type: mrr_at_3
value: 28.897000000000002
- type: mrr_at_5
value: 30.147000000000002
- type: ndcg_at_1
value: 22.015
- type: ndcg_at_10
value: 32.225
- type: ndcg_at_100
value: 38.405
- type: ndcg_at_1000
value: 40.932
- type: ndcg_at_3
value: 27.403
- type: ndcg_at_5
value: 29.587000000000003
- type: precision_at_1
value: 22.015
- type: precision_at_10
value: 5.9830000000000005
- type: precision_at_100
value: 1.051
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 13.391
- type: precision_at_5
value: 9.602
- type: recall_at_1
value: 17.757
- type: recall_at_10
value: 44.467
- type: recall_at_100
value: 71.53699999999999
- type: recall_at_1000
value: 89.281
- type: recall_at_3
value: 31.095
- type: recall_at_5
value: 36.818
- type: map_at_1
value: 30.354
- type: map_at_10
value: 42.134
- type: map_at_100
value: 43.429
- type: map_at_1000
value: 43.532
- type: map_at_3
value: 38.491
- type: map_at_5
value: 40.736
- type: mrr_at_1
value: 37.247
- type: mrr_at_10
value: 47.775
- type: mrr_at_100
value: 48.522999999999996
- type: mrr_at_1000
value: 48.567
- type: mrr_at_3
value: 45.059
- type: mrr_at_5
value: 46.811
- type: ndcg_at_1
value: 37.247
- type: ndcg_at_10
value: 48.609
- type: ndcg_at_100
value: 53.782
- type: ndcg_at_1000
value: 55.666000000000004
- type: ndcg_at_3
value: 42.866
- type: ndcg_at_5
value: 46.001
- type: precision_at_1
value: 37.247
- type: precision_at_10
value: 8.892999999999999
- type: precision_at_100
value: 1.341
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 20.5
- type: precision_at_5
value: 14.976
- type: recall_at_1
value: 30.354
- type: recall_at_10
value: 62.273
- type: recall_at_100
value: 83.65599999999999
- type: recall_at_1000
value: 95.82000000000001
- type: recall_at_3
value: 46.464
- type: recall_at_5
value: 54.225
- type: map_at_1
value: 26.949
- type: map_at_10
value: 37.230000000000004
- type: map_at_100
value: 38.644
- type: map_at_1000
value: 38.751999999999995
- type: map_at_3
value: 33.816
- type: map_at_5
value: 35.817
- type: mrr_at_1
value: 33.446999999999996
- type: mrr_at_10
value: 42.970000000000006
- type: mrr_at_100
value: 43.873
- type: mrr_at_1000
value: 43.922
- type: mrr_at_3
value: 40.467999999999996
- type: mrr_at_5
value: 41.861
- type: ndcg_at_1
value: 33.446999999999996
- type: ndcg_at_10
value: 43.403000000000006
- type: ndcg_at_100
value: 49.247
- type: ndcg_at_1000
value: 51.361999999999995
- type: ndcg_at_3
value: 38.155
- type: ndcg_at_5
value: 40.643
- type: precision_at_1
value: 33.446999999999996
- type: precision_at_10
value: 8.128
- type: precision_at_100
value: 1.274
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.493000000000002
- type: precision_at_5
value: 13.333
- type: recall_at_1
value: 26.949
- type: recall_at_10
value: 56.006
- type: recall_at_100
value: 80.99199999999999
- type: recall_at_1000
value: 95.074
- type: recall_at_3
value: 40.809
- type: recall_at_5
value: 47.57
- type: map_at_1
value: 27.243583333333333
- type: map_at_10
value: 37.193250000000006
- type: map_at_100
value: 38.44833333333334
- type: map_at_1000
value: 38.56083333333333
- type: map_at_3
value: 34.06633333333333
- type: map_at_5
value: 35.87858333333334
- type: mrr_at_1
value: 32.291583333333335
- type: mrr_at_10
value: 41.482749999999996
- type: mrr_at_100
value: 42.33583333333333
- type: mrr_at_1000
value: 42.38683333333333
- type: mrr_at_3
value: 38.952999999999996
- type: mrr_at_5
value: 40.45333333333333
- type: ndcg_at_1
value: 32.291583333333335
- type: ndcg_at_10
value: 42.90533333333334
- type: ndcg_at_100
value: 48.138666666666666
- type: ndcg_at_1000
value: 50.229083333333335
- type: ndcg_at_3
value: 37.76133333333334
- type: ndcg_at_5
value: 40.31033333333334
- type: precision_at_1
value: 32.291583333333335
- type: precision_at_10
value: 7.585583333333333
- type: precision_at_100
value: 1.2045000000000001
- type: precision_at_1000
value: 0.15733333333333335
- type: precision_at_3
value: 17.485416666666666
- type: precision_at_5
value: 12.5145
- type: recall_at_1
value: 27.243583333333333
- type: recall_at_10
value: 55.45108333333334
- type: recall_at_100
value: 78.25858333333335
- type: recall_at_1000
value: 92.61716666666665
- type: recall_at_3
value: 41.130583333333334
- type: recall_at_5
value: 47.73133333333334
- type: map_at_1
value: 26.325
- type: map_at_10
value: 32.795
- type: map_at_100
value: 33.96
- type: map_at_1000
value: 34.054
- type: map_at_3
value: 30.64
- type: map_at_5
value: 31.771
- type: mrr_at_1
value: 29.908
- type: mrr_at_10
value: 35.83
- type: mrr_at_100
value: 36.868
- type: mrr_at_1000
value: 36.928
- type: mrr_at_3
value: 33.896
- type: mrr_at_5
value: 34.893
- type: ndcg_at_1
value: 29.908
- type: ndcg_at_10
value: 36.746
- type: ndcg_at_100
value: 42.225
- type: ndcg_at_1000
value: 44.523
- type: ndcg_at_3
value: 32.82
- type: ndcg_at_5
value: 34.583000000000006
- type: precision_at_1
value: 29.908
- type: precision_at_10
value: 5.6129999999999995
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 13.753000000000002
- type: precision_at_5
value: 9.417
- type: recall_at_1
value: 26.325
- type: recall_at_10
value: 45.975
- type: recall_at_100
value: 70.393
- type: recall_at_1000
value: 87.217
- type: recall_at_3
value: 35.195
- type: recall_at_5
value: 39.69
- type: map_at_1
value: 17.828
- type: map_at_10
value: 25.759
- type: map_at_100
value: 26.961000000000002
- type: map_at_1000
value: 27.094
- type: map_at_3
value: 23.166999999999998
- type: map_at_5
value: 24.610000000000003
- type: mrr_at_1
value: 21.61
- type: mrr_at_10
value: 29.605999999999998
- type: mrr_at_100
value: 30.586000000000002
- type: mrr_at_1000
value: 30.664
- type: mrr_at_3
value: 27.214
- type: mrr_at_5
value: 28.571
- type: ndcg_at_1
value: 21.61
- type: ndcg_at_10
value: 30.740000000000002
- type: ndcg_at_100
value: 36.332
- type: ndcg_at_1000
value: 39.296
- type: ndcg_at_3
value: 26.11
- type: ndcg_at_5
value: 28.297
- type: precision_at_1
value: 21.61
- type: precision_at_10
value: 5.643
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 12.4
- type: precision_at_5
value: 9.119
- type: recall_at_1
value: 17.828
- type: recall_at_10
value: 41.876000000000005
- type: recall_at_100
value: 66.648
- type: recall_at_1000
value: 87.763
- type: recall_at_3
value: 28.957
- type: recall_at_5
value: 34.494
- type: map_at_1
value: 27.921000000000003
- type: map_at_10
value: 37.156
- type: map_at_100
value: 38.399
- type: map_at_1000
value: 38.498
- type: map_at_3
value: 34.134
- type: map_at_5
value: 35.936
- type: mrr_at_1
value: 32.649
- type: mrr_at_10
value: 41.19
- type: mrr_at_100
value: 42.102000000000004
- type: mrr_at_1000
value: 42.157
- type: mrr_at_3
value: 38.464
- type: mrr_at_5
value: 40.148
- type: ndcg_at_1
value: 32.649
- type: ndcg_at_10
value: 42.679
- type: ndcg_at_100
value: 48.27
- type: ndcg_at_1000
value: 50.312
- type: ndcg_at_3
value: 37.269000000000005
- type: ndcg_at_5
value: 40.055
- type: precision_at_1
value: 32.649
- type: precision_at_10
value: 7.155
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 16.791
- type: precision_at_5
value: 12.015
- type: recall_at_1
value: 27.921000000000003
- type: recall_at_10
value: 55.357
- type: recall_at_100
value: 79.476
- type: recall_at_1000
value: 93.314
- type: recall_at_3
value: 40.891
- type: recall_at_5
value: 47.851
- type: map_at_1
value: 25.524
- type: map_at_10
value: 35.135
- type: map_at_100
value: 36.665
- type: map_at_1000
value: 36.886
- type: map_at_3
value: 31.367
- type: map_at_5
value: 33.724
- type: mrr_at_1
value: 30.631999999999998
- type: mrr_at_10
value: 39.616
- type: mrr_at_100
value: 40.54
- type: mrr_at_1000
value: 40.585
- type: mrr_at_3
value: 36.462
- type: mrr_at_5
value: 38.507999999999996
- type: ndcg_at_1
value: 30.631999999999998
- type: ndcg_at_10
value: 41.61
- type: ndcg_at_100
value: 47.249
- type: ndcg_at_1000
value: 49.662
- type: ndcg_at_3
value: 35.421
- type: ndcg_at_5
value: 38.811
- type: precision_at_1
value: 30.631999999999998
- type: precision_at_10
value: 8.123
- type: precision_at_100
value: 1.5810000000000002
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 12.568999999999999
- type: recall_at_1
value: 25.524
- type: recall_at_10
value: 54.994
- type: recall_at_100
value: 80.03099999999999
- type: recall_at_1000
value: 95.25099999999999
- type: recall_at_3
value: 37.563
- type: recall_at_5
value: 46.428999999999995
- type: map_at_1
value: 22.224
- type: map_at_10
value: 30.599999999999998
- type: map_at_100
value: 31.526
- type: map_at_1000
value: 31.629
- type: map_at_3
value: 27.491
- type: map_at_5
value: 29.212
- type: mrr_at_1
value: 24.214
- type: mrr_at_10
value: 32.632
- type: mrr_at_100
value: 33.482
- type: mrr_at_1000
value: 33.550000000000004
- type: mrr_at_3
value: 29.852
- type: mrr_at_5
value: 31.451
- type: ndcg_at_1
value: 24.214
- type: ndcg_at_10
value: 35.802
- type: ndcg_at_100
value: 40.502
- type: ndcg_at_1000
value: 43.052
- type: ndcg_at_3
value: 29.847
- type: ndcg_at_5
value: 32.732
- type: precision_at_1
value: 24.214
- type: precision_at_10
value: 5.804
- type: precision_at_100
value: 0.885
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.692999999999998
- type: precision_at_5
value: 9.242
- type: recall_at_1
value: 22.224
- type: recall_at_10
value: 49.849
- type: recall_at_100
value: 71.45
- type: recall_at_1000
value: 90.583
- type: recall_at_3
value: 34.153
- type: recall_at_5
value: 41.004000000000005
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.386999999999999
- type: map_at_10
value: 20.182
- type: map_at_100
value: 21.86
- type: map_at_1000
value: 22.054000000000002
- type: map_at_3
value: 17.165
- type: map_at_5
value: 18.643
- type: mrr_at_1
value: 26.906000000000002
- type: mrr_at_10
value: 37.907999999999994
- type: mrr_at_100
value: 38.868
- type: mrr_at_1000
value: 38.913
- type: mrr_at_3
value: 34.853
- type: mrr_at_5
value: 36.567
- type: ndcg_at_1
value: 26.906000000000002
- type: ndcg_at_10
value: 28.103
- type: ndcg_at_100
value: 35.073
- type: ndcg_at_1000
value: 38.653
- type: ndcg_at_3
value: 23.345
- type: ndcg_at_5
value: 24.828
- type: precision_at_1
value: 26.906000000000002
- type: precision_at_10
value: 8.547
- type: precision_at_100
value: 1.617
- type: precision_at_1000
value: 0.22799999999999998
- type: precision_at_3
value: 17.025000000000002
- type: precision_at_5
value: 12.834000000000001
- type: recall_at_1
value: 12.386999999999999
- type: recall_at_10
value: 33.306999999999995
- type: recall_at_100
value: 57.516
- type: recall_at_1000
value: 77.74799999999999
- type: recall_at_3
value: 21.433
- type: recall_at_5
value: 25.915
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.322
- type: map_at_10
value: 20.469
- type: map_at_100
value: 28.638
- type: map_at_1000
value: 30.433
- type: map_at_3
value: 14.802000000000001
- type: map_at_5
value: 17.297
- type: mrr_at_1
value: 68.75
- type: mrr_at_10
value: 76.29599999999999
- type: mrr_at_100
value: 76.62400000000001
- type: mrr_at_1000
value: 76.633
- type: mrr_at_3
value: 75.083
- type: mrr_at_5
value: 75.771
- type: ndcg_at_1
value: 54.87499999999999
- type: ndcg_at_10
value: 41.185
- type: ndcg_at_100
value: 46.400000000000006
- type: ndcg_at_1000
value: 54.223
- type: ndcg_at_3
value: 45.489000000000004
- type: ndcg_at_5
value: 43.161
- type: precision_at_1
value: 68.75
- type: precision_at_10
value: 32.300000000000004
- type: precision_at_100
value: 10.607999999999999
- type: precision_at_1000
value: 2.237
- type: precision_at_3
value: 49.083
- type: precision_at_5
value: 41.6
- type: recall_at_1
value: 9.322
- type: recall_at_10
value: 25.696
- type: recall_at_100
value: 52.898
- type: recall_at_1000
value: 77.281
- type: recall_at_3
value: 15.943
- type: recall_at_5
value: 19.836000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.650000000000006
- type: f1
value: 43.528467245539396
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.56
- type: map_at_10
value: 76.767
- type: map_at_100
value: 77.054
- type: map_at_1000
value: 77.068
- type: map_at_3
value: 75.29299999999999
- type: map_at_5
value: 76.24
- type: mrr_at_1
value: 71.842
- type: mrr_at_10
value: 81.459
- type: mrr_at_100
value: 81.58800000000001
- type: mrr_at_1000
value: 81.59100000000001
- type: mrr_at_3
value: 80.188
- type: mrr_at_5
value: 81.038
- type: ndcg_at_1
value: 71.842
- type: ndcg_at_10
value: 81.51899999999999
- type: ndcg_at_100
value: 82.544
- type: ndcg_at_1000
value: 82.829
- type: ndcg_at_3
value: 78.92
- type: ndcg_at_5
value: 80.406
- type: precision_at_1
value: 71.842
- type: precision_at_10
value: 10.066
- type: precision_at_100
value: 1.076
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 30.703000000000003
- type: precision_at_5
value: 19.301
- type: recall_at_1
value: 66.56
- type: recall_at_10
value: 91.55
- type: recall_at_100
value: 95.67099999999999
- type: recall_at_1000
value: 97.539
- type: recall_at_3
value: 84.46900000000001
- type: recall_at_5
value: 88.201
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.087
- type: map_at_10
value: 32.830999999999996
- type: map_at_100
value: 34.814
- type: map_at_1000
value: 34.999
- type: map_at_3
value: 28.198
- type: map_at_5
value: 30.779
- type: mrr_at_1
value: 38.889
- type: mrr_at_10
value: 48.415
- type: mrr_at_100
value: 49.187
- type: mrr_at_1000
value: 49.226
- type: mrr_at_3
value: 45.705
- type: mrr_at_5
value: 47.225
- type: ndcg_at_1
value: 38.889
- type: ndcg_at_10
value: 40.758
- type: ndcg_at_100
value: 47.671
- type: ndcg_at_1000
value: 50.744
- type: ndcg_at_3
value: 36.296
- type: ndcg_at_5
value: 37.852999999999994
- type: precision_at_1
value: 38.889
- type: precision_at_10
value: 11.466
- type: precision_at_100
value: 1.8499999999999999
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 24.126
- type: precision_at_5
value: 18.21
- type: recall_at_1
value: 20.087
- type: recall_at_10
value: 48.042
- type: recall_at_100
value: 73.493
- type: recall_at_1000
value: 91.851
- type: recall_at_3
value: 32.694
- type: recall_at_5
value: 39.099000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.096000000000004
- type: map_at_10
value: 56.99999999999999
- type: map_at_100
value: 57.914
- type: map_at_1000
value: 57.984
- type: map_at_3
value: 53.900999999999996
- type: map_at_5
value: 55.827000000000005
- type: mrr_at_1
value: 76.19200000000001
- type: mrr_at_10
value: 81.955
- type: mrr_at_100
value: 82.164
- type: mrr_at_1000
value: 82.173
- type: mrr_at_3
value: 80.963
- type: mrr_at_5
value: 81.574
- type: ndcg_at_1
value: 76.19200000000001
- type: ndcg_at_10
value: 65.75
- type: ndcg_at_100
value: 68.949
- type: ndcg_at_1000
value: 70.342
- type: ndcg_at_3
value: 61.29
- type: ndcg_at_5
value: 63.747
- type: precision_at_1
value: 76.19200000000001
- type: precision_at_10
value: 13.571
- type: precision_at_100
value: 1.6070000000000002
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.663
- type: precision_at_5
value: 25.136999999999997
- type: recall_at_1
value: 38.096000000000004
- type: recall_at_10
value: 67.853
- type: recall_at_100
value: 80.365
- type: recall_at_1000
value: 89.629
- type: recall_at_3
value: 57.995
- type: recall_at_5
value: 62.843
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 85.95200000000001
- type: ap
value: 80.73847277002109
- type: f1
value: 85.92406135678594
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.916999999999998
- type: map_at_10
value: 33.23
- type: map_at_100
value: 34.427
- type: map_at_1000
value: 34.477000000000004
- type: map_at_3
value: 29.292
- type: map_at_5
value: 31.6
- type: mrr_at_1
value: 21.547
- type: mrr_at_10
value: 33.839999999999996
- type: mrr_at_100
value: 34.979
- type: mrr_at_1000
value: 35.022999999999996
- type: mrr_at_3
value: 29.988
- type: mrr_at_5
value: 32.259
- type: ndcg_at_1
value: 21.519
- type: ndcg_at_10
value: 40.209
- type: ndcg_at_100
value: 45.954
- type: ndcg_at_1000
value: 47.187
- type: ndcg_at_3
value: 32.227
- type: ndcg_at_5
value: 36.347
- type: precision_at_1
value: 21.519
- type: precision_at_10
value: 6.447
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.877999999999998
- type: precision_at_5
value: 10.404
- type: recall_at_1
value: 20.916999999999998
- type: recall_at_10
value: 61.7
- type: recall_at_100
value: 88.202
- type: recall_at_1000
value: 97.588
- type: recall_at_3
value: 40.044999999999995
- type: recall_at_5
value: 49.964999999999996
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.02781577747379
- type: f1
value: 92.83653922768306
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.04286365709075
- type: f1
value: 53.43867658525793
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.47276395427035
- type: f1
value: 69.77017399597342
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.3819771351715
- type: f1
value: 76.8484533435409
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.16515993299593
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.77145323314774
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.53637706586391
- type: mrr
value: 33.7312926288863
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.063999999999999
- type: map_at_10
value: 15.046999999999999
- type: map_at_100
value: 19.116
- type: map_at_1000
value: 20.702
- type: map_at_3
value: 10.932
- type: map_at_5
value: 12.751999999999999
- type: mrr_at_1
value: 50.464
- type: mrr_at_10
value: 58.189
- type: mrr_at_100
value: 58.733999999999995
- type: mrr_at_1000
value: 58.769000000000005
- type: mrr_at_3
value: 56.24400000000001
- type: mrr_at_5
value: 57.68299999999999
- type: ndcg_at_1
value: 48.142
- type: ndcg_at_10
value: 37.897
- type: ndcg_at_100
value: 35.264
- type: ndcg_at_1000
value: 44.033
- type: ndcg_at_3
value: 42.967
- type: ndcg_at_5
value: 40.815
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 28.235
- type: precision_at_100
value: 8.994
- type: precision_at_1000
value: 2.218
- type: precision_at_3
value: 40.041
- type: precision_at_5
value: 35.046
- type: recall_at_1
value: 7.063999999999999
- type: recall_at_10
value: 18.598
- type: recall_at_100
value: 35.577999999999996
- type: recall_at_1000
value: 67.43
- type: recall_at_3
value: 11.562999999999999
- type: recall_at_5
value: 14.771
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.046
- type: map_at_10
value: 44.808
- type: map_at_100
value: 45.898
- type: map_at_1000
value: 45.927
- type: map_at_3
value: 40.19
- type: map_at_5
value: 42.897
- type: mrr_at_1
value: 32.706
- type: mrr_at_10
value: 47.275
- type: mrr_at_100
value: 48.075
- type: mrr_at_1000
value: 48.095
- type: mrr_at_3
value: 43.463
- type: mrr_at_5
value: 45.741
- type: ndcg_at_1
value: 32.706
- type: ndcg_at_10
value: 52.835
- type: ndcg_at_100
value: 57.345
- type: ndcg_at_1000
value: 57.985
- type: ndcg_at_3
value: 44.171
- type: ndcg_at_5
value: 48.661
- type: precision_at_1
value: 32.706
- type: precision_at_10
value: 8.895999999999999
- type: precision_at_100
value: 1.143
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 20.238999999999997
- type: precision_at_5
value: 14.728
- type: recall_at_1
value: 29.046
- type: recall_at_10
value: 74.831
- type: recall_at_100
value: 94.192
- type: recall_at_1000
value: 98.897
- type: recall_at_3
value: 52.37500000000001
- type: recall_at_5
value: 62.732
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.38799999999999
- type: map_at_10
value: 84.315
- type: map_at_100
value: 84.955
- type: map_at_1000
value: 84.971
- type: map_at_3
value: 81.33399999999999
- type: map_at_5
value: 83.21300000000001
- type: mrr_at_1
value: 81.03
- type: mrr_at_10
value: 87.395
- type: mrr_at_100
value: 87.488
- type: mrr_at_1000
value: 87.48899999999999
- type: mrr_at_3
value: 86.41499999999999
- type: mrr_at_5
value: 87.074
- type: ndcg_at_1
value: 81.04
- type: ndcg_at_10
value: 88.151
- type: ndcg_at_100
value: 89.38199999999999
- type: ndcg_at_1000
value: 89.479
- type: ndcg_at_3
value: 85.24000000000001
- type: ndcg_at_5
value: 86.856
- type: precision_at_1
value: 81.04
- type: precision_at_10
value: 13.372
- type: precision_at_100
value: 1.526
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.217
- type: precision_at_5
value: 24.502
- type: recall_at_1
value: 70.38799999999999
- type: recall_at_10
value: 95.452
- type: recall_at_100
value: 99.59700000000001
- type: recall_at_1000
value: 99.988
- type: recall_at_3
value: 87.11
- type: recall_at_5
value: 91.662
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.334991029213235
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.586500854616666
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.153
- type: map_at_10
value: 14.277000000000001
- type: map_at_100
value: 16.922
- type: map_at_1000
value: 17.302999999999997
- type: map_at_3
value: 9.961
- type: map_at_5
value: 12.257
- type: mrr_at_1
value: 25.4
- type: mrr_at_10
value: 37.458000000000006
- type: mrr_at_100
value: 38.681
- type: mrr_at_1000
value: 38.722
- type: mrr_at_3
value: 34.1
- type: mrr_at_5
value: 36.17
- type: ndcg_at_1
value: 25.4
- type: ndcg_at_10
value: 23.132
- type: ndcg_at_100
value: 32.908
- type: ndcg_at_1000
value: 38.754
- type: ndcg_at_3
value: 21.82
- type: ndcg_at_5
value: 19.353
- type: precision_at_1
value: 25.4
- type: precision_at_10
value: 12.1
- type: precision_at_100
value: 2.628
- type: precision_at_1000
value: 0.402
- type: precision_at_3
value: 20.732999999999997
- type: precision_at_5
value: 17.34
- type: recall_at_1
value: 5.153
- type: recall_at_10
value: 24.54
- type: recall_at_100
value: 53.293
- type: recall_at_1000
value: 81.57
- type: recall_at_3
value: 12.613
- type: recall_at_5
value: 17.577
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.86284404925333
- type: cos_sim_spearman
value: 78.85870555294795
- type: euclidean_pearson
value: 82.20105295276093
- type: euclidean_spearman
value: 78.92125617009592
- type: manhattan_pearson
value: 82.15840025289069
- type: manhattan_spearman
value: 78.85955732900803
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.98747423389027
- type: cos_sim_spearman
value: 75.71298531799367
- type: euclidean_pearson
value: 81.59709559192291
- type: euclidean_spearman
value: 75.40622749225653
- type: manhattan_pearson
value: 81.55553547608804
- type: manhattan_spearman
value: 75.39380235424899
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.76861330695503
- type: cos_sim_spearman
value: 85.72991921531624
- type: euclidean_pearson
value: 84.84504307397536
- type: euclidean_spearman
value: 86.02679162824732
- type: manhattan_pearson
value: 84.79969439220142
- type: manhattan_spearman
value: 85.99238837291625
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.31929747511796
- type: cos_sim_spearman
value: 81.50806522502528
- type: euclidean_pearson
value: 82.93936686512777
- type: euclidean_spearman
value: 81.54403447993224
- type: manhattan_pearson
value: 82.89696981900828
- type: manhattan_spearman
value: 81.52817825470865
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.14413295332908
- type: cos_sim_spearman
value: 88.81032027008195
- type: euclidean_pearson
value: 88.19205563407645
- type: euclidean_spearman
value: 88.89738339479216
- type: manhattan_pearson
value: 88.11075942004189
- type: manhattan_spearman
value: 88.8297061675564
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.15980075557017
- type: cos_sim_spearman
value: 83.81896308594801
- type: euclidean_pearson
value: 83.11195254311338
- type: euclidean_spearman
value: 84.10479481755407
- type: manhattan_pearson
value: 83.13915225100556
- type: manhattan_spearman
value: 84.09895591027859
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.93669480147919
- type: cos_sim_spearman
value: 87.89861394614361
- type: euclidean_pearson
value: 88.37316413202339
- type: euclidean_spearman
value: 88.18033817842569
- type: manhattan_pearson
value: 88.39427578879469
- type: manhattan_spearman
value: 88.09185009236847
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 66.62215083348255
- type: cos_sim_spearman
value: 67.33243665716736
- type: euclidean_pearson
value: 67.60871701996284
- type: euclidean_spearman
value: 66.75929225238659
- type: manhattan_pearson
value: 67.63907838970992
- type: manhattan_spearman
value: 66.79313656754846
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.65549191934764
- type: cos_sim_spearman
value: 85.73266847750143
- type: euclidean_pearson
value: 85.75609932254318
- type: euclidean_spearman
value: 85.9452287759371
- type: manhattan_pearson
value: 85.69717413063573
- type: manhattan_spearman
value: 85.86546318377046
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.08164129085783
- type: mrr
value: 96.2877273416489
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 62.09400000000001
- type: map_at_10
value: 71.712
- type: map_at_100
value: 72.128
- type: map_at_1000
value: 72.14399999999999
- type: map_at_3
value: 68.93
- type: map_at_5
value: 70.694
- type: mrr_at_1
value: 65.0
- type: mrr_at_10
value: 72.572
- type: mrr_at_100
value: 72.842
- type: mrr_at_1000
value: 72.856
- type: mrr_at_3
value: 70.44399999999999
- type: mrr_at_5
value: 71.744
- type: ndcg_at_1
value: 65.0
- type: ndcg_at_10
value: 76.178
- type: ndcg_at_100
value: 77.887
- type: ndcg_at_1000
value: 78.227
- type: ndcg_at_3
value: 71.367
- type: ndcg_at_5
value: 73.938
- type: precision_at_1
value: 65.0
- type: precision_at_10
value: 10.033
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 18.4
- type: recall_at_1
value: 62.09400000000001
- type: recall_at_10
value: 89.022
- type: recall_at_100
value: 96.833
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 75.922
- type: recall_at_5
value: 82.428
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82178217821782
- type: cos_sim_ap
value: 95.71282508220798
- type: cos_sim_f1
value: 90.73120494335737
- type: cos_sim_precision
value: 93.52441613588111
- type: cos_sim_recall
value: 88.1
- type: dot_accuracy
value: 99.73960396039604
- type: dot_ap
value: 92.98534606529098
- type: dot_f1
value: 86.83024536805209
- type: dot_precision
value: 86.96088264794383
- type: dot_recall
value: 86.7
- type: euclidean_accuracy
value: 99.82475247524752
- type: euclidean_ap
value: 95.72927039014849
- type: euclidean_f1
value: 90.89974293059126
- type: euclidean_precision
value: 93.54497354497354
- type: euclidean_recall
value: 88.4
- type: manhattan_accuracy
value: 99.82574257425742
- type: manhattan_ap
value: 95.72142177390405
- type: manhattan_f1
value: 91.00152516522625
- type: manhattan_precision
value: 92.55429162357808
- type: manhattan_recall
value: 89.5
- type: max_accuracy
value: 99.82574257425742
- type: max_ap
value: 95.72927039014849
- type: max_f1
value: 91.00152516522625
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.63957663468679
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.003307257923964
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.005825525863905
- type: mrr
value: 53.854683919022165
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.503611569974098
- type: cos_sim_spearman
value: 31.17155564248449
- type: dot_pearson
value: 26.740428413981306
- type: dot_spearman
value: 26.55727635469746
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.23600000000000002
- type: map_at_10
value: 1.7670000000000001
- type: map_at_100
value: 10.208
- type: map_at_1000
value: 25.997999999999998
- type: map_at_3
value: 0.605
- type: map_at_5
value: 0.9560000000000001
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 90.167
- type: mrr_at_100
value: 90.167
- type: mrr_at_1000
value: 90.167
- type: mrr_at_3
value: 89.667
- type: mrr_at_5
value: 90.167
- type: ndcg_at_1
value: 77.0
- type: ndcg_at_10
value: 68.783
- type: ndcg_at_100
value: 54.196
- type: ndcg_at_1000
value: 52.077
- type: ndcg_at_3
value: 71.642
- type: ndcg_at_5
value: 70.45700000000001
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 73.0
- type: precision_at_100
value: 55.48
- type: precision_at_1000
value: 23.102
- type: precision_at_3
value: 76.0
- type: precision_at_5
value: 74.8
- type: recall_at_1
value: 0.23600000000000002
- type: recall_at_10
value: 1.9869999999999999
- type: recall_at_100
value: 13.749
- type: recall_at_1000
value: 50.157
- type: recall_at_3
value: 0.633
- type: recall_at_5
value: 1.0290000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.437
- type: map_at_10
value: 8.791
- type: map_at_100
value: 15.001999999999999
- type: map_at_1000
value: 16.549
- type: map_at_3
value: 3.8080000000000003
- type: map_at_5
value: 5.632000000000001
- type: mrr_at_1
value: 20.408
- type: mrr_at_10
value: 36.96
- type: mrr_at_100
value: 37.912
- type: mrr_at_1000
value: 37.912
- type: mrr_at_3
value: 29.592000000000002
- type: mrr_at_5
value: 34.489999999999995
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 22.554
- type: ndcg_at_100
value: 35.197
- type: ndcg_at_1000
value: 46.58
- type: ndcg_at_3
value: 20.285
- type: ndcg_at_5
value: 21.924
- type: precision_at_1
value: 20.408
- type: precision_at_10
value: 21.837
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.537
- type: precision_at_3
value: 21.769
- type: precision_at_5
value: 23.673
- type: recall_at_1
value: 1.437
- type: recall_at_10
value: 16.314999999999998
- type: recall_at_100
value: 47.635
- type: recall_at_1000
value: 82.963
- type: recall_at_3
value: 4.955
- type: recall_at_5
value: 8.805
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.6128
- type: ap
value: 14.279639861175664
- type: f1
value: 54.922292491204274
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.01188455008489
- type: f1
value: 57.377953019225515
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.306769136544254
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.64701674912082
- type: cos_sim_ap
value: 72.46600945328552
- type: cos_sim_f1
value: 67.96572367648784
- type: cos_sim_precision
value: 61.21801649397336
- type: cos_sim_recall
value: 76.38522427440633
- type: dot_accuracy
value: 82.33295583238957
- type: dot_ap
value: 62.54843443071716
- type: dot_f1
value: 60.38378562507096
- type: dot_precision
value: 52.99980067769583
- type: dot_recall
value: 70.15831134564644
- type: euclidean_accuracy
value: 85.7423854085951
- type: euclidean_ap
value: 72.76873850945174
- type: euclidean_f1
value: 68.23556960543262
- type: euclidean_precision
value: 61.3344559040202
- type: euclidean_recall
value: 76.88654353562005
- type: manhattan_accuracy
value: 85.74834594981225
- type: manhattan_ap
value: 72.66825372446462
- type: manhattan_f1
value: 68.21539194662853
- type: manhattan_precision
value: 62.185056472632496
- type: manhattan_recall
value: 75.54089709762533
- type: max_accuracy
value: 85.74834594981225
- type: max_ap
value: 72.76873850945174
- type: max_f1
value: 68.23556960543262
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.73171110334924
- type: cos_sim_ap
value: 85.51855542063649
- type: cos_sim_f1
value: 77.95706775700934
- type: cos_sim_precision
value: 74.12524298805887
- type: cos_sim_recall
value: 82.20665229442562
- type: dot_accuracy
value: 86.94842240074514
- type: dot_ap
value: 80.90995345771762
- type: dot_f1
value: 74.20765027322403
- type: dot_precision
value: 70.42594385285575
- type: dot_recall
value: 78.41854019094548
- type: euclidean_accuracy
value: 88.73753250281368
- type: euclidean_ap
value: 85.54712254033734
- type: euclidean_f1
value: 78.07565728654365
- type: euclidean_precision
value: 75.1120597652081
- type: euclidean_recall
value: 81.282722513089
- type: manhattan_accuracy
value: 88.72588970388482
- type: manhattan_ap
value: 85.52118291594071
- type: manhattan_f1
value: 78.04428724070593
- type: manhattan_precision
value: 74.83219105490002
- type: manhattan_recall
value: 81.54450261780106
- type: max_accuracy
value: 88.73753250281368
- type: max_ap
value: 85.54712254033734
- type: max_f1
value: 78.07565728654365
---
# gte-base
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base")
model = AutoModel.from_pretrained("thenlper/gte-base")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-base')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-small-en | BAAI | feature-extraction | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence transformers",
"en",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-05T08:04:07 | 2023-12-13T03:53:21 | 419,855 | 74 | ---
language:
- en
license: mit
tags:
- mteb
- sentence transformers
model-index:
- name: bge-small-en
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.34328358208955
- type: ap
value: 37.59947775195661
- type: f1
value: 68.548415491933
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.04527499999999
- type: ap
value: 89.60696356772135
- type: f1
value: 93.03361469382438
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.08
- type: f1
value: 45.66249835363254
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.205999999999996
- type: map_at_10
value: 50.782000000000004
- type: map_at_100
value: 51.547
- type: map_at_1000
value: 51.554
- type: map_at_3
value: 46.515
- type: map_at_5
value: 49.296
- type: mrr_at_1
value: 35.632999999999996
- type: mrr_at_10
value: 50.958999999999996
- type: mrr_at_100
value: 51.724000000000004
- type: mrr_at_1000
value: 51.731
- type: mrr_at_3
value: 46.669
- type: mrr_at_5
value: 49.439
- type: ndcg_at_1
value: 35.205999999999996
- type: ndcg_at_10
value: 58.835
- type: ndcg_at_100
value: 62.095
- type: ndcg_at_1000
value: 62.255
- type: ndcg_at_3
value: 50.255
- type: ndcg_at_5
value: 55.296
- type: precision_at_1
value: 35.205999999999996
- type: precision_at_10
value: 8.421
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.365
- type: precision_at_5
value: 14.680000000000001
- type: recall_at_1
value: 35.205999999999996
- type: recall_at_10
value: 84.211
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.095
- type: recall_at_5
value: 73.4
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.52644476278646
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.973045724188964
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.28285314871488
- type: mrr
value: 74.52743701358659
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 80.09041909160327
- type: cos_sim_spearman
value: 79.96266537706944
- type: euclidean_pearson
value: 79.50774978162241
- type: euclidean_spearman
value: 79.9144715078551
- type: manhattan_pearson
value: 79.2062139879302
- type: manhattan_spearman
value: 79.35000081468212
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.31493506493506
- type: f1
value: 85.2704557977762
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.6837242810816
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.38881249555897
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.884999999999998
- type: map_at_10
value: 39.574
- type: map_at_100
value: 40.993
- type: map_at_1000
value: 41.129
- type: map_at_3
value: 36.089
- type: map_at_5
value: 38.191
- type: mrr_at_1
value: 34.477999999999994
- type: mrr_at_10
value: 45.411
- type: mrr_at_100
value: 46.089999999999996
- type: mrr_at_1000
value: 46.147
- type: mrr_at_3
value: 42.346000000000004
- type: mrr_at_5
value: 44.292
- type: ndcg_at_1
value: 34.477999999999994
- type: ndcg_at_10
value: 46.123999999999995
- type: ndcg_at_100
value: 51.349999999999994
- type: ndcg_at_1000
value: 53.578
- type: ndcg_at_3
value: 40.824
- type: ndcg_at_5
value: 43.571
- type: precision_at_1
value: 34.477999999999994
- type: precision_at_10
value: 8.841000000000001
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.192
- type: precision_at_3
value: 19.742
- type: precision_at_5
value: 14.421000000000001
- type: recall_at_1
value: 27.884999999999998
- type: recall_at_10
value: 59.087
- type: recall_at_100
value: 80.609
- type: recall_at_1000
value: 95.054
- type: recall_at_3
value: 44.082
- type: recall_at_5
value: 51.593999999999994
- type: map_at_1
value: 30.639
- type: map_at_10
value: 40.047
- type: map_at_100
value: 41.302
- type: map_at_1000
value: 41.425
- type: map_at_3
value: 37.406
- type: map_at_5
value: 38.934000000000005
- type: mrr_at_1
value: 37.707
- type: mrr_at_10
value: 46.082
- type: mrr_at_100
value: 46.745
- type: mrr_at_1000
value: 46.786
- type: mrr_at_3
value: 43.980999999999995
- type: mrr_at_5
value: 45.287
- type: ndcg_at_1
value: 37.707
- type: ndcg_at_10
value: 45.525
- type: ndcg_at_100
value: 49.976
- type: ndcg_at_1000
value: 51.94499999999999
- type: ndcg_at_3
value: 41.704
- type: ndcg_at_5
value: 43.596000000000004
- type: precision_at_1
value: 37.707
- type: precision_at_10
value: 8.465
- type: precision_at_100
value: 1.375
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 19.979
- type: precision_at_5
value: 14.115
- type: recall_at_1
value: 30.639
- type: recall_at_10
value: 54.775
- type: recall_at_100
value: 73.678
- type: recall_at_1000
value: 86.142
- type: recall_at_3
value: 43.230000000000004
- type: recall_at_5
value: 48.622
- type: map_at_1
value: 38.038
- type: map_at_10
value: 49.922
- type: map_at_100
value: 51.032
- type: map_at_1000
value: 51.085
- type: map_at_3
value: 46.664
- type: map_at_5
value: 48.588
- type: mrr_at_1
value: 43.95
- type: mrr_at_10
value: 53.566
- type: mrr_at_100
value: 54.318999999999996
- type: mrr_at_1000
value: 54.348
- type: mrr_at_3
value: 51.066
- type: mrr_at_5
value: 52.649
- type: ndcg_at_1
value: 43.95
- type: ndcg_at_10
value: 55.676
- type: ndcg_at_100
value: 60.126000000000005
- type: ndcg_at_1000
value: 61.208
- type: ndcg_at_3
value: 50.20400000000001
- type: ndcg_at_5
value: 53.038
- type: precision_at_1
value: 43.95
- type: precision_at_10
value: 8.953
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.256999999999998
- type: precision_at_5
value: 15.524
- type: recall_at_1
value: 38.038
- type: recall_at_10
value: 69.15
- type: recall_at_100
value: 88.31599999999999
- type: recall_at_1000
value: 95.993
- type: recall_at_3
value: 54.663
- type: recall_at_5
value: 61.373
- type: map_at_1
value: 24.872
- type: map_at_10
value: 32.912
- type: map_at_100
value: 33.972
- type: map_at_1000
value: 34.046
- type: map_at_3
value: 30.361
- type: map_at_5
value: 31.704
- type: mrr_at_1
value: 26.779999999999998
- type: mrr_at_10
value: 34.812
- type: mrr_at_100
value: 35.754999999999995
- type: mrr_at_1000
value: 35.809000000000005
- type: mrr_at_3
value: 32.335
- type: mrr_at_5
value: 33.64
- type: ndcg_at_1
value: 26.779999999999998
- type: ndcg_at_10
value: 37.623
- type: ndcg_at_100
value: 42.924
- type: ndcg_at_1000
value: 44.856
- type: ndcg_at_3
value: 32.574
- type: ndcg_at_5
value: 34.842
- type: precision_at_1
value: 26.779999999999998
- type: precision_at_10
value: 5.729
- type: precision_at_100
value: 0.886
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.559
- type: precision_at_5
value: 9.469
- type: recall_at_1
value: 24.872
- type: recall_at_10
value: 50.400999999999996
- type: recall_at_100
value: 74.954
- type: recall_at_1000
value: 89.56
- type: recall_at_3
value: 36.726
- type: recall_at_5
value: 42.138999999999996
- type: map_at_1
value: 16.803
- type: map_at_10
value: 24.348
- type: map_at_100
value: 25.56
- type: map_at_1000
value: 25.668000000000003
- type: map_at_3
value: 21.811
- type: map_at_5
value: 23.287
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 28.961
- type: mrr_at_100
value: 29.979
- type: mrr_at_1000
value: 30.046
- type: mrr_at_3
value: 26.555
- type: mrr_at_5
value: 28.060000000000002
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 29.335
- type: ndcg_at_100
value: 35.188
- type: ndcg_at_1000
value: 37.812
- type: ndcg_at_3
value: 24.83
- type: ndcg_at_5
value: 27.119
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.4350000000000005
- type: precision_at_100
value: 0.9480000000000001
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.982
- type: precision_at_5
value: 8.831
- type: recall_at_1
value: 16.803
- type: recall_at_10
value: 40.039
- type: recall_at_100
value: 65.83200000000001
- type: recall_at_1000
value: 84.478
- type: recall_at_3
value: 27.682000000000002
- type: recall_at_5
value: 33.535
- type: map_at_1
value: 28.345
- type: map_at_10
value: 37.757000000000005
- type: map_at_100
value: 39.141
- type: map_at_1000
value: 39.262
- type: map_at_3
value: 35.183
- type: map_at_5
value: 36.592
- type: mrr_at_1
value: 34.649
- type: mrr_at_10
value: 43.586999999999996
- type: mrr_at_100
value: 44.481
- type: mrr_at_1000
value: 44.542
- type: mrr_at_3
value: 41.29
- type: mrr_at_5
value: 42.642
- type: ndcg_at_1
value: 34.649
- type: ndcg_at_10
value: 43.161
- type: ndcg_at_100
value: 48.734
- type: ndcg_at_1000
value: 51.046
- type: ndcg_at_3
value: 39.118
- type: ndcg_at_5
value: 41.022
- type: precision_at_1
value: 34.649
- type: precision_at_10
value: 7.603
- type: precision_at_100
value: 1.209
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 18.319
- type: precision_at_5
value: 12.839
- type: recall_at_1
value: 28.345
- type: recall_at_10
value: 53.367
- type: recall_at_100
value: 76.453
- type: recall_at_1000
value: 91.82000000000001
- type: recall_at_3
value: 41.636
- type: recall_at_5
value: 46.760000000000005
- type: map_at_1
value: 22.419
- type: map_at_10
value: 31.716
- type: map_at_100
value: 33.152
- type: map_at_1000
value: 33.267
- type: map_at_3
value: 28.74
- type: map_at_5
value: 30.48
- type: mrr_at_1
value: 28.310999999999996
- type: mrr_at_10
value: 37.039
- type: mrr_at_100
value: 38.09
- type: mrr_at_1000
value: 38.145
- type: mrr_at_3
value: 34.437
- type: mrr_at_5
value: 36.024
- type: ndcg_at_1
value: 28.310999999999996
- type: ndcg_at_10
value: 37.41
- type: ndcg_at_100
value: 43.647999999999996
- type: ndcg_at_1000
value: 46.007
- type: ndcg_at_3
value: 32.509
- type: ndcg_at_5
value: 34.943999999999996
- type: precision_at_1
value: 28.310999999999996
- type: precision_at_10
value: 6.963
- type: precision_at_100
value: 1.1860000000000002
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 15.867999999999999
- type: precision_at_5
value: 11.507000000000001
- type: recall_at_1
value: 22.419
- type: recall_at_10
value: 49.28
- type: recall_at_100
value: 75.802
- type: recall_at_1000
value: 92.032
- type: recall_at_3
value: 35.399
- type: recall_at_5
value: 42.027
- type: map_at_1
value: 24.669249999999998
- type: map_at_10
value: 33.332583333333325
- type: map_at_100
value: 34.557833333333335
- type: map_at_1000
value: 34.67141666666666
- type: map_at_3
value: 30.663166666666662
- type: map_at_5
value: 32.14883333333333
- type: mrr_at_1
value: 29.193833333333334
- type: mrr_at_10
value: 37.47625
- type: mrr_at_100
value: 38.3545
- type: mrr_at_1000
value: 38.413166666666676
- type: mrr_at_3
value: 35.06741666666667
- type: mrr_at_5
value: 36.450666666666656
- type: ndcg_at_1
value: 29.193833333333334
- type: ndcg_at_10
value: 38.505416666666676
- type: ndcg_at_100
value: 43.81125
- type: ndcg_at_1000
value: 46.09558333333333
- type: ndcg_at_3
value: 33.90916666666667
- type: ndcg_at_5
value: 36.07666666666666
- type: precision_at_1
value: 29.193833333333334
- type: precision_at_10
value: 6.7251666666666665
- type: precision_at_100
value: 1.1058333333333332
- type: precision_at_1000
value: 0.14833333333333332
- type: precision_at_3
value: 15.554166666666665
- type: precision_at_5
value: 11.079250000000002
- type: recall_at_1
value: 24.669249999999998
- type: recall_at_10
value: 49.75583333333332
- type: recall_at_100
value: 73.06908333333332
- type: recall_at_1000
value: 88.91316666666667
- type: recall_at_3
value: 36.913250000000005
- type: recall_at_5
value: 42.48641666666666
- type: map_at_1
value: 24.044999999999998
- type: map_at_10
value: 30.349999999999998
- type: map_at_100
value: 31.273
- type: map_at_1000
value: 31.362000000000002
- type: map_at_3
value: 28.508
- type: map_at_5
value: 29.369
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.12
- type: mrr_at_100
value: 33.904
- type: mrr_at_1000
value: 33.967000000000006
- type: mrr_at_3
value: 31.365
- type: mrr_at_5
value: 32.124
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.214
- type: ndcg_at_100
value: 38.681
- type: ndcg_at_1000
value: 40.926
- type: ndcg_at_3
value: 30.725
- type: ndcg_at_5
value: 31.967000000000002
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.215
- type: precision_at_100
value: 0.807
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 8.712
- type: recall_at_1
value: 24.044999999999998
- type: recall_at_10
value: 43.456
- type: recall_at_100
value: 63.675000000000004
- type: recall_at_1000
value: 80.05499999999999
- type: recall_at_3
value: 33.561
- type: recall_at_5
value: 36.767
- type: map_at_1
value: 15.672
- type: map_at_10
value: 22.641
- type: map_at_100
value: 23.75
- type: map_at_1000
value: 23.877000000000002
- type: map_at_3
value: 20.219
- type: map_at_5
value: 21.648
- type: mrr_at_1
value: 18.823
- type: mrr_at_10
value: 26.101999999999997
- type: mrr_at_100
value: 27.038
- type: mrr_at_1000
value: 27.118
- type: mrr_at_3
value: 23.669
- type: mrr_at_5
value: 25.173000000000002
- type: ndcg_at_1
value: 18.823
- type: ndcg_at_10
value: 27.176000000000002
- type: ndcg_at_100
value: 32.42
- type: ndcg_at_1000
value: 35.413
- type: ndcg_at_3
value: 22.756999999999998
- type: ndcg_at_5
value: 25.032
- type: precision_at_1
value: 18.823
- type: precision_at_10
value: 5.034000000000001
- type: precision_at_100
value: 0.895
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 10.771
- type: precision_at_5
value: 8.1
- type: recall_at_1
value: 15.672
- type: recall_at_10
value: 37.296
- type: recall_at_100
value: 60.863
- type: recall_at_1000
value: 82.234
- type: recall_at_3
value: 25.330000000000002
- type: recall_at_5
value: 30.964000000000002
- type: map_at_1
value: 24.633
- type: map_at_10
value: 32.858
- type: map_at_100
value: 34.038000000000004
- type: map_at_1000
value: 34.141
- type: map_at_3
value: 30.209000000000003
- type: map_at_5
value: 31.567
- type: mrr_at_1
value: 28.358
- type: mrr_at_10
value: 36.433
- type: mrr_at_100
value: 37.352000000000004
- type: mrr_at_1000
value: 37.41
- type: mrr_at_3
value: 34.033
- type: mrr_at_5
value: 35.246
- type: ndcg_at_1
value: 28.358
- type: ndcg_at_10
value: 37.973
- type: ndcg_at_100
value: 43.411
- type: ndcg_at_1000
value: 45.747
- type: ndcg_at_3
value: 32.934999999999995
- type: ndcg_at_5
value: 35.013
- type: precision_at_1
value: 28.358
- type: precision_at_10
value: 6.418
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 14.677000000000001
- type: precision_at_5
value: 10.335999999999999
- type: recall_at_1
value: 24.633
- type: recall_at_10
value: 50.048
- type: recall_at_100
value: 73.821
- type: recall_at_1000
value: 90.046
- type: recall_at_3
value: 36.284
- type: recall_at_5
value: 41.370000000000005
- type: map_at_1
value: 23.133
- type: map_at_10
value: 31.491999999999997
- type: map_at_100
value: 33.062000000000005
- type: map_at_1000
value: 33.256
- type: map_at_3
value: 28.886
- type: map_at_5
value: 30.262
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.144
- type: mrr_at_100
value: 37.14
- type: mrr_at_1000
value: 37.191
- type: mrr_at_3
value: 33.762
- type: mrr_at_5
value: 34.997
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 36.951
- type: ndcg_at_100
value: 43.287
- type: ndcg_at_1000
value: 45.777
- type: ndcg_at_3
value: 32.786
- type: ndcg_at_5
value: 34.65
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.055
- type: precision_at_100
value: 1.476
- type: precision_at_1000
value: 0.22899999999999998
- type: precision_at_3
value: 15.481
- type: precision_at_5
value: 11.186
- type: recall_at_1
value: 23.133
- type: recall_at_10
value: 47.285
- type: recall_at_100
value: 76.176
- type: recall_at_1000
value: 92.176
- type: recall_at_3
value: 35.223
- type: recall_at_5
value: 40.142
- type: map_at_1
value: 19.547
- type: map_at_10
value: 26.374
- type: map_at_100
value: 27.419
- type: map_at_1000
value: 27.539
- type: map_at_3
value: 23.882
- type: map_at_5
value: 25.163999999999998
- type: mrr_at_1
value: 21.442
- type: mrr_at_10
value: 28.458
- type: mrr_at_100
value: 29.360999999999997
- type: mrr_at_1000
value: 29.448999999999998
- type: mrr_at_3
value: 25.97
- type: mrr_at_5
value: 27.273999999999997
- type: ndcg_at_1
value: 21.442
- type: ndcg_at_10
value: 30.897000000000002
- type: ndcg_at_100
value: 35.99
- type: ndcg_at_1000
value: 38.832
- type: ndcg_at_3
value: 25.944
- type: ndcg_at_5
value: 28.126
- type: precision_at_1
value: 21.442
- type: precision_at_10
value: 4.9910000000000005
- type: precision_at_100
value: 0.8109999999999999
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.911
- type: recall_at_1
value: 19.547
- type: recall_at_10
value: 42.886
- type: recall_at_100
value: 66.64999999999999
- type: recall_at_1000
value: 87.368
- type: recall_at_3
value: 29.143
- type: recall_at_5
value: 34.544000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.572
- type: map_at_10
value: 25.312
- type: map_at_100
value: 27.062
- type: map_at_1000
value: 27.253
- type: map_at_3
value: 21.601
- type: map_at_5
value: 23.473
- type: mrr_at_1
value: 34.984
- type: mrr_at_10
value: 46.406
- type: mrr_at_100
value: 47.179
- type: mrr_at_1000
value: 47.21
- type: mrr_at_3
value: 43.485
- type: mrr_at_5
value: 45.322
- type: ndcg_at_1
value: 34.984
- type: ndcg_at_10
value: 34.344
- type: ndcg_at_100
value: 41.015
- type: ndcg_at_1000
value: 44.366
- type: ndcg_at_3
value: 29.119
- type: ndcg_at_5
value: 30.825999999999997
- type: precision_at_1
value: 34.984
- type: precision_at_10
value: 10.358
- type: precision_at_100
value: 1.762
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 21.368000000000002
- type: precision_at_5
value: 15.948
- type: recall_at_1
value: 15.572
- type: recall_at_10
value: 39.367999999999995
- type: recall_at_100
value: 62.183
- type: recall_at_1000
value: 80.92200000000001
- type: recall_at_3
value: 26.131999999999998
- type: recall_at_5
value: 31.635999999999996
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.848
- type: map_at_10
value: 19.25
- type: map_at_100
value: 27.193
- type: map_at_1000
value: 28.721999999999998
- type: map_at_3
value: 13.968
- type: map_at_5
value: 16.283
- type: mrr_at_1
value: 68.75
- type: mrr_at_10
value: 76.25
- type: mrr_at_100
value: 76.534
- type: mrr_at_1000
value: 76.53999999999999
- type: mrr_at_3
value: 74.667
- type: mrr_at_5
value: 75.86699999999999
- type: ndcg_at_1
value: 56.00000000000001
- type: ndcg_at_10
value: 41.426
- type: ndcg_at_100
value: 45.660000000000004
- type: ndcg_at_1000
value: 53.02
- type: ndcg_at_3
value: 46.581
- type: ndcg_at_5
value: 43.836999999999996
- type: precision_at_1
value: 68.75
- type: precision_at_10
value: 32.800000000000004
- type: precision_at_100
value: 10.440000000000001
- type: precision_at_1000
value: 1.9980000000000002
- type: precision_at_3
value: 49.667
- type: precision_at_5
value: 42.25
- type: recall_at_1
value: 8.848
- type: recall_at_10
value: 24.467
- type: recall_at_100
value: 51.344
- type: recall_at_1000
value: 75.235
- type: recall_at_3
value: 15.329
- type: recall_at_5
value: 18.892999999999997
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.95
- type: f1
value: 43.44563593360779
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 78.036
- type: map_at_10
value: 85.639
- type: map_at_100
value: 85.815
- type: map_at_1000
value: 85.829
- type: map_at_3
value: 84.795
- type: map_at_5
value: 85.336
- type: mrr_at_1
value: 84.353
- type: mrr_at_10
value: 90.582
- type: mrr_at_100
value: 90.617
- type: mrr_at_1000
value: 90.617
- type: mrr_at_3
value: 90.132
- type: mrr_at_5
value: 90.447
- type: ndcg_at_1
value: 84.353
- type: ndcg_at_10
value: 89.003
- type: ndcg_at_100
value: 89.60000000000001
- type: ndcg_at_1000
value: 89.836
- type: ndcg_at_3
value: 87.81400000000001
- type: ndcg_at_5
value: 88.478
- type: precision_at_1
value: 84.353
- type: precision_at_10
value: 10.482
- type: precision_at_100
value: 1.099
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 33.257999999999996
- type: precision_at_5
value: 20.465
- type: recall_at_1
value: 78.036
- type: recall_at_10
value: 94.517
- type: recall_at_100
value: 96.828
- type: recall_at_1000
value: 98.261
- type: recall_at_3
value: 91.12
- type: recall_at_5
value: 92.946
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.191
- type: map_at_10
value: 32.369
- type: map_at_100
value: 34.123999999999995
- type: map_at_1000
value: 34.317
- type: map_at_3
value: 28.71
- type: map_at_5
value: 30.607
- type: mrr_at_1
value: 40.894999999999996
- type: mrr_at_10
value: 48.842
- type: mrr_at_100
value: 49.599
- type: mrr_at_1000
value: 49.647000000000006
- type: mrr_at_3
value: 46.785
- type: mrr_at_5
value: 47.672
- type: ndcg_at_1
value: 40.894999999999996
- type: ndcg_at_10
value: 39.872
- type: ndcg_at_100
value: 46.126
- type: ndcg_at_1000
value: 49.476
- type: ndcg_at_3
value: 37.153000000000006
- type: ndcg_at_5
value: 37.433
- type: precision_at_1
value: 40.894999999999996
- type: precision_at_10
value: 10.818
- type: precision_at_100
value: 1.73
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 25.051000000000002
- type: precision_at_5
value: 17.531
- type: recall_at_1
value: 20.191
- type: recall_at_10
value: 45.768
- type: recall_at_100
value: 68.82000000000001
- type: recall_at_1000
value: 89.133
- type: recall_at_3
value: 33.296
- type: recall_at_5
value: 38.022
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.257
- type: map_at_10
value: 61.467000000000006
- type: map_at_100
value: 62.364
- type: map_at_1000
value: 62.424
- type: map_at_3
value: 58.228
- type: map_at_5
value: 60.283
- type: mrr_at_1
value: 78.515
- type: mrr_at_10
value: 84.191
- type: mrr_at_100
value: 84.378
- type: mrr_at_1000
value: 84.385
- type: mrr_at_3
value: 83.284
- type: mrr_at_5
value: 83.856
- type: ndcg_at_1
value: 78.515
- type: ndcg_at_10
value: 69.78999999999999
- type: ndcg_at_100
value: 72.886
- type: ndcg_at_1000
value: 74.015
- type: ndcg_at_3
value: 65.23
- type: ndcg_at_5
value: 67.80199999999999
- type: precision_at_1
value: 78.515
- type: precision_at_10
value: 14.519000000000002
- type: precision_at_100
value: 1.694
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 41.702
- type: precision_at_5
value: 27.046999999999997
- type: recall_at_1
value: 39.257
- type: recall_at_10
value: 72.59299999999999
- type: recall_at_100
value: 84.679
- type: recall_at_1000
value: 92.12
- type: recall_at_3
value: 62.552
- type: recall_at_5
value: 67.616
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.5152
- type: ap
value: 87.64584669595709
- type: f1
value: 91.50605576428437
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.926000000000002
- type: map_at_10
value: 34.049
- type: map_at_100
value: 35.213
- type: map_at_1000
value: 35.265
- type: map_at_3
value: 30.309
- type: map_at_5
value: 32.407000000000004
- type: mrr_at_1
value: 22.55
- type: mrr_at_10
value: 34.657
- type: mrr_at_100
value: 35.760999999999996
- type: mrr_at_1000
value: 35.807
- type: mrr_at_3
value: 30.989
- type: mrr_at_5
value: 33.039
- type: ndcg_at_1
value: 22.55
- type: ndcg_at_10
value: 40.842
- type: ndcg_at_100
value: 46.436
- type: ndcg_at_1000
value: 47.721999999999994
- type: ndcg_at_3
value: 33.209
- type: ndcg_at_5
value: 36.943
- type: precision_at_1
value: 22.55
- type: precision_at_10
value: 6.447
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.136000000000001
- type: precision_at_5
value: 10.381
- type: recall_at_1
value: 21.926000000000002
- type: recall_at_10
value: 61.724999999999994
- type: recall_at_100
value: 87.604
- type: recall_at_1000
value: 97.421
- type: recall_at_3
value: 40.944
- type: recall_at_5
value: 49.915
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.54765161878704
- type: f1
value: 93.3298945415573
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.71591427268582
- type: f1
value: 59.32113870474471
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.83053127101547
- type: f1
value: 73.60757944876475
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.72562205783457
- type: f1
value: 78.63761662505502
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.37935633767996
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.55270546130387
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.462692753143834
- type: mrr
value: 31.497569753511563
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.646
- type: map_at_10
value: 12.498
- type: map_at_100
value: 15.486
- type: map_at_1000
value: 16.805999999999997
- type: map_at_3
value: 9.325
- type: map_at_5
value: 10.751
- type: mrr_at_1
value: 43.034
- type: mrr_at_10
value: 52.662
- type: mrr_at_100
value: 53.189
- type: mrr_at_1000
value: 53.25
- type: mrr_at_3
value: 50.929
- type: mrr_at_5
value: 51.92
- type: ndcg_at_1
value: 41.796
- type: ndcg_at_10
value: 33.477000000000004
- type: ndcg_at_100
value: 29.996000000000002
- type: ndcg_at_1000
value: 38.864
- type: ndcg_at_3
value: 38.940000000000005
- type: ndcg_at_5
value: 36.689
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 24.799
- type: precision_at_100
value: 7.432999999999999
- type: precision_at_1000
value: 1.9929999999999999
- type: precision_at_3
value: 36.842000000000006
- type: precision_at_5
value: 32.135999999999996
- type: recall_at_1
value: 5.646
- type: recall_at_10
value: 15.963
- type: recall_at_100
value: 29.492
- type: recall_at_1000
value: 61.711000000000006
- type: recall_at_3
value: 10.585
- type: recall_at_5
value: 12.753999999999998
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.602
- type: map_at_10
value: 41.545
- type: map_at_100
value: 42.644999999999996
- type: map_at_1000
value: 42.685
- type: map_at_3
value: 37.261
- type: map_at_5
value: 39.706
- type: mrr_at_1
value: 31.141000000000002
- type: mrr_at_10
value: 44.139
- type: mrr_at_100
value: 44.997
- type: mrr_at_1000
value: 45.025999999999996
- type: mrr_at_3
value: 40.503
- type: mrr_at_5
value: 42.64
- type: ndcg_at_1
value: 31.141000000000002
- type: ndcg_at_10
value: 48.995
- type: ndcg_at_100
value: 53.788000000000004
- type: ndcg_at_1000
value: 54.730000000000004
- type: ndcg_at_3
value: 40.844
- type: ndcg_at_5
value: 44.955
- type: precision_at_1
value: 31.141000000000002
- type: precision_at_10
value: 8.233
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 18.579
- type: precision_at_5
value: 13.533999999999999
- type: recall_at_1
value: 27.602
- type: recall_at_10
value: 69.216
- type: recall_at_100
value: 90.252
- type: recall_at_1000
value: 97.27
- type: recall_at_3
value: 47.987
- type: recall_at_5
value: 57.438
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.949
- type: map_at_10
value: 84.89999999999999
- type: map_at_100
value: 85.531
- type: map_at_1000
value: 85.548
- type: map_at_3
value: 82.027
- type: map_at_5
value: 83.853
- type: mrr_at_1
value: 81.69999999999999
- type: mrr_at_10
value: 87.813
- type: mrr_at_100
value: 87.917
- type: mrr_at_1000
value: 87.91799999999999
- type: mrr_at_3
value: 86.938
- type: mrr_at_5
value: 87.53999999999999
- type: ndcg_at_1
value: 81.75
- type: ndcg_at_10
value: 88.55499999999999
- type: ndcg_at_100
value: 89.765
- type: ndcg_at_1000
value: 89.871
- type: ndcg_at_3
value: 85.905
- type: ndcg_at_5
value: 87.41
- type: precision_at_1
value: 81.75
- type: precision_at_10
value: 13.403
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.597
- type: precision_at_5
value: 24.69
- type: recall_at_1
value: 70.949
- type: recall_at_10
value: 95.423
- type: recall_at_100
value: 99.509
- type: recall_at_1000
value: 99.982
- type: recall_at_3
value: 87.717
- type: recall_at_5
value: 92.032
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 51.76962893449579
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.32897690686379
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.478
- type: map_at_10
value: 11.994
- type: map_at_100
value: 13.977
- type: map_at_1000
value: 14.295
- type: map_at_3
value: 8.408999999999999
- type: map_at_5
value: 10.024
- type: mrr_at_1
value: 22.1
- type: mrr_at_10
value: 33.526
- type: mrr_at_100
value: 34.577000000000005
- type: mrr_at_1000
value: 34.632000000000005
- type: mrr_at_3
value: 30.217
- type: mrr_at_5
value: 31.962000000000003
- type: ndcg_at_1
value: 22.1
- type: ndcg_at_10
value: 20.191
- type: ndcg_at_100
value: 27.954
- type: ndcg_at_1000
value: 33.491
- type: ndcg_at_3
value: 18.787000000000003
- type: ndcg_at_5
value: 16.378999999999998
- type: precision_at_1
value: 22.1
- type: precision_at_10
value: 10.69
- type: precision_at_100
value: 2.1919999999999997
- type: precision_at_1000
value: 0.35200000000000004
- type: precision_at_3
value: 17.732999999999997
- type: precision_at_5
value: 14.499999999999998
- type: recall_at_1
value: 4.478
- type: recall_at_10
value: 21.657
- type: recall_at_100
value: 44.54
- type: recall_at_1000
value: 71.542
- type: recall_at_3
value: 10.778
- type: recall_at_5
value: 14.687
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.82325259156718
- type: cos_sim_spearman
value: 79.2463589100662
- type: euclidean_pearson
value: 80.48318380496771
- type: euclidean_spearman
value: 79.34451935199979
- type: manhattan_pearson
value: 80.39041824178759
- type: manhattan_spearman
value: 79.23002892700211
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.74130231431258
- type: cos_sim_spearman
value: 78.36856568042397
- type: euclidean_pearson
value: 82.48301631890303
- type: euclidean_spearman
value: 78.28376980722732
- type: manhattan_pearson
value: 82.43552075450525
- type: manhattan_spearman
value: 78.22702443947126
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.96138619461459
- type: cos_sim_spearman
value: 81.85436343502379
- type: euclidean_pearson
value: 81.82895226665367
- type: euclidean_spearman
value: 82.22707349602916
- type: manhattan_pearson
value: 81.66303369445873
- type: manhattan_spearman
value: 82.05030197179455
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.05481244198648
- type: cos_sim_spearman
value: 80.85052504637808
- type: euclidean_pearson
value: 80.86728419744497
- type: euclidean_spearman
value: 81.033786401512
- type: manhattan_pearson
value: 80.90107531061103
- type: manhattan_spearman
value: 81.11374116827795
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 84.615220756399
- type: cos_sim_spearman
value: 86.46858500002092
- type: euclidean_pearson
value: 86.08307800247586
- type: euclidean_spearman
value: 86.72691443870013
- type: manhattan_pearson
value: 85.96155594487269
- type: manhattan_spearman
value: 86.605909505275
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.14363913634436
- type: cos_sim_spearman
value: 84.48430226487102
- type: euclidean_pearson
value: 83.75303424801902
- type: euclidean_spearman
value: 84.56762380734538
- type: manhattan_pearson
value: 83.6135447165928
- type: manhattan_spearman
value: 84.39898212616731
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.09909252554525
- type: cos_sim_spearman
value: 85.70951402743276
- type: euclidean_pearson
value: 87.1991936239908
- type: euclidean_spearman
value: 86.07745840612071
- type: manhattan_pearson
value: 87.25039137549952
- type: manhattan_spearman
value: 85.99938746659761
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.529332093413615
- type: cos_sim_spearman
value: 65.38177340147439
- type: euclidean_pearson
value: 66.35278011412136
- type: euclidean_spearman
value: 65.47147267032997
- type: manhattan_pearson
value: 66.71804682408693
- type: manhattan_spearman
value: 65.67406521423597
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.45802942885662
- type: cos_sim_spearman
value: 84.8853341842566
- type: euclidean_pearson
value: 84.60915021096707
- type: euclidean_spearman
value: 85.11181242913666
- type: manhattan_pearson
value: 84.38600521210364
- type: manhattan_spearman
value: 84.89045417981723
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.92793380635129
- type: mrr
value: 95.85834191226348
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.74400000000001
- type: map_at_10
value: 65.455
- type: map_at_100
value: 66.106
- type: map_at_1000
value: 66.129
- type: map_at_3
value: 62.719
- type: map_at_5
value: 64.441
- type: mrr_at_1
value: 58.667
- type: mrr_at_10
value: 66.776
- type: mrr_at_100
value: 67.363
- type: mrr_at_1000
value: 67.384
- type: mrr_at_3
value: 64.889
- type: mrr_at_5
value: 66.122
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_10
value: 69.904
- type: ndcg_at_100
value: 72.807
- type: ndcg_at_1000
value: 73.423
- type: ndcg_at_3
value: 65.405
- type: ndcg_at_5
value: 67.86999999999999
- type: precision_at_1
value: 58.667
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.08
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 17
- type: recall_at_1
value: 55.74400000000001
- type: recall_at_10
value: 82.122
- type: recall_at_100
value: 95.167
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 70.14399999999999
- type: recall_at_5
value: 76.417
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.86534653465347
- type: cos_sim_ap
value: 96.54142419791388
- type: cos_sim_f1
value: 93.07535641547861
- type: cos_sim_precision
value: 94.81327800829875
- type: cos_sim_recall
value: 91.4
- type: dot_accuracy
value: 99.86435643564356
- type: dot_ap
value: 96.53682260449868
- type: dot_f1
value: 92.98515104966718
- type: dot_precision
value: 95.27806925498426
- type: dot_recall
value: 90.8
- type: euclidean_accuracy
value: 99.86336633663366
- type: euclidean_ap
value: 96.5228676185697
- type: euclidean_f1
value: 92.9735234215886
- type: euclidean_precision
value: 94.70954356846472
- type: euclidean_recall
value: 91.3
- type: manhattan_accuracy
value: 99.85841584158416
- type: manhattan_ap
value: 96.50392760934032
- type: manhattan_f1
value: 92.84642321160581
- type: manhattan_precision
value: 92.8928928928929
- type: manhattan_recall
value: 92.80000000000001
- type: max_accuracy
value: 99.86534653465347
- type: max_ap
value: 96.54142419791388
- type: max_f1
value: 93.07535641547861
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 61.08285408766616
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.640675309010604
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.20333913710715
- type: mrr
value: 54.088813555725324
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.79465221925075
- type: cos_sim_spearman
value: 30.530816059163634
- type: dot_pearson
value: 31.364837244718043
- type: dot_spearman
value: 30.79726823684003
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22599999999999998
- type: map_at_10
value: 1.735
- type: map_at_100
value: 8.978
- type: map_at_1000
value: 20.851
- type: map_at_3
value: 0.613
- type: map_at_5
value: 0.964
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 92.867
- type: mrr_at_100
value: 92.867
- type: mrr_at_1000
value: 92.867
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 92.667
- type: ndcg_at_1
value: 82
- type: ndcg_at_10
value: 73.164
- type: ndcg_at_100
value: 51.878
- type: ndcg_at_1000
value: 44.864
- type: ndcg_at_3
value: 79.184
- type: ndcg_at_5
value: 76.39
- type: precision_at_1
value: 88
- type: precision_at_10
value: 76.2
- type: precision_at_100
value: 52.459999999999994
- type: precision_at_1000
value: 19.692
- type: precision_at_3
value: 82.667
- type: precision_at_5
value: 80
- type: recall_at_1
value: 0.22599999999999998
- type: recall_at_10
value: 1.942
- type: recall_at_100
value: 12.342
- type: recall_at_1000
value: 41.42
- type: recall_at_3
value: 0.637
- type: recall_at_5
value: 1.034
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.567
- type: map_at_10
value: 13.116
- type: map_at_100
value: 19.39
- type: map_at_1000
value: 20.988
- type: map_at_3
value: 7.109
- type: map_at_5
value: 9.950000000000001
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 57.404999999999994
- type: mrr_at_100
value: 58.021
- type: mrr_at_1000
value: 58.021
- type: mrr_at_3
value: 54.762
- type: mrr_at_5
value: 56.19
- type: ndcg_at_1
value: 38.775999999999996
- type: ndcg_at_10
value: 30.359
- type: ndcg_at_100
value: 41.284
- type: ndcg_at_1000
value: 52.30200000000001
- type: ndcg_at_3
value: 36.744
- type: ndcg_at_5
value: 34.326
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 26.122
- type: precision_at_100
value: 8.082
- type: precision_at_1000
value: 1.559
- type: precision_at_3
value: 40.136
- type: precision_at_5
value: 35.510000000000005
- type: recall_at_1
value: 3.567
- type: recall_at_10
value: 19.045
- type: recall_at_100
value: 49.979
- type: recall_at_1000
value: 84.206
- type: recall_at_3
value: 8.52
- type: recall_at_5
value: 13.103000000000002
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 68.8394
- type: ap
value: 13.454399712443099
- type: f1
value: 53.04963076364322
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.546123372948514
- type: f1
value: 60.86952793277713
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.10042955060234
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.03308100375514
- type: cos_sim_ap
value: 71.08284605869684
- type: cos_sim_f1
value: 65.42539436255494
- type: cos_sim_precision
value: 64.14807302231237
- type: cos_sim_recall
value: 66.75461741424802
- type: dot_accuracy
value: 84.68736961316088
- type: dot_ap
value: 69.20524036530992
- type: dot_f1
value: 63.54893953365829
- type: dot_precision
value: 63.45698500394633
- type: dot_recall
value: 63.641160949868066
- type: euclidean_accuracy
value: 85.07480479227513
- type: euclidean_ap
value: 71.14592761009864
- type: euclidean_f1
value: 65.43814432989691
- type: euclidean_precision
value: 63.95465994962216
- type: euclidean_recall
value: 66.99208443271768
- type: manhattan_accuracy
value: 85.06288370984085
- type: manhattan_ap
value: 71.07289742593868
- type: manhattan_f1
value: 65.37585421412301
- type: manhattan_precision
value: 62.816147859922175
- type: manhattan_recall
value: 68.15303430079156
- type: max_accuracy
value: 85.07480479227513
- type: max_ap
value: 71.14592761009864
- type: max_f1
value: 65.43814432989691
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.79058485659952
- type: cos_sim_ap
value: 83.7183187008759
- type: cos_sim_f1
value: 75.86921142180798
- type: cos_sim_precision
value: 73.00683371298405
- type: cos_sim_recall
value: 78.96519864490298
- type: dot_accuracy
value: 87.0085768618776
- type: dot_ap
value: 81.87467488474279
- type: dot_f1
value: 74.04188363990559
- type: dot_precision
value: 72.10507114191901
- type: dot_recall
value: 76.08561749307053
- type: euclidean_accuracy
value: 87.8332751193387
- type: euclidean_ap
value: 83.83585648120315
- type: euclidean_f1
value: 76.02582177042369
- type: euclidean_precision
value: 73.36388371759989
- type: euclidean_recall
value: 78.88820449645827
- type: manhattan_accuracy
value: 87.87208444910156
- type: manhattan_ap
value: 83.8101950642973
- type: manhattan_f1
value: 75.90454195535027
- type: manhattan_precision
value: 72.44419564761039
- type: manhattan_recall
value: 79.71204188481676
- type: max_accuracy
value: 87.87208444910156
- type: max_ap
value: 83.83585648120315
- type: max_f1
value: 76.02582177042369
---
**Recommend switching to newest [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focus on retrieval-augmented LLMs, consisting of following projects currently:
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding), [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## News
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [LM-Cocktail](https://huggingface.co/Shitao) | English | | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
Snowflake/snowflake-arctic-embed-m | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"arxiv:2407.18887",
"arxiv:2405.05374",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-11T11:07:56 | 2024-12-13T20:51:22 | 418,779 | 153 | ---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
new_version: Snowflake/snowflake-arctic-embed-m-v2.0
model-index:
- name: snowflake-arctic-embed-m
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.80597014925374
- type: ap
value: 39.31198155789558
- type: f1
value: 70.48198448222148
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 82.831525
- type: ap
value: 77.4474050181638
- type: f1
value: 82.77204845110204
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.93000000000001
- type: f1
value: 37.98013371053459
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.43
- type: map_at_100
value: 48.208
- type: map_at_1000
value: 48.211
- type: map_at_3
value: 42.579
- type: map_at_5
value: 45.263999999999996
- type: mrr_at_1
value: 31.65
- type: mrr_at_10
value: 47.573
- type: mrr_at_100
value: 48.359
- type: mrr_at_1000
value: 48.362
- type: mrr_at_3
value: 42.734
- type: mrr_at_5
value: 45.415
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 56.436
- type: ndcg_at_100
value: 59.657000000000004
- type: ndcg_at_1000
value: 59.731
- type: ndcg_at_3
value: 46.327
- type: ndcg_at_5
value: 51.178000000000004
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.527999999999999
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.061
- type: precision_at_5
value: 13.797999999999998
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.277
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 57.18299999999999
- type: recall_at_5
value: 68.99
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.23625429411296
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 37.433880471403654
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.53175025582013
- type: mrr
value: 74.51160796728664
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.93746103286769
- type: cos_sim_spearman
value: 86.62245567912619
- type: euclidean_pearson
value: 87.154173907501
- type: euclidean_spearman
value: 86.62245567912619
- type: manhattan_pearson
value: 87.17682026633462
- type: manhattan_spearman
value: 86.74775973908348
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.33766233766232
- type: f1
value: 79.64931422442245
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 19.116028913890613
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.966921852810174
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.98019698537654
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 34.079
- type: map_at_10
value: 46.35
- type: map_at_100
value: 47.785
- type: map_at_1000
value: 47.903
- type: map_at_3
value: 42.620999999999995
- type: map_at_5
value: 44.765
- type: mrr_at_1
value: 41.345
- type: mrr_at_10
value: 52.032000000000004
- type: mrr_at_100
value: 52.690000000000005
- type: mrr_at_1000
value: 52.727999999999994
- type: mrr_at_3
value: 49.428
- type: mrr_at_5
value: 51.093999999999994
- type: ndcg_at_1
value: 41.345
- type: ndcg_at_10
value: 53.027
- type: ndcg_at_100
value: 57.962
- type: ndcg_at_1000
value: 59.611999999999995
- type: ndcg_at_3
value: 47.687000000000005
- type: ndcg_at_5
value: 50.367
- type: precision_at_1
value: 41.345
- type: precision_at_10
value: 10.157
- type: precision_at_100
value: 1.567
- type: precision_at_1000
value: 0.199
- type: precision_at_3
value: 23.081
- type: precision_at_5
value: 16.738
- type: recall_at_1
value: 34.079
- type: recall_at_10
value: 65.93900000000001
- type: recall_at_100
value: 86.42699999999999
- type: recall_at_1000
value: 96.61
- type: recall_at_3
value: 50.56699999999999
- type: recall_at_5
value: 57.82000000000001
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 33.289
- type: map_at_10
value: 43.681
- type: map_at_100
value: 45.056000000000004
- type: map_at_1000
value: 45.171
- type: map_at_3
value: 40.702
- type: map_at_5
value: 42.292
- type: mrr_at_1
value: 41.146
- type: mrr_at_10
value: 49.604
- type: mrr_at_100
value: 50.28399999999999
- type: mrr_at_1000
value: 50.322
- type: mrr_at_3
value: 47.611
- type: mrr_at_5
value: 48.717
- type: ndcg_at_1
value: 41.146
- type: ndcg_at_10
value: 49.43
- type: ndcg_at_100
value: 54.01899999999999
- type: ndcg_at_1000
value: 55.803000000000004
- type: ndcg_at_3
value: 45.503
- type: ndcg_at_5
value: 47.198
- type: precision_at_1
value: 41.146
- type: precision_at_10
value: 9.268
- type: precision_at_100
value: 1.4749999999999999
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 21.932
- type: precision_at_5
value: 15.389
- type: recall_at_1
value: 33.289
- type: recall_at_10
value: 59.209999999999994
- type: recall_at_100
value: 78.676
- type: recall_at_1000
value: 89.84100000000001
- type: recall_at_3
value: 47.351
- type: recall_at_5
value: 52.178999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 44.483
- type: map_at_10
value: 56.862
- type: map_at_100
value: 57.901
- type: map_at_1000
value: 57.948
- type: map_at_3
value: 53.737
- type: map_at_5
value: 55.64
- type: mrr_at_1
value: 50.658
- type: mrr_at_10
value: 60.281
- type: mrr_at_100
value: 60.946
- type: mrr_at_1000
value: 60.967000000000006
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.531
- type: ndcg_at_1
value: 50.658
- type: ndcg_at_10
value: 62.339
- type: ndcg_at_100
value: 66.28399999999999
- type: ndcg_at_1000
value: 67.166
- type: ndcg_at_3
value: 57.458
- type: ndcg_at_5
value: 60.112
- type: precision_at_1
value: 50.658
- type: precision_at_10
value: 9.762
- type: precision_at_100
value: 1.26
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 25.329
- type: precision_at_5
value: 17.254
- type: recall_at_1
value: 44.483
- type: recall_at_10
value: 74.819
- type: recall_at_100
value: 91.702
- type: recall_at_1000
value: 97.84
- type: recall_at_3
value: 62.13999999999999
- type: recall_at_5
value: 68.569
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 26.489
- type: map_at_10
value: 37.004999999999995
- type: map_at_100
value: 38.001000000000005
- type: map_at_1000
value: 38.085
- type: map_at_3
value: 34.239999999999995
- type: map_at_5
value: 35.934
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 38.807
- type: mrr_at_100
value: 39.671
- type: mrr_at_1000
value: 39.736
- type: mrr_at_3
value: 36.29
- type: mrr_at_5
value: 37.906
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 42.510999999999996
- type: ndcg_at_100
value: 47.226
- type: ndcg_at_1000
value: 49.226
- type: ndcg_at_3
value: 37.295
- type: ndcg_at_5
value: 40.165
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.633
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 16.234
- type: precision_at_5
value: 11.434999999999999
- type: recall_at_1
value: 26.489
- type: recall_at_10
value: 57.457
- type: recall_at_100
value: 78.712
- type: recall_at_1000
value: 93.565
- type: recall_at_3
value: 43.748
- type: recall_at_5
value: 50.589
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 12.418999999999999
- type: map_at_10
value: 22.866
- type: map_at_100
value: 24.365000000000002
- type: map_at_1000
value: 24.479
- type: map_at_3
value: 19.965
- type: map_at_5
value: 21.684
- type: mrr_at_1
value: 14.677000000000001
- type: mrr_at_10
value: 26.316
- type: mrr_at_100
value: 27.514
- type: mrr_at_1000
value: 27.57
- type: mrr_at_3
value: 23.3
- type: mrr_at_5
value: 25.191000000000003
- type: ndcg_at_1
value: 14.677000000000001
- type: ndcg_at_10
value: 28.875
- type: ndcg_at_100
value: 35.607
- type: ndcg_at_1000
value: 38.237
- type: ndcg_at_3
value: 23.284
- type: ndcg_at_5
value: 26.226
- type: precision_at_1
value: 14.677000000000001
- type: precision_at_10
value: 5.771
- type: precision_at_100
value: 1.058
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 11.940000000000001
- type: precision_at_5
value: 9.229
- type: recall_at_1
value: 12.418999999999999
- type: recall_at_10
value: 43.333
- type: recall_at_100
value: 71.942
- type: recall_at_1000
value: 90.67399999999999
- type: recall_at_3
value: 28.787000000000003
- type: recall_at_5
value: 35.638
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.686999999999998
- type: map_at_10
value: 42.331
- type: map_at_100
value: 43.655
- type: map_at_1000
value: 43.771
- type: map_at_3
value: 38.944
- type: map_at_5
value: 40.991
- type: mrr_at_1
value: 37.921
- type: mrr_at_10
value: 47.534
- type: mrr_at_100
value: 48.362
- type: mrr_at_1000
value: 48.405
- type: mrr_at_3
value: 44.995000000000005
- type: mrr_at_5
value: 46.617
- type: ndcg_at_1
value: 37.921
- type: ndcg_at_10
value: 48.236000000000004
- type: ndcg_at_100
value: 53.705000000000005
- type: ndcg_at_1000
value: 55.596000000000004
- type: ndcg_at_3
value: 43.11
- type: ndcg_at_5
value: 45.862
- type: precision_at_1
value: 37.921
- type: precision_at_10
value: 8.643
- type: precision_at_100
value: 1.336
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 20.308
- type: precision_at_5
value: 14.514
- type: recall_at_1
value: 31.686999999999998
- type: recall_at_10
value: 60.126999999999995
- type: recall_at_100
value: 83.10600000000001
- type: recall_at_1000
value: 95.15
- type: recall_at_3
value: 46.098
- type: recall_at_5
value: 53.179
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.686
- type: map_at_10
value: 39.146
- type: map_at_100
value: 40.543
- type: map_at_1000
value: 40.644999999999996
- type: map_at_3
value: 36.195
- type: map_at_5
value: 37.919000000000004
- type: mrr_at_1
value: 35.160000000000004
- type: mrr_at_10
value: 44.711
- type: mrr_at_100
value: 45.609
- type: mrr_at_1000
value: 45.655
- type: mrr_at_3
value: 42.409
- type: mrr_at_5
value: 43.779
- type: ndcg_at_1
value: 35.160000000000004
- type: ndcg_at_10
value: 44.977000000000004
- type: ndcg_at_100
value: 50.663000000000004
- type: ndcg_at_1000
value: 52.794
- type: ndcg_at_3
value: 40.532000000000004
- type: ndcg_at_5
value: 42.641
- type: precision_at_1
value: 35.160000000000004
- type: precision_at_10
value: 8.014000000000001
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 19.444
- type: precision_at_5
value: 13.653
- type: recall_at_1
value: 28.686
- type: recall_at_10
value: 56.801
- type: recall_at_100
value: 80.559
- type: recall_at_1000
value: 95.052
- type: recall_at_3
value: 43.675999999999995
- type: recall_at_5
value: 49.703
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.173833333333338
- type: map_at_10
value: 38.202083333333334
- type: map_at_100
value: 39.47475
- type: map_at_1000
value: 39.586499999999994
- type: map_at_3
value: 35.17308333333334
- type: map_at_5
value: 36.914
- type: mrr_at_1
value: 32.92958333333333
- type: mrr_at_10
value: 42.16758333333333
- type: mrr_at_100
value: 43.04108333333333
- type: mrr_at_1000
value: 43.092499999999994
- type: mrr_at_3
value: 39.69166666666666
- type: mrr_at_5
value: 41.19458333333333
- type: ndcg_at_1
value: 32.92958333333333
- type: ndcg_at_10
value: 43.80583333333333
- type: ndcg_at_100
value: 49.060916666666664
- type: ndcg_at_1000
value: 51.127250000000004
- type: ndcg_at_3
value: 38.80383333333333
- type: ndcg_at_5
value: 41.29658333333333
- type: precision_at_1
value: 32.92958333333333
- type: precision_at_10
value: 7.655666666666666
- type: precision_at_100
value: 1.2094166666666668
- type: precision_at_1000
value: 0.15750000000000003
- type: precision_at_3
value: 17.87975
- type: precision_at_5
value: 12.741833333333332
- type: recall_at_1
value: 28.173833333333338
- type: recall_at_10
value: 56.219249999999995
- type: recall_at_100
value: 79.01416666666665
- type: recall_at_1000
value: 93.13425000000001
- type: recall_at_3
value: 42.39241666666667
- type: recall_at_5
value: 48.764833333333335
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.625999999999998
- type: map_at_10
value: 32.808
- type: map_at_100
value: 33.951
- type: map_at_1000
value: 34.052
- type: map_at_3
value: 30.536
- type: map_at_5
value: 31.77
- type: mrr_at_1
value: 28.374
- type: mrr_at_10
value: 35.527
- type: mrr_at_100
value: 36.451
- type: mrr_at_1000
value: 36.522
- type: mrr_at_3
value: 33.410000000000004
- type: mrr_at_5
value: 34.537
- type: ndcg_at_1
value: 28.374
- type: ndcg_at_10
value: 37.172
- type: ndcg_at_100
value: 42.474000000000004
- type: ndcg_at_1000
value: 44.853
- type: ndcg_at_3
value: 32.931
- type: ndcg_at_5
value: 34.882999999999996
- type: precision_at_1
value: 28.374
- type: precision_at_10
value: 5.813
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 14.008000000000001
- type: precision_at_5
value: 9.754999999999999
- type: recall_at_1
value: 25.625999999999998
- type: recall_at_10
value: 47.812
- type: recall_at_100
value: 71.61800000000001
- type: recall_at_1000
value: 88.881
- type: recall_at_3
value: 35.876999999999995
- type: recall_at_5
value: 40.839
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.233
- type: map_at_10
value: 26.375999999999998
- type: map_at_100
value: 27.575
- type: map_at_1000
value: 27.706999999999997
- type: map_at_3
value: 23.619
- type: map_at_5
value: 25.217
- type: mrr_at_1
value: 22.023
- type: mrr_at_10
value: 30.122
- type: mrr_at_100
value: 31.083
- type: mrr_at_1000
value: 31.163999999999998
- type: mrr_at_3
value: 27.541
- type: mrr_at_5
value: 29.061999999999998
- type: ndcg_at_1
value: 22.023
- type: ndcg_at_10
value: 31.476
- type: ndcg_at_100
value: 37.114000000000004
- type: ndcg_at_1000
value: 39.981
- type: ndcg_at_3
value: 26.538
- type: ndcg_at_5
value: 29.016
- type: precision_at_1
value: 22.023
- type: precision_at_10
value: 5.819
- type: precision_at_100
value: 1.018
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.583
- type: precision_at_5
value: 9.36
- type: recall_at_1
value: 18.233
- type: recall_at_10
value: 43.029
- type: recall_at_100
value: 68.253
- type: recall_at_1000
value: 88.319
- type: recall_at_3
value: 29.541
- type: recall_at_5
value: 35.783
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 28.923
- type: map_at_10
value: 39.231
- type: map_at_100
value: 40.483000000000004
- type: map_at_1000
value: 40.575
- type: map_at_3
value: 35.94
- type: map_at_5
value: 37.683
- type: mrr_at_1
value: 33.955
- type: mrr_at_10
value: 43.163000000000004
- type: mrr_at_100
value: 44.054
- type: mrr_at_1000
value: 44.099
- type: mrr_at_3
value: 40.361000000000004
- type: mrr_at_5
value: 41.905
- type: ndcg_at_1
value: 33.955
- type: ndcg_at_10
value: 45.068000000000005
- type: ndcg_at_100
value: 50.470000000000006
- type: ndcg_at_1000
value: 52.349000000000004
- type: ndcg_at_3
value: 39.298
- type: ndcg_at_5
value: 41.821999999999996
- type: precision_at_1
value: 33.955
- type: precision_at_10
value: 7.649
- type: precision_at_100
value: 1.173
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.817
- type: precision_at_5
value: 12.537
- type: recall_at_1
value: 28.923
- type: recall_at_10
value: 58.934
- type: recall_at_100
value: 81.809
- type: recall_at_1000
value: 94.71300000000001
- type: recall_at_3
value: 42.975
- type: recall_at_5
value: 49.501
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 28.596
- type: map_at_10
value: 38.735
- type: map_at_100
value: 40.264
- type: map_at_1000
value: 40.48
- type: map_at_3
value: 35.394999999999996
- type: map_at_5
value: 37.099
- type: mrr_at_1
value: 33.992
- type: mrr_at_10
value: 43.076
- type: mrr_at_100
value: 44.005
- type: mrr_at_1000
value: 44.043
- type: mrr_at_3
value: 40.415
- type: mrr_at_5
value: 41.957
- type: ndcg_at_1
value: 33.992
- type: ndcg_at_10
value: 44.896
- type: ndcg_at_100
value: 50.44499999999999
- type: ndcg_at_1000
value: 52.675000000000004
- type: ndcg_at_3
value: 39.783
- type: ndcg_at_5
value: 41.997
- type: precision_at_1
value: 33.992
- type: precision_at_10
value: 8.498
- type: precision_at_100
value: 1.585
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 18.511
- type: precision_at_5
value: 13.241
- type: recall_at_1
value: 28.596
- type: recall_at_10
value: 56.885
- type: recall_at_100
value: 82.306
- type: recall_at_1000
value: 95.813
- type: recall_at_3
value: 42.168
- type: recall_at_5
value: 48.32
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.576
- type: map_at_10
value: 33.034
- type: map_at_100
value: 34.117999999999995
- type: map_at_1000
value: 34.222
- type: map_at_3
value: 30.183
- type: map_at_5
value: 31.974000000000004
- type: mrr_at_1
value: 27.542
- type: mrr_at_10
value: 34.838
- type: mrr_at_100
value: 35.824
- type: mrr_at_1000
value: 35.899
- type: mrr_at_3
value: 32.348
- type: mrr_at_5
value: 34.039
- type: ndcg_at_1
value: 27.542
- type: ndcg_at_10
value: 37.663000000000004
- type: ndcg_at_100
value: 42.762
- type: ndcg_at_1000
value: 45.235
- type: ndcg_at_3
value: 32.227
- type: ndcg_at_5
value: 35.27
- type: precision_at_1
value: 27.542
- type: precision_at_10
value: 5.840999999999999
- type: precision_at_100
value: 0.895
- type: precision_at_1000
value: 0.123
- type: precision_at_3
value: 13.370000000000001
- type: precision_at_5
value: 9.797
- type: recall_at_1
value: 25.576
- type: recall_at_10
value: 50.285000000000004
- type: recall_at_100
value: 73.06
- type: recall_at_1000
value: 91.15299999999999
- type: recall_at_3
value: 35.781
- type: recall_at_5
value: 43.058
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 17.061
- type: map_at_10
value: 29.464000000000002
- type: map_at_100
value: 31.552999999999997
- type: map_at_1000
value: 31.707
- type: map_at_3
value: 24.834999999999997
- type: map_at_5
value: 27.355
- type: mrr_at_1
value: 38.958
- type: mrr_at_10
value: 51.578
- type: mrr_at_100
value: 52.262
- type: mrr_at_1000
value: 52.283
- type: mrr_at_3
value: 48.599
- type: mrr_at_5
value: 50.404
- type: ndcg_at_1
value: 38.958
- type: ndcg_at_10
value: 39.367999999999995
- type: ndcg_at_100
value: 46.521
- type: ndcg_at_1000
value: 49.086999999999996
- type: ndcg_at_3
value: 33.442
- type: ndcg_at_5
value: 35.515
- type: precision_at_1
value: 38.958
- type: precision_at_10
value: 12.110999999999999
- type: precision_at_100
value: 1.982
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 25.102999999999998
- type: precision_at_5
value: 18.971
- type: recall_at_1
value: 17.061
- type: recall_at_10
value: 45.198
- type: recall_at_100
value: 69.18900000000001
- type: recall_at_1000
value: 83.38499999999999
- type: recall_at_3
value: 30.241
- type: recall_at_5
value: 36.851
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.398
- type: map_at_10
value: 21.421
- type: map_at_100
value: 31.649
- type: map_at_1000
value: 33.469
- type: map_at_3
value: 15.310000000000002
- type: map_at_5
value: 17.946
- type: mrr_at_1
value: 71
- type: mrr_at_10
value: 78.92099999999999
- type: mrr_at_100
value: 79.225
- type: mrr_at_1000
value: 79.23
- type: mrr_at_3
value: 77.792
- type: mrr_at_5
value: 78.467
- type: ndcg_at_1
value: 57.99999999999999
- type: ndcg_at_10
value: 44.733000000000004
- type: ndcg_at_100
value: 50.646
- type: ndcg_at_1000
value: 57.903999999999996
- type: ndcg_at_3
value: 49.175999999999995
- type: ndcg_at_5
value: 46.800999999999995
- type: precision_at_1
value: 71
- type: precision_at_10
value: 36.25
- type: precision_at_100
value: 12.135
- type: precision_at_1000
value: 2.26
- type: precision_at_3
value: 52.75
- type: precision_at_5
value: 45.65
- type: recall_at_1
value: 9.398
- type: recall_at_10
value: 26.596999999999998
- type: recall_at_100
value: 57.943
- type: recall_at_1000
value: 81.147
- type: recall_at_3
value: 16.634
- type: recall_at_5
value: 20.7
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.535000000000004
- type: f1
value: 42.53702746452163
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 77.235
- type: map_at_10
value: 85.504
- type: map_at_100
value: 85.707
- type: map_at_1000
value: 85.718
- type: map_at_3
value: 84.425
- type: map_at_5
value: 85.13
- type: mrr_at_1
value: 83.363
- type: mrr_at_10
value: 89.916
- type: mrr_at_100
value: 89.955
- type: mrr_at_1000
value: 89.956
- type: mrr_at_3
value: 89.32600000000001
- type: mrr_at_5
value: 89.79
- type: ndcg_at_1
value: 83.363
- type: ndcg_at_10
value: 89.015
- type: ndcg_at_100
value: 89.649
- type: ndcg_at_1000
value: 89.825
- type: ndcg_at_3
value: 87.45100000000001
- type: ndcg_at_5
value: 88.39399999999999
- type: precision_at_1
value: 83.363
- type: precision_at_10
value: 10.659
- type: precision_at_100
value: 1.122
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.338
- type: precision_at_5
value: 20.671999999999997
- type: recall_at_1
value: 77.235
- type: recall_at_10
value: 95.389
- type: recall_at_100
value: 97.722
- type: recall_at_1000
value: 98.744
- type: recall_at_3
value: 91.19800000000001
- type: recall_at_5
value: 93.635
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 20.835
- type: map_at_10
value: 34.459
- type: map_at_100
value: 36.335
- type: map_at_1000
value: 36.518
- type: map_at_3
value: 30.581000000000003
- type: map_at_5
value: 32.859
- type: mrr_at_1
value: 40.894999999999996
- type: mrr_at_10
value: 50.491
- type: mrr_at_100
value: 51.243
- type: mrr_at_1000
value: 51.286
- type: mrr_at_3
value: 47.994
- type: mrr_at_5
value: 49.429
- type: ndcg_at_1
value: 40.894999999999996
- type: ndcg_at_10
value: 42.403
- type: ndcg_at_100
value: 48.954
- type: ndcg_at_1000
value: 51.961
- type: ndcg_at_3
value: 39.11
- type: ndcg_at_5
value: 40.152
- type: precision_at_1
value: 40.894999999999996
- type: precision_at_10
value: 11.466
- type: precision_at_100
value: 1.833
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 25.874000000000002
- type: precision_at_5
value: 19.012
- type: recall_at_1
value: 20.835
- type: recall_at_10
value: 49.535000000000004
- type: recall_at_100
value: 73.39099999999999
- type: recall_at_1000
value: 91.01599999999999
- type: recall_at_3
value: 36.379
- type: recall_at_5
value: 42.059999999999995
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 40.945
- type: map_at_10
value: 65.376
- type: map_at_100
value: 66.278
- type: map_at_1000
value: 66.33
- type: map_at_3
value: 61.753
- type: map_at_5
value: 64.077
- type: mrr_at_1
value: 81.891
- type: mrr_at_10
value: 87.256
- type: mrr_at_100
value: 87.392
- type: mrr_at_1000
value: 87.395
- type: mrr_at_3
value: 86.442
- type: mrr_at_5
value: 86.991
- type: ndcg_at_1
value: 81.891
- type: ndcg_at_10
value: 73.654
- type: ndcg_at_100
value: 76.62299999999999
- type: ndcg_at_1000
value: 77.60000000000001
- type: ndcg_at_3
value: 68.71199999999999
- type: ndcg_at_5
value: 71.563
- type: precision_at_1
value: 81.891
- type: precision_at_10
value: 15.409
- type: precision_at_100
value: 1.77
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 44.15
- type: precision_at_5
value: 28.732000000000003
- type: recall_at_1
value: 40.945
- type: recall_at_10
value: 77.04299999999999
- type: recall_at_100
value: 88.508
- type: recall_at_1000
value: 94.943
- type: recall_at_3
value: 66.226
- type: recall_at_5
value: 71.83
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 74.08200000000001
- type: ap
value: 68.10929101713998
- type: f1
value: 73.98447117652009
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.729000000000003
- type: map_at_10
value: 34.602
- type: map_at_100
value: 35.756
- type: map_at_1000
value: 35.803000000000004
- type: map_at_3
value: 30.619000000000003
- type: map_at_5
value: 32.914
- type: mrr_at_1
value: 22.364
- type: mrr_at_10
value: 35.183
- type: mrr_at_100
value: 36.287000000000006
- type: mrr_at_1000
value: 36.327999999999996
- type: mrr_at_3
value: 31.258000000000003
- type: mrr_at_5
value: 33.542
- type: ndcg_at_1
value: 22.364
- type: ndcg_at_10
value: 41.765
- type: ndcg_at_100
value: 47.293
- type: ndcg_at_1000
value: 48.457
- type: ndcg_at_3
value: 33.676
- type: ndcg_at_5
value: 37.783
- type: precision_at_1
value: 22.364
- type: precision_at_10
value: 6.662
- type: precision_at_100
value: 0.943
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.435999999999998
- type: precision_at_5
value: 10.764999999999999
- type: recall_at_1
value: 21.729000000000003
- type: recall_at_10
value: 63.815999999999995
- type: recall_at_100
value: 89.265
- type: recall_at_1000
value: 98.149
- type: recall_at_3
value: 41.898
- type: recall_at_5
value: 51.76500000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.73141814865483
- type: f1
value: 92.17518476408004
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.18011855905152
- type: f1
value: 46.70999638311856
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 75.24261603375525
- type: f1
value: 74.07895183913367
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 28.43855875387446
- type: v_measure
value: 29.05331990256969
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.92333557498318
- type: f1
value: 64.29789389602692
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.74714189643578
- type: f1
value: 71.672585608315
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.503564225501613
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.410225127136457
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.170019896091908
- type: mrr
value: 29.881276831500976
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.544
- type: map_at_10
value: 14.116999999999999
- type: map_at_100
value: 17.522
- type: map_at_1000
value: 19
- type: map_at_3
value: 10.369
- type: map_at_5
value: 12.189
- type: mrr_at_1
value: 47.988
- type: mrr_at_10
value: 56.84
- type: mrr_at_100
value: 57.367000000000004
- type: mrr_at_1000
value: 57.403000000000006
- type: mrr_at_3
value: 54.592
- type: mrr_at_5
value: 56.233
- type: ndcg_at_1
value: 45.82
- type: ndcg_at_10
value: 36.767
- type: ndcg_at_100
value: 33.356
- type: ndcg_at_1000
value: 42.062
- type: ndcg_at_3
value: 42.15
- type: ndcg_at_5
value: 40.355000000000004
- type: precision_at_1
value: 47.988
- type: precision_at_10
value: 27.121000000000002
- type: precision_at_100
value: 8.455
- type: precision_at_1000
value: 2.103
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 35.356
- type: recall_at_1
value: 6.544
- type: recall_at_10
value: 17.928
- type: recall_at_100
value: 32.843
- type: recall_at_1000
value: 65.752
- type: recall_at_3
value: 11.297
- type: recall_at_5
value: 14.357000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 39.262
- type: map_at_10
value: 55.095000000000006
- type: map_at_100
value: 55.93900000000001
- type: map_at_1000
value: 55.955999999999996
- type: map_at_3
value: 50.93
- type: map_at_5
value: 53.491
- type: mrr_at_1
value: 43.598
- type: mrr_at_10
value: 57.379999999999995
- type: mrr_at_100
value: 57.940999999999995
- type: mrr_at_1000
value: 57.952000000000005
- type: mrr_at_3
value: 53.998000000000005
- type: mrr_at_5
value: 56.128
- type: ndcg_at_1
value: 43.598
- type: ndcg_at_10
value: 62.427
- type: ndcg_at_100
value: 65.759
- type: ndcg_at_1000
value: 66.133
- type: ndcg_at_3
value: 54.745999999999995
- type: ndcg_at_5
value: 58.975
- type: precision_at_1
value: 43.598
- type: precision_at_10
value: 9.789
- type: precision_at_100
value: 1.171
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 24.295
- type: precision_at_5
value: 17.028
- type: recall_at_1
value: 39.262
- type: recall_at_10
value: 82.317
- type: recall_at_100
value: 96.391
- type: recall_at_1000
value: 99.116
- type: recall_at_3
value: 62.621
- type: recall_at_5
value: 72.357
- task:
type: Classification
dataset:
name: MTEB NewsClassification
type: ag_news
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy
value: 78.17500000000001
- type: f1
value: 78.01940892857273
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.89816700610999
- type: cos_sim_ap
value: 100
- type: cos_sim_f1
value: 99.9490575649516
- type: cos_sim_precision
value: 100
- type: cos_sim_recall
value: 99.89816700610999
- type: dot_accuracy
value: 99.89816700610999
- type: dot_ap
value: 100
- type: dot_f1
value: 99.9490575649516
- type: dot_precision
value: 100
- type: dot_recall
value: 99.89816700610999
- type: euclidean_accuracy
value: 99.89816700610999
- type: euclidean_ap
value: 100
- type: euclidean_f1
value: 99.9490575649516
- type: euclidean_precision
value: 100
- type: euclidean_recall
value: 99.89816700610999
- type: manhattan_accuracy
value: 99.89816700610999
- type: manhattan_ap
value: 100
- type: manhattan_f1
value: 99.9490575649516
- type: manhattan_precision
value: 100
- type: manhattan_recall
value: 99.89816700610999
- type: max_accuracy
value: 99.89816700610999
- type: max_ap
value: 100
- type: max_f1
value: 99.9490575649516
- task:
type: PairClassification
dataset:
name: MTEB PawsX (en)
type: paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61
- type: cos_sim_ap
value: 59.630757252602464
- type: cos_sim_f1
value: 62.37521514629949
- type: cos_sim_precision
value: 45.34534534534534
- type: cos_sim_recall
value: 99.88974641675854
- type: dot_accuracy
value: 61
- type: dot_ap
value: 59.631527308059006
- type: dot_f1
value: 62.37521514629949
- type: dot_precision
value: 45.34534534534534
- type: dot_recall
value: 99.88974641675854
- type: euclidean_accuracy
value: 61
- type: euclidean_ap
value: 59.630757252602464
- type: euclidean_f1
value: 62.37521514629949
- type: euclidean_precision
value: 45.34534534534534
- type: euclidean_recall
value: 99.88974641675854
- type: manhattan_accuracy
value: 60.9
- type: manhattan_ap
value: 59.613947780462254
- type: manhattan_f1
value: 62.37521514629949
- type: manhattan_precision
value: 45.34534534534534
- type: manhattan_recall
value: 99.88974641675854
- type: max_accuracy
value: 61
- type: max_ap
value: 59.631527308059006
- type: max_f1
value: 62.37521514629949
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 69.963
- type: map_at_10
value: 83.59400000000001
- type: map_at_100
value: 84.236
- type: map_at_1000
value: 84.255
- type: map_at_3
value: 80.69800000000001
- type: map_at_5
value: 82.568
- type: mrr_at_1
value: 80.58999999999999
- type: mrr_at_10
value: 86.78200000000001
- type: mrr_at_100
value: 86.89099999999999
- type: mrr_at_1000
value: 86.893
- type: mrr_at_3
value: 85.757
- type: mrr_at_5
value: 86.507
- type: ndcg_at_1
value: 80.60000000000001
- type: ndcg_at_10
value: 87.41799999999999
- type: ndcg_at_100
value: 88.723
- type: ndcg_at_1000
value: 88.875
- type: ndcg_at_3
value: 84.565
- type: ndcg_at_5
value: 86.236
- type: precision_at_1
value: 80.60000000000001
- type: precision_at_10
value: 13.239
- type: precision_at_100
value: 1.5150000000000001
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.947
- type: precision_at_5
value: 24.354
- type: recall_at_1
value: 69.963
- type: recall_at_10
value: 94.553
- type: recall_at_100
value: 99.104
- type: recall_at_1000
value: 99.872
- type: recall_at_3
value: 86.317
- type: recall_at_5
value: 91.023
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 47.52890410998761
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 62.760692287940486
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.093
- type: map_at_10
value: 12.695
- type: map_at_100
value: 14.824000000000002
- type: map_at_1000
value: 15.123000000000001
- type: map_at_3
value: 8.968
- type: map_at_5
value: 10.828
- type: mrr_at_1
value: 25.1
- type: mrr_at_10
value: 35.894999999999996
- type: mrr_at_100
value: 36.966
- type: mrr_at_1000
value: 37.019999999999996
- type: mrr_at_3
value: 32.467
- type: mrr_at_5
value: 34.416999999999994
- type: ndcg_at_1
value: 25.1
- type: ndcg_at_10
value: 21.096999999999998
- type: ndcg_at_100
value: 29.202
- type: ndcg_at_1000
value: 34.541
- type: ndcg_at_3
value: 19.875
- type: ndcg_at_5
value: 17.497
- type: precision_at_1
value: 25.1
- type: precision_at_10
value: 10.9
- type: precision_at_100
value: 2.255
- type: precision_at_1000
value: 0.35400000000000004
- type: precision_at_3
value: 18.367
- type: precision_at_5
value: 15.299999999999999
- type: recall_at_1
value: 5.093
- type: recall_at_10
value: 22.092
- type: recall_at_100
value: 45.778
- type: recall_at_1000
value: 71.985
- type: recall_at_3
value: 11.167
- type: recall_at_5
value: 15.501999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 74.04386981759481
- type: cos_sim_spearman
value: 69.12484963763646
- type: euclidean_pearson
value: 71.49384353291062
- type: euclidean_spearman
value: 69.12484548317074
- type: manhattan_pearson
value: 71.49828173987272
- type: manhattan_spearman
value: 69.08350274367014
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 66.95372527615659
- type: cos_sim_spearman
value: 66.96821894433991
- type: euclidean_pearson
value: 64.675348002074
- type: euclidean_spearman
value: 66.96821894433991
- type: manhattan_pearson
value: 64.5965887073831
- type: manhattan_spearman
value: 66.88569076794741
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.34698437961983
- type: cos_sim_spearman
value: 79.1153001117325
- type: euclidean_pearson
value: 78.53562874696966
- type: euclidean_spearman
value: 79.11530018205724
- type: manhattan_pearson
value: 78.46484988944093
- type: manhattan_spearman
value: 79.01416027493104
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 68.81220371935373
- type: cos_sim_spearman
value: 68.50538405089604
- type: euclidean_pearson
value: 68.69204272683749
- type: euclidean_spearman
value: 68.50534223912419
- type: manhattan_pearson
value: 68.67300120149523
- type: manhattan_spearman
value: 68.45404301623115
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 78.2464678879813
- type: cos_sim_spearman
value: 79.92003940566667
- type: euclidean_pearson
value: 79.8080778793964
- type: euclidean_spearman
value: 79.92003940566667
- type: manhattan_pearson
value: 79.80153621444681
- type: manhattan_spearman
value: 79.91293261418134
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 76.31179207708662
- type: cos_sim_spearman
value: 78.65597349856115
- type: euclidean_pearson
value: 78.76937027472678
- type: euclidean_spearman
value: 78.65597349856115
- type: manhattan_pearson
value: 78.77129513300605
- type: manhattan_spearman
value: 78.62640467680775
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.43158429552561
- type: cos_sim_spearman
value: 81.46108646565362
- type: euclidean_pearson
value: 81.47071791452292
- type: euclidean_spearman
value: 81.46108646565362
- type: manhattan_pearson
value: 81.56920643846031
- type: manhattan_spearman
value: 81.42226241399516
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 66.89546474141514
- type: cos_sim_spearman
value: 65.8393752170531
- type: euclidean_pearson
value: 67.2580522762307
- type: euclidean_spearman
value: 65.8393752170531
- type: manhattan_pearson
value: 67.45157729300522
- type: manhattan_spearman
value: 66.19470854403802
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 71.39566306334434
- type: cos_sim_spearman
value: 74.0981396086974
- type: euclidean_pearson
value: 73.7834496259745
- type: euclidean_spearman
value: 74.09803741302046
- type: manhattan_pearson
value: 73.79958138780945
- type: manhattan_spearman
value: 74.09894837555905
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: PhilipMay/stsb_multi_mt
config: en
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 71.39566311006806
- type: cos_sim_spearman
value: 74.0981396086974
- type: euclidean_pearson
value: 73.78344970897099
- type: euclidean_spearman
value: 74.09803741302046
- type: manhattan_pearson
value: 73.79958147136705
- type: manhattan_spearman
value: 74.09894837555905
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 80.81059564334683
- type: mrr
value: 94.62696617108381
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 57.760999999999996
- type: map_at_10
value: 68.614
- type: map_at_100
value: 69.109
- type: map_at_1000
value: 69.134
- type: map_at_3
value: 65.735
- type: map_at_5
value: 67.42099999999999
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 69.94200000000001
- type: mrr_at_100
value: 70.254
- type: mrr_at_1000
value: 70.28
- type: mrr_at_3
value: 67.72200000000001
- type: mrr_at_5
value: 69.18900000000001
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 73.548
- type: ndcg_at_100
value: 75.381
- type: ndcg_at_1000
value: 75.991
- type: ndcg_at_3
value: 68.685
- type: ndcg_at_5
value: 71.26
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.833
- type: precision_at_100
value: 1.08
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.889000000000003
- type: precision_at_5
value: 17.8
- type: recall_at_1
value: 57.760999999999996
- type: recall_at_10
value: 87.13300000000001
- type: recall_at_100
value: 95
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 74.211
- type: recall_at_5
value: 80.63900000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81881188118813
- type: cos_sim_ap
value: 95.21196473745837
- type: cos_sim_f1
value: 90.69767441860465
- type: cos_sim_precision
value: 91.71779141104295
- type: cos_sim_recall
value: 89.7
- type: dot_accuracy
value: 99.81881188118813
- type: dot_ap
value: 95.21196473745837
- type: dot_f1
value: 90.69767441860465
- type: dot_precision
value: 91.71779141104295
- type: dot_recall
value: 89.7
- type: euclidean_accuracy
value: 99.81881188118813
- type: euclidean_ap
value: 95.21196473745839
- type: euclidean_f1
value: 90.69767441860465
- type: euclidean_precision
value: 91.71779141104295
- type: euclidean_recall
value: 89.7
- type: manhattan_accuracy
value: 99.81287128712871
- type: manhattan_ap
value: 95.16667174835017
- type: manhattan_f1
value: 90.41095890410959
- type: manhattan_precision
value: 91.7610710607621
- type: manhattan_recall
value: 89.1
- type: max_accuracy
value: 99.81881188118813
- type: max_ap
value: 95.21196473745839
- type: max_f1
value: 90.69767441860465
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 59.54942204515638
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 39.42892282672948
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.189033075914324
- type: mrr
value: 51.97014790764791
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.09466569775977
- type: cos_sim_spearman
value: 30.31058660775912
- type: dot_pearson
value: 30.09466438861689
- type: dot_spearman
value: 30.31058660775912
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.253
- type: map_at_10
value: 2.07
- type: map_at_100
value: 12.679000000000002
- type: map_at_1000
value: 30.412
- type: map_at_3
value: 0.688
- type: map_at_5
value: 1.079
- type: mrr_at_1
value: 96
- type: mrr_at_10
value: 98
- type: mrr_at_100
value: 98
- type: mrr_at_1000
value: 98
- type: mrr_at_3
value: 98
- type: mrr_at_5
value: 98
- type: ndcg_at_1
value: 89
- type: ndcg_at_10
value: 79.646
- type: ndcg_at_100
value: 62.217999999999996
- type: ndcg_at_1000
value: 55.13400000000001
- type: ndcg_at_3
value: 83.458
- type: ndcg_at_5
value: 80.982
- type: precision_at_1
value: 96
- type: precision_at_10
value: 84.6
- type: precision_at_100
value: 64.34
- type: precision_at_1000
value: 24.534
- type: precision_at_3
value: 88.667
- type: precision_at_5
value: 85.6
- type: recall_at_1
value: 0.253
- type: recall_at_10
value: 2.253
- type: recall_at_100
value: 15.606
- type: recall_at_1000
value: 51.595
- type: recall_at_3
value: 0.7100000000000001
- type: recall_at_5
value: 1.139
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.0540000000000003
- type: map_at_10
value: 13.078999999999999
- type: map_at_100
value: 19.468
- type: map_at_1000
value: 21.006
- type: map_at_3
value: 6.8629999999999995
- type: map_at_5
value: 9.187
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 56.735
- type: mrr_at_100
value: 57.352000000000004
- type: mrr_at_1000
value: 57.352000000000004
- type: mrr_at_3
value: 52.721
- type: mrr_at_5
value: 54.66
- type: ndcg_at_1
value: 38.775999999999996
- type: ndcg_at_10
value: 31.469
- type: ndcg_at_100
value: 42.016999999999996
- type: ndcg_at_1000
value: 52.60399999999999
- type: ndcg_at_3
value: 35.894
- type: ndcg_at_5
value: 33.873
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 27.346999999999998
- type: precision_at_100
value: 8.327
- type: precision_at_1000
value: 1.551
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.469
- type: recall_at_1
value: 3.0540000000000003
- type: recall_at_10
value: 19.185
- type: recall_at_100
value: 51.056000000000004
- type: recall_at_1000
value: 82.814
- type: recall_at_3
value: 7.961
- type: recall_at_5
value: 11.829
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 64.9346
- type: ap
value: 12.121605736777527
- type: f1
value: 50.169902005887955
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.72608941709111
- type: f1
value: 57.0702928875253
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 37.72671554400943
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.84556237706384
- type: cos_sim_ap
value: 63.28364215788651
- type: cos_sim_f1
value: 60.00000000000001
- type: cos_sim_precision
value: 54.45161290322581
- type: cos_sim_recall
value: 66.80738786279683
- type: dot_accuracy
value: 82.84556237706384
- type: dot_ap
value: 63.28364302860433
- type: dot_f1
value: 60.00000000000001
- type: dot_precision
value: 54.45161290322581
- type: dot_recall
value: 66.80738786279683
- type: euclidean_accuracy
value: 82.84556237706384
- type: euclidean_ap
value: 63.28363625097978
- type: euclidean_f1
value: 60.00000000000001
- type: euclidean_precision
value: 54.45161290322581
- type: euclidean_recall
value: 66.80738786279683
- type: manhattan_accuracy
value: 82.86940454193241
- type: manhattan_ap
value: 63.244773709836764
- type: manhattan_f1
value: 60.12680942696495
- type: manhattan_precision
value: 55.00109433136353
- type: manhattan_recall
value: 66.3060686015831
- type: max_accuracy
value: 82.86940454193241
- type: max_ap
value: 63.28364302860433
- type: max_f1
value: 60.12680942696495
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.32033220786278
- type: cos_sim_ap
value: 84.71928176006863
- type: cos_sim_f1
value: 76.51483333969684
- type: cos_sim_precision
value: 75.89184276300841
- type: cos_sim_recall
value: 77.14813674160764
- type: dot_accuracy
value: 88.32033220786278
- type: dot_ap
value: 84.71928330149228
- type: dot_f1
value: 76.51483333969684
- type: dot_precision
value: 75.89184276300841
- type: dot_recall
value: 77.14813674160764
- type: euclidean_accuracy
value: 88.32033220786278
- type: euclidean_ap
value: 84.71928045384345
- type: euclidean_f1
value: 76.51483333969684
- type: euclidean_precision
value: 75.89184276300841
- type: euclidean_recall
value: 77.14813674160764
- type: manhattan_accuracy
value: 88.27570147863545
- type: manhattan_ap
value: 84.68523541579755
- type: manhattan_f1
value: 76.51512269355146
- type: manhattan_precision
value: 75.62608107091825
- type: manhattan_recall
value: 77.42531567600862
- type: max_accuracy
value: 88.32033220786278
- type: max_ap
value: 84.71928330149228
- type: max_f1
value: 76.51512269355146
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 85.30624598674467
---
<h1 align="center">Snowflake's Arctic-embed-m</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
## News
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!
07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
## Models
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| Google-gecko-text-embedding | 55.7 |
| text-embedding-3-large | 55.44 |
| Cohere-embed-english-v3.0 | 55.00 |
| bge-large-en-v1.5 | 54.29 |
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------- | -------------------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |
| GIST-all-MiniLM-L6-v2 | 45.12 |
| gte-tiny | 44.92 |
| all-MiniLM-L6-v2 | 41.95 |
| bge-micro-v2 | 42.56 |
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |
| bge-small-en-v1.5 | 51.68 |
| Cohere-embed-english-light-v3.0 | 51.34 |
| text-embedding-3-small | 51.08 |
| e5-small-v2 | 49.04 |
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |
| bge-base-en-v1.5 | 53.25 |
| nomic-embed-text-v1.5 | 53.25 |
| GIST-Embedding-v0 | 52.31 |
| gte-base | 52.31 |
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)
Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |
| nomic-embed-text-v1.5 | 53.01 |
| nomic-embed-text-v1 | 52.81 |
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| UAE-Large-V1 | 54.66 |
| bge-large-en-v1.5 | 54.29 |
| mxbai-embed-large-v1 | 54.39 |
| e5-Large-v2 | 50.56 |
## Usage
### Using Sentence Transformers
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m")
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = query_embeddings @ document_embeddings.T
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
Produces:
```
Query: what is snowflake?
0.2747492 The Data Cloud!
0.19998045 Mexico City of Course!
Query: Where can I get the best tacos?
0.29974818 Mexico City of Course!
0.2344071 The Data Cloud!
```
### Using Huggingface transformers
You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m', add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.15664823859882132, 0.24481869975470627]
```
## Using Infinity
OpenAI compatible API deployment with [Infinity](https://github.com/michaelfeil/infinity) and Docker.
```bash
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.70 \
v2 --model-id Snowflake/snowflake-arctic-embed-m --dtype float16 --batch-size 32 --engine torch --port 7997
```
## FAQ
TBD
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
## Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=bda4e7d8-e0d8-4f43-8ecc-7bc1d1c4ed04" /> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/e5-base-v2 | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-19T07:21:14 | 2025-02-17T03:25:40 | 396,638 | 110 | ---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-base-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.77611940298506
- type: ap
value: 42.052710266606056
- type: f1
value: 72.12040628266567
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.81012500000001
- type: ap
value: 89.4213700757244
- type: f1
value: 92.8039091197065
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.711999999999996
- type: f1
value: 46.11544975436018
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.186
- type: map_at_10
value: 36.632999999999996
- type: map_at_100
value: 37.842
- type: map_at_1000
value: 37.865
- type: map_at_3
value: 32.278
- type: map_at_5
value: 34.760999999999996
- type: mrr_at_1
value: 23.400000000000002
- type: mrr_at_10
value: 36.721
- type: mrr_at_100
value: 37.937
- type: mrr_at_1000
value: 37.96
- type: mrr_at_3
value: 32.302
- type: mrr_at_5
value: 34.894
- type: ndcg_at_1
value: 23.186
- type: ndcg_at_10
value: 44.49
- type: ndcg_at_100
value: 50.065000000000005
- type: ndcg_at_1000
value: 50.629999999999995
- type: ndcg_at_3
value: 35.461
- type: ndcg_at_5
value: 39.969
- type: precision_at_1
value: 23.186
- type: precision_at_10
value: 6.97
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.912
- type: precision_at_5
value: 11.152
- type: recall_at_1
value: 23.186
- type: recall_at_10
value: 69.70100000000001
- type: recall_at_100
value: 95.092
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 44.737
- type: recall_at_5
value: 55.761
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.10312401440185
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 39.67275326095384
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.97793816337376
- type: mrr
value: 72.76832431957087
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.11646947018187
- type: cos_sim_spearman
value: 81.40064994975234
- type: euclidean_pearson
value: 82.37355689019232
- type: euclidean_spearman
value: 81.6777646977348
- type: manhattan_pearson
value: 82.61101422716945
- type: manhattan_spearman
value: 81.80427360442245
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 83.52922077922076
- type: f1
value: 83.45298679360866
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.495115019668496
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.724792944166765
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.361000000000004
- type: map_at_10
value: 43.765
- type: map_at_100
value: 45.224
- type: map_at_1000
value: 45.35
- type: map_at_3
value: 40.353
- type: map_at_5
value: 42.195
- type: mrr_at_1
value: 40.629
- type: mrr_at_10
value: 50.458000000000006
- type: mrr_at_100
value: 51.06699999999999
- type: mrr_at_1000
value: 51.12
- type: mrr_at_3
value: 47.902
- type: mrr_at_5
value: 49.447
- type: ndcg_at_1
value: 40.629
- type: ndcg_at_10
value: 50.376
- type: ndcg_at_100
value: 55.065
- type: ndcg_at_1000
value: 57.196000000000005
- type: ndcg_at_3
value: 45.616
- type: ndcg_at_5
value: 47.646
- type: precision_at_1
value: 40.629
- type: precision_at_10
value: 9.785
- type: precision_at_100
value: 1.562
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.031
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 32.361000000000004
- type: recall_at_10
value: 62.214000000000006
- type: recall_at_100
value: 81.464
- type: recall_at_1000
value: 95.905
- type: recall_at_3
value: 47.5
- type: recall_at_5
value: 53.69500000000001
- type: map_at_1
value: 27.971
- type: map_at_10
value: 37.444
- type: map_at_100
value: 38.607
- type: map_at_1000
value: 38.737
- type: map_at_3
value: 34.504000000000005
- type: map_at_5
value: 36.234
- type: mrr_at_1
value: 35.35
- type: mrr_at_10
value: 43.441
- type: mrr_at_100
value: 44.147999999999996
- type: mrr_at_1000
value: 44.196000000000005
- type: mrr_at_3
value: 41.285
- type: mrr_at_5
value: 42.552
- type: ndcg_at_1
value: 35.35
- type: ndcg_at_10
value: 42.903999999999996
- type: ndcg_at_100
value: 47.406
- type: ndcg_at_1000
value: 49.588
- type: ndcg_at_3
value: 38.778
- type: ndcg_at_5
value: 40.788000000000004
- type: precision_at_1
value: 35.35
- type: precision_at_10
value: 8.083
- type: precision_at_100
value: 1.313
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 18.769
- type: precision_at_5
value: 13.439
- type: recall_at_1
value: 27.971
- type: recall_at_10
value: 52.492000000000004
- type: recall_at_100
value: 71.642
- type: recall_at_1000
value: 85.488
- type: recall_at_3
value: 40.1
- type: recall_at_5
value: 45.800000000000004
- type: map_at_1
value: 39.898
- type: map_at_10
value: 51.819
- type: map_at_100
value: 52.886
- type: map_at_1000
value: 52.941
- type: map_at_3
value: 48.619
- type: map_at_5
value: 50.493
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 55.230000000000004
- type: mrr_at_100
value: 55.887
- type: mrr_at_1000
value: 55.916
- type: mrr_at_3
value: 52.717000000000006
- type: mrr_at_5
value: 54.222
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 57.586999999999996
- type: ndcg_at_100
value: 61.745000000000005
- type: ndcg_at_1000
value: 62.83800000000001
- type: ndcg_at_3
value: 52.207
- type: ndcg_at_5
value: 54.925999999999995
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.21
- type: precision_at_100
value: 1.226
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 23.177
- type: precision_at_5
value: 16.038
- type: recall_at_1
value: 39.898
- type: recall_at_10
value: 71.18900000000001
- type: recall_at_100
value: 89.082
- type: recall_at_1000
value: 96.865
- type: recall_at_3
value: 56.907
- type: recall_at_5
value: 63.397999999999996
- type: map_at_1
value: 22.706
- type: map_at_10
value: 30.818
- type: map_at_100
value: 32.038
- type: map_at_1000
value: 32.123000000000005
- type: map_at_3
value: 28.077
- type: map_at_5
value: 29.709999999999997
- type: mrr_at_1
value: 24.407
- type: mrr_at_10
value: 32.555
- type: mrr_at_100
value: 33.692
- type: mrr_at_1000
value: 33.751
- type: mrr_at_3
value: 29.848999999999997
- type: mrr_at_5
value: 31.509999999999998
- type: ndcg_at_1
value: 24.407
- type: ndcg_at_10
value: 35.624
- type: ndcg_at_100
value: 41.454
- type: ndcg_at_1000
value: 43.556
- type: ndcg_at_3
value: 30.217
- type: ndcg_at_5
value: 33.111000000000004
- type: precision_at_1
value: 24.407
- type: precision_at_10
value: 5.548
- type: precision_at_100
value: 0.8869999999999999
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.731
- type: precision_at_5
value: 9.22
- type: recall_at_1
value: 22.706
- type: recall_at_10
value: 48.772
- type: recall_at_100
value: 75.053
- type: recall_at_1000
value: 90.731
- type: recall_at_3
value: 34.421
- type: recall_at_5
value: 41.427
- type: map_at_1
value: 13.424
- type: map_at_10
value: 21.09
- type: map_at_100
value: 22.264999999999997
- type: map_at_1000
value: 22.402
- type: map_at_3
value: 18.312
- type: map_at_5
value: 19.874
- type: mrr_at_1
value: 16.915
- type: mrr_at_10
value: 25.258000000000003
- type: mrr_at_100
value: 26.228
- type: mrr_at_1000
value: 26.31
- type: mrr_at_3
value: 22.492
- type: mrr_at_5
value: 24.04
- type: ndcg_at_1
value: 16.915
- type: ndcg_at_10
value: 26.266000000000002
- type: ndcg_at_100
value: 32.08
- type: ndcg_at_1000
value: 35.086
- type: ndcg_at_3
value: 21.049
- type: ndcg_at_5
value: 23.508000000000003
- type: precision_at_1
value: 16.915
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 10.282
- type: precision_at_5
value: 7.836
- type: recall_at_1
value: 13.424
- type: recall_at_10
value: 38.179
- type: recall_at_100
value: 63.906
- type: recall_at_1000
value: 84.933
- type: recall_at_3
value: 23.878
- type: recall_at_5
value: 30.037999999999997
- type: map_at_1
value: 26.154
- type: map_at_10
value: 35.912
- type: map_at_100
value: 37.211
- type: map_at_1000
value: 37.327
- type: map_at_3
value: 32.684999999999995
- type: map_at_5
value: 34.562
- type: mrr_at_1
value: 32.435
- type: mrr_at_10
value: 41.411
- type: mrr_at_100
value: 42.297000000000004
- type: mrr_at_1000
value: 42.345
- type: mrr_at_3
value: 38.771
- type: mrr_at_5
value: 40.33
- type: ndcg_at_1
value: 32.435
- type: ndcg_at_10
value: 41.785
- type: ndcg_at_100
value: 47.469
- type: ndcg_at_1000
value: 49.685
- type: ndcg_at_3
value: 36.618
- type: ndcg_at_5
value: 39.101
- type: precision_at_1
value: 32.435
- type: precision_at_10
value: 7.642
- type: precision_at_100
value: 1.244
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 17.485
- type: precision_at_5
value: 12.57
- type: recall_at_1
value: 26.154
- type: recall_at_10
value: 54.111
- type: recall_at_100
value: 78.348
- type: recall_at_1000
value: 92.996
- type: recall_at_3
value: 39.189
- type: recall_at_5
value: 45.852
- type: map_at_1
value: 26.308999999999997
- type: map_at_10
value: 35.524
- type: map_at_100
value: 36.774
- type: map_at_1000
value: 36.891
- type: map_at_3
value: 32.561
- type: map_at_5
value: 34.034
- type: mrr_at_1
value: 31.735000000000003
- type: mrr_at_10
value: 40.391
- type: mrr_at_100
value: 41.227000000000004
- type: mrr_at_1000
value: 41.288000000000004
- type: mrr_at_3
value: 37.938
- type: mrr_at_5
value: 39.193
- type: ndcg_at_1
value: 31.735000000000003
- type: ndcg_at_10
value: 41.166000000000004
- type: ndcg_at_100
value: 46.702
- type: ndcg_at_1000
value: 49.157000000000004
- type: ndcg_at_3
value: 36.274
- type: ndcg_at_5
value: 38.177
- type: precision_at_1
value: 31.735000000000003
- type: precision_at_10
value: 7.5569999999999995
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.199
- type: precision_at_5
value: 12.123000000000001
- type: recall_at_1
value: 26.308999999999997
- type: recall_at_10
value: 53.083000000000006
- type: recall_at_100
value: 76.922
- type: recall_at_1000
value: 93.767
- type: recall_at_3
value: 39.262
- type: recall_at_5
value: 44.413000000000004
- type: map_at_1
value: 24.391250000000003
- type: map_at_10
value: 33.280166666666666
- type: map_at_100
value: 34.49566666666667
- type: map_at_1000
value: 34.61533333333333
- type: map_at_3
value: 30.52183333333333
- type: map_at_5
value: 32.06608333333333
- type: mrr_at_1
value: 29.105083333333337
- type: mrr_at_10
value: 37.44766666666666
- type: mrr_at_100
value: 38.32491666666667
- type: mrr_at_1000
value: 38.385666666666665
- type: mrr_at_3
value: 35.06883333333333
- type: mrr_at_5
value: 36.42066666666667
- type: ndcg_at_1
value: 29.105083333333337
- type: ndcg_at_10
value: 38.54358333333333
- type: ndcg_at_100
value: 43.833583333333344
- type: ndcg_at_1000
value: 46.215333333333334
- type: ndcg_at_3
value: 33.876
- type: ndcg_at_5
value: 36.05208333333333
- type: precision_at_1
value: 29.105083333333337
- type: precision_at_10
value: 6.823416666666665
- type: precision_at_100
value: 1.1270833333333334
- type: precision_at_1000
value: 0.15208333333333332
- type: precision_at_3
value: 15.696750000000002
- type: precision_at_5
value: 11.193499999999998
- type: recall_at_1
value: 24.391250000000003
- type: recall_at_10
value: 49.98808333333333
- type: recall_at_100
value: 73.31616666666666
- type: recall_at_1000
value: 89.96291666666667
- type: recall_at_3
value: 36.86666666666667
- type: recall_at_5
value: 42.54350000000001
- type: map_at_1
value: 21.995
- type: map_at_10
value: 28.807
- type: map_at_100
value: 29.813000000000002
- type: map_at_1000
value: 29.903000000000002
- type: map_at_3
value: 26.636
- type: map_at_5
value: 27.912
- type: mrr_at_1
value: 24.847
- type: mrr_at_10
value: 31.494
- type: mrr_at_100
value: 32.381
- type: mrr_at_1000
value: 32.446999999999996
- type: mrr_at_3
value: 29.473
- type: mrr_at_5
value: 30.7
- type: ndcg_at_1
value: 24.847
- type: ndcg_at_10
value: 32.818999999999996
- type: ndcg_at_100
value: 37.835
- type: ndcg_at_1000
value: 40.226
- type: ndcg_at_3
value: 28.811999999999998
- type: ndcg_at_5
value: 30.875999999999998
- type: precision_at_1
value: 24.847
- type: precision_at_10
value: 5.244999999999999
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 12.577
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 21.995
- type: recall_at_10
value: 42.479
- type: recall_at_100
value: 65.337
- type: recall_at_1000
value: 83.23700000000001
- type: recall_at_3
value: 31.573
- type: recall_at_5
value: 36.684
- type: map_at_1
value: 15.751000000000001
- type: map_at_10
value: 21.909
- type: map_at_100
value: 23.064
- type: map_at_1000
value: 23.205000000000002
- type: map_at_3
value: 20.138
- type: map_at_5
value: 20.973
- type: mrr_at_1
value: 19.305
- type: mrr_at_10
value: 25.647
- type: mrr_at_100
value: 26.659
- type: mrr_at_1000
value: 26.748
- type: mrr_at_3
value: 23.933
- type: mrr_at_5
value: 24.754
- type: ndcg_at_1
value: 19.305
- type: ndcg_at_10
value: 25.886
- type: ndcg_at_100
value: 31.56
- type: ndcg_at_1000
value: 34.799
- type: ndcg_at_3
value: 22.708000000000002
- type: ndcg_at_5
value: 23.838
- type: precision_at_1
value: 19.305
- type: precision_at_10
value: 4.677
- type: precision_at_100
value: 0.895
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 10.771
- type: precision_at_5
value: 7.46
- type: recall_at_1
value: 15.751000000000001
- type: recall_at_10
value: 34.156
- type: recall_at_100
value: 59.899
- type: recall_at_1000
value: 83.08
- type: recall_at_3
value: 24.772
- type: recall_at_5
value: 28.009
- type: map_at_1
value: 23.34
- type: map_at_10
value: 32.383
- type: map_at_100
value: 33.629999999999995
- type: map_at_1000
value: 33.735
- type: map_at_3
value: 29.68
- type: map_at_5
value: 31.270999999999997
- type: mrr_at_1
value: 27.612
- type: mrr_at_10
value: 36.381
- type: mrr_at_100
value: 37.351
- type: mrr_at_1000
value: 37.411
- type: mrr_at_3
value: 33.893
- type: mrr_at_5
value: 35.353
- type: ndcg_at_1
value: 27.612
- type: ndcg_at_10
value: 37.714999999999996
- type: ndcg_at_100
value: 43.525000000000006
- type: ndcg_at_1000
value: 45.812999999999995
- type: ndcg_at_3
value: 32.796
- type: ndcg_at_5
value: 35.243
- type: precision_at_1
value: 27.612
- type: precision_at_10
value: 6.465
- type: precision_at_100
value: 1.0619999999999998
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.049999999999999
- type: precision_at_5
value: 10.764999999999999
- type: recall_at_1
value: 23.34
- type: recall_at_10
value: 49.856
- type: recall_at_100
value: 75.334
- type: recall_at_1000
value: 91.156
- type: recall_at_3
value: 36.497
- type: recall_at_5
value: 42.769
- type: map_at_1
value: 25.097
- type: map_at_10
value: 34.599999999999994
- type: map_at_100
value: 36.174
- type: map_at_1000
value: 36.398
- type: map_at_3
value: 31.781
- type: map_at_5
value: 33.22
- type: mrr_at_1
value: 31.225
- type: mrr_at_10
value: 39.873
- type: mrr_at_100
value: 40.853
- type: mrr_at_1000
value: 40.904
- type: mrr_at_3
value: 37.681
- type: mrr_at_5
value: 38.669
- type: ndcg_at_1
value: 31.225
- type: ndcg_at_10
value: 40.586
- type: ndcg_at_100
value: 46.226
- type: ndcg_at_1000
value: 48.788
- type: ndcg_at_3
value: 36.258
- type: ndcg_at_5
value: 37.848
- type: precision_at_1
value: 31.225
- type: precision_at_10
value: 7.707999999999999
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 17.26
- type: precision_at_5
value: 12.253
- type: recall_at_1
value: 25.097
- type: recall_at_10
value: 51.602000000000004
- type: recall_at_100
value: 76.854
- type: recall_at_1000
value: 93.303
- type: recall_at_3
value: 38.68
- type: recall_at_5
value: 43.258
- type: map_at_1
value: 17.689
- type: map_at_10
value: 25.291000000000004
- type: map_at_100
value: 26.262
- type: map_at_1000
value: 26.372
- type: map_at_3
value: 22.916
- type: map_at_5
value: 24.315
- type: mrr_at_1
value: 19.409000000000002
- type: mrr_at_10
value: 27.233
- type: mrr_at_100
value: 28.109
- type: mrr_at_1000
value: 28.192
- type: mrr_at_3
value: 24.892
- type: mrr_at_5
value: 26.278000000000002
- type: ndcg_at_1
value: 19.409000000000002
- type: ndcg_at_10
value: 29.809
- type: ndcg_at_100
value: 34.936
- type: ndcg_at_1000
value: 37.852000000000004
- type: ndcg_at_3
value: 25.179000000000002
- type: ndcg_at_5
value: 27.563
- type: precision_at_1
value: 19.409000000000002
- type: precision_at_10
value: 4.861
- type: precision_at_100
value: 0.8
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.985
- type: recall_at_1
value: 17.689
- type: recall_at_10
value: 41.724
- type: recall_at_100
value: 65.95299999999999
- type: recall_at_1000
value: 88.094
- type: recall_at_3
value: 29.621
- type: recall_at_5
value: 35.179
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.581
- type: map_at_10
value: 18.944
- type: map_at_100
value: 20.812
- type: map_at_1000
value: 21.002000000000002
- type: map_at_3
value: 15.661
- type: map_at_5
value: 17.502000000000002
- type: mrr_at_1
value: 23.388
- type: mrr_at_10
value: 34.263
- type: mrr_at_100
value: 35.364000000000004
- type: mrr_at_1000
value: 35.409
- type: mrr_at_3
value: 30.586000000000002
- type: mrr_at_5
value: 32.928000000000004
- type: ndcg_at_1
value: 23.388
- type: ndcg_at_10
value: 26.56
- type: ndcg_at_100
value: 34.248
- type: ndcg_at_1000
value: 37.779
- type: ndcg_at_3
value: 21.179000000000002
- type: ndcg_at_5
value: 23.504
- type: precision_at_1
value: 23.388
- type: precision_at_10
value: 8.476
- type: precision_at_100
value: 1.672
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 15.852
- type: precision_at_5
value: 12.73
- type: recall_at_1
value: 10.581
- type: recall_at_10
value: 32.512
- type: recall_at_100
value: 59.313
- type: recall_at_1000
value: 79.25
- type: recall_at_3
value: 19.912
- type: recall_at_5
value: 25.832
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.35
- type: map_at_10
value: 20.134
- type: map_at_100
value: 28.975
- type: map_at_1000
value: 30.709999999999997
- type: map_at_3
value: 14.513000000000002
- type: map_at_5
value: 16.671
- type: mrr_at_1
value: 69.75
- type: mrr_at_10
value: 77.67699999999999
- type: mrr_at_100
value: 77.97500000000001
- type: mrr_at_1000
value: 77.985
- type: mrr_at_3
value: 76.292
- type: mrr_at_5
value: 77.179
- type: ndcg_at_1
value: 56.49999999999999
- type: ndcg_at_10
value: 42.226
- type: ndcg_at_100
value: 47.562
- type: ndcg_at_1000
value: 54.923
- type: ndcg_at_3
value: 46.564
- type: ndcg_at_5
value: 43.830000000000005
- type: precision_at_1
value: 69.75
- type: precision_at_10
value: 33.525
- type: precision_at_100
value: 11.035
- type: precision_at_1000
value: 2.206
- type: precision_at_3
value: 49.75
- type: precision_at_5
value: 42
- type: recall_at_1
value: 9.35
- type: recall_at_10
value: 25.793
- type: recall_at_100
value: 54.186
- type: recall_at_1000
value: 77.81
- type: recall_at_3
value: 15.770000000000001
- type: recall_at_5
value: 19.09
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.945
- type: f1
value: 42.07407842992542
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.04599999999999
- type: map_at_10
value: 80.718
- type: map_at_100
value: 80.961
- type: map_at_1000
value: 80.974
- type: map_at_3
value: 79.49199999999999
- type: map_at_5
value: 80.32000000000001
- type: mrr_at_1
value: 76.388
- type: mrr_at_10
value: 85.214
- type: mrr_at_100
value: 85.302
- type: mrr_at_1000
value: 85.302
- type: mrr_at_3
value: 84.373
- type: mrr_at_5
value: 84.979
- type: ndcg_at_1
value: 76.388
- type: ndcg_at_10
value: 84.987
- type: ndcg_at_100
value: 85.835
- type: ndcg_at_1000
value: 86.04899999999999
- type: ndcg_at_3
value: 83.04
- type: ndcg_at_5
value: 84.22500000000001
- type: precision_at_1
value: 76.388
- type: precision_at_10
value: 10.35
- type: precision_at_100
value: 1.099
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 32.108
- type: precision_at_5
value: 20.033
- type: recall_at_1
value: 71.04599999999999
- type: recall_at_10
value: 93.547
- type: recall_at_100
value: 96.887
- type: recall_at_1000
value: 98.158
- type: recall_at_3
value: 88.346
- type: recall_at_5
value: 91.321
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.8
- type: map_at_10
value: 31.979999999999997
- type: map_at_100
value: 33.876
- type: map_at_1000
value: 34.056999999999995
- type: map_at_3
value: 28.067999999999998
- type: map_at_5
value: 30.066
- type: mrr_at_1
value: 38.735
- type: mrr_at_10
value: 47.749
- type: mrr_at_100
value: 48.605
- type: mrr_at_1000
value: 48.644999999999996
- type: mrr_at_3
value: 45.165
- type: mrr_at_5
value: 46.646
- type: ndcg_at_1
value: 38.735
- type: ndcg_at_10
value: 39.883
- type: ndcg_at_100
value: 46.983000000000004
- type: ndcg_at_1000
value: 50.043000000000006
- type: ndcg_at_3
value: 35.943000000000005
- type: ndcg_at_5
value: 37.119
- type: precision_at_1
value: 38.735
- type: precision_at_10
value: 10.940999999999999
- type: precision_at_100
value: 1.836
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 23.817
- type: precision_at_5
value: 17.346
- type: recall_at_1
value: 19.8
- type: recall_at_10
value: 47.082
- type: recall_at_100
value: 73.247
- type: recall_at_1000
value: 91.633
- type: recall_at_3
value: 33.201
- type: recall_at_5
value: 38.81
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.102999999999994
- type: map_at_10
value: 60.547
- type: map_at_100
value: 61.466
- type: map_at_1000
value: 61.526
- type: map_at_3
value: 56.973
- type: map_at_5
value: 59.244
- type: mrr_at_1
value: 76.205
- type: mrr_at_10
value: 82.816
- type: mrr_at_100
value: 83.002
- type: mrr_at_1000
value: 83.009
- type: mrr_at_3
value: 81.747
- type: mrr_at_5
value: 82.467
- type: ndcg_at_1
value: 76.205
- type: ndcg_at_10
value: 69.15
- type: ndcg_at_100
value: 72.297
- type: ndcg_at_1000
value: 73.443
- type: ndcg_at_3
value: 64.07000000000001
- type: ndcg_at_5
value: 66.96600000000001
- type: precision_at_1
value: 76.205
- type: precision_at_10
value: 14.601
- type: precision_at_100
value: 1.7049999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 41.202
- type: precision_at_5
value: 27.006000000000004
- type: recall_at_1
value: 38.102999999999994
- type: recall_at_10
value: 73.005
- type: recall_at_100
value: 85.253
- type: recall_at_1000
value: 92.795
- type: recall_at_3
value: 61.803
- type: recall_at_5
value: 67.515
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.15
- type: ap
value: 80.36282825265391
- type: f1
value: 86.07368510726472
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.6
- type: map_at_10
value: 34.887
- type: map_at_100
value: 36.069
- type: map_at_1000
value: 36.115
- type: map_at_3
value: 31.067
- type: map_at_5
value: 33.300000000000004
- type: mrr_at_1
value: 23.238
- type: mrr_at_10
value: 35.47
- type: mrr_at_100
value: 36.599
- type: mrr_at_1000
value: 36.64
- type: mrr_at_3
value: 31.735999999999997
- type: mrr_at_5
value: 33.939
- type: ndcg_at_1
value: 23.252
- type: ndcg_at_10
value: 41.765
- type: ndcg_at_100
value: 47.402
- type: ndcg_at_1000
value: 48.562
- type: ndcg_at_3
value: 34.016999999999996
- type: ndcg_at_5
value: 38.016
- type: precision_at_1
value: 23.252
- type: precision_at_10
value: 6.569
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.479000000000001
- type: precision_at_5
value: 10.722
- type: recall_at_1
value: 22.6
- type: recall_at_10
value: 62.919000000000004
- type: recall_at_100
value: 88.82
- type: recall_at_1000
value: 97.71600000000001
- type: recall_at_3
value: 41.896
- type: recall_at_5
value: 51.537
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.69357045143639
- type: f1
value: 93.55489858177597
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 75.31235750114
- type: f1
value: 57.891491963121155
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.04303967720243
- type: f1
value: 70.51516022297616
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.65299260255549
- type: f1
value: 77.49059766538576
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.458906115906597
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.9851513122443
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.2916268497217
- type: mrr
value: 32.328276715593816
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.3740000000000006
- type: map_at_10
value: 13.089999999999998
- type: map_at_100
value: 16.512
- type: map_at_1000
value: 18.014
- type: map_at_3
value: 9.671000000000001
- type: map_at_5
value: 11.199
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.367
- type: mrr_at_100
value: 56.021
- type: mrr_at_1000
value: 56.058
- type: mrr_at_3
value: 53.30200000000001
- type: mrr_at_5
value: 54.773
- type: ndcg_at_1
value: 45.046
- type: ndcg_at_10
value: 35.388999999999996
- type: ndcg_at_100
value: 32.175
- type: ndcg_at_1000
value: 41.018
- type: ndcg_at_3
value: 40.244
- type: ndcg_at_5
value: 38.267
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 26.563
- type: precision_at_100
value: 8.074
- type: precision_at_1000
value: 2.099
- type: precision_at_3
value: 37.358000000000004
- type: precision_at_5
value: 33.003
- type: recall_at_1
value: 6.3740000000000006
- type: recall_at_10
value: 16.805999999999997
- type: recall_at_100
value: 31.871
- type: recall_at_1000
value: 64.098
- type: recall_at_3
value: 10.383000000000001
- type: recall_at_5
value: 13.166
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.847
- type: map_at_10
value: 50.532
- type: map_at_100
value: 51.504000000000005
- type: map_at_1000
value: 51.528
- type: map_at_3
value: 46.219
- type: map_at_5
value: 48.868
- type: mrr_at_1
value: 39.137
- type: mrr_at_10
value: 53.157
- type: mrr_at_100
value: 53.839999999999996
- type: mrr_at_1000
value: 53.857
- type: mrr_at_3
value: 49.667
- type: mrr_at_5
value: 51.847
- type: ndcg_at_1
value: 39.108
- type: ndcg_at_10
value: 58.221000000000004
- type: ndcg_at_100
value: 62.021
- type: ndcg_at_1000
value: 62.57
- type: ndcg_at_3
value: 50.27199999999999
- type: ndcg_at_5
value: 54.623999999999995
- type: precision_at_1
value: 39.108
- type: precision_at_10
value: 9.397
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.644000000000002
- type: precision_at_5
value: 16.141
- type: recall_at_1
value: 34.847
- type: recall_at_10
value: 78.945
- type: recall_at_100
value: 94.793
- type: recall_at_1000
value: 98.904
- type: recall_at_3
value: 58.56
- type: recall_at_5
value: 68.535
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.728
- type: map_at_10
value: 82.537
- type: map_at_100
value: 83.218
- type: map_at_1000
value: 83.238
- type: map_at_3
value: 79.586
- type: map_at_5
value: 81.416
- type: mrr_at_1
value: 79.17999999999999
- type: mrr_at_10
value: 85.79299999999999
- type: mrr_at_100
value: 85.937
- type: mrr_at_1000
value: 85.938
- type: mrr_at_3
value: 84.748
- type: mrr_at_5
value: 85.431
- type: ndcg_at_1
value: 79.17
- type: ndcg_at_10
value: 86.555
- type: ndcg_at_100
value: 88.005
- type: ndcg_at_1000
value: 88.146
- type: ndcg_at_3
value: 83.557
- type: ndcg_at_5
value: 85.152
- type: precision_at_1
value: 79.17
- type: precision_at_10
value: 13.163
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.53
- type: precision_at_5
value: 24.046
- type: recall_at_1
value: 68.728
- type: recall_at_10
value: 94.217
- type: recall_at_100
value: 99.295
- type: recall_at_1000
value: 99.964
- type: recall_at_3
value: 85.646
- type: recall_at_5
value: 90.113
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.15680266226348
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.4318549229047
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.353
- type: map_at_10
value: 10.956000000000001
- type: map_at_100
value: 12.873999999999999
- type: map_at_1000
value: 13.177
- type: map_at_3
value: 7.854
- type: map_at_5
value: 9.327
- type: mrr_at_1
value: 21.4
- type: mrr_at_10
value: 31.948999999999998
- type: mrr_at_100
value: 33.039
- type: mrr_at_1000
value: 33.106
- type: mrr_at_3
value: 28.449999999999996
- type: mrr_at_5
value: 30.535
- type: ndcg_at_1
value: 21.4
- type: ndcg_at_10
value: 18.694
- type: ndcg_at_100
value: 26.275
- type: ndcg_at_1000
value: 31.836
- type: ndcg_at_3
value: 17.559
- type: ndcg_at_5
value: 15.372
- type: precision_at_1
value: 21.4
- type: precision_at_10
value: 9.790000000000001
- type: precision_at_100
value: 2.0709999999999997
- type: precision_at_1000
value: 0.34099999999999997
- type: precision_at_3
value: 16.467000000000002
- type: precision_at_5
value: 13.54
- type: recall_at_1
value: 4.353
- type: recall_at_10
value: 19.892000000000003
- type: recall_at_100
value: 42.067
- type: recall_at_1000
value: 69.268
- type: recall_at_3
value: 10.042
- type: recall_at_5
value: 13.741999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.75433886279843
- type: cos_sim_spearman
value: 78.29727771767095
- type: euclidean_pearson
value: 80.83057828506621
- type: euclidean_spearman
value: 78.35203149750356
- type: manhattan_pearson
value: 80.7403553891142
- type: manhattan_spearman
value: 78.33670488531051
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.59999465280839
- type: cos_sim_spearman
value: 75.79279003980383
- type: euclidean_pearson
value: 82.29895375956758
- type: euclidean_spearman
value: 77.33856514102094
- type: manhattan_pearson
value: 82.22694214534756
- type: manhattan_spearman
value: 77.3028993008695
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.09296929691297
- type: cos_sim_spearman
value: 83.58056936846941
- type: euclidean_pearson
value: 83.84067483060005
- type: euclidean_spearman
value: 84.45155680480985
- type: manhattan_pearson
value: 83.82353052971942
- type: manhattan_spearman
value: 84.43030567861112
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.74616852320915
- type: cos_sim_spearman
value: 79.948683747966
- type: euclidean_pearson
value: 81.55702283757084
- type: euclidean_spearman
value: 80.1721505114231
- type: manhattan_pearson
value: 81.52251518619441
- type: manhattan_spearman
value: 80.1469800135577
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.97170104226318
- type: cos_sim_spearman
value: 88.82021731518206
- type: euclidean_pearson
value: 87.92950547187615
- type: euclidean_spearman
value: 88.67043634645866
- type: manhattan_pearson
value: 87.90668112827639
- type: manhattan_spearman
value: 88.64471082785317
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.02790375770599
- type: cos_sim_spearman
value: 84.46308496590792
- type: euclidean_pearson
value: 84.29430000414911
- type: euclidean_spearman
value: 84.77298303589936
- type: manhattan_pearson
value: 84.23919291368665
- type: manhattan_spearman
value: 84.75272234871308
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.62885108477064
- type: cos_sim_spearman
value: 87.58456196391622
- type: euclidean_pearson
value: 88.2602775281007
- type: euclidean_spearman
value: 87.51556278299846
- type: manhattan_pearson
value: 88.11224053672842
- type: manhattan_spearman
value: 87.4336094383095
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.98187965128411
- type: cos_sim_spearman
value: 64.0653163219731
- type: euclidean_pearson
value: 62.30616725924099
- type: euclidean_spearman
value: 61.556971332295916
- type: manhattan_pearson
value: 62.07642330128549
- type: manhattan_spearman
value: 61.155494129828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.6089703921826
- type: cos_sim_spearman
value: 86.52303197250791
- type: euclidean_pearson
value: 85.95801955963246
- type: euclidean_spearman
value: 86.25242424112962
- type: manhattan_pearson
value: 85.88829100470312
- type: manhattan_spearman
value: 86.18742955805165
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.02282098487036
- type: mrr
value: 95.05126409538174
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.928
- type: map_at_10
value: 67.308
- type: map_at_100
value: 67.89500000000001
- type: map_at_1000
value: 67.91199999999999
- type: map_at_3
value: 65.091
- type: map_at_5
value: 66.412
- type: mrr_at_1
value: 58.667
- type: mrr_at_10
value: 68.401
- type: mrr_at_100
value: 68.804
- type: mrr_at_1000
value: 68.819
- type: mrr_at_3
value: 66.72200000000001
- type: mrr_at_5
value: 67.72200000000001
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_10
value: 71.944
- type: ndcg_at_100
value: 74.464
- type: ndcg_at_1000
value: 74.82799999999999
- type: ndcg_at_3
value: 68.257
- type: ndcg_at_5
value: 70.10300000000001
- type: precision_at_1
value: 58.667
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.222
- type: precision_at_5
value: 17.533
- type: recall_at_1
value: 55.928
- type: recall_at_10
value: 84.65
- type: recall_at_100
value: 96.267
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 74.656
- type: recall_at_5
value: 79.489
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 94.5795129511524
- type: cos_sim_f1
value: 89.34673366834171
- type: cos_sim_precision
value: 89.79797979797979
- type: cos_sim_recall
value: 88.9
- type: dot_accuracy
value: 99.53465346534654
- type: dot_ap
value: 81.56492504352725
- type: dot_f1
value: 76.33816908454227
- type: dot_precision
value: 76.37637637637637
- type: dot_recall
value: 76.3
- type: euclidean_accuracy
value: 99.78514851485149
- type: euclidean_ap
value: 94.59134620408962
- type: euclidean_f1
value: 88.96484375
- type: euclidean_precision
value: 86.92748091603053
- type: euclidean_recall
value: 91.10000000000001
- type: manhattan_accuracy
value: 99.78415841584159
- type: manhattan_ap
value: 94.5190197328845
- type: manhattan_f1
value: 88.84462151394423
- type: manhattan_precision
value: 88.4920634920635
- type: manhattan_recall
value: 89.2
- type: max_accuracy
value: 99.79009900990098
- type: max_ap
value: 94.59134620408962
- type: max_f1
value: 89.34673366834171
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.1487505617497
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.502518166001856
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.33775480236701
- type: mrr
value: 51.17302223919871
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.561111309808208
- type: cos_sim_spearman
value: 30.2839254379273
- type: dot_pearson
value: 29.560242291401973
- type: dot_spearman
value: 30.51527274679116
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.215
- type: map_at_10
value: 1.752
- type: map_at_100
value: 9.258
- type: map_at_1000
value: 23.438
- type: map_at_3
value: 0.6
- type: map_at_5
value: 0.968
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 91.333
- type: mrr_at_100
value: 91.333
- type: mrr_at_1000
value: 91.333
- type: mrr_at_3
value: 91.333
- type: mrr_at_5
value: 91.333
- type: ndcg_at_1
value: 75
- type: ndcg_at_10
value: 69.596
- type: ndcg_at_100
value: 51.970000000000006
- type: ndcg_at_1000
value: 48.864999999999995
- type: ndcg_at_3
value: 73.92699999999999
- type: ndcg_at_5
value: 73.175
- type: precision_at_1
value: 84
- type: precision_at_10
value: 74
- type: precision_at_100
value: 53.2
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 79.333
- type: precision_at_5
value: 78.4
- type: recall_at_1
value: 0.215
- type: recall_at_10
value: 1.9609999999999999
- type: recall_at_100
value: 12.809999999999999
- type: recall_at_1000
value: 46.418
- type: recall_at_3
value: 0.6479999999999999
- type: recall_at_5
value: 1.057
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.066
- type: map_at_10
value: 10.508000000000001
- type: map_at_100
value: 16.258
- type: map_at_1000
value: 17.705000000000002
- type: map_at_3
value: 6.157
- type: map_at_5
value: 7.510999999999999
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 48.786
- type: mrr_at_100
value: 49.619
- type: mrr_at_1000
value: 49.619
- type: mrr_at_3
value: 45.918
- type: mrr_at_5
value: 46.837
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 26.401999999999997
- type: ndcg_at_100
value: 37.139
- type: ndcg_at_1000
value: 48.012
- type: ndcg_at_3
value: 31.875999999999998
- type: ndcg_at_5
value: 27.383000000000003
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.611999999999999
- type: precision_at_1000
value: 1.492
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 16.239
- type: recall_at_100
value: 47.29
- type: recall_at_1000
value: 81.137
- type: recall_at_3
value: 7.069
- type: recall_at_5
value: 9.483
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.1126
- type: ap
value: 14.710862719285753
- type: f1
value: 55.437808972378846
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.39049235993209
- type: f1
value: 60.69810537250234
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.15576640316866
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.52917684925792
- type: cos_sim_ap
value: 75.97497873817315
- type: cos_sim_f1
value: 70.01151926276718
- type: cos_sim_precision
value: 67.98409147402435
- type: cos_sim_recall
value: 72.16358839050132
- type: dot_accuracy
value: 82.47004828038385
- type: dot_ap
value: 62.48739894974198
- type: dot_f1
value: 59.13107511045656
- type: dot_precision
value: 55.27765029830197
- type: dot_recall
value: 63.562005277044854
- type: euclidean_accuracy
value: 86.46361089586935
- type: euclidean_ap
value: 75.59282886839452
- type: euclidean_f1
value: 69.6465443945099
- type: euclidean_precision
value: 64.52847175331982
- type: euclidean_recall
value: 75.64643799472296
- type: manhattan_accuracy
value: 86.43380818978363
- type: manhattan_ap
value: 75.5742420974403
- type: manhattan_f1
value: 69.8636926889715
- type: manhattan_precision
value: 65.8644859813084
- type: manhattan_recall
value: 74.37994722955145
- type: max_accuracy
value: 86.52917684925792
- type: max_ap
value: 75.97497873817315
- type: max_f1
value: 70.01151926276718
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.29056545193464
- type: cos_sim_ap
value: 86.63028865482376
- type: cos_sim_f1
value: 79.18166458532285
- type: cos_sim_precision
value: 75.70585756426465
- type: cos_sim_recall
value: 82.99199260856174
- type: dot_accuracy
value: 85.23305002522606
- type: dot_ap
value: 76.0482687263196
- type: dot_f1
value: 70.80484330484332
- type: dot_precision
value: 65.86933474688577
- type: dot_recall
value: 76.53988296889437
- type: euclidean_accuracy
value: 89.26145845461248
- type: euclidean_ap
value: 86.54073288416006
- type: euclidean_f1
value: 78.9721371479794
- type: euclidean_precision
value: 76.68649354417525
- type: euclidean_recall
value: 81.39821373575609
- type: manhattan_accuracy
value: 89.22847052431405
- type: manhattan_ap
value: 86.51250729037905
- type: manhattan_f1
value: 78.94601825044894
- type: manhattan_precision
value: 75.32694594027555
- type: manhattan_recall
value: 82.93039728980598
- type: max_accuracy
value: 89.29056545193464
- type: max_ap
value: 86.63028865482376
- type: max_f1
value: 79.18166458532285
---
# E5-base-v2
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 768.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base-v2')
model = AutoModel.from_pretrained('intfloat/e5-base-v2')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-base-v2')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
minishlab/potion-base-8M | minishlab | null | [
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"mteb",
"sentence-transformers",
"license:mit",
"model-index",
"region:us"
] | 2024-10-29T09:35:47 | 2025-01-21T17:53:06 | 325,384 | 50 | ---
library_name: model2vec
license: mit
tags:
- embeddings
- static-embeddings
- mteb
- sentence-transformers
model-index:
- name: potion-base-8M
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.15142428785607
- type: ap
value: 20.626102291010103
- type: ap_weighted
value: 20.626102291010103
- type: f1
value: 59.187001923736894
- type: f1_weighted
value: 77.34906471545477
- type: main_score
value: 72.15142428785607
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.7910447761194
- type: ap
value: 33.038020188116036
- type: ap_weighted
value: 33.038020188116036
- type: f1
value: 65.03799728338926
- type: f1_weighted
value: 74.32788084269461
- type: main_score
value: 71.7910447761194
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 72.47644999999999
- type: ap
value: 66.91002822830875
- type: ap_weighted
value: 66.91002822830875
- type: f1
value: 72.2600863044581
- type: f1_weighted
value: 72.2600863044581
- type: main_score
value: 72.47644999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.012
- type: f1
value: 35.38209336470206
- type: f1_weighted
value: 35.38209336470206
- type: main_score
value: 36.012
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 41.966
- type: map_at_1
value: 21.124000000000002
- type: map_at_10
value: 34.335
- type: map_at_100
value: 35.618
- type: map_at_1000
value: 35.653
- type: map_at_20
value: 35.21
- type: map_at_3
value: 30.287
- type: map_at_5
value: 32.364
- type: mrr_at_1
value: 21.62162162162162
- type: mrr_at_10
value: 34.509104969631224
- type: mrr_at_100
value: 35.79229946325059
- type: mrr_at_1000
value: 35.82767320968403
- type: mrr_at_20
value: 35.38485605181455
- type: mrr_at_3
value: 30.405405405405343
- type: mrr_at_5
value: 32.539118065433755
- type: nauc_map_at_1000_diff1
value: 7.960826255212609
- type: nauc_map_at_1000_max
value: -0.036381315067780806
- type: nauc_map_at_1000_std
value: 4.317766293607543
- type: nauc_map_at_100_diff1
value: 7.96318422584977
- type: nauc_map_at_100_max
value: -0.007800758201736421
- type: nauc_map_at_100_std
value: 4.362078927714198
- type: nauc_map_at_10_diff1
value: 7.718022643886373
- type: nauc_map_at_10_max
value: -0.28312250079415263
- type: nauc_map_at_10_std
value: 4.079196099329437
- type: nauc_map_at_1_diff1
value: 9.240393281366906
- type: nauc_map_at_1_max
value: -4.35798405693968
- type: nauc_map_at_1_std
value: 1.5076565659508505
- type: nauc_map_at_20_diff1
value: 8.028053857747947
- type: nauc_map_at_20_max
value: 0.0719807687813251
- type: nauc_map_at_20_std
value: 4.394812024847373
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type: Clustering
dataset:
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type: mteb/arxiv-clustering-p2p
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dataset:
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type: mteb/arxiv-clustering-s2s
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
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dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
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dataset:
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dataset:
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type: mteb/cqadupstack-android
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split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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dataset:
name: MTEB CQADupstackEnglishRetrieval (default)
type: mteb/cqadupstack-english
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type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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dataset:
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type: mteb/nq
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value: 77.27600787572996
- type: euclidean_pearson
value: 76.77960647262235
- type: euclidean_spearman
value: 77.27600787572996
- type: main_score
value: 77.27600787572996
- type: manhattan_pearson
value: 76.37651436440808
- type: manhattan_spearman
value: 76.85568457177312
- type: pearson
value: 76.53983680018591
- type: spearman
value: 77.27600787572996
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 76.20854411766629
- type: cosine_spearman
value: 71.914099628002
- type: euclidean_pearson
value: 74.5273047891339
- type: euclidean_spearman
value: 71.914099628002
- type: main_score
value: 71.914099628002
- type: manhattan_pearson
value: 74.53275458017302
- type: manhattan_spearman
value: 71.9720930787841
- type: pearson
value: 76.20854411766629
- type: spearman
value: 71.914099628002
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 79.24273419832653
- type: cosine_spearman
value: 79.75345871163103
- type: euclidean_pearson
value: 79.31395801169265
- type: euclidean_spearman
value: 79.75345871163103
- type: main_score
value: 79.75345871163103
- type: manhattan_pearson
value: 79.24199238927697
- type: manhattan_spearman
value: 79.64058599210834
- type: pearson
value: 79.24273419832653
- type: spearman
value: 79.75345871163103
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 75.64452330127995
- type: cosine_spearman
value: 76.26343823222666
- type: euclidean_pearson
value: 75.64112047932008
- type: euclidean_spearman
value: 76.26343823222666
- type: main_score
value: 76.26343823222666
- type: manhattan_pearson
value: 75.32718809126764
- type: manhattan_spearman
value: 75.9420892784719
- type: pearson
value: 75.64452330127995
- type: spearman
value: 76.26343823222666
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 17.52217310066287
- type: cosine_spearman
value: 14.729958484232528
- type: euclidean_pearson
value: 17.507234354096582
- type: euclidean_spearman
value: 14.729958484232528
- type: main_score
value: 14.729958484232528
- type: manhattan_pearson
value: 15.286020788097272
- type: manhattan_spearman
value: 11.320242312589713
- type: pearson
value: 17.52217310066287
- type: spearman
value: 14.729958484232528
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 84.67406984717113
- type: cosine_spearman
value: 85.96709815630739
- type: euclidean_pearson
value: 84.7186375682207
- type: euclidean_spearman
value: 85.96709815630739
- type: main_score
value: 85.96709815630739
- type: manhattan_pearson
value: 85.07894758059129
- type: manhattan_spearman
value: 86.57110045700985
- type: pearson
value: 84.67406984717113
- type: spearman
value: 85.96709815630739
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 36.02331692863771
- type: cosine_spearman
value: 34.28540470062557
- type: euclidean_pearson
value: 35.996881386631514
- type: euclidean_spearman
value: 34.28540470062557
- type: main_score
value: 34.28540470062557
- type: manhattan_pearson
value: 35.47246063445784
- type: manhattan_spearman
value: 34.83247787211397
- type: pearson
value: 36.02331692863771
- type: spearman
value: 34.28540470062557
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 13.925983981770388
- type: cosine_spearman
value: 11.193291331109325
- type: euclidean_pearson
value: 13.9151651239108
- type: euclidean_spearman
value: 11.193291331109325
- type: main_score
value: 11.193291331109325
- type: manhattan_pearson
value: 12.652407957594654
- type: manhattan_spearman
value: 9.888358907769014
- type: pearson
value: 13.925983981770388
- type: spearman
value: 11.193291331109325
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 26.77839285232968
- type: cosine_spearman
value: 23.010015986939717
- type: euclidean_pearson
value: 27.13668235790385
- type: euclidean_spearman
value: 23.010015986939717
- type: main_score
value: 23.010015986939717
- type: manhattan_pearson
value: 27.02698710744775
- type: manhattan_spearman
value: 23.038730409304936
- type: pearson
value: 26.77839285232968
- type: spearman
value: 23.010015986939717
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 25.330935194314364
- type: cosine_spearman
value: 23.143555348782797
- type: euclidean_pearson
value: 24.670147594978143
- type: euclidean_spearman
value: 23.143555348782797
- type: main_score
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- type: manhattan_pearson
value: 24.879695698914418
- type: manhattan_spearman
value: 25.916630507885134
- type: pearson
value: 25.330935194314364
- type: spearman
value: 23.143555348782797
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 6.61651078645899
- type: cosine_spearman
value: 5.415104433010482
- type: euclidean_pearson
value: 6.791575957480809
- type: euclidean_spearman
value: 5.415104433010482
- type: main_score
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- type: manhattan_pearson
value: 3.6585407382250987
- type: manhattan_spearman
value: 4.566044103659472
- type: pearson
value: 6.61651078645899
- type: spearman
value: 5.415104433010482
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 32.718045784523184
- type: cosine_spearman
value: 27.52844368619317
- type: euclidean_pearson
value: 32.98978359596458
- type: euclidean_spearman
value: 27.52844368619317
- type: main_score
value: 27.52844368619317
- type: manhattan_pearson
value: 35.57923949366344
- type: manhattan_spearman
value: 34.27137422651138
- type: pearson
value: 32.718045784523184
- type: spearman
value: 27.52844368619317
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 9.98410299881163
- type: cosine_spearman
value: 10.98684405086525
- type: euclidean_pearson
value: 9.461680781495218
- type: euclidean_spearman
value: 10.9925413190658
- type: main_score
value: 10.98684405086525
- type: manhattan_pearson
value: 9.442055271895944
- type: manhattan_spearman
value: 11.226101908391069
- type: pearson
value: 9.98410299881163
- type: spearman
value: 10.98684405086525
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 59.3180680265132
- type: cosine_spearman
value: 63.07956002739231
- type: euclidean_pearson
value: 62.46424835000928
- type: euclidean_spearman
value: 63.07956002739231
- type: main_score
value: 63.07956002739231
- type: manhattan_pearson
value: 62.048137683643766
- type: manhattan_spearman
value: 61.83898606879604
- type: pearson
value: 59.3180680265132
- type: spearman
value: 63.07956002739231
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 29.061215770374826
- type: cosine_spearman
value: 36.21441725938738
- type: euclidean_pearson
value: 28.44045530150387
- type: euclidean_spearman
value: 36.21441725938738
- type: main_score
value: 36.21441725938738
- type: manhattan_pearson
value: 29.32403221599612
- type: manhattan_spearman
value: 38.914481153396494
- type: pearson
value: 29.061215770374826
- type: spearman
value: 36.21441725938738
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 11.266385865086239
- type: cosine_spearman
value: 17.291293843893733
- type: euclidean_pearson
value: 10.045897285683115
- type: euclidean_spearman
value: 17.321323804048646
- type: main_score
value: 17.291293843893733
- type: manhattan_pearson
value: 15.333482209624194
- type: manhattan_spearman
value: 20.399166731513915
- type: pearson
value: 11.266385865086239
- type: spearman
value: 17.291293843893733
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 9.647587208410648
- type: cosine_spearman
value: 21.33739699413266
- type: euclidean_pearson
value: 7.451981822243237
- type: euclidean_spearman
value: 21.33739699413266
- type: main_score
value: 21.33739699413266
- type: manhattan_pearson
value: 10.05280275870948
- type: manhattan_spearman
value: 22.233400969472218
- type: pearson
value: 9.647587208410648
- type: spearman
value: 21.33739699413266
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 77.2598255013409
- type: cosine_spearman
value: 75.40519061413276
- type: euclidean_pearson
value: 77.19878276657876
- type: euclidean_spearman
value: 75.40519061413276
- type: main_score
value: 75.40519061413276
- type: manhattan_pearson
value: 77.04099640594512
- type: manhattan_spearman
value: 75.32219501493076
- type: pearson
value: 77.2598255013409
- type: spearman
value: 75.40519061413276
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 72.10127087089839
- type: map
value: 72.10127087089839
- type: mrr
value: 90.62288020621355
- type: nAUC_map_diff1
value: 8.726677558277695
- type: nAUC_map_max
value: 54.59636736704295
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value: 67.36367052533402
- type: nAUC_mrr_diff1
value: 47.77588337162405
- type: nAUC_mrr_max
value: 74.90946175462605
- type: nAUC_mrr_std
value: 71.81332269641806
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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- type: map_at_1
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- type: map_at_100
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- type: map_at_1000
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- type: nauc_map_at_1000_diff1
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
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dataset:
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type: mteb/stackexchange-clustering-p2p
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value: -29.877869977138204
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value: -33.58926486109219
- type: ndcg_at_1
value: 12.245000000000001
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value: 18.618000000000002
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value: 28.488000000000003
- type: ndcg_at_1000
value: 41.208
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value: 19.536
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value: 15.045
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value: 16.359
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value: 19.796
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- type: precision_at_1000
value: 1.471
- type: precision_at_20
value: 15.204
- type: precision_at_3
value: 18.367
- type: precision_at_5
value: 18.776
- type: recall_at_1
value: 1.22
- type: recall_at_10
value: 13.763
- type: recall_at_100
value: 40.107
- type: recall_at_1000
value: 79.06800000000001
- type: recall_at_20
value: 20.049
- type: recall_at_3
value: 4.2540000000000004
- type: recall_at_5
value: 7.142999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 69.0625
- type: ap
value: 12.429057046174089
- type: ap_weighted
value: 12.429057046174089
- type: f1
value: 52.366056859622454
- type: f1_weighted
value: 75.91632061778698
- type: main_score
value: 69.0625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 55.387662705149964
- type: f1
value: 55.62292803889264
- type: f1_weighted
value: 55.01561915660653
- type: main_score
value: 55.387662705149964
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 33.535908963951435
- type: v_measure
value: 33.535908963951435
- type: v_measure_std
value: 1.8862804680454297
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 81.57000655659535
- type: cosine_accuracy_threshold
value: 76.01186428039885
- type: cosine_ap
value: 57.187252502171674
- type: cosine_f1
value: 54.94480738905159
- type: cosine_f1_threshold
value: 63.27845286960887
- type: cosine_precision
value: 47.93632075471698
- type: cosine_recall
value: 64.35356200527704
- type: dot_accuracy
value: 81.57000655659535
- type: dot_accuracy_threshold
value: 76.01186510638954
- type: dot_ap
value: 57.1872568788409
- type: dot_f1
value: 54.94480738905159
- type: dot_f1_threshold
value: 63.27845437266042
- type: dot_precision
value: 47.93632075471698
- type: dot_recall
value: 64.35356200527704
- type: euclidean_accuracy
value: 81.57000655659535
- type: euclidean_accuracy_threshold
value: 69.2649048666448
- type: euclidean_ap
value: 57.18724194735979
- type: euclidean_f1
value: 54.94480738905159
- type: euclidean_f1_threshold
value: 85.69894748780587
- type: euclidean_precision
value: 47.93632075471698
- type: euclidean_recall
value: 64.35356200527704
- type: main_score
value: 57.516050924090266
- type: manhattan_accuracy
value: 81.71902008702389
- type: manhattan_accuracy_threshold
value: 856.8997862166725
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value: 57.516050924090266
- type: manhattan_f1
value: 55.16339869281046
- type: manhattan_f1_threshold
value: 1035.858379830097
- type: manhattan_precision
value: 50.18378378378379
- type: manhattan_recall
value: 61.24010554089709
- type: max_accuracy
value: 81.71902008702389
- type: max_ap
value: 57.516050924090266
- type: max_f1
value: 55.16339869281046
- type: max_precision
value: 50.18378378378379
- type: max_recall
value: 64.35356200527704
- type: similarity_accuracy
value: 81.57000655659535
- type: similarity_accuracy_threshold
value: 76.01186428039885
- type: similarity_ap
value: 57.187252502171674
- type: similarity_f1
value: 54.94480738905159
- type: similarity_f1_threshold
value: 63.27845286960887
- type: similarity_precision
value: 47.93632075471698
- type: similarity_recall
value: 64.35356200527704
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
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value: 87.09977878682035
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value: 63.00089389314832
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value: 81.9487582699938
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value: 74.04089724292375
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value: 70.7599466704091
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value: 77.64089929165382
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value: 63.00089560728222
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value: 81.94879514546079
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value: 74.04089724292375
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value: 56.350250341728405
- type: dot_precision
value: 70.7599466704091
- type: dot_recall
value: 77.64089929165382
- type: euclidean_accuracy
value: 87.09977878682035
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value: 86.02221469735642
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value: 81.94875892553148
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value: 74.04089724292375
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value: 93.43420484744681
- type: euclidean_precision
value: 70.7599466704091
- type: euclidean_recall
value: 77.64089929165382
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value: 82.13756947863085
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value: 87.19292117825125
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value: 1148.366796662276
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value: 71.32051463311183
- type: manhattan_recall
value: 77.6793963658762
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value: 87.19292117825125
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value: 82.13756947863085
- type: max_f1
value: 74.36426623424485
- type: max_precision
value: 71.32051463311183
- type: max_recall
value: 77.6793963658762
- type: similarity_accuracy
value: 87.09977878682035
- type: similarity_accuracy_threshold
value: 63.00089389314832
- type: similarity_ap
value: 81.9487582699938
- type: similarity_f1
value: 74.04089724292375
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value: 56.35024835869245
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value: 70.7599466704091
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value: 77.64089929165382
---
# potion-base-8M Model Card
<div align="center">
<img width="35%" alt="Model2Vec logo" src="https://raw.githubusercontent.com/MinishLab/model2vec/main/assets/images/logo_v2.png">
</div>
This [Model2Vec](https://github.com/MinishLab/model2vec) model is pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). It is a distilled version of the [baai/bge-base-en-v1.5](https://huggingface.co/baai/bge-base-en-v1.5) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/potion-base-8M")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
## How it works
Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on [MTEB](https://huggingface.co/spaces/mteb/leaderboard). This model is pre-trained using [Tokenlearn](https://github.com/MinishLab/tokenlearn). It's created using the following steps:
- Distillation: first, a model is distilled from a sentence transformer model using Model2Vec.
- Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus.
- Training: the distilled model is trained on the training data using Tokenlearn.
- Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx).
The results for this model can be found on the [Model2Vec results page](https://github.com/MinishLab/model2vec/blob/main/results/README.md).
## Additional Resources
- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Tokenlearn repo](https://github.com/MinishLab/tokenlearn)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/blob/main/results/README.md)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
NovaSearch/stella_en_400M_v5 | NovaSearch | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2412.19048",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-12T15:52:33 | 2025-03-05T13:57:29 | 315,187 | 192 | ---
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
model-index:
- name: stella_en_400M_v5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 92.35820895522387
- type: ap
value: 70.81322736988783
- type: ap_weighted
value: 70.81322736988783
- type: f1
value: 88.9505466159595
- type: f1_weighted
value: 92.68630932872613
- type: main_score
value: 92.35820895522387
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.1945
- type: ap
value: 96.08192192244094
- type: ap_weighted
value: 96.08192192244094
- type: f1
value: 97.1936887167346
- type: f1_weighted
value: 97.1936887167346
- type: main_score
value: 97.1945
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 59.528000000000006
- type: f1
value: 59.21016819840188
- type: f1_weighted
value: 59.21016819840188
- type: main_score
value: 59.528000000000006
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 64.24
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value: 40.398
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- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
value: 56.747
- type: map_at_3
value: 52.181
- type: map_at_5
value: 54.628
- type: mrr_at_1
value: 41.25177809388336
- type: mrr_at_10
value: 56.570762491815216
- type: mrr_at_100
value: 57.17548614361504
- type: mrr_at_1000
value: 57.176650626377466
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- type: mrr_at_3
value: 52.47747747747754
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type: mteb/arxiv-clustering-p2p
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metrics:
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dataset:
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type: mteb/askubuntudupquestions-reranking
config: default
split: test
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revision: c5a29a104738b98a9e76336939199e264163d4a0
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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dataset:
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type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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value: 92.29822161422709
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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value: 92.908
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type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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value: 71.18966762070158
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type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 74.42014716862516
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value: 9.909739891410648
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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value: 86.57284584320382
- type: cosine_spearman
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- type: euclidean_pearson
value: 83.94261758556554
- type: euclidean_spearman
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value: 82.20531642680812
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value: 84.15902154703083
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value: 82.19506027155957
- type: pearson
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- type: spearman
value: 82.20531642680812
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type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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value: 86.28047602146931
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value: 79.51504881448884
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value: 83.10545189967856
- type: euclidean_spearman
value: 79.50586960492797
- type: main_score
value: 79.51504881448884
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value: 79.730303339846
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value: 86.28047602146931
- type: spearman
value: 79.51504881448884
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type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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value: 88.74723553048702
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value: 89.18936052329725
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value: 88.90400878928668
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value: 89.18936052329725
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type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 86.45403437836023
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value: 85.14654611519086
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value: 85.87509624462743
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value: 85.14654611519086
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value: 85.14654611519086
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type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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value: 87.84742260009705
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value: 88.97393286325477
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value: 89.1014105509662
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value: 87.84742260009705
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value: 89.10215217191254
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type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 85.22397535461835
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value: 86.31393364087295
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value: 87.14066355879785
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value: 87.14066355879785
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 90.66131612061355
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value: 90.98181906744969
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value: 90.97082650129164
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 67.270656447085
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value: 67.82870469746828
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value: 69.03857775285664
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value: 68.00987097916055
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value: 67.82870469746828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 87.17245205384889
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value: 87.7360146030987
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value: 87.48919412794656
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value: 87.95220889254693
- type: pearson
value: 87.17245205384889
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value: 87.7360146030987
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 88.43547871921146
- type: map
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- type: mrr
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dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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dataset:
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split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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dataset:
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type: mteb/stackoverflowdupquestions-reranking
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split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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value: 48.82198285255887
- type: nauc_precision_at_3_diff1
value: 6.871296905858807
- type: nauc_precision_at_3_max
value: 70.54805793285054
- type: nauc_precision_at_3_std
value: 44.65108624094803
- type: nauc_precision_at_5_diff1
value: -9.074932448759695
- type: nauc_precision_at_5_max
value: 67.41284242437573
- type: nauc_precision_at_5_std
value: 23.876891983919577
- type: nauc_recall_at_1000_diff1
value: 8.142288830293255
- type: nauc_recall_at_1000_max
value: 38.85182826835104
- type: nauc_recall_at_1000_std
value: 68.60783819217335
- type: nauc_recall_at_100_diff1
value: 34.262914076287466
- type: nauc_recall_at_100_max
value: 12.87009658528838
- type: nauc_recall_at_100_std
value: 56.21330603762995
- type: nauc_recall_at_10_diff1
value: 49.33830945338758
- type: nauc_recall_at_10_max
value: 0.3539875530671406
- type: nauc_recall_at_10_std
value: 26.85864465557644
- type: nauc_recall_at_1_diff1
value: 53.48238396620123
- type: nauc_recall_at_1_max
value: 0.33476619393733076
- type: nauc_recall_at_1_std
value: 8.906362219128463
- type: nauc_recall_at_20_diff1
value: 44.21928181266254
- type: nauc_recall_at_20_max
value: -0.9198356057088594
- type: nauc_recall_at_20_std
value: 31.484376992896784
- type: nauc_recall_at_3_diff1
value: 53.038093080990876
- type: nauc_recall_at_3_max
value: -1.4170895916973003
- type: nauc_recall_at_3_std
value: 21.890202855574497
- type: nauc_recall_at_5_diff1
value: 49.39742214825278
- type: nauc_recall_at_5_max
value: 2.8412267611894517
- type: nauc_recall_at_5_std
value: 18.01598921859512
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 85.206
- type: ndcg_at_100
value: 67.29
- type: ndcg_at_1000
value: 60.584
- type: ndcg_at_20
value: 82.321
- type: ndcg_at_3
value: 88.642
- type: ndcg_at_5
value: 87.063
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 89.8
- type: precision_at_100
value: 69.78
- type: precision_at_1000
value: 26.738
- type: precision_at_20
value: 87.2
- type: precision_at_3
value: 92.0
- type: precision_at_5
value: 90.8
- type: recall_at_1
value: 0.246
- type: recall_at_10
value: 2.344
- type: recall_at_100
value: 16.962
- type: recall_at_1000
value: 57.325
- type: recall_at_20
value: 4.517
- type: recall_at_3
value: 0.731
- type: recall_at_5
value: 1.1780000000000002
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
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value: 31.455
- type: map_at_1
value: 2.9739999999999998
- type: map_at_10
value: 12.183
- type: map_at_100
value: 18.772
- type: map_at_1000
value: 20.415
- type: map_at_20
value: 14.451
- type: map_at_3
value: 6.507000000000001
- type: map_at_5
value: 8.66
- type: mrr_at_1
value: 40.816326530612244
- type: mrr_at_10
value: 57.70975056689341
- type: mrr_at_100
value: 58.18379126542391
- type: mrr_at_1000
value: 58.18379126542391
- type: mrr_at_20
value: 57.85552316164561
- type: mrr_at_3
value: 54.08163265306123
- type: mrr_at_5
value: 56.42857142857143
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value: 3.1567471051481437
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value: -1.5882060729791523
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value: 18.69622198722074
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value: 3.3449677678147536
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value: -2.8928606866168405
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value: 15.789984947653412
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value: 2.9696743570444264
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value: -9.096749212011876
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value: -5.38545817258353
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value: 20.680780404542546
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value: -7.04722927447817
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value: -7.062494733973898
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value: 4.070437790119271
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value: -4.84491434686032
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value: 0.5846341109021014
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value: 11.9634978045925
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value: -8.27834591046608
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value: -8.687615453381065
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value: 0.9195191526009436
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value: -1.673813362719489
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value: -6.67549753473631
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value: 19.877993208719573
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value: -10.37776706406218
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value: 7.132169578056367
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value: 19.877993208719573
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value: -10.37776706406218
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value: 7.132169578056367
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value: 20.414285568401457
- type: nauc_mrr_at_10_max
value: -9.677800295687861
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value: 8.001103690180859
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value: 22.393284073955723
- type: nauc_mrr_at_1_max
value: -5.889370191243167
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value: -1.5183536173658247
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value: 20.455564720604055
- type: nauc_mrr_at_20_max
value: -10.230642830103074
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value: 7.863582453266621
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value: 17.554895390732618
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value: -15.618463505555052
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value: 5.913231577966864
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value: 18.393678507779914
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value: 7.580745996262831
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value: 13.746937095530473
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value: -0.9319249687895838
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value: 38.56328031451904
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value: -7.142142012591404
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value: 35.61341954818848
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value: 9.010144273248759
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value: -15.320014897424574
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value: 2.84883880489144
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value: -0.5258673122126726
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value: 14.660827309009496
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value: 8.22391881710838
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value: 8.085466231900622
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value: 3.5794092637248505
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value: 22.720784312185856
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value: 8.958548406995279
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- type: nauc_precision_at_100_std
value: 71.04548238175113
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value: 12.4698194690008
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value: -15.84870544871496
- type: nauc_precision_at_10_std
value: 7.575297622501928
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value: 22.393284073955723
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value: -5.889370191243167
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value: -1.5183536173658247
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value: 15.393505718138758
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value: -3.70684298539384
- type: nauc_precision_at_20_std
value: 29.426137824970304
- type: nauc_precision_at_3_diff1
value: 9.997768085465394
- type: nauc_precision_at_3_max
value: -17.12224314347674
- type: nauc_precision_at_3_std
value: -1.343018166772313
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value: 3.8936997437913554
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value: -5.689104289687632
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value: 3.181098051304285
- type: nauc_recall_at_1000_diff1
value: 9.908303508158387
- type: nauc_recall_at_1000_max
value: 6.174506592699848
- type: nauc_recall_at_1000_std
value: 77.41931114780012
- type: nauc_recall_at_100_diff1
value: 10.286839241876192
- type: nauc_recall_at_100_max
value: -6.6138697026666815
- type: nauc_recall_at_100_std
value: 49.608313692633224
- type: nauc_recall_at_10_diff1
value: 2.215545846659851
- type: nauc_recall_at_10_max
value: -17.83025802478445
- type: nauc_recall_at_10_std
value: -3.3784768673705465
- type: nauc_recall_at_1_diff1
value: 20.680780404542546
- type: nauc_recall_at_1_max
value: -7.04722927447817
- type: nauc_recall_at_1_std
value: -7.062494733973898
- type: nauc_recall_at_20_diff1
value: 6.974410239251615
- type: nauc_recall_at_20_max
value: -14.161147924731646
- type: nauc_recall_at_20_std
value: 9.328412057721454
- type: nauc_recall_at_3_diff1
value: 7.904589805754212
- type: nauc_recall_at_3_max
value: -12.1912388648593
- type: nauc_recall_at_3_std
value: -9.221542013385555
- type: nauc_recall_at_5_diff1
value: -3.2604132752706914
- type: nauc_recall_at_5_max
value: -6.886351441658915
- type: nauc_recall_at_5_std
value: -7.014252851712789
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 31.455
- type: ndcg_at_100
value: 42.388999999999996
- type: ndcg_at_1000
value: 53.556000000000004
- type: ndcg_at_20
value: 30.808000000000003
- type: ndcg_at_3
value: 35.831
- type: ndcg_at_5
value: 32.845
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.449
- type: precision_at_1000
value: 1.6179999999999999
- type: precision_at_20
value: 19.387999999999998
- type: precision_at_3
value: 35.374
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 2.9739999999999998
- type: recall_at_10
value: 19.39
- type: recall_at_100
value: 51.636
- type: recall_at_1000
value: 86.99900000000001
- type: recall_at_20
value: 26.478
- type: recall_at_3
value: 7.703
- type: recall_at_5
value: 11.42
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 86.9384765625
- type: ap
value: 31.737513704141552
- type: ap_weighted
value: 31.737513704141552
- type: f1
value: 71.5490757306975
- type: f1_weighted
value: 89.14632533489856
- type: main_score
value: 86.9384765625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 73.57668364459535
- type: f1
value: 73.90467103648074
- type: f1_weighted
value: 73.42158415034704
- type: main_score
value: 73.57668364459535
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 58.574148097494685
- type: v_measure
value: 58.574148097494685
- type: v_measure_std
value: 0.9443161637490822
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 88.1385229778864
- type: cosine_accuracy_threshold
value: 83.86307954788208
- type: cosine_ap
value: 80.17965893449055
- type: cosine_f1
value: 73.0614300100705
- type: cosine_f1_threshold
value: 80.7942807674408
- type: cosine_precision
value: 69.8603755416466
- type: cosine_recall
value: 76.56992084432717
- type: dot_accuracy
value: 88.2100494724921
- type: dot_accuracy_threshold
value: 83.84793996810913
- type: dot_ap
value: 80.18603932881858
- type: dot_f1
value: 73.07643714466204
- type: dot_f1_threshold
value: 80.87586164474487
- type: dot_precision
value: 70.10909090909091
- type: dot_recall
value: 76.3060686015831
- type: euclidean_accuracy
value: 88.1385229778864
- type: euclidean_accuracy_threshold
value: 56.77661895751953
- type: euclidean_ap
value: 80.1784070881624
- type: euclidean_f1
value: 73.04830369529574
- type: euclidean_f1_threshold
value: 61.91838979721069
- type: euclidean_precision
value: 69.96859144720948
- type: euclidean_recall
value: 76.41160949868075
- type: main_score
value: 80.18603932881858
- type: manhattan_accuracy
value: 88.0431543184121
- type: manhattan_accuracy_threshold
value: 3755.6137084960938
- type: manhattan_ap
value: 79.98270453664578
- type: manhattan_f1
value: 72.68242015061023
- type: manhattan_f1_threshold
value: 3892.494583129883
- type: manhattan_precision
value: 71.54907975460122
- type: manhattan_recall
value: 73.85224274406332
- type: max_ap
value: 80.18603932881858
- type: max_f1
value: 73.07643714466204
- type: max_precision
value: 71.54907975460122
- type: max_recall
value: 76.56992084432717
- type: similarity_accuracy
value: 88.1385229778864
- type: similarity_accuracy_threshold
value: 83.86307954788208
- type: similarity_ap
value: 80.17965893449055
- type: similarity_f1
value: 73.0614300100705
- type: similarity_f1_threshold
value: 80.7942807674408
- type: similarity_precision
value: 69.8603755416466
- type: similarity_recall
value: 76.56992084432717
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.7892653393876
- type: cosine_accuracy_threshold
value: 79.69566583633423
- type: cosine_ap
value: 87.4579867302024
- type: cosine_f1
value: 79.91620843152658
- type: cosine_f1_threshold
value: 78.53609323501587
- type: cosine_precision
value: 77.7155329210622
- type: cosine_recall
value: 82.24514936864799
- type: dot_accuracy
value: 89.78732487289945
- type: dot_accuracy_threshold
value: 80.05315661430359
- type: dot_ap
value: 87.44916182456272
- type: dot_f1
value: 79.90419878751591
- type: dot_f1_threshold
value: 78.57890725135803
- type: dot_precision
value: 77.73409057812728
- type: dot_recall
value: 82.19895287958116
- type: euclidean_accuracy
value: 89.78538440641131
- type: euclidean_accuracy_threshold
value: 62.29925751686096
- type: euclidean_ap
value: 87.45904868911386
- type: euclidean_f1
value: 79.93127404474657
- type: euclidean_f1_threshold
value: 65.61101078987122
- type: euclidean_precision
value: 77.62060210373595
- type: euclidean_recall
value: 82.38373883584848
- type: main_score
value: 87.46554314325058
- type: manhattan_accuracy
value: 89.76597974152986
- type: manhattan_accuracy_threshold
value: 3988.5299682617188
- type: manhattan_ap
value: 87.46554314325058
- type: manhattan_f1
value: 79.97181740645973
- type: manhattan_f1_threshold
value: 4235.905838012695
- type: manhattan_precision
value: 77.13713427283783
- type: manhattan_recall
value: 83.02279026793964
- type: max_ap
value: 87.46554314325058
- type: max_f1
value: 79.97181740645973
- type: max_precision
value: 77.73409057812728
- type: max_recall
value: 83.02279026793964
- type: similarity_accuracy
value: 89.7892653393876
- type: similarity_accuracy_threshold
value: 79.69566583633423
- type: similarity_ap
value: 87.4579867302024
- type: similarity_f1
value: 79.91620843152658
- type: similarity_f1_threshold
value: 78.53609323501587
- type: similarity_precision
value: 77.7155329210622
- type: similarity_recall
value: 82.24514936864799
---
# Updates
We released a Jasper and Stella model technology report and code.(2025.1)
**Report:** https://arxiv.org/abs/2412.19048
**Codes:** https://github.com/NLPJCL/RAG-Retrieval
# Introduction
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for
their contributions!
**We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.**
Prompt of s2p task(e.g. retrieve task):
```text
Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}
```
Prompt of s2s task(e.g. semantic textual similarity task):
```text
Instruct: Retrieve semantically similar text.\nQuery: {query}
```
The models are finally trained by [MRL](https://arxiv.org/abs/2205.13147), so they have multiple dimensions: 512, 768,
1024, 2048, 4096, 6144 and 8192.
The higher the dimension, the better the performance.
**Generally speaking, 1024d is good enough.** The MTEB score of 1024d is only 0.001 lower than 8192d.
# Model directory structure
The model directory structure is very simple, it is a standard SentenceTransformer directory **with a series
of `2_Dense_{dims}`
folders**, where `dims` represents the final vector dimension.
For example, the `2_Dense_256` folder stores Linear weights that convert vector dimensions to 256 dimensions.
Please refer to the following chapters for specific instructions on how to use them.
# Usage
You can use `SentenceTransformers` or `transformers` library to encode text.
## Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`
query_prompt_name = "s2p_query"
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
# on gpu
model = SentenceTransformer("dunzhang/stella_en_400M_v5", trust_remote_code=True).cuda()
# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU.
# model = SentenceTransformer(
# "dunzhang/stella_en_400M_v5",
# trust_remote_code=True,
# device="cpu",
# config_kwargs={"use_memory_efficient_attention": False, "unpad_inputs": False}
# )
query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
doc_embeddings = model.encode(docs)
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 1024) (2, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.8398, 0.2990],
# [0.3282, 0.8095]])
```
## Transformers
```python
import os
import torch
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# The path of your model after cloning it
model_dir = "{Your MODEL_PATH}"
vector_dim = 1024
vector_linear_directory = f"2_Dense_{vector_dim}"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU.
# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True,use_memory_efficient_attention=False,unpad_inputs=False).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim)
vector_linear_dict = {
k.replace("linear.", ""): v for k, v in
torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items()
}
vector_linear.load_state_dict(vector_linear_dict)
vector_linear.cuda()
# Embed the queries
with torch.no_grad():
input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_vectors = normalize(vector_linear(query_vectors).cpu().numpy())
# Embed the documents
with torch.no_grad():
input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy())
print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)
similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8397531 0.29900077]
# [0.32818374 0.80954516]]
```
### infinity_emb
Usage via [infinity, MIT Licensed](https://github.com/michaelfeil/infinity).
```bash
docker run \
--gpus all -p "7997":"7997" \
michaelf34/infinity:0.0.69 \
v2 --model-id dunzhang/stella_en_400M_v5 --revision "refs/pr/24" --dtype bfloat16 --batch-size 16 --device cuda --engine torch --port 7997 --no-bettertransformer
```
# Citation
```
@misc{zhang2025jasperstelladistillationsota,
title={Jasper and Stella: distillation of SOTA embedding models},
author={Dun Zhang and Jiacheng Li and Ziyang Zeng and Fulong Wang},
year={2025},
eprint={2412.19048},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2412.19048},
}
```
# FAQ
Q: The details of training?
A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper)
Q: How to choose a suitable prompt for my own task?
A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training
data.
Q: How to reproduce MTEB results?
A: Please use evaluation scripts in `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `intfloat/e5-mistral-7b-instruct`
Q: Why each dimension has a linear weight?
A: MRL has multiple training methods, we choose this method which has the best performance.
Q: What is the sequence length of models?
A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the
model is trained on datasets of 512 length. This may be an optimization term.
If you have any questions, please start a discussion on community. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
intfloat/e5-base | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2022-12-26T05:58:05 | 2023-08-07T04:59:19 | 294,150 | 20 | ---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-base
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 79.71641791044777
- type: ap
value: 44.15426065428253
- type: f1
value: 73.89474407693241
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.9649
- type: ap
value: 84.10171551915973
- type: f1
value: 87.94148377827356
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.645999999999994
- type: f1
value: 42.230574673549
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.814
- type: map_at_10
value: 42.681999999999995
- type: map_at_100
value: 43.714
- type: map_at_1000
value: 43.724000000000004
- type: map_at_3
value: 38.11
- type: map_at_5
value: 40.666999999999994
- type: mrr_at_1
value: 27.168999999999997
- type: mrr_at_10
value: 42.84
- type: mrr_at_100
value: 43.864
- type: mrr_at_1000
value: 43.875
- type: mrr_at_3
value: 38.193
- type: mrr_at_5
value: 40.793
- type: ndcg_at_1
value: 26.814
- type: ndcg_at_10
value: 51.410999999999994
- type: ndcg_at_100
value: 55.713
- type: ndcg_at_1000
value: 55.957
- type: ndcg_at_3
value: 41.955
- type: ndcg_at_5
value: 46.558
- type: precision_at_1
value: 26.814
- type: precision_at_10
value: 7.922999999999999
- type: precision_at_100
value: 0.9780000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.71
- type: precision_at_5
value: 12.859000000000002
- type: recall_at_1
value: 26.814
- type: recall_at_10
value: 79.232
- type: recall_at_100
value: 97.795
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 53.129000000000005
- type: recall_at_5
value: 64.29599999999999
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.56933066536439
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.47647746165173
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.65675531567043
- type: mrr
value: 72.95255683067317
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.83147014162338
- type: cos_sim_spearman
value: 85.1031439521441
- type: euclidean_pearson
value: 83.53609085510973
- type: euclidean_spearman
value: 84.59650590202833
- type: manhattan_pearson
value: 83.14611947586386
- type: manhattan_spearman
value: 84.13384475757064
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 83.32792207792208
- type: f1
value: 83.32037485050513
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.18605446588703
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.72379130181917
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.659
- type: map_at_10
value: 40.333999999999996
- type: map_at_100
value: 41.763
- type: map_at_1000
value: 41.894
- type: map_at_3
value: 37.561
- type: map_at_5
value: 39.084
- type: mrr_at_1
value: 37.482
- type: mrr_at_10
value: 45.736
- type: mrr_at_100
value: 46.591
- type: mrr_at_1000
value: 46.644999999999996
- type: mrr_at_3
value: 43.491
- type: mrr_at_5
value: 44.75
- type: ndcg_at_1
value: 37.482
- type: ndcg_at_10
value: 45.606
- type: ndcg_at_100
value: 51.172
- type: ndcg_at_1000
value: 53.407000000000004
- type: ndcg_at_3
value: 41.808
- type: ndcg_at_5
value: 43.449
- type: precision_at_1
value: 37.482
- type: precision_at_10
value: 8.254999999999999
- type: precision_at_100
value: 1.3719999999999999
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 19.695
- type: precision_at_5
value: 13.847999999999999
- type: recall_at_1
value: 30.659
- type: recall_at_10
value: 55.409
- type: recall_at_100
value: 78.687
- type: recall_at_1000
value: 93.068
- type: recall_at_3
value: 43.891999999999996
- type: recall_at_5
value: 48.678
- type: map_at_1
value: 30.977
- type: map_at_10
value: 40.296
- type: map_at_100
value: 41.453
- type: map_at_1000
value: 41.581
- type: map_at_3
value: 37.619
- type: map_at_5
value: 39.181
- type: mrr_at_1
value: 39.108
- type: mrr_at_10
value: 46.894000000000005
- type: mrr_at_100
value: 47.55
- type: mrr_at_1000
value: 47.598
- type: mrr_at_3
value: 44.766
- type: mrr_at_5
value: 46.062999999999995
- type: ndcg_at_1
value: 39.108
- type: ndcg_at_10
value: 45.717
- type: ndcg_at_100
value: 49.941
- type: ndcg_at_1000
value: 52.138
- type: ndcg_at_3
value: 42.05
- type: ndcg_at_5
value: 43.893
- type: precision_at_1
value: 39.108
- type: precision_at_10
value: 8.306
- type: precision_at_100
value: 1.3419999999999999
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 19.979
- type: precision_at_5
value: 14.038
- type: recall_at_1
value: 30.977
- type: recall_at_10
value: 54.688
- type: recall_at_100
value: 72.556
- type: recall_at_1000
value: 86.53800000000001
- type: recall_at_3
value: 43.388
- type: recall_at_5
value: 48.717
- type: map_at_1
value: 39.812
- type: map_at_10
value: 50.1
- type: map_at_100
value: 51.193999999999996
- type: map_at_1000
value: 51.258
- type: map_at_3
value: 47.510999999999996
- type: map_at_5
value: 48.891
- type: mrr_at_1
value: 45.266
- type: mrr_at_10
value: 53.459999999999994
- type: mrr_at_100
value: 54.19199999999999
- type: mrr_at_1000
value: 54.228
- type: mrr_at_3
value: 51.296
- type: mrr_at_5
value: 52.495999999999995
- type: ndcg_at_1
value: 45.266
- type: ndcg_at_10
value: 55.034000000000006
- type: ndcg_at_100
value: 59.458
- type: ndcg_at_1000
value: 60.862
- type: ndcg_at_3
value: 50.52799999999999
- type: ndcg_at_5
value: 52.564
- type: precision_at_1
value: 45.266
- type: precision_at_10
value: 8.483
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 21.944
- type: precision_at_5
value: 14.721
- type: recall_at_1
value: 39.812
- type: recall_at_10
value: 66.36
- type: recall_at_100
value: 85.392
- type: recall_at_1000
value: 95.523
- type: recall_at_3
value: 54.127
- type: recall_at_5
value: 59.245000000000005
- type: map_at_1
value: 26.186
- type: map_at_10
value: 33.18
- type: map_at_100
value: 34.052
- type: map_at_1000
value: 34.149
- type: map_at_3
value: 31.029
- type: map_at_5
value: 32.321
- type: mrr_at_1
value: 28.136
- type: mrr_at_10
value: 35.195
- type: mrr_at_100
value: 35.996
- type: mrr_at_1000
value: 36.076
- type: mrr_at_3
value: 33.051
- type: mrr_at_5
value: 34.407
- type: ndcg_at_1
value: 28.136
- type: ndcg_at_10
value: 37.275999999999996
- type: ndcg_at_100
value: 41.935
- type: ndcg_at_1000
value: 44.389
- type: ndcg_at_3
value: 33.059
- type: ndcg_at_5
value: 35.313
- type: precision_at_1
value: 28.136
- type: precision_at_10
value: 5.457999999999999
- type: precision_at_100
value: 0.826
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 13.522
- type: precision_at_5
value: 9.424000000000001
- type: recall_at_1
value: 26.186
- type: recall_at_10
value: 47.961999999999996
- type: recall_at_100
value: 70.072
- type: recall_at_1000
value: 88.505
- type: recall_at_3
value: 36.752
- type: recall_at_5
value: 42.168
- type: map_at_1
value: 16.586000000000002
- type: map_at_10
value: 23.637
- type: map_at_100
value: 24.82
- type: map_at_1000
value: 24.95
- type: map_at_3
value: 21.428
- type: map_at_5
value: 22.555
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 27.839999999999996
- type: mrr_at_100
value: 28.887
- type: mrr_at_1000
value: 28.967
- type: mrr_at_3
value: 25.56
- type: mrr_at_5
value: 26.723000000000003
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 28.255000000000003
- type: ndcg_at_100
value: 33.886
- type: ndcg_at_1000
value: 36.963
- type: ndcg_at_3
value: 24.056
- type: ndcg_at_5
value: 25.818
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.1
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 11.526
- type: precision_at_5
value: 8.158999999999999
- type: recall_at_1
value: 16.586000000000002
- type: recall_at_10
value: 38.456
- type: recall_at_100
value: 62.666
- type: recall_at_1000
value: 84.47
- type: recall_at_3
value: 26.765
- type: recall_at_5
value: 31.297000000000004
- type: map_at_1
value: 28.831
- type: map_at_10
value: 37.545
- type: map_at_100
value: 38.934999999999995
- type: map_at_1000
value: 39.044000000000004
- type: map_at_3
value: 34.601
- type: map_at_5
value: 36.302
- type: mrr_at_1
value: 34.264
- type: mrr_at_10
value: 42.569
- type: mrr_at_100
value: 43.514
- type: mrr_at_1000
value: 43.561
- type: mrr_at_3
value: 40.167
- type: mrr_at_5
value: 41.678
- type: ndcg_at_1
value: 34.264
- type: ndcg_at_10
value: 42.914
- type: ndcg_at_100
value: 48.931999999999995
- type: ndcg_at_1000
value: 51.004000000000005
- type: ndcg_at_3
value: 38.096999999999994
- type: ndcg_at_5
value: 40.509
- type: precision_at_1
value: 34.264
- type: precision_at_10
value: 7.642
- type: precision_at_100
value: 1.258
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 17.453
- type: precision_at_5
value: 12.608
- type: recall_at_1
value: 28.831
- type: recall_at_10
value: 53.56999999999999
- type: recall_at_100
value: 79.26100000000001
- type: recall_at_1000
value: 92.862
- type: recall_at_3
value: 40.681
- type: recall_at_5
value: 46.597
- type: map_at_1
value: 27.461000000000002
- type: map_at_10
value: 35.885
- type: map_at_100
value: 37.039
- type: map_at_1000
value: 37.16
- type: map_at_3
value: 33.451
- type: map_at_5
value: 34.807
- type: mrr_at_1
value: 34.018
- type: mrr_at_10
value: 41.32
- type: mrr_at_100
value: 42.157
- type: mrr_at_1000
value: 42.223
- type: mrr_at_3
value: 39.288000000000004
- type: mrr_at_5
value: 40.481
- type: ndcg_at_1
value: 34.018
- type: ndcg_at_10
value: 40.821000000000005
- type: ndcg_at_100
value: 46.053
- type: ndcg_at_1000
value: 48.673
- type: ndcg_at_3
value: 36.839
- type: ndcg_at_5
value: 38.683
- type: precision_at_1
value: 34.018
- type: precision_at_10
value: 7.009
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 16.933
- type: precision_at_5
value: 11.826
- type: recall_at_1
value: 27.461000000000002
- type: recall_at_10
value: 50.285000000000004
- type: recall_at_100
value: 73.25500000000001
- type: recall_at_1000
value: 91.17699999999999
- type: recall_at_3
value: 39.104
- type: recall_at_5
value: 43.968
- type: map_at_1
value: 26.980083333333337
- type: map_at_10
value: 34.47208333333333
- type: map_at_100
value: 35.609249999999996
- type: map_at_1000
value: 35.72833333333333
- type: map_at_3
value: 32.189416666666666
- type: map_at_5
value: 33.44683333333334
- type: mrr_at_1
value: 31.731666666666662
- type: mrr_at_10
value: 38.518
- type: mrr_at_100
value: 39.38166666666667
- type: mrr_at_1000
value: 39.446999999999996
- type: mrr_at_3
value: 36.49966666666668
- type: mrr_at_5
value: 37.639916666666664
- type: ndcg_at_1
value: 31.731666666666662
- type: ndcg_at_10
value: 38.92033333333333
- type: ndcg_at_100
value: 44.01675
- type: ndcg_at_1000
value: 46.51075
- type: ndcg_at_3
value: 35.09766666666667
- type: ndcg_at_5
value: 36.842999999999996
- type: precision_at_1
value: 31.731666666666662
- type: precision_at_10
value: 6.472583333333332
- type: precision_at_100
value: 1.0665
- type: precision_at_1000
value: 0.14725000000000002
- type: precision_at_3
value: 15.659083333333331
- type: precision_at_5
value: 10.878833333333333
- type: recall_at_1
value: 26.980083333333337
- type: recall_at_10
value: 48.13925
- type: recall_at_100
value: 70.70149999999998
- type: recall_at_1000
value: 88.10775000000001
- type: recall_at_3
value: 37.30091666666667
- type: recall_at_5
value: 41.90358333333333
- type: map_at_1
value: 25.607999999999997
- type: map_at_10
value: 30.523
- type: map_at_100
value: 31.409
- type: map_at_1000
value: 31.507
- type: map_at_3
value: 28.915000000000003
- type: map_at_5
value: 29.756
- type: mrr_at_1
value: 28.681
- type: mrr_at_10
value: 33.409
- type: mrr_at_100
value: 34.241
- type: mrr_at_1000
value: 34.313
- type: mrr_at_3
value: 32.029999999999994
- type: mrr_at_5
value: 32.712
- type: ndcg_at_1
value: 28.681
- type: ndcg_at_10
value: 33.733000000000004
- type: ndcg_at_100
value: 38.32
- type: ndcg_at_1000
value: 40.937
- type: ndcg_at_3
value: 30.898999999999997
- type: ndcg_at_5
value: 32.088
- type: precision_at_1
value: 28.681
- type: precision_at_10
value: 4.968999999999999
- type: precision_at_100
value: 0.79
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 12.73
- type: precision_at_5
value: 8.558
- type: recall_at_1
value: 25.607999999999997
- type: recall_at_10
value: 40.722
- type: recall_at_100
value: 61.956999999999994
- type: recall_at_1000
value: 81.43
- type: recall_at_3
value: 32.785
- type: recall_at_5
value: 35.855
- type: map_at_1
value: 20.399
- type: map_at_10
value: 25.968000000000004
- type: map_at_100
value: 26.985999999999997
- type: map_at_1000
value: 27.105
- type: map_at_3
value: 24.215
- type: map_at_5
value: 25.157
- type: mrr_at_1
value: 24.708
- type: mrr_at_10
value: 29.971999999999998
- type: mrr_at_100
value: 30.858
- type: mrr_at_1000
value: 30.934
- type: mrr_at_3
value: 28.304000000000002
- type: mrr_at_5
value: 29.183999999999997
- type: ndcg_at_1
value: 24.708
- type: ndcg_at_10
value: 29.676000000000002
- type: ndcg_at_100
value: 34.656
- type: ndcg_at_1000
value: 37.588
- type: ndcg_at_3
value: 26.613
- type: ndcg_at_5
value: 27.919
- type: precision_at_1
value: 24.708
- type: precision_at_10
value: 5.01
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.975
- type: precision_at_5
value: 8.279
- type: recall_at_1
value: 20.399
- type: recall_at_10
value: 36.935
- type: recall_at_100
value: 59.532
- type: recall_at_1000
value: 80.58
- type: recall_at_3
value: 27.979
- type: recall_at_5
value: 31.636999999999997
- type: map_at_1
value: 27.606
- type: map_at_10
value: 34.213
- type: map_at_100
value: 35.339999999999996
- type: map_at_1000
value: 35.458
- type: map_at_3
value: 31.987
- type: map_at_5
value: 33.322
- type: mrr_at_1
value: 31.53
- type: mrr_at_10
value: 37.911
- type: mrr_at_100
value: 38.879000000000005
- type: mrr_at_1000
value: 38.956
- type: mrr_at_3
value: 35.868
- type: mrr_at_5
value: 37.047999999999995
- type: ndcg_at_1
value: 31.53
- type: ndcg_at_10
value: 38.312000000000005
- type: ndcg_at_100
value: 43.812
- type: ndcg_at_1000
value: 46.414
- type: ndcg_at_3
value: 34.319
- type: ndcg_at_5
value: 36.312
- type: precision_at_1
value: 31.53
- type: precision_at_10
value: 5.970000000000001
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 14.738999999999999
- type: precision_at_5
value: 10.242999999999999
- type: recall_at_1
value: 27.606
- type: recall_at_10
value: 47.136
- type: recall_at_100
value: 71.253
- type: recall_at_1000
value: 89.39399999999999
- type: recall_at_3
value: 36.342
- type: recall_at_5
value: 41.388999999999996
- type: map_at_1
value: 24.855
- type: map_at_10
value: 31.963
- type: map_at_100
value: 33.371
- type: map_at_1000
value: 33.584
- type: map_at_3
value: 29.543999999999997
- type: map_at_5
value: 30.793
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 35.601
- type: mrr_at_100
value: 36.551
- type: mrr_at_1000
value: 36.623
- type: mrr_at_3
value: 33.399
- type: mrr_at_5
value: 34.575
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 36.521
- type: ndcg_at_100
value: 42.087
- type: ndcg_at_1000
value: 45.119
- type: ndcg_at_3
value: 32.797
- type: ndcg_at_5
value: 34.208
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.22899999999999998
- type: precision_at_3
value: 15.152
- type: precision_at_5
value: 10.671999999999999
- type: recall_at_1
value: 24.855
- type: recall_at_10
value: 45.449
- type: recall_at_100
value: 70.921
- type: recall_at_1000
value: 90.629
- type: recall_at_3
value: 33.526
- type: recall_at_5
value: 37.848
- type: map_at_1
value: 24.781
- type: map_at_10
value: 30.020999999999997
- type: map_at_100
value: 30.948999999999998
- type: map_at_1000
value: 31.05
- type: map_at_3
value: 28.412
- type: map_at_5
value: 29.193
- type: mrr_at_1
value: 27.172
- type: mrr_at_10
value: 32.309
- type: mrr_at_100
value: 33.164
- type: mrr_at_1000
value: 33.239999999999995
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 31.562
- type: ndcg_at_1
value: 27.172
- type: ndcg_at_10
value: 33.178999999999995
- type: ndcg_at_100
value: 37.949
- type: ndcg_at_1000
value: 40.635
- type: ndcg_at_3
value: 30.107
- type: ndcg_at_5
value: 31.36
- type: precision_at_1
value: 27.172
- type: precision_at_10
value: 4.769
- type: precision_at_100
value: 0.769
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 12.261
- type: precision_at_5
value: 8.17
- type: recall_at_1
value: 24.781
- type: recall_at_10
value: 40.699000000000005
- type: recall_at_100
value: 62.866
- type: recall_at_1000
value: 83.11699999999999
- type: recall_at_3
value: 32.269999999999996
- type: recall_at_5
value: 35.443999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.2139999999999995
- type: map_at_10
value: 9.986
- type: map_at_100
value: 11.343
- type: map_at_1000
value: 11.55
- type: map_at_3
value: 7.961
- type: map_at_5
value: 8.967
- type: mrr_at_1
value: 12.052
- type: mrr_at_10
value: 20.165
- type: mrr_at_100
value: 21.317
- type: mrr_at_1000
value: 21.399
- type: mrr_at_3
value: 17.079
- type: mrr_at_5
value: 18.695
- type: ndcg_at_1
value: 12.052
- type: ndcg_at_10
value: 15.375
- type: ndcg_at_100
value: 21.858
- type: ndcg_at_1000
value: 26.145000000000003
- type: ndcg_at_3
value: 11.334
- type: ndcg_at_5
value: 12.798000000000002
- type: precision_at_1
value: 12.052
- type: precision_at_10
value: 5.16
- type: precision_at_100
value: 1.206
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 8.73
- type: precision_at_5
value: 7.114
- type: recall_at_1
value: 5.2139999999999995
- type: recall_at_10
value: 20.669999999999998
- type: recall_at_100
value: 43.901
- type: recall_at_1000
value: 68.447
- type: recall_at_3
value: 11.049000000000001
- type: recall_at_5
value: 14.652999999999999
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.511000000000001
- type: map_at_10
value: 19.503
- type: map_at_100
value: 27.46
- type: map_at_1000
value: 29.187
- type: map_at_3
value: 14.030999999999999
- type: map_at_5
value: 16.329
- type: mrr_at_1
value: 63.74999999999999
- type: mrr_at_10
value: 73.419
- type: mrr_at_100
value: 73.691
- type: mrr_at_1000
value: 73.697
- type: mrr_at_3
value: 71.792
- type: mrr_at_5
value: 72.979
- type: ndcg_at_1
value: 53.125
- type: ndcg_at_10
value: 41.02
- type: ndcg_at_100
value: 45.407
- type: ndcg_at_1000
value: 52.68000000000001
- type: ndcg_at_3
value: 46.088
- type: ndcg_at_5
value: 43.236000000000004
- type: precision_at_1
value: 63.74999999999999
- type: precision_at_10
value: 32.35
- type: precision_at_100
value: 10.363
- type: precision_at_1000
value: 2.18
- type: precision_at_3
value: 49.667
- type: precision_at_5
value: 41.5
- type: recall_at_1
value: 8.511000000000001
- type: recall_at_10
value: 24.851
- type: recall_at_100
value: 50.745
- type: recall_at_1000
value: 73.265
- type: recall_at_3
value: 15.716
- type: recall_at_5
value: 19.256
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.43500000000001
- type: f1
value: 44.56288273966374
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.858
- type: map_at_10
value: 52.276
- type: map_at_100
value: 52.928
- type: map_at_1000
value: 52.966
- type: map_at_3
value: 49.729
- type: map_at_5
value: 51.27
- type: mrr_at_1
value: 43.624
- type: mrr_at_10
value: 55.22899999999999
- type: mrr_at_100
value: 55.823
- type: mrr_at_1000
value: 55.85
- type: mrr_at_3
value: 52.739999999999995
- type: mrr_at_5
value: 54.251000000000005
- type: ndcg_at_1
value: 43.624
- type: ndcg_at_10
value: 58.23500000000001
- type: ndcg_at_100
value: 61.315
- type: ndcg_at_1000
value: 62.20099999999999
- type: ndcg_at_3
value: 53.22
- type: ndcg_at_5
value: 55.88999999999999
- type: precision_at_1
value: 43.624
- type: precision_at_10
value: 8.068999999999999
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 21.752
- type: precision_at_5
value: 14.515
- type: recall_at_1
value: 40.858
- type: recall_at_10
value: 73.744
- type: recall_at_100
value: 87.667
- type: recall_at_1000
value: 94.15599999999999
- type: recall_at_3
value: 60.287
- type: recall_at_5
value: 66.703
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.864
- type: map_at_10
value: 28.592000000000002
- type: map_at_100
value: 30.165
- type: map_at_1000
value: 30.364
- type: map_at_3
value: 24.586
- type: map_at_5
value: 26.717000000000002
- type: mrr_at_1
value: 35.031
- type: mrr_at_10
value: 43.876
- type: mrr_at_100
value: 44.683
- type: mrr_at_1000
value: 44.736
- type: mrr_at_3
value: 40.998000000000005
- type: mrr_at_5
value: 42.595
- type: ndcg_at_1
value: 35.031
- type: ndcg_at_10
value: 36.368
- type: ndcg_at_100
value: 42.472
- type: ndcg_at_1000
value: 45.973000000000006
- type: ndcg_at_3
value: 31.915
- type: ndcg_at_5
value: 33.394
- type: precision_at_1
value: 35.031
- type: precision_at_10
value: 10.139
- type: precision_at_100
value: 1.6420000000000001
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 21.142
- type: precision_at_5
value: 15.772
- type: recall_at_1
value: 17.864
- type: recall_at_10
value: 43.991
- type: recall_at_100
value: 66.796
- type: recall_at_1000
value: 87.64
- type: recall_at_3
value: 28.915999999999997
- type: recall_at_5
value: 35.185
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.556
- type: map_at_10
value: 53.056000000000004
- type: map_at_100
value: 53.909
- type: map_at_1000
value: 53.98
- type: map_at_3
value: 49.982
- type: map_at_5
value: 51.9
- type: mrr_at_1
value: 73.113
- type: mrr_at_10
value: 79.381
- type: mrr_at_100
value: 79.60300000000001
- type: mrr_at_1000
value: 79.617
- type: mrr_at_3
value: 78.298
- type: mrr_at_5
value: 78.995
- type: ndcg_at_1
value: 73.113
- type: ndcg_at_10
value: 62.21
- type: ndcg_at_100
value: 65.242
- type: ndcg_at_1000
value: 66.667
- type: ndcg_at_3
value: 57.717
- type: ndcg_at_5
value: 60.224
- type: precision_at_1
value: 73.113
- type: precision_at_10
value: 12.842999999999998
- type: precision_at_100
value: 1.522
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 36.178
- type: precision_at_5
value: 23.695
- type: recall_at_1
value: 36.556
- type: recall_at_10
value: 64.213
- type: recall_at_100
value: 76.077
- type: recall_at_1000
value: 85.53699999999999
- type: recall_at_3
value: 54.266999999999996
- type: recall_at_5
value: 59.236999999999995
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 75.958
- type: ap
value: 69.82869527654348
- type: f1
value: 75.89120903005633
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.608
- type: map_at_10
value: 36.144
- type: map_at_100
value: 37.244
- type: map_at_1000
value: 37.291999999999994
- type: map_at_3
value: 32.287
- type: map_at_5
value: 34.473
- type: mrr_at_1
value: 24.226
- type: mrr_at_10
value: 36.711
- type: mrr_at_100
value: 37.758
- type: mrr_at_1000
value: 37.8
- type: mrr_at_3
value: 32.92
- type: mrr_at_5
value: 35.104
- type: ndcg_at_1
value: 24.269
- type: ndcg_at_10
value: 43.138
- type: ndcg_at_100
value: 48.421
- type: ndcg_at_1000
value: 49.592000000000006
- type: ndcg_at_3
value: 35.269
- type: ndcg_at_5
value: 39.175
- type: precision_at_1
value: 24.269
- type: precision_at_10
value: 6.755999999999999
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.938
- type: precision_at_5
value: 10.934000000000001
- type: recall_at_1
value: 23.608
- type: recall_at_10
value: 64.679
- type: recall_at_100
value: 89.027
- type: recall_at_1000
value: 97.91
- type: recall_at_3
value: 43.25
- type: recall_at_5
value: 52.617000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.21477428180576
- type: f1
value: 92.92502305092152
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.76744186046511
- type: f1
value: 59.19855520057899
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.24613315400134
- type: f1
value: 70.19950395651232
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.75857431069268
- type: f1
value: 76.5433450230191
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.525463791623604
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.28695907385136
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.068174046665224
- type: mrr
value: 30.827586642840803
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.322
- type: map_at_10
value: 13.919999999999998
- type: map_at_100
value: 17.416
- type: map_at_1000
value: 18.836
- type: map_at_3
value: 10.111
- type: map_at_5
value: 11.991999999999999
- type: mrr_at_1
value: 48.297000000000004
- type: mrr_at_10
value: 57.114
- type: mrr_at_100
value: 57.713
- type: mrr_at_1000
value: 57.751
- type: mrr_at_3
value: 55.108000000000004
- type: mrr_at_5
value: 56.533
- type: ndcg_at_1
value: 46.44
- type: ndcg_at_10
value: 36.589
- type: ndcg_at_100
value: 33.202
- type: ndcg_at_1000
value: 41.668
- type: ndcg_at_3
value: 41.302
- type: ndcg_at_5
value: 39.829
- type: precision_at_1
value: 47.988
- type: precision_at_10
value: 27.059
- type: precision_at_100
value: 8.235000000000001
- type: precision_at_1000
value: 2.091
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 34.365
- type: recall_at_1
value: 6.322
- type: recall_at_10
value: 18.288
- type: recall_at_100
value: 32.580999999999996
- type: recall_at_1000
value: 63.605999999999995
- type: recall_at_3
value: 11.266
- type: recall_at_5
value: 14.69
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.586999999999996
- type: map_at_10
value: 52.464
- type: map_at_100
value: 53.384
- type: map_at_1000
value: 53.405
- type: map_at_3
value: 48.408
- type: map_at_5
value: 50.788999999999994
- type: mrr_at_1
value: 40.904
- type: mrr_at_10
value: 54.974000000000004
- type: mrr_at_100
value: 55.60699999999999
- type: mrr_at_1000
value: 55.623
- type: mrr_at_3
value: 51.73799999999999
- type: mrr_at_5
value: 53.638
- type: ndcg_at_1
value: 40.904
- type: ndcg_at_10
value: 59.965999999999994
- type: ndcg_at_100
value: 63.613
- type: ndcg_at_1000
value: 64.064
- type: ndcg_at_3
value: 52.486
- type: ndcg_at_5
value: 56.377
- type: precision_at_1
value: 40.904
- type: precision_at_10
value: 9.551
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.552
- type: precision_at_5
value: 16.436999999999998
- type: recall_at_1
value: 36.586999999999996
- type: recall_at_10
value: 80.094
- type: recall_at_100
value: 95.515
- type: recall_at_1000
value: 98.803
- type: recall_at_3
value: 60.907
- type: recall_at_5
value: 69.817
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.422
- type: map_at_10
value: 84.113
- type: map_at_100
value: 84.744
- type: map_at_1000
value: 84.762
- type: map_at_3
value: 81.171
- type: map_at_5
value: 83.039
- type: mrr_at_1
value: 81.12
- type: mrr_at_10
value: 87.277
- type: mrr_at_100
value: 87.384
- type: mrr_at_1000
value: 87.385
- type: mrr_at_3
value: 86.315
- type: mrr_at_5
value: 86.981
- type: ndcg_at_1
value: 81.12
- type: ndcg_at_10
value: 87.92
- type: ndcg_at_100
value: 89.178
- type: ndcg_at_1000
value: 89.29899999999999
- type: ndcg_at_3
value: 85.076
- type: ndcg_at_5
value: 86.67099999999999
- type: precision_at_1
value: 81.12
- type: precision_at_10
value: 13.325999999999999
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.16
- type: precision_at_5
value: 24.456
- type: recall_at_1
value: 70.422
- type: recall_at_10
value: 95.00800000000001
- type: recall_at_100
value: 99.38
- type: recall_at_1000
value: 99.94800000000001
- type: recall_at_3
value: 86.809
- type: recall_at_5
value: 91.334
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 48.18491891699636
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.190639679711914
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.478
- type: map_at_10
value: 11.268
- type: map_at_100
value: 13.129
- type: map_at_1000
value: 13.41
- type: map_at_3
value: 8.103
- type: map_at_5
value: 9.609
- type: mrr_at_1
value: 22
- type: mrr_at_10
value: 32.248
- type: mrr_at_100
value: 33.355000000000004
- type: mrr_at_1000
value: 33.42
- type: mrr_at_3
value: 29.15
- type: mrr_at_5
value: 30.785
- type: ndcg_at_1
value: 22
- type: ndcg_at_10
value: 18.990000000000002
- type: ndcg_at_100
value: 26.302999999999997
- type: ndcg_at_1000
value: 31.537
- type: ndcg_at_3
value: 18.034
- type: ndcg_at_5
value: 15.655
- type: precision_at_1
value: 22
- type: precision_at_10
value: 9.91
- type: precision_at_100
value: 2.0420000000000003
- type: precision_at_1000
value: 0.33
- type: precision_at_3
value: 16.933
- type: precision_at_5
value: 13.719999999999999
- type: recall_at_1
value: 4.478
- type: recall_at_10
value: 20.087
- type: recall_at_100
value: 41.457
- type: recall_at_1000
value: 67.10199999999999
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 13.927999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.27341574565806
- type: cos_sim_spearman
value: 79.66419880841734
- type: euclidean_pearson
value: 81.32473321838208
- type: euclidean_spearman
value: 79.29828832085133
- type: manhattan_pearson
value: 81.25554065883132
- type: manhattan_spearman
value: 79.23275543279853
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.40468875905418
- type: cos_sim_spearman
value: 74.2189990321174
- type: euclidean_pearson
value: 80.74376966290956
- type: euclidean_spearman
value: 74.97663839079335
- type: manhattan_pearson
value: 80.69779331646207
- type: manhattan_spearman
value: 75.00225252917613
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.5745290053095
- type: cos_sim_spearman
value: 83.31401180333397
- type: euclidean_pearson
value: 82.96500607325534
- type: euclidean_spearman
value: 83.8534967935793
- type: manhattan_pearson
value: 82.83112050632508
- type: manhattan_spearman
value: 83.70877296557838
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.67833656607704
- type: cos_sim_spearman
value: 78.52252410630707
- type: euclidean_pearson
value: 80.071189514343
- type: euclidean_spearman
value: 78.95143545742796
- type: manhattan_pearson
value: 80.0128926165121
- type: manhattan_spearman
value: 78.91236678732628
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.48437639980746
- type: cos_sim_spearman
value: 88.34876527774259
- type: euclidean_pearson
value: 87.64898081823888
- type: euclidean_spearman
value: 88.58937180804213
- type: manhattan_pearson
value: 87.5942417815288
- type: manhattan_spearman
value: 88.53013922267687
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.69189187164781
- type: cos_sim_spearman
value: 84.15327883572112
- type: euclidean_pearson
value: 83.64202266685898
- type: euclidean_spearman
value: 84.6219602318862
- type: manhattan_pearson
value: 83.53256698709998
- type: manhattan_spearman
value: 84.49260712904946
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.09508017611589
- type: cos_sim_spearman
value: 87.23010990417097
- type: euclidean_pearson
value: 87.62545569077133
- type: euclidean_spearman
value: 86.71152051711714
- type: manhattan_pearson
value: 87.5057154278377
- type: manhattan_spearman
value: 86.60611898281267
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 61.72129893941176
- type: cos_sim_spearman
value: 62.87871412069194
- type: euclidean_pearson
value: 63.21077648290454
- type: euclidean_spearman
value: 63.03263080805978
- type: manhattan_pearson
value: 63.20740860135976
- type: manhattan_spearman
value: 62.89930471802817
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.039118236799
- type: cos_sim_spearman
value: 86.18102563389962
- type: euclidean_pearson
value: 85.62977041471879
- type: euclidean_spearman
value: 86.02478990544347
- type: manhattan_pearson
value: 85.60786740521806
- type: manhattan_spearman
value: 85.99546210442547
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.89875069737266
- type: mrr
value: 95.42621322033087
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.660999999999994
- type: map_at_10
value: 68.738
- type: map_at_100
value: 69.33200000000001
- type: map_at_1000
value: 69.352
- type: map_at_3
value: 66.502
- type: map_at_5
value: 67.686
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 70.003
- type: mrr_at_100
value: 70.441
- type: mrr_at_1000
value: 70.46
- type: mrr_at_3
value: 68.278
- type: mrr_at_5
value: 69.194
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 73.083
- type: ndcg_at_100
value: 75.56
- type: ndcg_at_1000
value: 76.01400000000001
- type: ndcg_at_3
value: 69.28699999999999
- type: ndcg_at_5
value: 70.85000000000001
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 58.660999999999994
- type: recall_at_10
value: 85.02199999999999
- type: recall_at_100
value: 95.933
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 74.506
- type: recall_at_5
value: 78.583
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8029702970297
- type: cos_sim_ap
value: 94.87673936635738
- type: cos_sim_f1
value: 90.00502260170768
- type: cos_sim_precision
value: 90.41372351160445
- type: cos_sim_recall
value: 89.60000000000001
- type: dot_accuracy
value: 99.57524752475247
- type: dot_ap
value: 84.81717934496321
- type: dot_f1
value: 78.23026646556059
- type: dot_precision
value: 78.66531850353893
- type: dot_recall
value: 77.8
- type: euclidean_accuracy
value: 99.8029702970297
- type: euclidean_ap
value: 94.74658253135284
- type: euclidean_f1
value: 90.08470353761834
- type: euclidean_precision
value: 89.77159880834161
- type: euclidean_recall
value: 90.4
- type: manhattan_accuracy
value: 99.8
- type: manhattan_ap
value: 94.69224030742787
- type: manhattan_f1
value: 89.9502487562189
- type: manhattan_precision
value: 89.50495049504951
- type: manhattan_recall
value: 90.4
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 94.87673936635738
- type: max_f1
value: 90.08470353761834
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 63.906039623153035
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.56053830923281
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.15326538775145
- type: mrr
value: 50.99279295051355
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.44030762047337
- type: cos_sim_spearman
value: 31.00910300264562
- type: dot_pearson
value: 26.88257194766013
- type: dot_spearman
value: 27.646202679013577
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.247
- type: map_at_10
value: 1.9429999999999998
- type: map_at_100
value: 10.82
- type: map_at_1000
value: 25.972
- type: map_at_3
value: 0.653
- type: map_at_5
value: 1.057
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 96.333
- type: mrr_at_100
value: 96.333
- type: mrr_at_1000
value: 96.333
- type: mrr_at_3
value: 96.333
- type: mrr_at_5
value: 96.333
- type: ndcg_at_1
value: 89
- type: ndcg_at_10
value: 79.63799999999999
- type: ndcg_at_100
value: 57.961
- type: ndcg_at_1000
value: 50.733
- type: ndcg_at_3
value: 84.224
- type: ndcg_at_5
value: 82.528
- type: precision_at_1
value: 94
- type: precision_at_10
value: 84.2
- type: precision_at_100
value: 59.36
- type: precision_at_1000
value: 22.738
- type: precision_at_3
value: 88
- type: precision_at_5
value: 86.8
- type: recall_at_1
value: 0.247
- type: recall_at_10
value: 2.131
- type: recall_at_100
value: 14.035
- type: recall_at_1000
value: 47.457
- type: recall_at_3
value: 0.6779999999999999
- type: recall_at_5
value: 1.124
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.603
- type: map_at_10
value: 11.667
- type: map_at_100
value: 16.474
- type: map_at_1000
value: 18.074
- type: map_at_3
value: 6.03
- type: map_at_5
value: 8.067
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 51.063
- type: mrr_at_100
value: 51.908
- type: mrr_at_1000
value: 51.908
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 49.694
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 28.305000000000003
- type: ndcg_at_100
value: 35.311
- type: ndcg_at_1000
value: 47.644999999999996
- type: ndcg_at_3
value: 32.187
- type: ndcg_at_5
value: 29.134999999999998
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 26.122
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.467
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 30.203999999999997
- type: recall_at_1
value: 2.603
- type: recall_at_10
value: 18.716
- type: recall_at_100
value: 42.512
- type: recall_at_1000
value: 79.32000000000001
- type: recall_at_3
value: 7.59
- type: recall_at_5
value: 10.949
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 74.117
- type: ap
value: 15.89357321699319
- type: f1
value: 57.14385866369257
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.38370118845502
- type: f1
value: 61.67038693866553
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 42.57754941537969
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.1775049174465
- type: cos_sim_ap
value: 74.3994879581554
- type: cos_sim_f1
value: 69.32903671308551
- type: cos_sim_precision
value: 61.48193508879363
- type: cos_sim_recall
value: 79.47229551451187
- type: dot_accuracy
value: 81.65345413363534
- type: dot_ap
value: 59.690898346685096
- type: dot_f1
value: 57.27622826467499
- type: dot_precision
value: 51.34965473948525
- type: dot_recall
value: 64.74934036939314
- type: euclidean_accuracy
value: 86.04637301066937
- type: euclidean_ap
value: 74.33009001775268
- type: euclidean_f1
value: 69.2458374142997
- type: euclidean_precision
value: 64.59570580173595
- type: euclidean_recall
value: 74.6174142480211
- type: manhattan_accuracy
value: 86.11193896405793
- type: manhattan_ap
value: 74.2964140130421
- type: manhattan_f1
value: 69.11601528788066
- type: manhattan_precision
value: 64.86924323073363
- type: manhattan_recall
value: 73.95778364116094
- type: max_accuracy
value: 86.1775049174465
- type: max_ap
value: 74.3994879581554
- type: max_f1
value: 69.32903671308551
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.01501921061823
- type: cos_sim_ap
value: 85.97819287477351
- type: cos_sim_f1
value: 78.33882858518875
- type: cos_sim_precision
value: 75.49446626204926
- type: cos_sim_recall
value: 81.40591315060055
- type: dot_accuracy
value: 86.47494857763806
- type: dot_ap
value: 78.77420360340282
- type: dot_f1
value: 73.06433247936238
- type: dot_precision
value: 67.92140777983595
- type: dot_recall
value: 79.04989220819218
- type: euclidean_accuracy
value: 88.7297706368611
- type: euclidean_ap
value: 85.61550568529317
- type: euclidean_f1
value: 77.84805525263539
- type: euclidean_precision
value: 73.73639994491117
- type: euclidean_recall
value: 82.44533415460425
- type: manhattan_accuracy
value: 88.75111576823068
- type: manhattan_ap
value: 85.58701671476263
- type: manhattan_f1
value: 77.70169909067856
- type: manhattan_precision
value: 73.37666780704755
- type: manhattan_recall
value: 82.5685247921158
- type: max_accuracy
value: 89.01501921061823
- type: max_ap
value: 85.97819287477351
- type: max_f1
value: 78.33882858518875
---
## E5-base
**News (May 2023): please switch to [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2), which has better performance and same method of usage.**
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 768.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base')
model = AutoModel.from_pretrained('intfloat/e5-base')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-base')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Snowflake/snowflake-arctic-embed-xs | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"arxiv:2407.18887",
"arxiv:2405.05374",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-12T13:54:17 | 2024-12-13T20:54:05 | 270,601 | 35 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-snowflake-arctic-embed-xs
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 65.08955223880598
- type: ap
value: 28.514291209445364
- type: f1
value: 59.2604580112738
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 70.035375
- type: ap
value: 64.29444264250405
- type: f1
value: 69.78382333907138
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 35.343999999999994
- type: f1
value: 34.69618251902858
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 28.592000000000002
- type: map_at_10
value: 43.597
- type: map_at_100
value: 44.614
- type: map_at_1000
value: 44.624
- type: map_at_3
value: 38.928000000000004
- type: map_at_5
value: 41.453
- type: mrr_at_1
value: 29.232000000000003
- type: mrr_at_10
value: 43.829
- type: mrr_at_100
value: 44.852
- type: mrr_at_1000
value: 44.862
- type: mrr_at_3
value: 39.118
- type: mrr_at_5
value: 41.703
- type: ndcg_at_1
value: 28.592000000000002
- type: ndcg_at_10
value: 52.081
- type: ndcg_at_100
value: 56.37
- type: ndcg_at_1000
value: 56.598000000000006
- type: ndcg_at_3
value: 42.42
- type: ndcg_at_5
value: 46.965
- type: precision_at_1
value: 28.592000000000002
- type: precision_at_10
value: 7.922999999999999
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 17.52
- type: precision_at_5
value: 12.717
- type: recall_at_1
value: 28.592000000000002
- type: recall_at_10
value: 79.232
- type: recall_at_100
value: 97.866
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 52.559999999999995
- type: recall_at_5
value: 63.585
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.50220588953974
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.08725826118282
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.25381587694928
- type: mrr
value: 73.79776194873148
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.47489332445278
- type: cos_sim_spearman
value: 84.05432487336698
- type: euclidean_pearson
value: 84.5108222177219
- type: euclidean_spearman
value: 84.05432487336698
- type: manhattan_pearson
value: 84.20440618321464
- type: manhattan_spearman
value: 83.9290208134097
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 76.37337662337663
- type: f1
value: 75.33296834885043
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 21.31174373264835
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 34.481973521597844
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 26.14094256567341
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 32.527
- type: map_at_10
value: 43.699
- type: map_at_100
value: 45.03
- type: map_at_1000
value: 45.157000000000004
- type: map_at_3
value: 39.943
- type: map_at_5
value: 42.324
- type: mrr_at_1
value: 39.771
- type: mrr_at_10
value: 49.277
- type: mrr_at_100
value: 49.956
- type: mrr_at_1000
value: 50.005
- type: mrr_at_3
value: 46.304
- type: mrr_at_5
value: 48.493
- type: ndcg_at_1
value: 39.771
- type: ndcg_at_10
value: 49.957
- type: ndcg_at_100
value: 54.678000000000004
- type: ndcg_at_1000
value: 56.751
- type: ndcg_at_3
value: 44.608
- type: ndcg_at_5
value: 47.687000000000005
- type: precision_at_1
value: 39.771
- type: precision_at_10
value: 9.557
- type: precision_at_100
value: 1.5010000000000001
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 21.173000000000002
- type: precision_at_5
value: 15.794
- type: recall_at_1
value: 32.527
- type: recall_at_10
value: 61.791
- type: recall_at_100
value: 81.49300000000001
- type: recall_at_1000
value: 95.014
- type: recall_at_3
value: 46.605000000000004
- type: recall_at_5
value: 54.83
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 29.424
- type: map_at_10
value: 38.667
- type: map_at_100
value: 39.771
- type: map_at_1000
value: 39.899
- type: map_at_3
value: 35.91
- type: map_at_5
value: 37.45
- type: mrr_at_1
value: 36.687999999999995
- type: mrr_at_10
value: 44.673
- type: mrr_at_100
value: 45.289
- type: mrr_at_1000
value: 45.338
- type: mrr_at_3
value: 42.601
- type: mrr_at_5
value: 43.875
- type: ndcg_at_1
value: 36.687999999999995
- type: ndcg_at_10
value: 44.013000000000005
- type: ndcg_at_100
value: 48.13
- type: ndcg_at_1000
value: 50.294000000000004
- type: ndcg_at_3
value: 40.056999999999995
- type: ndcg_at_5
value: 41.902
- type: precision_at_1
value: 36.687999999999995
- type: precision_at_10
value: 8.158999999999999
- type: precision_at_100
value: 1.321
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 19.045
- type: precision_at_5
value: 13.427
- type: recall_at_1
value: 29.424
- type: recall_at_10
value: 53.08500000000001
- type: recall_at_100
value: 70.679
- type: recall_at_1000
value: 84.66
- type: recall_at_3
value: 41.399
- type: recall_at_5
value: 46.632
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.747
- type: map_at_10
value: 51.452
- type: map_at_100
value: 52.384
- type: map_at_1000
value: 52.437
- type: map_at_3
value: 48.213
- type: map_at_5
value: 50.195
- type: mrr_at_1
value: 45.391999999999996
- type: mrr_at_10
value: 54.928
- type: mrr_at_100
value: 55.532000000000004
- type: mrr_at_1000
value: 55.565
- type: mrr_at_3
value: 52.456
- type: mrr_at_5
value: 54.054
- type: ndcg_at_1
value: 45.391999999999996
- type: ndcg_at_10
value: 57.055
- type: ndcg_at_100
value: 60.751999999999995
- type: ndcg_at_1000
value: 61.864
- type: ndcg_at_3
value: 51.662
- type: ndcg_at_5
value: 54.613
- type: precision_at_1
value: 45.391999999999996
- type: precision_at_10
value: 9.103
- type: precision_at_100
value: 1.1780000000000002
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 22.717000000000002
- type: precision_at_5
value: 15.812000000000001
- type: recall_at_1
value: 39.747
- type: recall_at_10
value: 70.10499999999999
- type: recall_at_100
value: 86.23100000000001
- type: recall_at_1000
value: 94.025
- type: recall_at_3
value: 55.899
- type: recall_at_5
value: 63.05500000000001
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.168999999999997
- type: map_at_10
value: 34.975
- type: map_at_100
value: 35.94
- type: map_at_1000
value: 36.021
- type: map_at_3
value: 32.35
- type: map_at_5
value: 33.831
- type: mrr_at_1
value: 28.701
- type: mrr_at_10
value: 36.698
- type: mrr_at_100
value: 37.546
- type: mrr_at_1000
value: 37.613
- type: mrr_at_3
value: 34.256
- type: mrr_at_5
value: 35.685
- type: ndcg_at_1
value: 28.701
- type: ndcg_at_10
value: 39.639
- type: ndcg_at_100
value: 44.389
- type: ndcg_at_1000
value: 46.46
- type: ndcg_at_3
value: 34.52
- type: ndcg_at_5
value: 37.076
- type: precision_at_1
value: 28.701
- type: precision_at_10
value: 5.955
- type: precision_at_100
value: 0.8880000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 14.274999999999999
- type: precision_at_5
value: 10.011000000000001
- type: recall_at_1
value: 27.168999999999997
- type: recall_at_10
value: 52.347
- type: recall_at_100
value: 74.1
- type: recall_at_1000
value: 89.739
- type: recall_at_3
value: 38.567
- type: recall_at_5
value: 44.767
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 15.872
- type: map_at_10
value: 23.153000000000002
- type: map_at_100
value: 24.311
- type: map_at_1000
value: 24.432000000000002
- type: map_at_3
value: 20.707
- type: map_at_5
value: 21.921
- type: mrr_at_1
value: 19.776
- type: mrr_at_10
value: 27.755999999999997
- type: mrr_at_100
value: 28.709
- type: mrr_at_1000
value: 28.778
- type: mrr_at_3
value: 25.186999999999998
- type: mrr_at_5
value: 26.43
- type: ndcg_at_1
value: 19.776
- type: ndcg_at_10
value: 28.288999999999998
- type: ndcg_at_100
value: 34.011
- type: ndcg_at_1000
value: 36.916
- type: ndcg_at_3
value: 23.551
- type: ndcg_at_5
value: 25.429000000000002
- type: precision_at_1
value: 19.776
- type: precision_at_10
value: 5.311
- type: precision_at_100
value: 0.9440000000000001
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 11.360000000000001
- type: precision_at_5
value: 8.209
- type: recall_at_1
value: 15.872
- type: recall_at_10
value: 39.726
- type: recall_at_100
value: 65.035
- type: recall_at_1000
value: 85.846
- type: recall_at_3
value: 26.432
- type: recall_at_5
value: 31.22
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 28.126
- type: map_at_10
value: 37.537
- type: map_at_100
value: 38.807
- type: map_at_1000
value: 38.923
- type: map_at_3
value: 34.65
- type: map_at_5
value: 36.248000000000005
- type: mrr_at_1
value: 34.649
- type: mrr_at_10
value: 42.893
- type: mrr_at_100
value: 43.721
- type: mrr_at_1000
value: 43.775999999999996
- type: mrr_at_3
value: 40.488
- type: mrr_at_5
value: 41.729
- type: ndcg_at_1
value: 34.649
- type: ndcg_at_10
value: 43.072
- type: ndcg_at_100
value: 48.464
- type: ndcg_at_1000
value: 50.724000000000004
- type: ndcg_at_3
value: 38.506
- type: ndcg_at_5
value: 40.522000000000006
- type: precision_at_1
value: 34.649
- type: precision_at_10
value: 7.68
- type: precision_at_100
value: 1.214
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 18.029999999999998
- type: precision_at_5
value: 12.666
- type: recall_at_1
value: 28.126
- type: recall_at_10
value: 54.396
- type: recall_at_100
value: 76.988
- type: recall_at_1000
value: 91.85799999999999
- type: recall_at_3
value: 41.169
- type: recall_at_5
value: 46.658
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 26.68
- type: map_at_10
value: 35.702
- type: map_at_100
value: 36.864999999999995
- type: map_at_1000
value: 36.977
- type: map_at_3
value: 32.828
- type: map_at_5
value: 34.481
- type: mrr_at_1
value: 32.991
- type: mrr_at_10
value: 40.993
- type: mrr_at_100
value: 41.827
- type: mrr_at_1000
value: 41.887
- type: mrr_at_3
value: 38.623000000000005
- type: mrr_at_5
value: 40.021
- type: ndcg_at_1
value: 32.991
- type: ndcg_at_10
value: 41.036
- type: ndcg_at_100
value: 46.294000000000004
- type: ndcg_at_1000
value: 48.644
- type: ndcg_at_3
value: 36.419000000000004
- type: ndcg_at_5
value: 38.618
- type: precision_at_1
value: 32.991
- type: precision_at_10
value: 7.385999999999999
- type: precision_at_100
value: 1.176
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 17.122999999999998
- type: precision_at_5
value: 12.215
- type: recall_at_1
value: 26.68
- type: recall_at_10
value: 51.644
- type: recall_at_100
value: 74.55000000000001
- type: recall_at_1000
value: 90.825
- type: recall_at_3
value: 38.579
- type: recall_at_5
value: 44.512
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 26.30825
- type: map_at_10
value: 34.97866666666666
- type: map_at_100
value: 36.109249999999996
- type: map_at_1000
value: 36.22508333333333
- type: map_at_3
value: 32.239083333333326
- type: map_at_5
value: 33.75933333333334
- type: mrr_at_1
value: 31.05308333333333
- type: mrr_at_10
value: 39.099833333333336
- type: mrr_at_100
value: 39.92008333333334
- type: mrr_at_1000
value: 39.980000000000004
- type: mrr_at_3
value: 36.75958333333333
- type: mrr_at_5
value: 38.086416666666665
- type: ndcg_at_1
value: 31.05308333333333
- type: ndcg_at_10
value: 40.11558333333334
- type: ndcg_at_100
value: 45.05966666666667
- type: ndcg_at_1000
value: 47.36516666666667
- type: ndcg_at_3
value: 35.490833333333335
- type: ndcg_at_5
value: 37.64541666666666
- type: precision_at_1
value: 31.05308333333333
- type: precision_at_10
value: 6.968416666666666
- type: precision_at_100
value: 1.1156666666666666
- type: precision_at_1000
value: 0.14950000000000002
- type: precision_at_3
value: 16.123
- type: precision_at_5
value: 11.451166666666666
- type: recall_at_1
value: 26.30825
- type: recall_at_10
value: 51.19283333333333
- type: recall_at_100
value: 73.0285
- type: recall_at_1000
value: 89.11133333333333
- type: recall_at_3
value: 38.26208333333333
- type: recall_at_5
value: 43.855916666666666
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 23.363999999999997
- type: map_at_10
value: 30.606
- type: map_at_100
value: 31.491999999999997
- type: map_at_1000
value: 31.578
- type: map_at_3
value: 28.610000000000003
- type: map_at_5
value: 29.602
- type: mrr_at_1
value: 26.38
- type: mrr_at_10
value: 33.472
- type: mrr_at_100
value: 34.299
- type: mrr_at_1000
value: 34.361999999999995
- type: mrr_at_3
value: 31.696999999999996
- type: mrr_at_5
value: 32.503
- type: ndcg_at_1
value: 26.38
- type: ndcg_at_10
value: 34.772999999999996
- type: ndcg_at_100
value: 39.334
- type: ndcg_at_1000
value: 41.676
- type: ndcg_at_3
value: 31.097
- type: ndcg_at_5
value: 32.561
- type: precision_at_1
value: 26.38
- type: precision_at_10
value: 5.475
- type: precision_at_100
value: 0.84
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.395000000000001
- type: precision_at_5
value: 9.11
- type: recall_at_1
value: 23.363999999999997
- type: recall_at_10
value: 44.656
- type: recall_at_100
value: 65.77199999999999
- type: recall_at_1000
value: 83.462
- type: recall_at_3
value: 34.213
- type: recall_at_5
value: 38.091
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 17.971999999999998
- type: map_at_10
value: 24.913
- type: map_at_100
value: 25.916
- type: map_at_1000
value: 26.049
- type: map_at_3
value: 22.569
- type: map_at_5
value: 23.858999999999998
- type: mrr_at_1
value: 21.748
- type: mrr_at_10
value: 28.711
- type: mrr_at_100
value: 29.535
- type: mrr_at_1000
value: 29.621
- type: mrr_at_3
value: 26.484999999999996
- type: mrr_at_5
value: 27.701999999999998
- type: ndcg_at_1
value: 21.748
- type: ndcg_at_10
value: 29.412
- type: ndcg_at_100
value: 34.204
- type: ndcg_at_1000
value: 37.358000000000004
- type: ndcg_at_3
value: 25.202
- type: ndcg_at_5
value: 27.128000000000004
- type: precision_at_1
value: 21.748
- type: precision_at_10
value: 5.279
- type: precision_at_100
value: 0.902
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 11.551
- type: precision_at_5
value: 8.437999999999999
- type: recall_at_1
value: 17.971999999999998
- type: recall_at_10
value: 39.186
- type: recall_at_100
value: 60.785999999999994
- type: recall_at_1000
value: 83.372
- type: recall_at_3
value: 27.584999999999997
- type: recall_at_5
value: 32.448
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.684
- type: map_at_10
value: 35.188
- type: map_at_100
value: 36.379
- type: map_at_1000
value: 36.481
- type: map_at_3
value: 32.401
- type: map_at_5
value: 34.132
- type: mrr_at_1
value: 31.063000000000002
- type: mrr_at_10
value: 39.104
- type: mrr_at_100
value: 40.062999999999995
- type: mrr_at_1000
value: 40.119
- type: mrr_at_3
value: 36.692
- type: mrr_at_5
value: 38.161
- type: ndcg_at_1
value: 31.063000000000002
- type: ndcg_at_10
value: 40.096
- type: ndcg_at_100
value: 45.616
- type: ndcg_at_1000
value: 47.869
- type: ndcg_at_3
value: 35.256
- type: ndcg_at_5
value: 37.826
- type: precision_at_1
value: 31.063000000000002
- type: precision_at_10
value: 6.622999999999999
- type: precision_at_100
value: 1.046
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 15.641
- type: precision_at_5
value: 11.231
- type: recall_at_1
value: 26.684
- type: recall_at_10
value: 51.092999999999996
- type: recall_at_100
value: 75.099
- type: recall_at_1000
value: 90.644
- type: recall_at_3
value: 38.063
- type: recall_at_5
value: 44.518
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 26.249
- type: map_at_10
value: 34.694
- type: map_at_100
value: 36.208
- type: map_at_1000
value: 36.443
- type: map_at_3
value: 31.868000000000002
- type: map_at_5
value: 33.018
- type: mrr_at_1
value: 31.818
- type: mrr_at_10
value: 39.416000000000004
- type: mrr_at_100
value: 40.327
- type: mrr_at_1000
value: 40.388000000000005
- type: mrr_at_3
value: 37.120999999999995
- type: mrr_at_5
value: 38.07
- type: ndcg_at_1
value: 31.818
- type: ndcg_at_10
value: 40.405
- type: ndcg_at_100
value: 45.816
- type: ndcg_at_1000
value: 48.403
- type: ndcg_at_3
value: 35.823
- type: ndcg_at_5
value: 37.191
- type: precision_at_1
value: 31.818
- type: precision_at_10
value: 7.806
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 16.535
- type: precision_at_5
value: 11.738999999999999
- type: recall_at_1
value: 26.249
- type: recall_at_10
value: 50.928
- type: recall_at_100
value: 75.271
- type: recall_at_1000
value: 91.535
- type: recall_at_3
value: 37.322
- type: recall_at_5
value: 41.318
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 21.884999999999998
- type: map_at_10
value: 29.158
- type: map_at_100
value: 30.208000000000002
- type: map_at_1000
value: 30.304
- type: map_at_3
value: 26.82
- type: map_at_5
value: 28.051
- type: mrr_at_1
value: 23.66
- type: mrr_at_10
value: 31.277
- type: mrr_at_100
value: 32.237
- type: mrr_at_1000
value: 32.308
- type: mrr_at_3
value: 29.205
- type: mrr_at_5
value: 30.314000000000004
- type: ndcg_at_1
value: 23.66
- type: ndcg_at_10
value: 33.64
- type: ndcg_at_100
value: 39.028
- type: ndcg_at_1000
value: 41.423
- type: ndcg_at_3
value: 29.189
- type: ndcg_at_5
value: 31.191999999999997
- type: precision_at_1
value: 23.66
- type: precision_at_10
value: 5.287
- type: precision_at_100
value: 0.86
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 12.631
- type: precision_at_5
value: 8.762
- type: recall_at_1
value: 21.884999999999998
- type: recall_at_10
value: 45.357
- type: recall_at_100
value: 70.338
- type: recall_at_1000
value: 88.356
- type: recall_at_3
value: 33.312000000000005
- type: recall_at_5
value: 38.222
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.058
- type: map_at_10
value: 21.549
- type: map_at_100
value: 23.287
- type: map_at_1000
value: 23.444000000000003
- type: map_at_3
value: 18.18
- type: map_at_5
value: 19.886
- type: mrr_at_1
value: 28.73
- type: mrr_at_10
value: 40.014
- type: mrr_at_100
value: 40.827000000000005
- type: mrr_at_1000
value: 40.866
- type: mrr_at_3
value: 36.602000000000004
- type: mrr_at_5
value: 38.702
- type: ndcg_at_1
value: 28.73
- type: ndcg_at_10
value: 29.881
- type: ndcg_at_100
value: 36.662
- type: ndcg_at_1000
value: 39.641999999999996
- type: ndcg_at_3
value: 24.661
- type: ndcg_at_5
value: 26.548
- type: precision_at_1
value: 28.73
- type: precision_at_10
value: 9.094
- type: precision_at_100
value: 1.6480000000000001
- type: precision_at_1000
value: 0.22100000000000003
- type: precision_at_3
value: 17.98
- type: precision_at_5
value: 13.811000000000002
- type: recall_at_1
value: 13.058
- type: recall_at_10
value: 35.458
- type: recall_at_100
value: 58.719
- type: recall_at_1000
value: 75.495
- type: recall_at_3
value: 22.607
- type: recall_at_5
value: 28.067999999999998
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.811
- type: map_at_10
value: 19.134999999999998
- type: map_at_100
value: 26.905
- type: map_at_1000
value: 28.503
- type: map_at_3
value: 13.863
- type: map_at_5
value: 16.062
- type: mrr_at_1
value: 67
- type: mrr_at_10
value: 74.607
- type: mrr_at_100
value: 74.941
- type: mrr_at_1000
value: 74.954
- type: mrr_at_3
value: 73.042
- type: mrr_at_5
value: 73.992
- type: ndcg_at_1
value: 52.87500000000001
- type: ndcg_at_10
value: 40.199
- type: ndcg_at_100
value: 44.901
- type: ndcg_at_1000
value: 52.239999999999995
- type: ndcg_at_3
value: 44.983000000000004
- type: ndcg_at_5
value: 42.137
- type: precision_at_1
value: 67
- type: precision_at_10
value: 31.8
- type: precision_at_100
value: 10.315000000000001
- type: precision_at_1000
value: 2.0420000000000003
- type: precision_at_3
value: 48.667
- type: precision_at_5
value: 40.9
- type: recall_at_1
value: 8.811
- type: recall_at_10
value: 24.503
- type: recall_at_100
value: 51.288999999999994
- type: recall_at_1000
value: 74.827
- type: recall_at_3
value: 15.254999999999999
- type: recall_at_5
value: 18.698999999999998
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 41.839999999999996
- type: f1
value: 37.78718146306379
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 68.47999999999999
- type: map_at_10
value: 78.782
- type: map_at_100
value: 79.021
- type: map_at_1000
value: 79.035
- type: map_at_3
value: 77.389
- type: map_at_5
value: 78.347
- type: mrr_at_1
value: 73.837
- type: mrr_at_10
value: 83.41499999999999
- type: mrr_at_100
value: 83.53399999999999
- type: mrr_at_1000
value: 83.535
- type: mrr_at_3
value: 82.32300000000001
- type: mrr_at_5
value: 83.13000000000001
- type: ndcg_at_1
value: 73.837
- type: ndcg_at_10
value: 83.404
- type: ndcg_at_100
value: 84.287
- type: ndcg_at_1000
value: 84.52199999999999
- type: ndcg_at_3
value: 81.072
- type: ndcg_at_5
value: 82.537
- type: precision_at_1
value: 73.837
- type: precision_at_10
value: 10.254000000000001
- type: precision_at_100
value: 1.088
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.538
- type: precision_at_5
value: 19.811
- type: recall_at_1
value: 68.47999999999999
- type: recall_at_10
value: 92.98100000000001
- type: recall_at_100
value: 96.50800000000001
- type: recall_at_1000
value: 97.925
- type: recall_at_3
value: 86.764
- type: recall_at_5
value: 90.39
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 16.786
- type: map_at_10
value: 26.97
- type: map_at_100
value: 28.488000000000003
- type: map_at_1000
value: 28.665000000000003
- type: map_at_3
value: 23.3
- type: map_at_5
value: 25.249
- type: mrr_at_1
value: 33.025
- type: mrr_at_10
value: 41.86
- type: mrr_at_100
value: 42.673
- type: mrr_at_1000
value: 42.714
- type: mrr_at_3
value: 39.403
- type: mrr_at_5
value: 40.723
- type: ndcg_at_1
value: 33.025
- type: ndcg_at_10
value: 34.522999999999996
- type: ndcg_at_100
value: 40.831
- type: ndcg_at_1000
value: 44.01
- type: ndcg_at_3
value: 30.698999999999998
- type: ndcg_at_5
value: 31.832
- type: precision_at_1
value: 33.025
- type: precision_at_10
value: 9.583
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.22100000000000003
- type: precision_at_3
value: 20.216
- type: precision_at_5
value: 15.031
- type: recall_at_1
value: 16.786
- type: recall_at_10
value: 41.969
- type: recall_at_100
value: 66.353
- type: recall_at_1000
value: 85.299
- type: recall_at_3
value: 28.111000000000004
- type: recall_at_5
value: 33.645
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 37.346000000000004
- type: map_at_10
value: 56.184999999999995
- type: map_at_100
value: 57.062000000000005
- type: map_at_1000
value: 57.126999999999995
- type: map_at_3
value: 52.815
- type: map_at_5
value: 54.893
- type: mrr_at_1
value: 74.693
- type: mrr_at_10
value: 81.128
- type: mrr_at_100
value: 81.356
- type: mrr_at_1000
value: 81.363
- type: mrr_at_3
value: 80.05600000000001
- type: mrr_at_5
value: 80.74
- type: ndcg_at_1
value: 74.693
- type: ndcg_at_10
value: 65.249
- type: ndcg_at_100
value: 68.357
- type: ndcg_at_1000
value: 69.64200000000001
- type: ndcg_at_3
value: 60.377
- type: ndcg_at_5
value: 63.044
- type: precision_at_1
value: 74.693
- type: precision_at_10
value: 13.630999999999998
- type: precision_at_100
value: 1.606
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 38.222
- type: precision_at_5
value: 25.040000000000003
- type: recall_at_1
value: 37.346000000000004
- type: recall_at_10
value: 68.157
- type: recall_at_100
value: 80.297
- type: recall_at_1000
value: 88.832
- type: recall_at_3
value: 57.333
- type: recall_at_5
value: 62.6
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 62.80240000000001
- type: ap
value: 58.22949464075975
- type: f1
value: 62.55694937343487
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 20.918
- type: map_at_10
value: 32.732
- type: map_at_100
value: 33.922000000000004
- type: map_at_1000
value: 33.976
- type: map_at_3
value: 29.051
- type: map_at_5
value: 31.101
- type: mrr_at_1
value: 21.418
- type: mrr_at_10
value: 33.284000000000006
- type: mrr_at_100
value: 34.426
- type: mrr_at_1000
value: 34.473
- type: mrr_at_3
value: 29.644
- type: mrr_at_5
value: 31.691000000000003
- type: ndcg_at_1
value: 21.418
- type: ndcg_at_10
value: 39.427
- type: ndcg_at_100
value: 45.190999999999995
- type: ndcg_at_1000
value: 46.544000000000004
- type: ndcg_at_3
value: 31.885
- type: ndcg_at_5
value: 35.555
- type: precision_at_1
value: 21.418
- type: precision_at_10
value: 6.254999999999999
- type: precision_at_100
value: 0.915
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.591000000000001
- type: precision_at_5
value: 10.011000000000001
- type: recall_at_1
value: 20.918
- type: recall_at_10
value: 60.074000000000005
- type: recall_at_100
value: 86.726
- type: recall_at_1000
value: 97.116
- type: recall_at_3
value: 39.506
- type: recall_at_5
value: 48.319
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.79799361605106
- type: f1
value: 90.0757957511057
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 58.00501595987233
- type: f1
value: 39.85731569133947
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 77.10970464135022
- type: f1
value: 76.12037616356896
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 69.81323966287493
- type: v_measure
value: 33.112774215788455
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.51042367182246
- type: f1
value: 60.99310361578824
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.0053799596503
- type: f1
value: 69.7794673003686
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.56899174856954
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 26.21848014733929
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.256308756916646
- type: mrr
value: 31.123872086825656
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.07
- type: map_at_10
value: 11.286999999999999
- type: map_at_100
value: 13.630999999999998
- type: map_at_1000
value: 14.844
- type: map_at_3
value: 8.395
- type: map_at_5
value: 9.721
- type: mrr_at_1
value: 41.486000000000004
- type: mrr_at_10
value: 51.041000000000004
- type: mrr_at_100
value: 51.661
- type: mrr_at_1000
value: 51.7
- type: mrr_at_3
value: 49.226
- type: mrr_at_5
value: 50.526
- type: ndcg_at_1
value: 39.783
- type: ndcg_at_10
value: 30.885
- type: ndcg_at_100
value: 27.459
- type: ndcg_at_1000
value: 35.988
- type: ndcg_at_3
value: 36.705
- type: ndcg_at_5
value: 34.156
- type: precision_at_1
value: 41.486000000000004
- type: precision_at_10
value: 22.415
- type: precision_at_100
value: 6.819999999999999
- type: precision_at_1000
value: 1.8980000000000001
- type: precision_at_3
value: 34.572
- type: precision_at_5
value: 29.287999999999997
- type: recall_at_1
value: 5.07
- type: recall_at_10
value: 14.576
- type: recall_at_100
value: 27.112000000000002
- type: recall_at_1000
value: 57.995
- type: recall_at_3
value: 9.242
- type: recall_at_5
value: 11.668000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 32.263999999999996
- type: map_at_10
value: 47.219
- type: map_at_100
value: 48.209999999999994
- type: map_at_1000
value: 48.24
- type: map_at_3
value: 42.905
- type: map_at_5
value: 45.501000000000005
- type: mrr_at_1
value: 36.153
- type: mrr_at_10
value: 49.636
- type: mrr_at_100
value: 50.357
- type: mrr_at_1000
value: 50.378
- type: mrr_at_3
value: 46.094
- type: mrr_at_5
value: 48.233
- type: ndcg_at_1
value: 36.124
- type: ndcg_at_10
value: 54.764
- type: ndcg_at_100
value: 58.867999999999995
- type: ndcg_at_1000
value: 59.548
- type: ndcg_at_3
value: 46.717999999999996
- type: ndcg_at_5
value: 50.981
- type: precision_at_1
value: 36.124
- type: precision_at_10
value: 8.931000000000001
- type: precision_at_100
value: 1.126
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 21.051000000000002
- type: precision_at_5
value: 15.104000000000001
- type: recall_at_1
value: 32.263999999999996
- type: recall_at_10
value: 75.39099999999999
- type: recall_at_100
value: 93.038
- type: recall_at_1000
value: 98.006
- type: recall_at_3
value: 54.562999999999995
- type: recall_at_5
value: 64.352
- task:
type: Classification
dataset:
name: MTEB NewsClassification
type: ag_news
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy
value: 77.75
- type: f1
value: 77.504243291547
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.89816700610999
- type: cos_sim_ap
value: 100
- type: cos_sim_f1
value: 99.9490575649516
- type: cos_sim_precision
value: 100
- type: cos_sim_recall
value: 99.89816700610999
- type: dot_accuracy
value: 99.89816700610999
- type: dot_ap
value: 100
- type: dot_f1
value: 99.9490575649516
- type: dot_precision
value: 100
- type: dot_recall
value: 99.89816700610999
- type: euclidean_accuracy
value: 99.89816700610999
- type: euclidean_ap
value: 100
- type: euclidean_f1
value: 99.9490575649516
- type: euclidean_precision
value: 100
- type: euclidean_recall
value: 99.89816700610999
- type: manhattan_accuracy
value: 99.89816700610999
- type: manhattan_ap
value: 100
- type: manhattan_f1
value: 99.9490575649516
- type: manhattan_precision
value: 100
- type: manhattan_recall
value: 99.89816700610999
- type: max_accuracy
value: 99.89816700610999
- type: max_ap
value: 100
- type: max_f1
value: 99.9490575649516
- task:
type: PairClassification
dataset:
name: MTEB PawsX (en)
type: paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.75000000000001
- type: cos_sim_ap
value: 57.9482264289061
- type: cos_sim_f1
value: 62.444061962134256
- type: cos_sim_precision
value: 45.3953953953954
- type: cos_sim_recall
value: 100
- type: dot_accuracy
value: 61.75000000000001
- type: dot_ap
value: 57.94808038610475
- type: dot_f1
value: 62.444061962134256
- type: dot_precision
value: 45.3953953953954
- type: dot_recall
value: 100
- type: euclidean_accuracy
value: 61.75000000000001
- type: euclidean_ap
value: 57.94808038610475
- type: euclidean_f1
value: 62.444061962134256
- type: euclidean_precision
value: 45.3953953953954
- type: euclidean_recall
value: 100
- type: manhattan_accuracy
value: 61.7
- type: manhattan_ap
value: 57.996119308184966
- type: manhattan_f1
value: 62.46078773091669
- type: manhattan_precision
value: 45.66768603465851
- type: manhattan_recall
value: 98.78721058434398
- type: max_accuracy
value: 61.75000000000001
- type: max_ap
value: 57.996119308184966
- type: max_f1
value: 62.46078773091669
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 69.001
- type: map_at_10
value: 82.573
- type: map_at_100
value: 83.226
- type: map_at_1000
value: 83.246
- type: map_at_3
value: 79.625
- type: map_at_5
value: 81.491
- type: mrr_at_1
value: 79.44
- type: mrr_at_10
value: 85.928
- type: mrr_at_100
value: 86.05199999999999
- type: mrr_at_1000
value: 86.054
- type: mrr_at_3
value: 84.847
- type: mrr_at_5
value: 85.596
- type: ndcg_at_1
value: 79.41
- type: ndcg_at_10
value: 86.568
- type: ndcg_at_100
value: 87.965
- type: ndcg_at_1000
value: 88.134
- type: ndcg_at_3
value: 83.55900000000001
- type: ndcg_at_5
value: 85.244
- type: precision_at_1
value: 79.41
- type: precision_at_10
value: 13.108
- type: precision_at_100
value: 1.509
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.443
- type: precision_at_5
value: 24.03
- type: recall_at_1
value: 69.001
- type: recall_at_10
value: 94.132
- type: recall_at_100
value: 99.043
- type: recall_at_1000
value: 99.878
- type: recall_at_3
value: 85.492
- type: recall_at_5
value: 90.226
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 48.3161352736264
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 57.83784484156747
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.403
- type: map_at_10
value: 10.922
- type: map_at_100
value: 12.626000000000001
- type: map_at_1000
value: 12.883
- type: map_at_3
value: 7.982
- type: map_at_5
value: 9.442
- type: mrr_at_1
value: 21.7
- type: mrr_at_10
value: 31.653
- type: mrr_at_100
value: 32.757999999999996
- type: mrr_at_1000
value: 32.824999999999996
- type: mrr_at_3
value: 28.266999999999996
- type: mrr_at_5
value: 30.127
- type: ndcg_at_1
value: 21.7
- type: ndcg_at_10
value: 18.355
- type: ndcg_at_100
value: 25.228
- type: ndcg_at_1000
value: 30.164
- type: ndcg_at_3
value: 17.549
- type: ndcg_at_5
value: 15.260000000000002
- type: precision_at_1
value: 21.7
- type: precision_at_10
value: 9.47
- type: precision_at_100
value: 1.9290000000000003
- type: precision_at_1000
value: 0.312
- type: precision_at_3
value: 16.3
- type: precision_at_5
value: 13.28
- type: recall_at_1
value: 4.403
- type: recall_at_10
value: 19.18
- type: recall_at_100
value: 39.182
- type: recall_at_1000
value: 63.378
- type: recall_at_3
value: 9.934999999999999
- type: recall_at_5
value: 13.459999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 76.90841073432534
- type: cos_sim_spearman
value: 69.2566375434526
- type: euclidean_pearson
value: 73.00183878559413
- type: euclidean_spearman
value: 69.25664656235413
- type: manhattan_pearson
value: 72.89594756197533
- type: manhattan_spearman
value: 69.23247111043545
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 69.60878511794063
- type: cos_sim_spearman
value: 65.89916377105551
- type: euclidean_pearson
value: 66.90761876557181
- type: euclidean_spearman
value: 65.89915018368384
- type: manhattan_pearson
value: 66.78502575257721
- type: manhattan_spearman
value: 65.79977053467938
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 77.2869334987418
- type: cos_sim_spearman
value: 77.86961921643416
- type: euclidean_pearson
value: 77.43179820479914
- type: euclidean_spearman
value: 77.86961921643416
- type: manhattan_pearson
value: 77.18900647348373
- type: manhattan_spearman
value: 77.61209060062608
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 76.26453932960364
- type: cos_sim_spearman
value: 72.81574657995401
- type: euclidean_pearson
value: 75.0708953437423
- type: euclidean_spearman
value: 72.81574657995401
- type: manhattan_pearson
value: 74.88396609999512
- type: manhattan_spearman
value: 72.65437562156805
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 82.37827653919395
- type: cos_sim_spearman
value: 83.4885552472602
- type: euclidean_pearson
value: 82.89377087926749
- type: euclidean_spearman
value: 83.4885552472602
- type: manhattan_pearson
value: 82.82440771787735
- type: manhattan_spearman
value: 83.41449537888975
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.7995043673964
- type: cos_sim_spearman
value: 80.57804447517638
- type: euclidean_pearson
value: 80.03013884278195
- type: euclidean_spearman
value: 80.57804447517638
- type: manhattan_pearson
value: 80.13406111544424
- type: manhattan_spearman
value: 80.65354602648962
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.63565989937278
- type: cos_sim_spearman
value: 84.4948593656943
- type: euclidean_pearson
value: 84.68743074820951
- type: euclidean_spearman
value: 84.4948593656943
- type: manhattan_pearson
value: 84.43639397781811
- type: manhattan_spearman
value: 84.32595552115242
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.06382649277246
- type: cos_sim_spearman
value: 66.28447782018655
- type: euclidean_pearson
value: 67.09895930908392
- type: euclidean_spearman
value: 66.28447782018655
- type: manhattan_pearson
value: 66.96342453888376
- type: manhattan_spearman
value: 66.33876259551842
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 78.43883428940346
- type: cos_sim_spearman
value: 79.18395553127085
- type: euclidean_pearson
value: 79.22986635457109
- type: euclidean_spearman
value: 79.18395553127085
- type: manhattan_pearson
value: 79.10921229934691
- type: manhattan_spearman
value: 79.02283553930171
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: PhilipMay/stsb_multi_mt
config: en
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 78.43883433444418
- type: cos_sim_spearman
value: 79.18395553127085
- type: euclidean_pearson
value: 79.22986642351681
- type: euclidean_spearman
value: 79.18395553127085
- type: manhattan_pearson
value: 79.10921236746302
- type: manhattan_spearman
value: 79.02283553930171
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 76.9361627171417
- type: mrr
value: 93.06577046773126
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 50.693999999999996
- type: map_at_10
value: 59.784000000000006
- type: map_at_100
value: 60.443000000000005
- type: map_at_1000
value: 60.480000000000004
- type: map_at_3
value: 57.028
- type: map_at_5
value: 58.306999999999995
- type: mrr_at_1
value: 53.333
- type: mrr_at_10
value: 61.565000000000005
- type: mrr_at_100
value: 62.095
- type: mrr_at_1000
value: 62.131
- type: mrr_at_3
value: 59.721999999999994
- type: mrr_at_5
value: 60.589000000000006
- type: ndcg_at_1
value: 53.333
- type: ndcg_at_10
value: 64.512
- type: ndcg_at_100
value: 67.366
- type: ndcg_at_1000
value: 68.46799999999999
- type: ndcg_at_3
value: 59.748999999999995
- type: ndcg_at_5
value: 61.526
- type: precision_at_1
value: 53.333
- type: precision_at_10
value: 8.733
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 23.222
- type: precision_at_5
value: 15.2
- type: recall_at_1
value: 50.693999999999996
- type: recall_at_10
value: 77.333
- type: recall_at_100
value: 90.10000000000001
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 64.39399999999999
- type: recall_at_5
value: 68.7
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81386138613861
- type: cos_sim_ap
value: 94.96375600031361
- type: cos_sim_f1
value: 90.36885245901641
- type: cos_sim_precision
value: 92.64705882352942
- type: cos_sim_recall
value: 88.2
- type: dot_accuracy
value: 99.81386138613861
- type: dot_ap
value: 94.96375600031361
- type: dot_f1
value: 90.36885245901641
- type: dot_precision
value: 92.64705882352942
- type: dot_recall
value: 88.2
- type: euclidean_accuracy
value: 99.81386138613861
- type: euclidean_ap
value: 94.96375600031361
- type: euclidean_f1
value: 90.36885245901641
- type: euclidean_precision
value: 92.64705882352942
- type: euclidean_recall
value: 88.2
- type: manhattan_accuracy
value: 99.81287128712871
- type: manhattan_ap
value: 94.92563500640084
- type: manhattan_f1
value: 90.27277406073082
- type: manhattan_precision
value: 93.00106044538707
- type: manhattan_recall
value: 87.7
- type: max_accuracy
value: 99.81386138613861
- type: max_ap
value: 94.96375600031361
- type: max_f1
value: 90.36885245901641
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.486984956276274
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.58453023612073
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.16317315282306
- type: mrr
value: 50.82617137764197
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.2927995133324
- type: cos_sim_spearman
value: 30.09648622523191
- type: dot_pearson
value: 30.29279853541771
- type: dot_spearman
value: 30.09648622523191
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.23500000000000001
- type: map_at_10
value: 2.01
- type: map_at_100
value: 12.064
- type: map_at_1000
value: 27.437
- type: map_at_3
value: 0.6649999999999999
- type: map_at_5
value: 1.0959999999999999
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 92.667
- type: mrr_at_100
value: 92.667
- type: mrr_at_1000
value: 92.667
- type: mrr_at_3
value: 91.667
- type: mrr_at_5
value: 92.667
- type: ndcg_at_1
value: 84
- type: ndcg_at_10
value: 79.431
- type: ndcg_at_100
value: 60.914
- type: ndcg_at_1000
value: 52.005
- type: ndcg_at_3
value: 82.285
- type: ndcg_at_5
value: 81.565
- type: precision_at_1
value: 88
- type: precision_at_10
value: 84.8
- type: precision_at_100
value: 62.32
- type: precision_at_1000
value: 23.014000000000003
- type: precision_at_3
value: 86.667
- type: precision_at_5
value: 87.2
- type: recall_at_1
value: 0.23500000000000001
- type: recall_at_10
value: 2.19
- type: recall_at_100
value: 14.904
- type: recall_at_1000
value: 47.875
- type: recall_at_3
value: 0.695
- type: recall_at_5
value: 1.165
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.639
- type: map_at_10
value: 14.184
- type: map_at_100
value: 20.61
- type: map_at_1000
value: 22.377
- type: map_at_3
value: 9.163
- type: map_at_5
value: 10.773000000000001
- type: mrr_at_1
value: 46.939
- type: mrr_at_10
value: 59.345000000000006
- type: mrr_at_100
value: 60.07599999999999
- type: mrr_at_1000
value: 60.07599999999999
- type: mrr_at_3
value: 55.782
- type: mrr_at_5
value: 58.231
- type: ndcg_at_1
value: 41.837
- type: ndcg_at_10
value: 32.789
- type: ndcg_at_100
value: 42.232
- type: ndcg_at_1000
value: 53.900999999999996
- type: ndcg_at_3
value: 41.963
- type: ndcg_at_5
value: 35.983
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 28.163
- type: precision_at_100
value: 8.102
- type: precision_at_1000
value: 1.59
- type: precision_at_3
value: 44.897999999999996
- type: precision_at_5
value: 34.694
- type: recall_at_1
value: 3.639
- type: recall_at_10
value: 19.308
- type: recall_at_100
value: 48.992000000000004
- type: recall_at_1000
value: 84.59400000000001
- type: recall_at_3
value: 9.956
- type: recall_at_5
value: 12.33
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 64.305
- type: ap
value: 11.330746746072599
- type: f1
value: 49.290704382387865
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.1941143180532
- type: f1
value: 56.40189765095578
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 36.28189332526842
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.1912737676581
- type: cos_sim_ap
value: 64.31536990146257
- type: cos_sim_f1
value: 61.095167030191696
- type: cos_sim_precision
value: 54.074375127006704
- type: cos_sim_recall
value: 70.21108179419525
- type: dot_accuracy
value: 83.1912737676581
- type: dot_ap
value: 64.31539216162541
- type: dot_f1
value: 61.095167030191696
- type: dot_precision
value: 54.074375127006704
- type: dot_recall
value: 70.21108179419525
- type: euclidean_accuracy
value: 83.1912737676581
- type: euclidean_ap
value: 64.31538391358727
- type: euclidean_f1
value: 61.095167030191696
- type: euclidean_precision
value: 54.074375127006704
- type: euclidean_recall
value: 70.21108179419525
- type: manhattan_accuracy
value: 83.07206294331525
- type: manhattan_ap
value: 64.14646315556838
- type: manhattan_f1
value: 61.194029850746254
- type: manhattan_precision
value: 54.166666666666664
- type: manhattan_recall
value: 70.31662269129288
- type: max_accuracy
value: 83.1912737676581
- type: max_ap
value: 64.31539216162541
- type: max_f1
value: 61.194029850746254
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.38242713548337
- type: cos_sim_ap
value: 84.70041255196017
- type: cos_sim_f1
value: 77.13222561986515
- type: cos_sim_precision
value: 73.95266690215472
- type: cos_sim_recall
value: 80.59747459193102
- type: dot_accuracy
value: 88.38242713548337
- type: dot_ap
value: 84.7004118720222
- type: dot_f1
value: 77.13222561986515
- type: dot_precision
value: 73.95266690215472
- type: dot_recall
value: 80.59747459193102
- type: euclidean_accuracy
value: 88.38242713548337
- type: euclidean_ap
value: 84.70041593996575
- type: euclidean_f1
value: 77.13222561986515
- type: euclidean_precision
value: 73.95266690215472
- type: euclidean_recall
value: 80.59747459193102
- type: manhattan_accuracy
value: 88.36108200411378
- type: manhattan_ap
value: 84.66897701572054
- type: manhattan_f1
value: 77.00707640360645
- type: manhattan_precision
value: 72.17695778062082
- type: manhattan_recall
value: 82.53002771789343
- type: max_accuracy
value: 88.38242713548337
- type: max_ap
value: 84.70041593996575
- type: max_f1
value: 77.13222561986515
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 81.46426354153643
---
<h1 align="center">Snowflake's Arctic-embed-xs</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
## News
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!
07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
## Models
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| Google-gecko-text-embedding | 55.7 |
| text-embedding-3-large | 55.44 |
| Cohere-embed-english-v3.0 | 55.00 |
| bge-large-en-v1.5 | 54.29 |
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------- | -------------------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |
| GIST-all-MiniLM-L6-v2 | 45.12 |
| gte-tiny | 44.92 |
| all-MiniLM-L6-v2 | 41.95 |
| bge-micro-v2 | 42.56 |
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
Based on the [infloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |
| bge-small-en-v1.5 | 51.68 |
| Cohere-embed-english-light-v3.0 | 51.34 |
| text-embedding-3-small | 51.08 |
| e5-small-v2 | 49.04 |
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |
| bge-base-en-v1.5 | 53.25 |
| nomic-embed-text-v1.5 | 53.25 |
| GIST-Embedding-v0 | 52.31 |
| gte-base | 52.31 |
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)
Based on the [nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |
| nomic-embed-text-v1.5 | 53.01 |
| nomic-embed-text-v1 | 52.81 |
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| UAE-Large-V1 | 54.66 |
| bge-large-en-v1.5 | 54.29 |
| mxbai-embed-large-v1 | 54.39 |
| e5-Large-v2 | 50.56 |
## Usage
### Using Sentence Transformers
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-xs")
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = query_embeddings @ document_embeddings.T
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
```
Query: what is snowflake?
0.57515126 The Data Cloud!
0.45798576 Mexico City of Course!
Query: Where can I get the best tacos?
0.5636022 Mexico City of Course!
0.5044898 The Data Cloud!
```
### Using Huggingface transformers
You can use the transformers package for a snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-xs')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-xs', add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-xs', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.5044895661144148, 0.5636021124426508]
```
## FAQ
TBD
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
## Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=15cd6ef8-397b-4e85-9d74-27ebdc7e9765" /> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
nvidia/NV-Embed-v2 | nvidia | feature-extraction | [
"transformers",
"safetensors",
"nvembed",
"feature-extraction",
"mteb",
"sentence-transformers",
"custom_code",
"en",
"arxiv:2405.17428",
"arxiv:2407.15831",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | 2024-08-29T13:00:32 | 2025-02-23T18:17:44 | 231,116 | 397 | ---
language:
- en
library_name: transformers
license: cc-by-nc-4.0
tags:
- mteb
- sentence-transformers
model-index:
- name: NV-Embed-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 94.28358208955224
- type: accuracy_stderr
value: 0.40076780842082305
- type: ap
value: 76.49097318319616
- type: ap_stderr
value: 1.2418692675183929
- type: f1
value: 91.41982003001168
- type: f1_stderr
value: 0.5043921413093579
- type: main_score
value: 94.28358208955224
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.74185000000001
- type: accuracy_stderr
value: 0.07420471683120942
- type: ap
value: 96.4737144875525
- type: ap_stderr
value: 0.2977518241541558
- type: f1
value: 97.7417581594921
- type: f1_stderr
value: 0.07428763617010377
- type: main_score
value: 97.74185000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 63.96000000000001
- type: accuracy_stderr
value: 1.815555011559825
- type: f1
value: 62.49361841640459
- type: f1_stderr
value: 2.829339314126457
- type: main_score
value: 63.96000000000001
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 46.515
- type: map_at_10
value: 62.392
- type: map_at_100
value: 62.732
- type: map_at_1000
value: 62.733000000000004
- type: map_at_3
value: 58.701
- type: map_at_5
value: 61.027
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 46.515
- type: ndcg_at_10
value: 70.074
- type: ndcg_at_100
value: 71.395
- type: ndcg_at_1000
value: 71.405
- type: ndcg_at_3
value: 62.643
- type: ndcg_at_5
value: 66.803
- type: precision_at_1
value: 46.515
- type: precision_at_10
value: 9.41
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.68
- type: precision_at_5
value: 16.814
- type: recall_at_1
value: 46.515
- type: recall_at_10
value: 94.097
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 74.03999999999999
- type: recall_at_5
value: 84.068
- type: main_score
value: 70.074
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 55.79933795955242
- type: v_measure
value: 55.79933795955242
- type: v_measure_std
value: 14.575108141916148
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 51.262845995850334
- type: v_measure
value: 51.262845995850334
- type: v_measure_std
value: 14.727824473104173
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.46477327480808
- type: mrr
value: 79.50160488941653
- type: main_score
value: 67.46477327480808
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cosine_pearson
value: 89.74311007980987
- type: cosine_spearman
value: 87.41644967443246
- type: manhattan_pearson
value: 88.57457108347744
- type: manhattan_spearman
value: 87.59295972042997
- type: euclidean_pearson
value: 88.27108977118459
- type: euclidean_spearman
value: 87.41644967443246
- type: main_score
value: 87.41644967443246
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 92.41558441558443
- type: accuracy_stderr
value: 0.37701502251934443
- type: f1
value: 92.38130170447671
- type: f1_stderr
value: 0.39115151225617767
- type: main_score
value: 92.41558441558443
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 54.08649516394218
- type: v_measure
value: 54.08649516394218
- type: v_measure_std
value: 0.5303233693045373
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 49.60352214167779
- type: v_measure
value: 49.60352214167779
- type: v_measure_std
value: 0.7176198612516721
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: CQADupstackRetrieval_is_a_combined_dataset
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 31.913249999999998
- type: map_at_10
value: 43.87733333333334
- type: map_at_100
value: 45.249916666666664
- type: map_at_1000
value: 45.350583333333326
- type: map_at_3
value: 40.316833333333335
- type: map_at_5
value: 42.317083333333336
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 38.30616666666667
- type: ndcg_at_10
value: 50.24175000000001
- type: ndcg_at_100
value: 55.345333333333336
- type: ndcg_at_1000
value: 56.91225000000001
- type: ndcg_at_3
value: 44.67558333333333
- type: ndcg_at_5
value: 47.32333333333334
- type: precision_at_1
value: 38.30616666666667
- type: precision_at_10
value: 9.007416666666666
- type: precision_at_100
value: 1.3633333333333333
- type: precision_at_1000
value: 0.16691666666666666
- type: precision_at_3
value: 20.895666666666667
- type: precision_at_5
value: 14.871666666666666
- type: recall_at_1
value: 31.913249999999998
- type: recall_at_10
value: 64.11891666666666
- type: recall_at_100
value: 85.91133333333333
- type: recall_at_1000
value: 96.28225
- type: recall_at_3
value: 48.54749999999999
- type: recall_at_5
value: 55.44283333333334
- type: main_score
value: 50.24175000000001
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 19.556
- type: map_at_10
value: 34.623
- type: map_at_100
value: 36.97
- type: map_at_1000
value: 37.123
- type: map_at_3
value: 28.904999999999998
- type: map_at_5
value: 31.955
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 44.104
- type: ndcg_at_10
value: 45.388
- type: ndcg_at_100
value: 52.793
- type: ndcg_at_1000
value: 55.108999999999995
- type: ndcg_at_3
value: 38.604
- type: ndcg_at_5
value: 40.806
- type: precision_at_1
value: 44.104
- type: precision_at_10
value: 14.143
- type: precision_at_100
value: 2.2190000000000003
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 29.316
- type: precision_at_5
value: 21.98
- type: recall_at_1
value: 19.556
- type: recall_at_10
value: 52.120999999999995
- type: recall_at_100
value: 76.509
- type: recall_at_1000
value: 89.029
- type: recall_at_3
value: 34.919
- type: recall_at_5
value: 42.18
- type: main_score
value: 45.388
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 10.714
- type: map_at_10
value: 25.814999999999998
- type: map_at_100
value: 37.845
- type: map_at_1000
value: 39.974
- type: map_at_3
value: 17.201
- type: map_at_5
value: 21.062
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 53.496
- type: ndcg_at_100
value: 58.053
- type: ndcg_at_1000
value: 64.886
- type: ndcg_at_3
value: 57.656
- type: ndcg_at_5
value: 55.900000000000006
- type: precision_at_1
value: 77.25
- type: precision_at_10
value: 43.65
- type: precision_at_100
value: 13.76
- type: precision_at_1000
value: 2.5940000000000003
- type: precision_at_3
value: 61.0
- type: precision_at_5
value: 54.65
- type: recall_at_1
value: 10.714
- type: recall_at_10
value: 31.173000000000002
- type: recall_at_100
value: 63.404
- type: recall_at_1000
value: 85.874
- type: recall_at_3
value: 18.249000000000002
- type: recall_at_5
value: 23.69
- type: main_score
value: 53.496
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 93.38499999999999
- type: accuracy_stderr
value: 0.13793114224133846
- type: f1
value: 90.12141028353496
- type: f1_stderr
value: 0.174640257706043
- type: main_score
value: 93.38499999999999
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 84.66900000000001
- type: map_at_10
value: 91.52799999999999
- type: map_at_100
value: 91.721
- type: map_at_1000
value: 91.73
- type: map_at_3
value: 90.752
- type: map_at_5
value: 91.262
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 91.20899999999999
- type: ndcg_at_10
value: 93.74900000000001
- type: ndcg_at_100
value: 94.279
- type: ndcg_at_1000
value: 94.408
- type: ndcg_at_3
value: 92.923
- type: ndcg_at_5
value: 93.376
- type: precision_at_1
value: 91.20899999999999
- type: precision_at_10
value: 11.059
- type: precision_at_100
value: 1.1560000000000001
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 35.129
- type: precision_at_5
value: 21.617
- type: recall_at_1
value: 84.66900000000001
- type: recall_at_10
value: 97.03399999999999
- type: recall_at_100
value: 98.931
- type: recall_at_1000
value: 99.65899999999999
- type: recall_at_3
value: 94.76299999999999
- type: recall_at_5
value: 95.968
- type: main_score
value: 93.74900000000001
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 34.866
- type: map_at_10
value: 58.06099999999999
- type: map_at_100
value: 60.028999999999996
- type: map_at_1000
value: 60.119
- type: map_at_3
value: 51.304
- type: map_at_5
value: 55.054
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 64.815
- type: ndcg_at_10
value: 65.729
- type: ndcg_at_100
value: 71.14
- type: ndcg_at_1000
value: 72.336
- type: ndcg_at_3
value: 61.973
- type: ndcg_at_5
value: 62.858000000000004
- type: precision_at_1
value: 64.815
- type: precision_at_10
value: 17.87
- type: precision_at_100
value: 2.373
- type: precision_at_1000
value: 0.258
- type: precision_at_3
value: 41.152
- type: precision_at_5
value: 29.568
- type: recall_at_1
value: 34.866
- type: recall_at_10
value: 72.239
- type: recall_at_100
value: 91.19
- type: recall_at_1000
value: 98.154
- type: recall_at_3
value: 56.472
- type: recall_at_5
value: 63.157
- type: main_score
value: 65.729
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 44.651999999999994
- type: map_at_10
value: 79.95100000000001
- type: map_at_100
value: 80.51700000000001
- type: map_at_1000
value: 80.542
- type: map_at_3
value: 77.008
- type: map_at_5
value: 78.935
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 89.305
- type: ndcg_at_10
value: 85.479
- type: ndcg_at_100
value: 87.235
- type: ndcg_at_1000
value: 87.669
- type: ndcg_at_3
value: 81.648
- type: ndcg_at_5
value: 83.88600000000001
- type: precision_at_1
value: 89.305
- type: precision_at_10
value: 17.807000000000002
- type: precision_at_100
value: 1.9140000000000001
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 53.756
- type: precision_at_5
value: 34.018
- type: recall_at_1
value: 44.651999999999994
- type: recall_at_10
value: 89.034
- type: recall_at_100
value: 95.719
- type: recall_at_1000
value: 98.535
- type: recall_at_3
value: 80.635
- type: recall_at_5
value: 85.044
- type: main_score
value: 85.479
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 97.1376
- type: accuracy_stderr
value: 0.04571914259913447
- type: ap
value: 95.92783808558808
- type: ap_stderr
value: 0.05063782483358255
- type: f1
value: 97.13755519177172
- type: f1_stderr
value: 0.04575943074086138
- type: main_score
value: 97.1376
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 0.0
- type: map_at_10
value: 38.342
- type: map_at_100
value: 0.0
- type: map_at_1000
value: 0.0
- type: map_at_3
value: 0.0
- type: map_at_5
value: 0.0
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 0.0
- type: ndcg_at_10
value: 45.629999999999995
- type: ndcg_at_100
value: 0.0
- type: ndcg_at_1000
value: 0.0
- type: ndcg_at_3
value: 0.0
- type: ndcg_at_5
value: 0.0
- type: precision_at_1
value: 0.0
- type: precision_at_10
value: 7.119000000000001
- type: precision_at_100
value: 0.0
- type: precision_at_1000
value: 0.0
- type: precision_at_3
value: 0.0
- type: precision_at_5
value: 0.0
- type: recall_at_1
value: 0.0
- type: recall_at_10
value: 67.972
- type: recall_at_100
value: 0.0
- type: recall_at_1000
value: 0.0
- type: recall_at_3
value: 0.0
- type: recall_at_5
value: 0.0
- type: main_score
value: 45.629999999999995
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 99.24988600091199
- type: accuracy_stderr
value: 0.04496826931900734
- type: f1
value: 99.15933275095276
- type: f1_stderr
value: 0.05565039139747446
- type: main_score
value: 99.24988600091199
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 94.3684450524396
- type: accuracy_stderr
value: 0.8436548701322188
- type: f1
value: 77.33022623133307
- type: f1_stderr
value: 0.9228425861187275
- type: main_score
value: 94.3684450524396
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 86.09616677874916
- type: accuracy_stderr
value: 0.9943208055590853
- type: f1
value: 83.4902056490062
- type: f1_stderr
value: 0.7626189310074184
- type: main_score
value: 86.09616677874916
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 92.17215870880968
- type: accuracy_stderr
value: 0.25949941333658166
- type: f1
value: 91.36757392422702
- type: f1_stderr
value: 0.29139507298154815
- type: main_score
value: 92.17215870880968
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: main_score
value: 46.09497344077905
- type: v_measure
value: 46.09497344077905
- type: v_measure_std
value: 1.44871520869784
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: main_score
value: 44.861049989560684
- type: v_measure
value: 44.861049989560684
- type: v_measure_std
value: 1.432199293162203
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.75936162919999
- type: mrr
value: 32.966812736541236
- type: main_score
value: 31.75936162919999
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 7.893999999999999
- type: map_at_10
value: 17.95
- type: map_at_100
value: 23.474
- type: map_at_1000
value: 25.412000000000003
- type: map_at_3
value: 12.884
- type: map_at_5
value: 15.171000000000001
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 55.728
- type: ndcg_at_10
value: 45.174
- type: ndcg_at_100
value: 42.18
- type: ndcg_at_1000
value: 50.793
- type: ndcg_at_3
value: 50.322
- type: ndcg_at_5
value: 48.244
- type: precision_at_1
value: 57.276
- type: precision_at_10
value: 33.437
- type: precision_at_100
value: 10.671999999999999
- type: precision_at_1000
value: 2.407
- type: precision_at_3
value: 46.646
- type: precision_at_5
value: 41.672
- type: recall_at_1
value: 7.893999999999999
- type: recall_at_10
value: 22.831000000000003
- type: recall_at_100
value: 43.818
- type: recall_at_1000
value: 75.009
- type: recall_at_3
value: 14.371
- type: recall_at_5
value: 17.752000000000002
- type: main_score
value: 45.174
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 49.351
- type: map_at_10
value: 66.682
- type: map_at_100
value: 67.179
- type: map_at_1000
value: 67.18499999999999
- type: map_at_3
value: 62.958999999999996
- type: map_at_5
value: 65.364
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 55.417
- type: ndcg_at_10
value: 73.568
- type: ndcg_at_100
value: 75.35
- type: ndcg_at_1000
value: 75.478
- type: ndcg_at_3
value: 67.201
- type: ndcg_at_5
value: 70.896
- type: precision_at_1
value: 55.417
- type: precision_at_10
value: 11.036999999999999
- type: precision_at_100
value: 1.204
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 29.654000000000003
- type: precision_at_5
value: 20.006
- type: recall_at_1
value: 49.351
- type: recall_at_10
value: 91.667
- type: recall_at_100
value: 98.89
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 75.715
- type: recall_at_5
value: 84.072
- type: main_score
value: 73.568
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.358
- type: map_at_10
value: 85.474
- type: map_at_100
value: 86.101
- type: map_at_1000
value: 86.114
- type: map_at_3
value: 82.562
- type: map_at_5
value: 84.396
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 82.12
- type: ndcg_at_10
value: 89.035
- type: ndcg_at_100
value: 90.17399999999999
- type: ndcg_at_1000
value: 90.243
- type: ndcg_at_3
value: 86.32300000000001
- type: ndcg_at_5
value: 87.85
- type: precision_at_1
value: 82.12
- type: precision_at_10
value: 13.55
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.89
- type: precision_at_5
value: 24.9
- type: recall_at_1
value: 71.358
- type: recall_at_10
value: 95.855
- type: recall_at_100
value: 99.711
- type: recall_at_1000
value: 99.994
- type: recall_at_3
value: 88.02
- type: recall_at_5
value: 92.378
- type: main_score
value: 89.035
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 71.0984522742521
- type: v_measure
value: 71.0984522742521
- type: v_measure_std
value: 3.5668139917058044
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: main_score
value: 74.94499641904133
- type: v_measure
value: 74.94499641904133
- type: v_measure_std
value: 11.419672879389248
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.343
- type: map_at_10
value: 13.044
- type: map_at_100
value: 15.290999999999999
- type: map_at_1000
value: 15.609
- type: map_at_3
value: 9.227
- type: map_at_5
value: 11.158
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 26.3
- type: ndcg_at_10
value: 21.901
- type: ndcg_at_100
value: 30.316
- type: ndcg_at_1000
value: 35.547000000000004
- type: ndcg_at_3
value: 20.560000000000002
- type: ndcg_at_5
value: 18.187
- type: precision_at_1
value: 26.3
- type: precision_at_10
value: 11.34
- type: precision_at_100
value: 2.344
- type: precision_at_1000
value: 0.359
- type: precision_at_3
value: 18.967
- type: precision_at_5
value: 15.920000000000002
- type: recall_at_1
value: 5.343
- type: recall_at_10
value: 22.997
- type: recall_at_100
value: 47.562
- type: recall_at_1000
value: 72.94500000000001
- type: recall_at_3
value: 11.533
- type: recall_at_5
value: 16.148
- type: main_score
value: 21.901
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 87.3054603493591
- type: cosine_spearman
value: 82.14763206055602
- type: manhattan_pearson
value: 84.78737790237557
- type: manhattan_spearman
value: 81.88455356002758
- type: euclidean_pearson
value: 85.00668629311117
- type: euclidean_spearman
value: 82.14763037860851
- type: main_score
value: 82.14763206055602
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 86.6911864687294
- type: cosine_spearman
value: 77.89286260403269
- type: manhattan_pearson
value: 82.87240347680857
- type: manhattan_spearman
value: 78.10055393740326
- type: euclidean_pearson
value: 82.72282535777123
- type: euclidean_spearman
value: 77.89256648406325
- type: main_score
value: 77.89286260403269
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 87.7220832598633
- type: cosine_spearman
value: 88.30238972017452
- type: manhattan_pearson
value: 87.88214789140248
- type: manhattan_spearman
value: 88.24770220032391
- type: euclidean_pearson
value: 87.98610386257103
- type: euclidean_spearman
value: 88.30238972017452
- type: main_score
value: 88.30238972017452
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 85.70614623247714
- type: cosine_spearman
value: 84.29920990970672
- type: manhattan_pearson
value: 84.9836190531721
- type: manhattan_spearman
value: 84.40933470597638
- type: euclidean_pearson
value: 84.96652336693347
- type: euclidean_spearman
value: 84.29920989531965
- type: main_score
value: 84.29920990970672
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 88.4169972425264
- type: cosine_spearman
value: 89.03555007807218
- type: manhattan_pearson
value: 88.83068699455478
- type: manhattan_spearman
value: 89.21877175674125
- type: euclidean_pearson
value: 88.7251052947544
- type: euclidean_spearman
value: 89.03557389893083
- type: main_score
value: 89.03555007807218
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 85.63830579034632
- type: cosine_spearman
value: 86.77353371581373
- type: manhattan_pearson
value: 86.24830492396637
- type: manhattan_spearman
value: 86.96754348626189
- type: euclidean_pearson
value: 86.09837038778359
- type: euclidean_spearman
value: 86.77353371581373
- type: main_score
value: 86.77353371581373
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cosine_pearson
value: 91.2204675588959
- type: cosine_spearman
value: 90.66976712249057
- type: manhattan_pearson
value: 91.11007808242346
- type: manhattan_spearman
value: 90.51739232964488
- type: euclidean_pearson
value: 91.19588941007903
- type: euclidean_spearman
value: 90.66976712249057
- type: main_score
value: 90.66976712249057
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cosine_pearson
value: 69.34416749707114
- type: cosine_spearman
value: 68.11632448161046
- type: manhattan_pearson
value: 68.99243488935281
- type: manhattan_spearman
value: 67.8398546438258
- type: euclidean_pearson
value: 69.06376010216088
- type: euclidean_spearman
value: 68.11632448161046
- type: main_score
value: 68.11632448161046
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 88.10309739429758
- type: cosine_spearman
value: 88.40520383147418
- type: manhattan_pearson
value: 88.50753383813232
- type: manhattan_spearman
value: 88.66382629460927
- type: euclidean_pearson
value: 88.35050664609376
- type: euclidean_spearman
value: 88.40520383147418
- type: main_score
value: 88.40520383147418
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.58627126942797
- type: mrr
value: 97.01098103058887
- type: main_score
value: 87.58627126942797
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 62.883
- type: map_at_10
value: 75.371
- type: map_at_100
value: 75.66000000000001
- type: map_at_1000
value: 75.667
- type: map_at_3
value: 72.741
- type: map_at_5
value: 74.74
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_10
value: 80.12700000000001
- type: ndcg_at_100
value: 81.291
- type: ndcg_at_1000
value: 81.464
- type: ndcg_at_3
value: 76.19
- type: ndcg_at_5
value: 78.827
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 10.567
- type: precision_at_100
value: 1.117
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 30.333
- type: precision_at_5
value: 20.133000000000003
- type: recall_at_1
value: 62.883
- type: recall_at_10
value: 93.556
- type: recall_at_100
value: 98.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 83.322
- type: recall_at_5
value: 89.756
- type: main_score
value: 80.12700000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87524752475248
- type: cos_sim_accuracy_threshold
value: 74.86587762832642
- type: cos_sim_ap
value: 97.02222446606328
- type: cos_sim_f1
value: 93.66197183098592
- type: cos_sim_f1_threshold
value: 74.74223375320435
- type: cos_sim_precision
value: 94.23076923076923
- type: cos_sim_recall
value: 93.10000000000001
- type: dot_accuracy
value: 99.87524752475248
- type: dot_accuracy_threshold
value: 74.86587762832642
- type: dot_ap
value: 97.02222688043362
- type: dot_f1
value: 93.66197183098592
- type: dot_f1_threshold
value: 74.74223375320435
- type: dot_precision
value: 94.23076923076923
- type: dot_recall
value: 93.10000000000001
- type: euclidean_accuracy
value: 99.87524752475248
- type: euclidean_accuracy_threshold
value: 70.9000825881958
- type: euclidean_ap
value: 97.02222446606329
- type: euclidean_f1
value: 93.66197183098592
- type: euclidean_f1_threshold
value: 71.07426524162292
- type: euclidean_precision
value: 94.23076923076923
- type: euclidean_recall
value: 93.10000000000001
- type: manhattan_accuracy
value: 99.87623762376238
- type: manhattan_accuracy_threshold
value: 3588.5040283203125
- type: manhattan_ap
value: 97.09194643777883
- type: manhattan_f1
value: 93.7375745526839
- type: manhattan_f1_threshold
value: 3664.3760681152344
- type: manhattan_precision
value: 93.18181818181817
- type: manhattan_recall
value: 94.3
- type: max_accuracy
value: 99.87623762376238
- type: max_ap
value: 97.09194643777883
- type: max_f1
value: 93.7375745526839
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 82.10134099988541
- type: v_measure
value: 82.10134099988541
- type: v_measure_std
value: 2.7926349897769533
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 48.357450742397404
- type: v_measure
value: 48.357450742397404
- type: v_measure_std
value: 1.520118876440547
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.79277200802986
- type: mrr
value: 56.742517082590616
- type: main_score
value: 55.79277200802986
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_spearman
value: 30.701215774712693
- type: cosine_pearson
value: 31.26740037278488
- type: dot_spearman
value: 30.701215774712693
- type: dot_pearson
value: 31.267404144879997
- type: main_score
value: 30.701215774712693
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.23800000000000002
- type: map_at_10
value: 2.31
- type: map_at_100
value: 15.495000000000001
- type: map_at_1000
value: 38.829
- type: map_at_3
value: 0.72
- type: map_at_5
value: 1.185
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 71.39
- type: ndcg_at_1000
value: 64.153
- type: ndcg_at_3
value: 89.877
- type: ndcg_at_5
value: 89.562
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 92.60000000000001
- type: precision_at_100
value: 73.74000000000001
- type: precision_at_1000
value: 28.222
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 93.60000000000001
- type: recall_at_1
value: 0.23800000000000002
- type: recall_at_10
value: 2.428
- type: recall_at_100
value: 18.099999999999998
- type: recall_at_1000
value: 60.79599999999999
- type: recall_at_3
value: 0.749
- type: recall_at_5
value: 1.238
- type: main_score
value: 88.442
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.4939999999999998
- type: map_at_10
value: 12.531999999999998
- type: map_at_100
value: 19.147
- type: map_at_1000
value: 20.861
- type: map_at_3
value: 7.558
- type: map_at_5
value: 9.49
- type: mrr_at_1
value: 0.0
- type: mrr_at_10
value: 0.0
- type: mrr_at_100
value: 0.0
- type: mrr_at_1000
value: 0.0
- type: mrr_at_3
value: 0.0
- type: mrr_at_5
value: 0.0
- type: ndcg_at_1
value: 47.959
- type: ndcg_at_10
value: 31.781
- type: ndcg_at_100
value: 42.131
- type: ndcg_at_1000
value: 53.493
- type: ndcg_at_3
value: 39.204
- type: ndcg_at_5
value: 34.635
- type: precision_at_1
value: 48.980000000000004
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.224
- type: precision_at_1000
value: 1.584
- type: precision_at_3
value: 38.775999999999996
- type: precision_at_5
value: 33.061
- type: recall_at_1
value: 3.4939999999999998
- type: recall_at_10
value: 18.895
- type: recall_at_100
value: 50.192
- type: recall_at_1000
value: 85.167
- type: recall_at_3
value: 8.703
- type: recall_at_5
value: 11.824
- type: main_score
value: 31.781
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 92.7402
- type: accuracy_stderr
value: 1.020764595781027
- type: ap
value: 44.38594756333084
- type: ap_stderr
value: 1.817150701258273
- type: f1
value: 79.95699280019547
- type: f1_stderr
value: 1.334582498702029
- type: main_score
value: 92.7402
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 80.86870401810978
- type: accuracy_stderr
value: 0.22688467782004712
- type: f1
value: 81.1829040745744
- type: f1_stderr
value: 0.19774920574849694
- type: main_score
value: 80.86870401810978
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 64.82048869927482
- type: v_measure
value: 64.82048869927482
- type: v_measure_std
value: 0.9170394252450564
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.44251057996067
- type: cos_sim_accuracy_threshold
value: 70.2150285243988
- type: cos_sim_ap
value: 81.11422351199913
- type: cos_sim_f1
value: 73.71062868615887
- type: cos_sim_f1_threshold
value: 66.507488489151
- type: cos_sim_precision
value: 70.2799712849964
- type: cos_sim_recall
value: 77.4934036939314
- type: dot_accuracy
value: 88.44251057996067
- type: dot_accuracy_threshold
value: 70.2150285243988
- type: dot_ap
value: 81.11420529068658
- type: dot_f1
value: 73.71062868615887
- type: dot_f1_threshold
value: 66.50749444961548
- type: dot_precision
value: 70.2799712849964
- type: dot_recall
value: 77.4934036939314
- type: euclidean_accuracy
value: 88.44251057996067
- type: euclidean_accuracy_threshold
value: 77.18156576156616
- type: euclidean_ap
value: 81.11422421732487
- type: euclidean_f1
value: 73.71062868615887
- type: euclidean_f1_threshold
value: 81.84436559677124
- type: euclidean_precision
value: 70.2799712849964
- type: euclidean_recall
value: 77.4934036939314
- type: manhattan_accuracy
value: 88.26369434344639
- type: manhattan_accuracy_threshold
value: 3837.067413330078
- type: manhattan_ap
value: 80.81442360477725
- type: manhattan_f1
value: 73.39883099117024
- type: manhattan_f1_threshold
value: 4098.833847045898
- type: manhattan_precision
value: 69.41896024464832
- type: manhattan_recall
value: 77.86279683377309
- type: max_accuracy
value: 88.44251057996067
- type: max_ap
value: 81.11422421732487
- type: max_f1
value: 73.71062868615887
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 90.03182365040556
- type: cos_sim_accuracy_threshold
value: 64.46443796157837
- type: cos_sim_ap
value: 87.86649113691112
- type: cos_sim_f1
value: 80.45644844577821
- type: cos_sim_f1_threshold
value: 61.40774488449097
- type: cos_sim_precision
value: 77.54052702992216
- type: cos_sim_recall
value: 83.60024638127503
- type: dot_accuracy
value: 90.03182365040556
- type: dot_accuracy_threshold
value: 64.46444988250732
- type: dot_ap
value: 87.86649011954319
- type: dot_f1
value: 80.45644844577821
- type: dot_f1_threshold
value: 61.407750844955444
- type: dot_precision
value: 77.54052702992216
- type: dot_recall
value: 83.60024638127503
- type: euclidean_accuracy
value: 90.03182365040556
- type: euclidean_accuracy_threshold
value: 84.30368900299072
- type: euclidean_ap
value: 87.86649114275045
- type: euclidean_f1
value: 80.45644844577821
- type: euclidean_f1_threshold
value: 87.8547191619873
- type: euclidean_precision
value: 77.54052702992216
- type: euclidean_recall
value: 83.60024638127503
- type: manhattan_accuracy
value: 89.99883572010712
- type: manhattan_accuracy_threshold
value: 4206.838607788086
- type: manhattan_ap
value: 87.8600826607838
- type: manhattan_f1
value: 80.44054508120217
- type: manhattan_f1_threshold
value: 4372.755432128906
- type: manhattan_precision
value: 78.08219178082192
- type: manhattan_recall
value: 82.94579611949491
- type: max_accuracy
value: 90.03182365040556
- type: max_ap
value: 87.86649114275045
- type: max_f1
value: 80.45644844577821
---
## Introduction
We present NV-Embed-v2, a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark ([MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard))(as of Aug 30, 2024) with a score of 72.31 across 56 text embedding tasks. It also holds the No. 1 in the retrieval sub-category (a score of 62.65 across 15 tasks) in the leaderboard, which is essential to the development of RAG technology.
NV-Embed-v2 presents several new designs, including having the LLM attend to latent vectors for better pooled embedding output, and demonstrating a two-staged instruction tuning method to enhance the accuracy of both retrieval and non-retrieval tasks. Additionally, NV-Embed-v2 incorporates a novel hard-negative mining methods that take into account the positive relevance score for better false negatives removal.
For more technical details, refer to our paper: [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://arxiv.org/pdf/2405.17428).
## Model Details
- Base Decoder-only LLM: [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- Pooling Type: Latent-Attention
- Embedding Dimension: 4096
## How to use
Here is an example of how to encode queries and passages using Huggingface-transformer and Sentence-transformer. Please find the required package version [here](https://huggingface.co/nvidia/NV-Embed-v2#2-required-packages).
### Usage (HuggingFace Transformers)
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}
query_prefix = "Instruct: "+task_name_to_instruct["example"]+"\nQuery: "
queries = [
'are judo throws allowed in wrestling?',
'how to become a radiology technician in michigan?'
]
# No instruction needed for retrieval passages
passage_prefix = ""
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]
# load model with tokenizer
model = AutoModel.from_pretrained('nvidia/NV-Embed-v2', trust_remote_code=True)
# get the embeddings
max_length = 32768
query_embeddings = model.encode(queries, instruction=query_prefix, max_length=max_length)
passage_embeddings = model.encode(passages, instruction=passage_prefix, max_length=max_length)
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1)
# get the embeddings with DataLoader (spliting the datasets into multiple mini-batches)
# batch_size=2
# query_embeddings = model._do_encode(queries, batch_size=batch_size, instruction=query_prefix, max_length=max_length, num_workers=32, return_numpy=True)
# passage_embeddings = model._do_encode(passages, batch_size=batch_size, instruction=passage_prefix, max_length=max_length, num_workers=32, return_numpy=True)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
# [[87.42693328857422, 0.46283677220344543], [0.965264618396759, 86.03721618652344]]
```
### Usage (Sentence-Transformers)
```python
import torch
from sentence_transformers import SentenceTransformer
# Each query needs to be accompanied by an corresponding instruction describing the task.
task_name_to_instruct = {"example": "Given a question, retrieve passages that answer the question",}
query_prefix = "Instruct: "+task_name_to_instruct["example"]+"\nQuery: "
queries = [
'are judo throws allowed in wrestling?',
'how to become a radiology technician in michigan?'
]
# No instruction needed for retrieval passages
passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
]
# load model with tokenizer
model = SentenceTransformer('nvidia/NV-Embed-v2', trust_remote_code=True)
model.max_seq_length = 32768
model.tokenizer.padding_side="right"
def add_eos(input_examples):
input_examples = [input_example + model.tokenizer.eos_token for input_example in input_examples]
return input_examples
# get the embeddings
batch_size = 2
query_embeddings = model.encode(add_eos(queries), batch_size=batch_size, prompt=query_prefix, normalize_embeddings=True)
passage_embeddings = model.encode(add_eos(passages), batch_size=batch_size, normalize_embeddings=True)
scores = (query_embeddings @ passage_embeddings.T) * 100
print(scores.tolist())
```
## License
This model should not be used for any commercial purpose. Refer the [license](https://spdx.org/licenses/CC-BY-NC-4.0) for the detailed terms.
For commercial purpose, we recommend you to use the models of [NeMo Retriever Microservices (NIMs)](https://build.nvidia.com/explore/retrieval).
## Correspondence to
Chankyu Lee ([email protected]), Rajarshi Roy ([email protected]), Wei Ping ([email protected])
## Citation
If you find this code useful in your research, please consider citing:
```bibtex
@article{lee2024nv,
title={NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models},
author={Lee, Chankyu and Roy, Rajarshi and Xu, Mengyao and Raiman, Jonathan and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
journal={arXiv preprint arXiv:2405.17428},
year={2024}
}
```
```bibtex
@article{moreira2024nv,
title={NV-Retriever: Improving text embedding models with effective hard-negative mining},
author={Moreira, Gabriel de Souza P and Osmulski, Radek and Xu, Mengyao and Ak, Ronay and Schifferer, Benedikt and Oldridge, Even},
journal={arXiv preprint arXiv:2407.15831},
year={2024}
}
```
## Troubleshooting
#### 1. Instruction template for MTEB benchmarks
For MTEB sub-tasks for retrieval, STS, summarization, please use the instruction prefix template in [instructions.json](https://huggingface.co/nvidia/NV-Embed-v2/blob/main/instructions.json). For classification, clustering and reranking, please use the instructions provided in Table. 7 in [NV-Embed paper](https://arxiv.org/pdf/2405.17428).
#### 2. Required Packages
If you have trouble, try installing the python packages as below
```python
pip uninstall -y transformer-engine
pip install torch==2.2.0
pip install transformers==4.42.4
pip install flash-attn==2.2.0
pip install sentence-transformers==2.7.0
```
#### 3. How to enable Multi-GPU (Note, this is the case for HuggingFace Transformers)
```python
from transformers import AutoModel
from torch.nn import DataParallel
embedding_model = AutoModel.from_pretrained("nvidia/NV-Embed-v2")
for module_key, module in embedding_model._modules.items():
embedding_model._modules[module_key] = DataParallel(module)
```
#### 4. Fixing "nvidia/NV-Embed-v2 is not the path to a directory containing a file named config.json"
Switch to your local model path，and open config.json and change the value of **"_name_or_path"** and replace it with your local model path.
#### 5. Access to model nvidia/NV-Embed-v2 is restricted. You must be authenticated to access it
Use your huggingface access [token](https://huggingface.co/settings/tokens) to execute *"huggingface-cli login"*.
#### 6. How to resolve slight mismatch in Sentence transformer results.
A slight mismatch in the Sentence Transformer implementation is caused by a discrepancy in the calculation of the instruction prefix length within the Sentence Transformer package.
To fix this issue, you need to build the Sentence Transformer package from source, making the necessary modification in this [line](https://github.com/UKPLab/sentence-transformers/blob/v2.7-release/sentence_transformers/SentenceTransformer.py#L353) as below.
```python
git clone https://github.com/UKPLab/sentence-transformers.git
cd sentence-transformers
git checkout v2.7-release
# Modify L353 in SentenceTransformer.py to **'extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1]'**.
pip install -e .
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
jinaai/jina-embeddings-v2-base-en | jinaai | feature-extraction | [
"sentence-transformers",
"pytorch",
"coreml",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"custom_code",
"en",
"dataset:allenai/c4",
"arxiv:2108.12409",
"arxiv:2310.19923",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"region:us"
] | 2023-09-27T17:04:00 | 2025-01-06T16:24:38 | 227,825 | 716 | ---
datasets:
- allenai/c4
language: en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
inference: false
model-index:
- name: jina-embedding-b-en-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.73134328358209
- type: ap
value: 37.765427081831035
- type: f1
value: 68.79367444339518
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.544275
- type: ap
value: 84.61328675662887
- type: f1
value: 88.51879035862375
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.263999999999996
- type: f1
value: 43.778759656699435
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.693
- type: map_at_10
value: 35.487
- type: map_at_100
value: 36.862
- type: map_at_1000
value: 36.872
- type: map_at_3
value: 30.049999999999997
- type: map_at_5
value: 32.966
- type: mrr_at_1
value: 21.977
- type: mrr_at_10
value: 35.565999999999995
- type: mrr_at_100
value: 36.948
- type: mrr_at_1000
value: 36.958
- type: mrr_at_3
value: 30.121
- type: mrr_at_5
value: 33.051
- type: ndcg_at_1
value: 21.693
- type: ndcg_at_10
value: 44.181
- type: ndcg_at_100
value: 49.982
- type: ndcg_at_1000
value: 50.233000000000004
- type: ndcg_at_3
value: 32.830999999999996
- type: ndcg_at_5
value: 38.080000000000005
- type: precision_at_1
value: 21.693
- type: precision_at_10
value: 7.248
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 13.632
- type: precision_at_5
value: 10.725
- type: recall_at_1
value: 21.693
- type: recall_at_10
value: 72.475
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 40.896
- type: recall_at_5
value: 53.627
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.39242428696777
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.675626784714
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.247725694904034
- type: mrr
value: 74.91359978894604
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.68003802970496
- type: cos_sim_spearman
value: 81.23438110096286
- type: euclidean_pearson
value: 81.87462986142582
- type: euclidean_spearman
value: 81.23438110096286
- type: manhattan_pearson
value: 81.61162566600755
- type: manhattan_spearman
value: 81.11329400456184
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.01298701298701
- type: f1
value: 83.31690714969382
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.050108150972086
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.15731442819715
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.391999999999996
- type: map_at_10
value: 42.597
- type: map_at_100
value: 44.07
- type: map_at_1000
value: 44.198
- type: map_at_3
value: 38.957
- type: map_at_5
value: 40.961
- type: mrr_at_1
value: 37.196
- type: mrr_at_10
value: 48.152
- type: mrr_at_100
value: 48.928
- type: mrr_at_1000
value: 48.964999999999996
- type: mrr_at_3
value: 45.446
- type: mrr_at_5
value: 47.205999999999996
- type: ndcg_at_1
value: 37.196
- type: ndcg_at_10
value: 49.089
- type: ndcg_at_100
value: 54.471000000000004
- type: ndcg_at_1000
value: 56.385
- type: ndcg_at_3
value: 43.699
- type: ndcg_at_5
value: 46.22
- type: precision_at_1
value: 37.196
- type: precision_at_10
value: 9.313
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 20.839
- type: precision_at_5
value: 14.936
- type: recall_at_1
value: 31.391999999999996
- type: recall_at_10
value: 61.876
- type: recall_at_100
value: 84.214
- type: recall_at_1000
value: 95.985
- type: recall_at_3
value: 46.6
- type: recall_at_5
value: 53.588
- type: map_at_1
value: 29.083
- type: map_at_10
value: 38.812999999999995
- type: map_at_100
value: 40.053
- type: map_at_1000
value: 40.188
- type: map_at_3
value: 36.111
- type: map_at_5
value: 37.519000000000005
- type: mrr_at_1
value: 36.497
- type: mrr_at_10
value: 44.85
- type: mrr_at_100
value: 45.546
- type: mrr_at_1000
value: 45.593
- type: mrr_at_3
value: 42.686
- type: mrr_at_5
value: 43.909
- type: ndcg_at_1
value: 36.497
- type: ndcg_at_10
value: 44.443
- type: ndcg_at_100
value: 48.979
- type: ndcg_at_1000
value: 51.154999999999994
- type: ndcg_at_3
value: 40.660000000000004
- type: ndcg_at_5
value: 42.193000000000005
- type: precision_at_1
value: 36.497
- type: precision_at_10
value: 8.433
- type: precision_at_100
value: 1.369
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 19.894000000000002
- type: precision_at_5
value: 13.873
- type: recall_at_1
value: 29.083
- type: recall_at_10
value: 54.313
- type: recall_at_100
value: 73.792
- type: recall_at_1000
value: 87.629
- type: recall_at_3
value: 42.257
- type: recall_at_5
value: 47.066
- type: map_at_1
value: 38.556000000000004
- type: map_at_10
value: 50.698
- type: map_at_100
value: 51.705
- type: map_at_1000
value: 51.768
- type: map_at_3
value: 47.848
- type: map_at_5
value: 49.358000000000004
- type: mrr_at_1
value: 43.95
- type: mrr_at_10
value: 54.191
- type: mrr_at_100
value: 54.852999999999994
- type: mrr_at_1000
value: 54.885
- type: mrr_at_3
value: 51.954
- type: mrr_at_5
value: 53.13
- type: ndcg_at_1
value: 43.95
- type: ndcg_at_10
value: 56.516
- type: ndcg_at_100
value: 60.477000000000004
- type: ndcg_at_1000
value: 61.746
- type: ndcg_at_3
value: 51.601
- type: ndcg_at_5
value: 53.795
- type: precision_at_1
value: 43.95
- type: precision_at_10
value: 9.009
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.989
- type: precision_at_5
value: 15.473
- type: recall_at_1
value: 38.556000000000004
- type: recall_at_10
value: 70.159
- type: recall_at_100
value: 87.132
- type: recall_at_1000
value: 96.16
- type: recall_at_3
value: 56.906
- type: recall_at_5
value: 62.332
- type: map_at_1
value: 24.238
- type: map_at_10
value: 32.5
- type: map_at_100
value: 33.637
- type: map_at_1000
value: 33.719
- type: map_at_3
value: 30.026999999999997
- type: map_at_5
value: 31.555
- type: mrr_at_1
value: 26.328000000000003
- type: mrr_at_10
value: 34.44
- type: mrr_at_100
value: 35.455999999999996
- type: mrr_at_1000
value: 35.521
- type: mrr_at_3
value: 32.034
- type: mrr_at_5
value: 33.565
- type: ndcg_at_1
value: 26.328000000000003
- type: ndcg_at_10
value: 37.202
- type: ndcg_at_100
value: 42.728
- type: ndcg_at_1000
value: 44.792
- type: ndcg_at_3
value: 32.368
- type: ndcg_at_5
value: 35.008
- type: precision_at_1
value: 26.328000000000003
- type: precision_at_10
value: 5.7059999999999995
- type: precision_at_100
value: 0.8880000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.672
- type: precision_at_5
value: 9.74
- type: recall_at_1
value: 24.238
- type: recall_at_10
value: 49.829
- type: recall_at_100
value: 75.21
- type: recall_at_1000
value: 90.521
- type: recall_at_3
value: 36.867
- type: recall_at_5
value: 43.241
- type: map_at_1
value: 15.378
- type: map_at_10
value: 22.817999999999998
- type: map_at_100
value: 23.977999999999998
- type: map_at_1000
value: 24.108
- type: map_at_3
value: 20.719
- type: map_at_5
value: 21.889
- type: mrr_at_1
value: 19.03
- type: mrr_at_10
value: 27.022000000000002
- type: mrr_at_100
value: 28.011999999999997
- type: mrr_at_1000
value: 28.096
- type: mrr_at_3
value: 24.855
- type: mrr_at_5
value: 26.029999999999998
- type: ndcg_at_1
value: 19.03
- type: ndcg_at_10
value: 27.526
- type: ndcg_at_100
value: 33.040000000000006
- type: ndcg_at_1000
value: 36.187000000000005
- type: ndcg_at_3
value: 23.497
- type: ndcg_at_5
value: 25.334
- type: precision_at_1
value: 19.03
- type: precision_at_10
value: 4.963
- type: precision_at_100
value: 0.893
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 11.360000000000001
- type: precision_at_5
value: 8.134
- type: recall_at_1
value: 15.378
- type: recall_at_10
value: 38.061
- type: recall_at_100
value: 61.754
- type: recall_at_1000
value: 84.259
- type: recall_at_3
value: 26.788
- type: recall_at_5
value: 31.326999999999998
- type: map_at_1
value: 27.511999999999997
- type: map_at_10
value: 37.429
- type: map_at_100
value: 38.818000000000005
- type: map_at_1000
value: 38.924
- type: map_at_3
value: 34.625
- type: map_at_5
value: 36.064
- type: mrr_at_1
value: 33.300999999999995
- type: mrr_at_10
value: 43.036
- type: mrr_at_100
value: 43.894
- type: mrr_at_1000
value: 43.936
- type: mrr_at_3
value: 40.825
- type: mrr_at_5
value: 42.028
- type: ndcg_at_1
value: 33.300999999999995
- type: ndcg_at_10
value: 43.229
- type: ndcg_at_100
value: 48.992000000000004
- type: ndcg_at_1000
value: 51.02100000000001
- type: ndcg_at_3
value: 38.794000000000004
- type: ndcg_at_5
value: 40.65
- type: precision_at_1
value: 33.300999999999995
- type: precision_at_10
value: 7.777000000000001
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.163
- type: precision_at_3
value: 18.351
- type: precision_at_5
value: 12.762
- type: recall_at_1
value: 27.511999999999997
- type: recall_at_10
value: 54.788000000000004
- type: recall_at_100
value: 79.105
- type: recall_at_1000
value: 92.49199999999999
- type: recall_at_3
value: 41.924
- type: recall_at_5
value: 47.026
- type: map_at_1
value: 24.117
- type: map_at_10
value: 33.32
- type: map_at_100
value: 34.677
- type: map_at_1000
value: 34.78
- type: map_at_3
value: 30.233999999999998
- type: map_at_5
value: 31.668000000000003
- type: mrr_at_1
value: 29.566
- type: mrr_at_10
value: 38.244
- type: mrr_at_100
value: 39.245000000000005
- type: mrr_at_1000
value: 39.296
- type: mrr_at_3
value: 35.864000000000004
- type: mrr_at_5
value: 36.919999999999995
- type: ndcg_at_1
value: 29.566
- type: ndcg_at_10
value: 39.127
- type: ndcg_at_100
value: 44.989000000000004
- type: ndcg_at_1000
value: 47.189
- type: ndcg_at_3
value: 34.039
- type: ndcg_at_5
value: 35.744
- type: precision_at_1
value: 29.566
- type: precision_at_10
value: 7.385999999999999
- type: precision_at_100
value: 1.204
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.286
- type: precision_at_5
value: 11.484
- type: recall_at_1
value: 24.117
- type: recall_at_10
value: 51.559999999999995
- type: recall_at_100
value: 77.104
- type: recall_at_1000
value: 91.79899999999999
- type: recall_at_3
value: 36.82
- type: recall_at_5
value: 41.453
- type: map_at_1
value: 25.17625
- type: map_at_10
value: 34.063916666666664
- type: map_at_100
value: 35.255500000000005
- type: map_at_1000
value: 35.37275
- type: map_at_3
value: 31.351666666666667
- type: map_at_5
value: 32.80608333333333
- type: mrr_at_1
value: 29.59783333333333
- type: mrr_at_10
value: 38.0925
- type: mrr_at_100
value: 38.957249999999995
- type: mrr_at_1000
value: 39.01608333333333
- type: mrr_at_3
value: 35.77625
- type: mrr_at_5
value: 37.04991666666667
- type: ndcg_at_1
value: 29.59783333333333
- type: ndcg_at_10
value: 39.343666666666664
- type: ndcg_at_100
value: 44.488249999999994
- type: ndcg_at_1000
value: 46.83358333333334
- type: ndcg_at_3
value: 34.69708333333333
- type: ndcg_at_5
value: 36.75075
- type: precision_at_1
value: 29.59783333333333
- type: precision_at_10
value: 6.884083333333332
- type: precision_at_100
value: 1.114
- type: precision_at_1000
value: 0.15108333333333332
- type: precision_at_3
value: 15.965250000000003
- type: precision_at_5
value: 11.246500000000001
- type: recall_at_1
value: 25.17625
- type: recall_at_10
value: 51.015999999999984
- type: recall_at_100
value: 73.60174999999998
- type: recall_at_1000
value: 89.849
- type: recall_at_3
value: 37.88399999999999
- type: recall_at_5
value: 43.24541666666666
- type: map_at_1
value: 24.537
- type: map_at_10
value: 31.081999999999997
- type: map_at_100
value: 32.042
- type: map_at_1000
value: 32.141
- type: map_at_3
value: 29.137
- type: map_at_5
value: 30.079
- type: mrr_at_1
value: 27.454
- type: mrr_at_10
value: 33.694
- type: mrr_at_100
value: 34.579
- type: mrr_at_1000
value: 34.649
- type: mrr_at_3
value: 32.004
- type: mrr_at_5
value: 32.794000000000004
- type: ndcg_at_1
value: 27.454
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.641
- type: ndcg_at_1000
value: 42.105
- type: ndcg_at_3
value: 31.276
- type: ndcg_at_5
value: 32.65
- type: precision_at_1
value: 27.454
- type: precision_at_10
value: 5.337
- type: precision_at_100
value: 0.8250000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 13.241
- type: precision_at_5
value: 8.895999999999999
- type: recall_at_1
value: 24.537
- type: recall_at_10
value: 44.324999999999996
- type: recall_at_100
value: 65.949
- type: recall_at_1000
value: 84.017
- type: recall_at_3
value: 33.857
- type: recall_at_5
value: 37.316
- type: map_at_1
value: 17.122
- type: map_at_10
value: 24.32
- type: map_at_100
value: 25.338
- type: map_at_1000
value: 25.462
- type: map_at_3
value: 22.064
- type: map_at_5
value: 23.322000000000003
- type: mrr_at_1
value: 20.647
- type: mrr_at_10
value: 27.858
- type: mrr_at_100
value: 28.743999999999996
- type: mrr_at_1000
value: 28.819
- type: mrr_at_3
value: 25.769
- type: mrr_at_5
value: 26.964
- type: ndcg_at_1
value: 20.647
- type: ndcg_at_10
value: 28.849999999999998
- type: ndcg_at_100
value: 33.849000000000004
- type: ndcg_at_1000
value: 36.802
- type: ndcg_at_3
value: 24.799
- type: ndcg_at_5
value: 26.682
- type: precision_at_1
value: 20.647
- type: precision_at_10
value: 5.2170000000000005
- type: precision_at_100
value: 0.906
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 11.769
- type: precision_at_5
value: 8.486
- type: recall_at_1
value: 17.122
- type: recall_at_10
value: 38.999
- type: recall_at_100
value: 61.467000000000006
- type: recall_at_1000
value: 82.716
- type: recall_at_3
value: 27.601
- type: recall_at_5
value: 32.471
- type: map_at_1
value: 24.396
- type: map_at_10
value: 33.415
- type: map_at_100
value: 34.521
- type: map_at_1000
value: 34.631
- type: map_at_3
value: 30.703999999999997
- type: map_at_5
value: 32.166
- type: mrr_at_1
value: 28.825
- type: mrr_at_10
value: 37.397000000000006
- type: mrr_at_100
value: 38.286
- type: mrr_at_1000
value: 38.346000000000004
- type: mrr_at_3
value: 35.028
- type: mrr_at_5
value: 36.32
- type: ndcg_at_1
value: 28.825
- type: ndcg_at_10
value: 38.656
- type: ndcg_at_100
value: 43.856
- type: ndcg_at_1000
value: 46.31
- type: ndcg_at_3
value: 33.793
- type: ndcg_at_5
value: 35.909
- type: precision_at_1
value: 28.825
- type: precision_at_10
value: 6.567
- type: precision_at_100
value: 1.0330000000000001
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 15.516
- type: precision_at_5
value: 10.914
- type: recall_at_1
value: 24.396
- type: recall_at_10
value: 50.747
- type: recall_at_100
value: 73.477
- type: recall_at_1000
value: 90.801
- type: recall_at_3
value: 37.1
- type: recall_at_5
value: 42.589
- type: map_at_1
value: 25.072
- type: map_at_10
value: 34.307
- type: map_at_100
value: 35.725
- type: map_at_1000
value: 35.943999999999996
- type: map_at_3
value: 30.906
- type: map_at_5
value: 32.818000000000005
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.673
- type: mrr_at_100
value: 39.459
- type: mrr_at_1000
value: 39.527
- type: mrr_at_3
value: 35.771
- type: mrr_at_5
value: 37.332
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 40.548
- type: ndcg_at_100
value: 45.678999999999995
- type: ndcg_at_1000
value: 48.488
- type: ndcg_at_3
value: 34.887
- type: ndcg_at_5
value: 37.543
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.688000000000001
- type: precision_at_100
value: 1.482
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 16.206
- type: precision_at_5
value: 12.016
- type: recall_at_1
value: 25.072
- type: recall_at_10
value: 53.478
- type: recall_at_100
value: 76.07300000000001
- type: recall_at_1000
value: 93.884
- type: recall_at_3
value: 37.583
- type: recall_at_5
value: 44.464
- type: map_at_1
value: 20.712
- type: map_at_10
value: 27.467999999999996
- type: map_at_100
value: 28.502
- type: map_at_1000
value: 28.610000000000003
- type: map_at_3
value: 24.887999999999998
- type: map_at_5
value: 26.273999999999997
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 29.553
- type: mrr_at_100
value: 30.485
- type: mrr_at_1000
value: 30.56
- type: mrr_at_3
value: 27.078999999999997
- type: mrr_at_5
value: 28.401
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 32.023
- type: ndcg_at_100
value: 37.158
- type: ndcg_at_1000
value: 39.823
- type: ndcg_at_3
value: 26.951999999999998
- type: ndcg_at_5
value: 29.281000000000002
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 5.213
- type: precision_at_100
value: 0.832
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.459999999999999
- type: precision_at_5
value: 8.244
- type: recall_at_1
value: 20.712
- type: recall_at_10
value: 44.057
- type: recall_at_100
value: 67.944
- type: recall_at_1000
value: 87.925
- type: recall_at_3
value: 30.305
- type: recall_at_5
value: 36.071999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.181999999999999
- type: map_at_10
value: 16.66
- type: map_at_100
value: 18.273
- type: map_at_1000
value: 18.45
- type: map_at_3
value: 14.141
- type: map_at_5
value: 15.455
- type: mrr_at_1
value: 22.15
- type: mrr_at_10
value: 32.062000000000005
- type: mrr_at_100
value: 33.116
- type: mrr_at_1000
value: 33.168
- type: mrr_at_3
value: 28.827
- type: mrr_at_5
value: 30.892999999999997
- type: ndcg_at_1
value: 22.15
- type: ndcg_at_10
value: 23.532
- type: ndcg_at_100
value: 30.358
- type: ndcg_at_1000
value: 33.783
- type: ndcg_at_3
value: 19.222
- type: ndcg_at_5
value: 20.919999999999998
- type: precision_at_1
value: 22.15
- type: precision_at_10
value: 7.185999999999999
- type: precision_at_100
value: 1.433
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 13.941
- type: precision_at_5
value: 10.906
- type: recall_at_1
value: 10.181999999999999
- type: recall_at_10
value: 28.104000000000003
- type: recall_at_100
value: 51.998999999999995
- type: recall_at_1000
value: 71.311
- type: recall_at_3
value: 17.698
- type: recall_at_5
value: 22.262999999999998
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.669
- type: map_at_10
value: 15.552
- type: map_at_100
value: 21.865000000000002
- type: map_at_1000
value: 23.268
- type: map_at_3
value: 11.309
- type: map_at_5
value: 13.084000000000001
- type: mrr_at_1
value: 55.50000000000001
- type: mrr_at_10
value: 66.46600000000001
- type: mrr_at_100
value: 66.944
- type: mrr_at_1000
value: 66.956
- type: mrr_at_3
value: 64.542
- type: mrr_at_5
value: 65.717
- type: ndcg_at_1
value: 44.75
- type: ndcg_at_10
value: 35.049
- type: ndcg_at_100
value: 39.073
- type: ndcg_at_1000
value: 46.208
- type: ndcg_at_3
value: 39.525
- type: ndcg_at_5
value: 37.156
- type: precision_at_1
value: 55.50000000000001
- type: precision_at_10
value: 27.800000000000004
- type: precision_at_100
value: 9.013
- type: precision_at_1000
value: 1.8800000000000001
- type: precision_at_3
value: 42.667
- type: precision_at_5
value: 36.0
- type: recall_at_1
value: 6.669
- type: recall_at_10
value: 21.811
- type: recall_at_100
value: 45.112
- type: recall_at_1000
value: 67.806
- type: recall_at_3
value: 13.373
- type: recall_at_5
value: 16.615
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.769999999999996
- type: f1
value: 42.91448356376592
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.013
- type: map_at_10
value: 66.239
- type: map_at_100
value: 66.62599999999999
- type: map_at_1000
value: 66.644
- type: map_at_3
value: 63.965
- type: map_at_5
value: 65.45400000000001
- type: mrr_at_1
value: 58.221000000000004
- type: mrr_at_10
value: 70.43700000000001
- type: mrr_at_100
value: 70.744
- type: mrr_at_1000
value: 70.75099999999999
- type: mrr_at_3
value: 68.284
- type: mrr_at_5
value: 69.721
- type: ndcg_at_1
value: 58.221000000000004
- type: ndcg_at_10
value: 72.327
- type: ndcg_at_100
value: 73.953
- type: ndcg_at_1000
value: 74.312
- type: ndcg_at_3
value: 68.062
- type: ndcg_at_5
value: 70.56400000000001
- type: precision_at_1
value: 58.221000000000004
- type: precision_at_10
value: 9.521
- type: precision_at_100
value: 1.045
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 27.348
- type: precision_at_5
value: 17.794999999999998
- type: recall_at_1
value: 54.013
- type: recall_at_10
value: 86.957
- type: recall_at_100
value: 93.911
- type: recall_at_1000
value: 96.38
- type: recall_at_3
value: 75.555
- type: recall_at_5
value: 81.671
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.254
- type: map_at_10
value: 33.723
- type: map_at_100
value: 35.574
- type: map_at_1000
value: 35.730000000000004
- type: map_at_3
value: 29.473
- type: map_at_5
value: 31.543
- type: mrr_at_1
value: 41.358
- type: mrr_at_10
value: 49.498
- type: mrr_at_100
value: 50.275999999999996
- type: mrr_at_1000
value: 50.308
- type: mrr_at_3
value: 47.016000000000005
- type: mrr_at_5
value: 48.336
- type: ndcg_at_1
value: 41.358
- type: ndcg_at_10
value: 41.579
- type: ndcg_at_100
value: 48.455
- type: ndcg_at_1000
value: 51.165000000000006
- type: ndcg_at_3
value: 37.681
- type: ndcg_at_5
value: 38.49
- type: precision_at_1
value: 41.358
- type: precision_at_10
value: 11.543000000000001
- type: precision_at_100
value: 1.87
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 24.743000000000002
- type: precision_at_5
value: 17.994
- type: recall_at_1
value: 21.254
- type: recall_at_10
value: 48.698
- type: recall_at_100
value: 74.588
- type: recall_at_1000
value: 91.00200000000001
- type: recall_at_3
value: 33.939
- type: recall_at_5
value: 39.367000000000004
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.922
- type: map_at_10
value: 52.32599999999999
- type: map_at_100
value: 53.18000000000001
- type: map_at_1000
value: 53.245
- type: map_at_3
value: 49.294
- type: map_at_5
value: 51.202999999999996
- type: mrr_at_1
value: 71.843
- type: mrr_at_10
value: 78.24600000000001
- type: mrr_at_100
value: 78.515
- type: mrr_at_1000
value: 78.527
- type: mrr_at_3
value: 77.17500000000001
- type: mrr_at_5
value: 77.852
- type: ndcg_at_1
value: 71.843
- type: ndcg_at_10
value: 61.379
- type: ndcg_at_100
value: 64.535
- type: ndcg_at_1000
value: 65.888
- type: ndcg_at_3
value: 56.958
- type: ndcg_at_5
value: 59.434
- type: precision_at_1
value: 71.843
- type: precision_at_10
value: 12.686
- type: precision_at_100
value: 1.517
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 35.778
- type: precision_at_5
value: 23.422
- type: recall_at_1
value: 35.922
- type: recall_at_10
value: 63.43
- type: recall_at_100
value: 75.868
- type: recall_at_1000
value: 84.88900000000001
- type: recall_at_3
value: 53.666000000000004
- type: recall_at_5
value: 58.555
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 79.4408
- type: ap
value: 73.52820871620366
- type: f1
value: 79.36240238685001
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.826999999999998
- type: map_at_10
value: 34.04
- type: map_at_100
value: 35.226
- type: map_at_1000
value: 35.275
- type: map_at_3
value: 30.165999999999997
- type: map_at_5
value: 32.318000000000005
- type: mrr_at_1
value: 22.464000000000002
- type: mrr_at_10
value: 34.631
- type: mrr_at_100
value: 35.752
- type: mrr_at_1000
value: 35.795
- type: mrr_at_3
value: 30.798
- type: mrr_at_5
value: 32.946999999999996
- type: ndcg_at_1
value: 22.464000000000002
- type: ndcg_at_10
value: 40.919
- type: ndcg_at_100
value: 46.632
- type: ndcg_at_1000
value: 47.833
- type: ndcg_at_3
value: 32.992
- type: ndcg_at_5
value: 36.834
- type: precision_at_1
value: 22.464000000000002
- type: precision_at_10
value: 6.494
- type: precision_at_100
value: 0.9369999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.021
- type: precision_at_5
value: 10.347000000000001
- type: recall_at_1
value: 21.826999999999998
- type: recall_at_10
value: 62.132
- type: recall_at_100
value: 88.55199999999999
- type: recall_at_1000
value: 97.707
- type: recall_at_3
value: 40.541
- type: recall_at_5
value: 49.739
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.68399452804377
- type: f1
value: 95.25490609832268
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 83.15321477428182
- type: f1
value: 60.35476439087966
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.92669804976462
- type: f1
value: 69.22815107207565
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4855413584398
- type: f1
value: 72.92107516103387
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.412679360205544
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.09211869875204
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.540919056982545
- type: mrr
value: 31.529904607063536
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.745
- type: map_at_10
value: 12.013
- type: map_at_100
value: 15.040000000000001
- type: map_at_1000
value: 16.427
- type: map_at_3
value: 8.841000000000001
- type: map_at_5
value: 10.289
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 53.483999999999995
- type: mrr_at_100
value: 54.20700000000001
- type: mrr_at_1000
value: 54.252
- type: mrr_at_3
value: 51.29
- type: mrr_at_5
value: 52.73
- type: ndcg_at_1
value: 43.808
- type: ndcg_at_10
value: 32.445
- type: ndcg_at_100
value: 30.031000000000002
- type: ndcg_at_1000
value: 39.007
- type: ndcg_at_3
value: 37.204
- type: ndcg_at_5
value: 35.07
- type: precision_at_1
value: 45.201
- type: precision_at_10
value: 23.684
- type: precision_at_100
value: 7.600999999999999
- type: precision_at_1000
value: 2.043
- type: precision_at_3
value: 33.953
- type: precision_at_5
value: 29.412
- type: recall_at_1
value: 5.745
- type: recall_at_10
value: 16.168
- type: recall_at_100
value: 30.875999999999998
- type: recall_at_1000
value: 62.686
- type: recall_at_3
value: 9.75
- type: recall_at_5
value: 12.413
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.828
- type: map_at_10
value: 53.239000000000004
- type: map_at_100
value: 54.035999999999994
- type: map_at_1000
value: 54.067
- type: map_at_3
value: 49.289
- type: map_at_5
value: 51.784
- type: mrr_at_1
value: 42.497
- type: mrr_at_10
value: 55.916999999999994
- type: mrr_at_100
value: 56.495
- type: mrr_at_1000
value: 56.516999999999996
- type: mrr_at_3
value: 52.800000000000004
- type: mrr_at_5
value: 54.722
- type: ndcg_at_1
value: 42.468
- type: ndcg_at_10
value: 60.437
- type: ndcg_at_100
value: 63.731
- type: ndcg_at_1000
value: 64.41799999999999
- type: ndcg_at_3
value: 53.230999999999995
- type: ndcg_at_5
value: 57.26
- type: precision_at_1
value: 42.468
- type: precision_at_10
value: 9.47
- type: precision_at_100
value: 1.1360000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.724999999999998
- type: precision_at_5
value: 16.593
- type: recall_at_1
value: 37.828
- type: recall_at_10
value: 79.538
- type: recall_at_100
value: 93.646
- type: recall_at_1000
value: 98.72999999999999
- type: recall_at_3
value: 61.134
- type: recall_at_5
value: 70.377
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.548
- type: map_at_10
value: 84.466
- type: map_at_100
value: 85.10600000000001
- type: map_at_1000
value: 85.123
- type: map_at_3
value: 81.57600000000001
- type: map_at_5
value: 83.399
- type: mrr_at_1
value: 81.24
- type: mrr_at_10
value: 87.457
- type: mrr_at_100
value: 87.574
- type: mrr_at_1000
value: 87.575
- type: mrr_at_3
value: 86.507
- type: mrr_at_5
value: 87.205
- type: ndcg_at_1
value: 81.25
- type: ndcg_at_10
value: 88.203
- type: ndcg_at_100
value: 89.457
- type: ndcg_at_1000
value: 89.563
- type: ndcg_at_3
value: 85.465
- type: ndcg_at_5
value: 87.007
- type: precision_at_1
value: 81.25
- type: precision_at_10
value: 13.373
- type: precision_at_100
value: 1.5270000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.417
- type: precision_at_5
value: 24.556
- type: recall_at_1
value: 70.548
- type: recall_at_10
value: 95.208
- type: recall_at_100
value: 99.514
- type: recall_at_1000
value: 99.988
- type: recall_at_3
value: 87.214
- type: recall_at_5
value: 91.696
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 53.04822095496839
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.30778476474675
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.692
- type: map_at_10
value: 11.766
- type: map_at_100
value: 13.904
- type: map_at_1000
value: 14.216999999999999
- type: map_at_3
value: 8.245
- type: map_at_5
value: 9.92
- type: mrr_at_1
value: 23.0
- type: mrr_at_10
value: 33.78
- type: mrr_at_100
value: 34.922
- type: mrr_at_1000
value: 34.973
- type: mrr_at_3
value: 30.2
- type: mrr_at_5
value: 32.565
- type: ndcg_at_1
value: 23.0
- type: ndcg_at_10
value: 19.863
- type: ndcg_at_100
value: 28.141
- type: ndcg_at_1000
value: 33.549
- type: ndcg_at_3
value: 18.434
- type: ndcg_at_5
value: 16.384
- type: precision_at_1
value: 23.0
- type: precision_at_10
value: 10.39
- type: precision_at_100
value: 2.235
- type: precision_at_1000
value: 0.35300000000000004
- type: precision_at_3
value: 17.133000000000003
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 4.692
- type: recall_at_10
value: 21.025
- type: recall_at_100
value: 45.324999999999996
- type: recall_at_1000
value: 71.675
- type: recall_at_3
value: 10.440000000000001
- type: recall_at_5
value: 14.64
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.96178184892842
- type: cos_sim_spearman
value: 79.6487740813199
- type: euclidean_pearson
value: 82.06661161625023
- type: euclidean_spearman
value: 79.64876769031183
- type: manhattan_pearson
value: 82.07061164575131
- type: manhattan_spearman
value: 79.65197039464537
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.15305604100027
- type: cos_sim_spearman
value: 74.27447427941591
- type: euclidean_pearson
value: 80.52737337565307
- type: euclidean_spearman
value: 74.27416077132192
- type: manhattan_pearson
value: 80.53728571140387
- type: manhattan_spearman
value: 74.28853605753457
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.44386080639279
- type: cos_sim_spearman
value: 84.17947648159536
- type: euclidean_pearson
value: 83.34145388129387
- type: euclidean_spearman
value: 84.17947648159536
- type: manhattan_pearson
value: 83.30699061927966
- type: manhattan_spearman
value: 84.18125737380451
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.57392220985612
- type: cos_sim_spearman
value: 78.80745014464101
- type: euclidean_pearson
value: 80.01660371487199
- type: euclidean_spearman
value: 78.80741240102256
- type: manhattan_pearson
value: 79.96810779507953
- type: manhattan_spearman
value: 78.75600400119448
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.85421063026625
- type: cos_sim_spearman
value: 87.55320285299192
- type: euclidean_pearson
value: 86.69750143323517
- type: euclidean_spearman
value: 87.55320284326378
- type: manhattan_pearson
value: 86.63379169960379
- type: manhattan_spearman
value: 87.4815029877984
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.31314130411842
- type: cos_sim_spearman
value: 85.3489588181433
- type: euclidean_pearson
value: 84.13240933463535
- type: euclidean_spearman
value: 85.34902871403281
- type: manhattan_pearson
value: 84.01183086503559
- type: manhattan_spearman
value: 85.19316703166102
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.09979781689536
- type: cos_sim_spearman
value: 88.87813323759015
- type: euclidean_pearson
value: 88.65413031123792
- type: euclidean_spearman
value: 88.87813323759015
- type: manhattan_pearson
value: 88.61818758256024
- type: manhattan_spearman
value: 88.81044100494604
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 62.30693258111531
- type: cos_sim_spearman
value: 62.195516523251946
- type: euclidean_pearson
value: 62.951283701049476
- type: euclidean_spearman
value: 62.195516523251946
- type: manhattan_pearson
value: 63.068322281439535
- type: manhattan_spearman
value: 62.10621171028406
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.27092833763909
- type: cos_sim_spearman
value: 84.84429717949759
- type: euclidean_pearson
value: 84.8516966060792
- type: euclidean_spearman
value: 84.84429717949759
- type: manhattan_pearson
value: 84.82203139242881
- type: manhattan_spearman
value: 84.8358503952945
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 83.10290863981409
- type: mrr
value: 95.31168450286097
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.161
- type: map_at_10
value: 62.138000000000005
- type: map_at_100
value: 62.769
- type: map_at_1000
value: 62.812
- type: map_at_3
value: 59.111000000000004
- type: map_at_5
value: 60.995999999999995
- type: mrr_at_1
value: 55.333
- type: mrr_at_10
value: 63.504000000000005
- type: mrr_at_100
value: 64.036
- type: mrr_at_1000
value: 64.08
- type: mrr_at_3
value: 61.278
- type: mrr_at_5
value: 62.778
- type: ndcg_at_1
value: 55.333
- type: ndcg_at_10
value: 66.678
- type: ndcg_at_100
value: 69.415
- type: ndcg_at_1000
value: 70.453
- type: ndcg_at_3
value: 61.755
- type: ndcg_at_5
value: 64.546
- type: precision_at_1
value: 55.333
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.333000000000002
- type: recall_at_1
value: 52.161
- type: recall_at_10
value: 79.156
- type: recall_at_100
value: 91.333
- type: recall_at_1000
value: 99.333
- type: recall_at_3
value: 66.43299999999999
- type: recall_at_5
value: 73.272
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81287128712871
- type: cos_sim_ap
value: 95.30034785910676
- type: cos_sim_f1
value: 90.28629856850716
- type: cos_sim_precision
value: 92.36401673640168
- type: cos_sim_recall
value: 88.3
- type: dot_accuracy
value: 99.81287128712871
- type: dot_ap
value: 95.30034785910676
- type: dot_f1
value: 90.28629856850716
- type: dot_precision
value: 92.36401673640168
- type: dot_recall
value: 88.3
- type: euclidean_accuracy
value: 99.81287128712871
- type: euclidean_ap
value: 95.30034785910676
- type: euclidean_f1
value: 90.28629856850716
- type: euclidean_precision
value: 92.36401673640168
- type: euclidean_recall
value: 88.3
- type: manhattan_accuracy
value: 99.80990099009901
- type: manhattan_ap
value: 95.26880751950654
- type: manhattan_f1
value: 90.22177419354838
- type: manhattan_precision
value: 90.95528455284553
- type: manhattan_recall
value: 89.5
- type: max_accuracy
value: 99.81287128712871
- type: max_ap
value: 95.30034785910676
- type: max_f1
value: 90.28629856850716
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 58.518662504351184
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.96168178378587
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.04862593471896
- type: mrr
value: 52.97238402936932
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.092545236479946
- type: cos_sim_spearman
value: 31.599851000175498
- type: dot_pearson
value: 30.092542723901676
- type: dot_spearman
value: 31.599851000175498
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.189
- type: map_at_10
value: 1.662
- type: map_at_100
value: 9.384
- type: map_at_1000
value: 22.669
- type: map_at_3
value: 0.5559999999999999
- type: map_at_5
value: 0.9039999999999999
- type: mrr_at_1
value: 68.0
- type: mrr_at_10
value: 81.01899999999999
- type: mrr_at_100
value: 81.01899999999999
- type: mrr_at_1000
value: 81.01899999999999
- type: mrr_at_3
value: 79.333
- type: mrr_at_5
value: 80.733
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 65.913
- type: ndcg_at_100
value: 51.895
- type: ndcg_at_1000
value: 46.967
- type: ndcg_at_3
value: 65.49199999999999
- type: ndcg_at_5
value: 66.69699999999999
- type: precision_at_1
value: 68.0
- type: precision_at_10
value: 71.6
- type: precision_at_100
value: 53.66
- type: precision_at_1000
value: 21.124000000000002
- type: precision_at_3
value: 72.667
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.189
- type: recall_at_10
value: 1.913
- type: recall_at_100
value: 12.601999999999999
- type: recall_at_1000
value: 44.296
- type: recall_at_3
value: 0.605
- type: recall_at_5
value: 1.018
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.701
- type: map_at_10
value: 10.445
- type: map_at_100
value: 17.324
- type: map_at_1000
value: 19.161
- type: map_at_3
value: 5.497
- type: map_at_5
value: 7.278
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 45.534
- type: mrr_at_100
value: 45.792
- type: mrr_at_1000
value: 45.806999999999995
- type: mrr_at_3
value: 37.755
- type: mrr_at_5
value: 43.469
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 26.235000000000003
- type: ndcg_at_100
value: 39.17
- type: ndcg_at_1000
value: 51.038
- type: ndcg_at_3
value: 23.625
- type: ndcg_at_5
value: 24.338
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 24.285999999999998
- type: precision_at_100
value: 8.224
- type: precision_at_1000
value: 1.6179999999999999
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.701
- type: recall_at_10
value: 17.997
- type: recall_at_100
value: 51.766999999999996
- type: recall_at_1000
value: 87.863
- type: recall_at_3
value: 6.295000000000001
- type: recall_at_5
value: 9.993
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 73.3474
- type: ap
value: 15.393431414459924
- type: f1
value: 56.466681887882416
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.062818336163
- type: f1
value: 62.11230840463252
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 42.464892820845115
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.15962329379508
- type: cos_sim_ap
value: 74.73674057919256
- type: cos_sim_f1
value: 68.81245642574947
- type: cos_sim_precision
value: 61.48255813953488
- type: cos_sim_recall
value: 78.12664907651715
- type: dot_accuracy
value: 86.15962329379508
- type: dot_ap
value: 74.7367634988281
- type: dot_f1
value: 68.81245642574947
- type: dot_precision
value: 61.48255813953488
- type: dot_recall
value: 78.12664907651715
- type: euclidean_accuracy
value: 86.15962329379508
- type: euclidean_ap
value: 74.7367761466634
- type: euclidean_f1
value: 68.81245642574947
- type: euclidean_precision
value: 61.48255813953488
- type: euclidean_recall
value: 78.12664907651715
- type: manhattan_accuracy
value: 86.21326816474935
- type: manhattan_ap
value: 74.64416473733951
- type: manhattan_f1
value: 68.80924855491331
- type: manhattan_precision
value: 61.23456790123457
- type: manhattan_recall
value: 78.52242744063325
- type: max_accuracy
value: 86.21326816474935
- type: max_ap
value: 74.7367761466634
- type: max_f1
value: 68.81245642574947
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.97620988085536
- type: cos_sim_ap
value: 86.08680845745758
- type: cos_sim_f1
value: 78.02793637114438
- type: cos_sim_precision
value: 73.11082699683736
- type: cos_sim_recall
value: 83.65414228518632
- type: dot_accuracy
value: 88.97620988085536
- type: dot_ap
value: 86.08681149437946
- type: dot_f1
value: 78.02793637114438
- type: dot_precision
value: 73.11082699683736
- type: dot_recall
value: 83.65414228518632
- type: euclidean_accuracy
value: 88.97620988085536
- type: euclidean_ap
value: 86.08681215460771
- type: euclidean_f1
value: 78.02793637114438
- type: euclidean_precision
value: 73.11082699683736
- type: euclidean_recall
value: 83.65414228518632
- type: manhattan_accuracy
value: 88.88888888888889
- type: manhattan_ap
value: 86.02916327562438
- type: manhattan_f1
value: 78.02063045516843
- type: manhattan_precision
value: 73.38851947346994
- type: manhattan_recall
value: 83.2768709578072
- type: max_accuracy
value: 88.97620988085536
- type: max_ap
value: 86.08681215460771
- type: max_f1
value: 78.02793637114438
---
<!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
## Quick Start
The easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
## Intended Usage & Model Info
`jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset.
The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference.
Additionally, we provide the following embedding models:
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters **(you are here)**.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): Spanish-English Bilingual embeddings.
## Data & Parameters
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
## Usage
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>
### Why mean pooling?
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.
However, if you would like to do it without using the default `encode` function:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['How is the weather today?', 'What is the current weather like today?']
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
You can use Jina Embedding models directly from transformers package.
```python
!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))
```
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(
['Very long ... document'],
max_length=2048
)
```
Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"jinaai/jina-embeddings-v2-base-en", # switch to en/zh for English or Chinese
trust_remote_code=True
)
# control your input sequence length up to 8192
model.max_seq_length = 1024
embeddings = model.encode([
'How is the weather today?',
'What is the current weather like today?'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
## Alternatives to Using Transformers (or SentencTransformers) Package
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
## Use Jina Embeddings for RAG
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
## Plans
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
2. Multimodal embedding models enable Multimodal RAG applications.
3. High-performt rerankers.
## Trouble Shooting
**Loading of Model Code failed**
If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
```bash
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
```
**User is not logged into Huggingface**
The model is only availabe under [gated access](https://huggingface.co/docs/hub/models-gated).
This means you need to be logged into huggingface load load it.
If you receive the following error, you need to provide an access token, either by using the huggingface-cli or providing the token via an environment variable as described above:
```bash
OSError: jinaai/jina-embeddings-v2-base-en is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'
If this is a private repository, make sure to pass a token having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and pass `use_auth_token=True`.
```
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
```
@misc{günther2023jina,
title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents},
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
year={2023},
eprint={2310.19923},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-large-zh-v1.5 | BAAI | feature-extraction | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"zh",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T05:22:11 | 2024-04-02T14:00:04 | 210,152 | 490 | ---
language:
- zh
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. | [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
intfloat/e5-small-v2 | intfloat | sentence-similarity | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"en",
"arxiv:2212.03533",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-05-19T06:45:35 | 2025-02-17T03:24:44 | 209,296 | 85 | ---
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: e5-small-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.59701492537313
- type: ap
value: 41.67064885731708
- type: f1
value: 71.86465946398573
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.265875
- type: ap
value: 87.67633085349644
- type: f1
value: 91.24297521425744
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 45.882000000000005
- type: f1
value: 45.08058870381236
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.697
- type: map_at_10
value: 33.975
- type: map_at_100
value: 35.223
- type: map_at_1000
value: 35.260000000000005
- type: map_at_3
value: 29.776999999999997
- type: map_at_5
value: 32.035000000000004
- type: mrr_at_1
value: 20.982
- type: mrr_at_10
value: 34.094
- type: mrr_at_100
value: 35.343
- type: mrr_at_1000
value: 35.38
- type: mrr_at_3
value: 29.884
- type: mrr_at_5
value: 32.141999999999996
- type: ndcg_at_1
value: 20.697
- type: ndcg_at_10
value: 41.668
- type: ndcg_at_100
value: 47.397
- type: ndcg_at_1000
value: 48.305
- type: ndcg_at_3
value: 32.928000000000004
- type: ndcg_at_5
value: 36.998999999999995
- type: precision_at_1
value: 20.697
- type: precision_at_10
value: 6.636
- type: precision_at_100
value: 0.924
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 14.035
- type: precision_at_5
value: 10.398
- type: recall_at_1
value: 20.697
- type: recall_at_10
value: 66.35799999999999
- type: recall_at_100
value: 92.39
- type: recall_at_1000
value: 99.36
- type: recall_at_3
value: 42.105
- type: recall_at_5
value: 51.991
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 42.1169517447068
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.79553720107097
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.10811337308168
- type: mrr
value: 71.56410763751482
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 78.46834918248696
- type: cos_sim_spearman
value: 79.4289182755206
- type: euclidean_pearson
value: 76.26662973727008
- type: euclidean_spearman
value: 78.11744260952536
- type: manhattan_pearson
value: 76.08175262609434
- type: manhattan_spearman
value: 78.29395265552289
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.63636363636364
- type: f1
value: 81.55779952376953
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.88541137137571
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.05205685274407
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.293999999999997
- type: map_at_10
value: 39.876
- type: map_at_100
value: 41.315000000000005
- type: map_at_1000
value: 41.451
- type: map_at_3
value: 37.194
- type: map_at_5
value: 38.728
- type: mrr_at_1
value: 37.053000000000004
- type: mrr_at_10
value: 45.281
- type: mrr_at_100
value: 46.188
- type: mrr_at_1000
value: 46.245999999999995
- type: mrr_at_3
value: 43.228
- type: mrr_at_5
value: 44.366
- type: ndcg_at_1
value: 37.053000000000004
- type: ndcg_at_10
value: 45.086
- type: ndcg_at_100
value: 50.756
- type: ndcg_at_1000
value: 53.123
- type: ndcg_at_3
value: 41.416
- type: ndcg_at_5
value: 43.098
- type: precision_at_1
value: 37.053000000000004
- type: precision_at_10
value: 8.34
- type: precision_at_100
value: 1.346
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 19.647000000000002
- type: precision_at_5
value: 13.877
- type: recall_at_1
value: 30.293999999999997
- type: recall_at_10
value: 54.309
- type: recall_at_100
value: 78.59
- type: recall_at_1000
value: 93.82300000000001
- type: recall_at_3
value: 43.168
- type: recall_at_5
value: 48.192
- type: map_at_1
value: 28.738000000000003
- type: map_at_10
value: 36.925999999999995
- type: map_at_100
value: 38.017
- type: map_at_1000
value: 38.144
- type: map_at_3
value: 34.446
- type: map_at_5
value: 35.704
- type: mrr_at_1
value: 35.478
- type: mrr_at_10
value: 42.786
- type: mrr_at_100
value: 43.458999999999996
- type: mrr_at_1000
value: 43.507
- type: mrr_at_3
value: 40.648
- type: mrr_at_5
value: 41.804
- type: ndcg_at_1
value: 35.478
- type: ndcg_at_10
value: 42.044
- type: ndcg_at_100
value: 46.249
- type: ndcg_at_1000
value: 48.44
- type: ndcg_at_3
value: 38.314
- type: ndcg_at_5
value: 39.798
- type: precision_at_1
value: 35.478
- type: precision_at_10
value: 7.764
- type: precision_at_100
value: 1.253
- type: precision_at_1000
value: 0.174
- type: precision_at_3
value: 18.047
- type: precision_at_5
value: 12.637
- type: recall_at_1
value: 28.738000000000003
- type: recall_at_10
value: 50.659
- type: recall_at_100
value: 68.76299999999999
- type: recall_at_1000
value: 82.811
- type: recall_at_3
value: 39.536
- type: recall_at_5
value: 43.763999999999996
- type: map_at_1
value: 38.565
- type: map_at_10
value: 50.168
- type: map_at_100
value: 51.11
- type: map_at_1000
value: 51.173
- type: map_at_3
value: 47.044000000000004
- type: map_at_5
value: 48.838
- type: mrr_at_1
value: 44.201
- type: mrr_at_10
value: 53.596999999999994
- type: mrr_at_100
value: 54.211
- type: mrr_at_1000
value: 54.247
- type: mrr_at_3
value: 51.202000000000005
- type: mrr_at_5
value: 52.608999999999995
- type: ndcg_at_1
value: 44.201
- type: ndcg_at_10
value: 55.694
- type: ndcg_at_100
value: 59.518
- type: ndcg_at_1000
value: 60.907
- type: ndcg_at_3
value: 50.395999999999994
- type: ndcg_at_5
value: 53.022999999999996
- type: precision_at_1
value: 44.201
- type: precision_at_10
value: 8.84
- type: precision_at_100
value: 1.162
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 22.153
- type: precision_at_5
value: 15.260000000000002
- type: recall_at_1
value: 38.565
- type: recall_at_10
value: 68.65
- type: recall_at_100
value: 85.37400000000001
- type: recall_at_1000
value: 95.37400000000001
- type: recall_at_3
value: 54.645999999999994
- type: recall_at_5
value: 60.958
- type: map_at_1
value: 23.945
- type: map_at_10
value: 30.641000000000002
- type: map_at_100
value: 31.599
- type: map_at_1000
value: 31.691000000000003
- type: map_at_3
value: 28.405
- type: map_at_5
value: 29.704000000000004
- type: mrr_at_1
value: 25.537
- type: mrr_at_10
value: 32.22
- type: mrr_at_100
value: 33.138
- type: mrr_at_1000
value: 33.214
- type: mrr_at_3
value: 30.151
- type: mrr_at_5
value: 31.298
- type: ndcg_at_1
value: 25.537
- type: ndcg_at_10
value: 34.638000000000005
- type: ndcg_at_100
value: 39.486
- type: ndcg_at_1000
value: 41.936
- type: ndcg_at_3
value: 30.333
- type: ndcg_at_5
value: 32.482
- type: precision_at_1
value: 25.537
- type: precision_at_10
value: 5.153
- type: precision_at_100
value: 0.7929999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 12.429
- type: precision_at_5
value: 8.723
- type: recall_at_1
value: 23.945
- type: recall_at_10
value: 45.412
- type: recall_at_100
value: 67.836
- type: recall_at_1000
value: 86.467
- type: recall_at_3
value: 34.031
- type: recall_at_5
value: 39.039
- type: map_at_1
value: 14.419
- type: map_at_10
value: 20.858999999999998
- type: map_at_100
value: 22.067999999999998
- type: map_at_1000
value: 22.192
- type: map_at_3
value: 18.673000000000002
- type: map_at_5
value: 19.968
- type: mrr_at_1
value: 17.785999999999998
- type: mrr_at_10
value: 24.878
- type: mrr_at_100
value: 26.021
- type: mrr_at_1000
value: 26.095000000000002
- type: mrr_at_3
value: 22.616
- type: mrr_at_5
value: 23.785
- type: ndcg_at_1
value: 17.785999999999998
- type: ndcg_at_10
value: 25.153
- type: ndcg_at_100
value: 31.05
- type: ndcg_at_1000
value: 34.052
- type: ndcg_at_3
value: 21.117
- type: ndcg_at_5
value: 23.048
- type: precision_at_1
value: 17.785999999999998
- type: precision_at_10
value: 4.590000000000001
- type: precision_at_100
value: 0.864
- type: precision_at_1000
value: 0.125
- type: precision_at_3
value: 9.908999999999999
- type: precision_at_5
value: 7.313
- type: recall_at_1
value: 14.419
- type: recall_at_10
value: 34.477999999999994
- type: recall_at_100
value: 60.02499999999999
- type: recall_at_1000
value: 81.646
- type: recall_at_3
value: 23.515
- type: recall_at_5
value: 28.266999999999996
- type: map_at_1
value: 26.268
- type: map_at_10
value: 35.114000000000004
- type: map_at_100
value: 36.212
- type: map_at_1000
value: 36.333
- type: map_at_3
value: 32.436
- type: map_at_5
value: 33.992
- type: mrr_at_1
value: 31.761
- type: mrr_at_10
value: 40.355999999999995
- type: mrr_at_100
value: 41.125
- type: mrr_at_1000
value: 41.186
- type: mrr_at_3
value: 37.937
- type: mrr_at_5
value: 39.463
- type: ndcg_at_1
value: 31.761
- type: ndcg_at_10
value: 40.422000000000004
- type: ndcg_at_100
value: 45.458999999999996
- type: ndcg_at_1000
value: 47.951
- type: ndcg_at_3
value: 35.972
- type: ndcg_at_5
value: 38.272
- type: precision_at_1
value: 31.761
- type: precision_at_10
value: 7.103
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 16.779
- type: precision_at_5
value: 11.877
- type: recall_at_1
value: 26.268
- type: recall_at_10
value: 51.053000000000004
- type: recall_at_100
value: 72.702
- type: recall_at_1000
value: 89.521
- type: recall_at_3
value: 38.619
- type: recall_at_5
value: 44.671
- type: map_at_1
value: 25.230999999999998
- type: map_at_10
value: 34.227000000000004
- type: map_at_100
value: 35.370000000000005
- type: map_at_1000
value: 35.488
- type: map_at_3
value: 31.496000000000002
- type: map_at_5
value: 33.034
- type: mrr_at_1
value: 30.822
- type: mrr_at_10
value: 39.045
- type: mrr_at_100
value: 39.809
- type: mrr_at_1000
value: 39.873
- type: mrr_at_3
value: 36.663000000000004
- type: mrr_at_5
value: 37.964
- type: ndcg_at_1
value: 30.822
- type: ndcg_at_10
value: 39.472
- type: ndcg_at_100
value: 44.574999999999996
- type: ndcg_at_1000
value: 47.162
- type: ndcg_at_3
value: 34.929
- type: ndcg_at_5
value: 37.002
- type: precision_at_1
value: 30.822
- type: precision_at_10
value: 7.055
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 16.591
- type: precision_at_5
value: 11.667
- type: recall_at_1
value: 25.230999999999998
- type: recall_at_10
value: 50.42100000000001
- type: recall_at_100
value: 72.685
- type: recall_at_1000
value: 90.469
- type: recall_at_3
value: 37.503
- type: recall_at_5
value: 43.123
- type: map_at_1
value: 24.604166666666664
- type: map_at_10
value: 32.427166666666665
- type: map_at_100
value: 33.51474999999999
- type: map_at_1000
value: 33.6345
- type: map_at_3
value: 30.02366666666667
- type: map_at_5
value: 31.382333333333328
- type: mrr_at_1
value: 29.001166666666666
- type: mrr_at_10
value: 36.3315
- type: mrr_at_100
value: 37.16683333333333
- type: mrr_at_1000
value: 37.23341666666668
- type: mrr_at_3
value: 34.19916666666667
- type: mrr_at_5
value: 35.40458333333334
- type: ndcg_at_1
value: 29.001166666666666
- type: ndcg_at_10
value: 37.06883333333334
- type: ndcg_at_100
value: 41.95816666666666
- type: ndcg_at_1000
value: 44.501583333333336
- type: ndcg_at_3
value: 32.973499999999994
- type: ndcg_at_5
value: 34.90833333333334
- type: precision_at_1
value: 29.001166666666666
- type: precision_at_10
value: 6.336
- type: precision_at_100
value: 1.0282499999999999
- type: precision_at_1000
value: 0.14391666666666664
- type: precision_at_3
value: 14.932499999999996
- type: precision_at_5
value: 10.50825
- type: recall_at_1
value: 24.604166666666664
- type: recall_at_10
value: 46.9525
- type: recall_at_100
value: 68.67816666666667
- type: recall_at_1000
value: 86.59783333333334
- type: recall_at_3
value: 35.49783333333333
- type: recall_at_5
value: 40.52525000000001
- type: map_at_1
value: 23.559
- type: map_at_10
value: 29.023
- type: map_at_100
value: 29.818
- type: map_at_1000
value: 29.909000000000002
- type: map_at_3
value: 27.037
- type: map_at_5
value: 28.225
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 31.962000000000003
- type: mrr_at_100
value: 32.726
- type: mrr_at_1000
value: 32.800000000000004
- type: mrr_at_3
value: 30.266
- type: mrr_at_5
value: 31.208999999999996
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 32.53
- type: ndcg_at_100
value: 36.758
- type: ndcg_at_1000
value: 39.362
- type: ndcg_at_3
value: 28.985
- type: ndcg_at_5
value: 30.757
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 4.968999999999999
- type: precision_at_100
value: 0.759
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 12.219
- type: precision_at_5
value: 8.527999999999999
- type: recall_at_1
value: 23.559
- type: recall_at_10
value: 40.585
- type: recall_at_100
value: 60.306000000000004
- type: recall_at_1000
value: 80.11
- type: recall_at_3
value: 30.794
- type: recall_at_5
value: 35.186
- type: map_at_1
value: 16.384999999999998
- type: map_at_10
value: 22.142
- type: map_at_100
value: 23.057
- type: map_at_1000
value: 23.177
- type: map_at_3
value: 20.29
- type: map_at_5
value: 21.332
- type: mrr_at_1
value: 19.89
- type: mrr_at_10
value: 25.771
- type: mrr_at_100
value: 26.599
- type: mrr_at_1000
value: 26.680999999999997
- type: mrr_at_3
value: 23.962
- type: mrr_at_5
value: 24.934
- type: ndcg_at_1
value: 19.89
- type: ndcg_at_10
value: 25.97
- type: ndcg_at_100
value: 30.605
- type: ndcg_at_1000
value: 33.619
- type: ndcg_at_3
value: 22.704
- type: ndcg_at_5
value: 24.199
- type: precision_at_1
value: 19.89
- type: precision_at_10
value: 4.553
- type: precision_at_100
value: 0.8049999999999999
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 10.541
- type: precision_at_5
value: 7.46
- type: recall_at_1
value: 16.384999999999998
- type: recall_at_10
value: 34.001
- type: recall_at_100
value: 55.17100000000001
- type: recall_at_1000
value: 77.125
- type: recall_at_3
value: 24.618000000000002
- type: recall_at_5
value: 28.695999999999998
- type: map_at_1
value: 23.726
- type: map_at_10
value: 31.227
- type: map_at_100
value: 32.311
- type: map_at_1000
value: 32.419
- type: map_at_3
value: 28.765
- type: map_at_5
value: 30.229
- type: mrr_at_1
value: 27.705000000000002
- type: mrr_at_10
value: 35.085
- type: mrr_at_100
value: 35.931000000000004
- type: mrr_at_1000
value: 36
- type: mrr_at_3
value: 32.603
- type: mrr_at_5
value: 34.117999999999995
- type: ndcg_at_1
value: 27.705000000000002
- type: ndcg_at_10
value: 35.968
- type: ndcg_at_100
value: 41.197
- type: ndcg_at_1000
value: 43.76
- type: ndcg_at_3
value: 31.304
- type: ndcg_at_5
value: 33.661
- type: precision_at_1
value: 27.705000000000002
- type: precision_at_10
value: 5.942
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 13.868
- type: precision_at_5
value: 9.944
- type: recall_at_1
value: 23.726
- type: recall_at_10
value: 46.786
- type: recall_at_100
value: 70.072
- type: recall_at_1000
value: 88.2
- type: recall_at_3
value: 33.981
- type: recall_at_5
value: 39.893
- type: map_at_1
value: 23.344
- type: map_at_10
value: 31.636999999999997
- type: map_at_100
value: 33.065
- type: map_at_1000
value: 33.300000000000004
- type: map_at_3
value: 29.351
- type: map_at_5
value: 30.432
- type: mrr_at_1
value: 27.866000000000003
- type: mrr_at_10
value: 35.587
- type: mrr_at_100
value: 36.52
- type: mrr_at_1000
value: 36.597
- type: mrr_at_3
value: 33.696
- type: mrr_at_5
value: 34.713
- type: ndcg_at_1
value: 27.866000000000003
- type: ndcg_at_10
value: 36.61
- type: ndcg_at_100
value: 41.88
- type: ndcg_at_1000
value: 45.105000000000004
- type: ndcg_at_3
value: 33.038000000000004
- type: ndcg_at_5
value: 34.331
- type: precision_at_1
value: 27.866000000000003
- type: precision_at_10
value: 6.917
- type: precision_at_100
value: 1.3599999999999999
- type: precision_at_1000
value: 0.233
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 10.791
- type: recall_at_1
value: 23.344
- type: recall_at_10
value: 45.782000000000004
- type: recall_at_100
value: 69.503
- type: recall_at_1000
value: 90.742
- type: recall_at_3
value: 35.160000000000004
- type: recall_at_5
value: 39.058
- type: map_at_1
value: 20.776
- type: map_at_10
value: 27.285999999999998
- type: map_at_100
value: 28.235
- type: map_at_1000
value: 28.337
- type: map_at_3
value: 25.147000000000002
- type: map_at_5
value: 26.401999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 29.409999999999997
- type: mrr_at_100
value: 30.275000000000002
- type: mrr_at_1000
value: 30.354999999999997
- type: mrr_at_3
value: 27.418
- type: mrr_at_5
value: 28.592000000000002
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 31.239
- type: ndcg_at_100
value: 35.965
- type: ndcg_at_1000
value: 38.602
- type: ndcg_at_3
value: 27.174
- type: ndcg_at_5
value: 29.229
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 4.806
- type: precision_at_100
value: 0.776
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 11.459999999999999
- type: precision_at_5
value: 8.022
- type: recall_at_1
value: 20.776
- type: recall_at_10
value: 41.294
- type: recall_at_100
value: 63.111
- type: recall_at_1000
value: 82.88600000000001
- type: recall_at_3
value: 30.403000000000002
- type: recall_at_5
value: 35.455999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.376
- type: map_at_10
value: 15.926000000000002
- type: map_at_100
value: 17.585
- type: map_at_1000
value: 17.776
- type: map_at_3
value: 13.014000000000001
- type: map_at_5
value: 14.417
- type: mrr_at_1
value: 20.195
- type: mrr_at_10
value: 29.95
- type: mrr_at_100
value: 31.052000000000003
- type: mrr_at_1000
value: 31.108000000000004
- type: mrr_at_3
value: 26.667
- type: mrr_at_5
value: 28.458
- type: ndcg_at_1
value: 20.195
- type: ndcg_at_10
value: 22.871
- type: ndcg_at_100
value: 29.921999999999997
- type: ndcg_at_1000
value: 33.672999999999995
- type: ndcg_at_3
value: 17.782999999999998
- type: ndcg_at_5
value: 19.544
- type: precision_at_1
value: 20.195
- type: precision_at_10
value: 7.394
- type: precision_at_100
value: 1.493
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 13.073
- type: precision_at_5
value: 10.436
- type: recall_at_1
value: 9.376
- type: recall_at_10
value: 28.544999999999998
- type: recall_at_100
value: 53.147999999999996
- type: recall_at_1000
value: 74.62
- type: recall_at_3
value: 16.464000000000002
- type: recall_at_5
value: 21.004
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.415000000000001
- type: map_at_10
value: 18.738
- type: map_at_100
value: 27.291999999999998
- type: map_at_1000
value: 28.992
- type: map_at_3
value: 13.196
- type: map_at_5
value: 15.539
- type: mrr_at_1
value: 66.5
- type: mrr_at_10
value: 74.518
- type: mrr_at_100
value: 74.86
- type: mrr_at_1000
value: 74.87
- type: mrr_at_3
value: 72.375
- type: mrr_at_5
value: 73.86200000000001
- type: ndcg_at_1
value: 54.37499999999999
- type: ndcg_at_10
value: 41.317
- type: ndcg_at_100
value: 45.845
- type: ndcg_at_1000
value: 52.92
- type: ndcg_at_3
value: 44.983000000000004
- type: ndcg_at_5
value: 42.989
- type: precision_at_1
value: 66.5
- type: precision_at_10
value: 33.6
- type: precision_at_100
value: 10.972999999999999
- type: precision_at_1000
value: 2.214
- type: precision_at_3
value: 48.583
- type: precision_at_5
value: 42.15
- type: recall_at_1
value: 8.415000000000001
- type: recall_at_10
value: 24.953
- type: recall_at_100
value: 52.48199999999999
- type: recall_at_1000
value: 75.093
- type: recall_at_3
value: 14.341000000000001
- type: recall_at_5
value: 18.468
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.06499999999999
- type: f1
value: 41.439327599975385
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.02
- type: map_at_10
value: 76.68599999999999
- type: map_at_100
value: 76.959
- type: map_at_1000
value: 76.972
- type: map_at_3
value: 75.024
- type: map_at_5
value: 76.153
- type: mrr_at_1
value: 71.197
- type: mrr_at_10
value: 81.105
- type: mrr_at_100
value: 81.232
- type: mrr_at_1000
value: 81.233
- type: mrr_at_3
value: 79.758
- type: mrr_at_5
value: 80.69
- type: ndcg_at_1
value: 71.197
- type: ndcg_at_10
value: 81.644
- type: ndcg_at_100
value: 82.645
- type: ndcg_at_1000
value: 82.879
- type: ndcg_at_3
value: 78.792
- type: ndcg_at_5
value: 80.528
- type: precision_at_1
value: 71.197
- type: precision_at_10
value: 10.206999999999999
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 30.868000000000002
- type: precision_at_5
value: 19.559
- type: recall_at_1
value: 66.02
- type: recall_at_10
value: 92.50699999999999
- type: recall_at_100
value: 96.497
- type: recall_at_1000
value: 97.956
- type: recall_at_3
value: 84.866
- type: recall_at_5
value: 89.16199999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.948
- type: map_at_10
value: 29.833
- type: map_at_100
value: 31.487
- type: map_at_1000
value: 31.674000000000003
- type: map_at_3
value: 26.029999999999998
- type: map_at_5
value: 28.038999999999998
- type: mrr_at_1
value: 34.721999999999994
- type: mrr_at_10
value: 44.214999999999996
- type: mrr_at_100
value: 44.994
- type: mrr_at_1000
value: 45.051
- type: mrr_at_3
value: 41.667
- type: mrr_at_5
value: 43.032
- type: ndcg_at_1
value: 34.721999999999994
- type: ndcg_at_10
value: 37.434
- type: ndcg_at_100
value: 43.702000000000005
- type: ndcg_at_1000
value: 46.993
- type: ndcg_at_3
value: 33.56
- type: ndcg_at_5
value: 34.687
- type: precision_at_1
value: 34.721999999999994
- type: precision_at_10
value: 10.401
- type: precision_at_100
value: 1.7049999999999998
- type: precision_at_1000
value: 0.22799999999999998
- type: precision_at_3
value: 22.531000000000002
- type: precision_at_5
value: 16.42
- type: recall_at_1
value: 17.948
- type: recall_at_10
value: 45.062999999999995
- type: recall_at_100
value: 68.191
- type: recall_at_1000
value: 87.954
- type: recall_at_3
value: 31.112000000000002
- type: recall_at_5
value: 36.823
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.644
- type: map_at_10
value: 57.658
- type: map_at_100
value: 58.562000000000005
- type: map_at_1000
value: 58.62500000000001
- type: map_at_3
value: 54.022999999999996
- type: map_at_5
value: 56.293000000000006
- type: mrr_at_1
value: 73.288
- type: mrr_at_10
value: 80.51700000000001
- type: mrr_at_100
value: 80.72
- type: mrr_at_1000
value: 80.728
- type: mrr_at_3
value: 79.33200000000001
- type: mrr_at_5
value: 80.085
- type: ndcg_at_1
value: 73.288
- type: ndcg_at_10
value: 66.61
- type: ndcg_at_100
value: 69.723
- type: ndcg_at_1000
value: 70.96000000000001
- type: ndcg_at_3
value: 61.358999999999995
- type: ndcg_at_5
value: 64.277
- type: precision_at_1
value: 73.288
- type: precision_at_10
value: 14.17
- type: precision_at_100
value: 1.659
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 39.487
- type: precision_at_5
value: 25.999
- type: recall_at_1
value: 36.644
- type: recall_at_10
value: 70.851
- type: recall_at_100
value: 82.94399999999999
- type: recall_at_1000
value: 91.134
- type: recall_at_3
value: 59.230000000000004
- type: recall_at_5
value: 64.997
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.00280000000001
- type: ap
value: 80.46302061021223
- type: f1
value: 85.9592921596419
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.541
- type: map_at_10
value: 34.625
- type: map_at_100
value: 35.785
- type: map_at_1000
value: 35.831
- type: map_at_3
value: 30.823
- type: map_at_5
value: 32.967999999999996
- type: mrr_at_1
value: 23.180999999999997
- type: mrr_at_10
value: 35.207
- type: mrr_at_100
value: 36.315
- type: mrr_at_1000
value: 36.355
- type: mrr_at_3
value: 31.483
- type: mrr_at_5
value: 33.589999999999996
- type: ndcg_at_1
value: 23.195
- type: ndcg_at_10
value: 41.461
- type: ndcg_at_100
value: 47.032000000000004
- type: ndcg_at_1000
value: 48.199999999999996
- type: ndcg_at_3
value: 33.702
- type: ndcg_at_5
value: 37.522
- type: precision_at_1
value: 23.195
- type: precision_at_10
value: 6.526999999999999
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.308000000000002
- type: precision_at_5
value: 10.507
- type: recall_at_1
value: 22.541
- type: recall_at_10
value: 62.524
- type: recall_at_100
value: 88.228
- type: recall_at_1000
value: 97.243
- type: recall_at_3
value: 41.38
- type: recall_at_5
value: 50.55
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.69949840401279
- type: f1
value: 92.54141471311786
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.56041951664386
- type: f1
value: 55.88499977508287
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.62071284465365
- type: f1
value: 69.36717546572152
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.35843981170142
- type: f1
value: 76.15496453538884
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.33664956793118
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.883839621715524
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.096874986740758
- type: mrr
value: 30.97300481932132
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.4
- type: map_at_10
value: 11.852
- type: map_at_100
value: 14.758
- type: map_at_1000
value: 16.134
- type: map_at_3
value: 8.558
- type: map_at_5
value: 10.087
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 52.05800000000001
- type: mrr_at_100
value: 52.689
- type: mrr_at_1000
value: 52.742999999999995
- type: mrr_at_3
value: 50.205999999999996
- type: mrr_at_5
value: 51.367
- type: ndcg_at_1
value: 42.57
- type: ndcg_at_10
value: 32.449
- type: ndcg_at_100
value: 29.596
- type: ndcg_at_1000
value: 38.351
- type: ndcg_at_3
value: 37.044
- type: ndcg_at_5
value: 35.275
- type: precision_at_1
value: 44.272
- type: precision_at_10
value: 23.87
- type: precision_at_100
value: 7.625
- type: precision_at_1000
value: 2.045
- type: precision_at_3
value: 34.365
- type: precision_at_5
value: 30.341
- type: recall_at_1
value: 5.4
- type: recall_at_10
value: 15.943999999999999
- type: recall_at_100
value: 29.805
- type: recall_at_1000
value: 61.695
- type: recall_at_3
value: 9.539
- type: recall_at_5
value: 12.127
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.047000000000004
- type: map_at_10
value: 51.6
- type: map_at_100
value: 52.449999999999996
- type: map_at_1000
value: 52.476
- type: map_at_3
value: 47.452
- type: map_at_5
value: 49.964
- type: mrr_at_1
value: 40.382
- type: mrr_at_10
value: 54.273
- type: mrr_at_100
value: 54.859
- type: mrr_at_1000
value: 54.876000000000005
- type: mrr_at_3
value: 51.014
- type: mrr_at_5
value: 52.983999999999995
- type: ndcg_at_1
value: 40.353
- type: ndcg_at_10
value: 59.11300000000001
- type: ndcg_at_100
value: 62.604000000000006
- type: ndcg_at_1000
value: 63.187000000000005
- type: ndcg_at_3
value: 51.513
- type: ndcg_at_5
value: 55.576
- type: precision_at_1
value: 40.353
- type: precision_at_10
value: 9.418
- type: precision_at_100
value: 1.1440000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 23.078000000000003
- type: precision_at_5
value: 16.250999999999998
- type: recall_at_1
value: 36.047000000000004
- type: recall_at_10
value: 79.22200000000001
- type: recall_at_100
value: 94.23
- type: recall_at_1000
value: 98.51100000000001
- type: recall_at_3
value: 59.678
- type: recall_at_5
value: 68.967
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.232
- type: map_at_10
value: 81.674
- type: map_at_100
value: 82.338
- type: map_at_1000
value: 82.36099999999999
- type: map_at_3
value: 78.833
- type: map_at_5
value: 80.58
- type: mrr_at_1
value: 78.64
- type: mrr_at_10
value: 85.164
- type: mrr_at_100
value: 85.317
- type: mrr_at_1000
value: 85.319
- type: mrr_at_3
value: 84.127
- type: mrr_at_5
value: 84.789
- type: ndcg_at_1
value: 78.63
- type: ndcg_at_10
value: 85.711
- type: ndcg_at_100
value: 87.238
- type: ndcg_at_1000
value: 87.444
- type: ndcg_at_3
value: 82.788
- type: ndcg_at_5
value: 84.313
- type: precision_at_1
value: 78.63
- type: precision_at_10
value: 12.977
- type: precision_at_100
value: 1.503
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.113
- type: precision_at_5
value: 23.71
- type: recall_at_1
value: 68.232
- type: recall_at_10
value: 93.30199999999999
- type: recall_at_100
value: 98.799
- type: recall_at_1000
value: 99.885
- type: recall_at_3
value: 84.827
- type: recall_at_5
value: 89.188
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.71879170816294
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 59.65866311751794
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.218
- type: map_at_10
value: 10.337
- type: map_at_100
value: 12.131
- type: map_at_1000
value: 12.411
- type: map_at_3
value: 7.4270000000000005
- type: map_at_5
value: 8.913
- type: mrr_at_1
value: 20.8
- type: mrr_at_10
value: 30.868000000000002
- type: mrr_at_100
value: 31.903
- type: mrr_at_1000
value: 31.972
- type: mrr_at_3
value: 27.367
- type: mrr_at_5
value: 29.372
- type: ndcg_at_1
value: 20.8
- type: ndcg_at_10
value: 17.765
- type: ndcg_at_100
value: 24.914
- type: ndcg_at_1000
value: 30.206
- type: ndcg_at_3
value: 16.64
- type: ndcg_at_5
value: 14.712
- type: precision_at_1
value: 20.8
- type: precision_at_10
value: 9.24
- type: precision_at_100
value: 1.9560000000000002
- type: precision_at_1000
value: 0.32299999999999995
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.94
- type: recall_at_1
value: 4.218
- type: recall_at_10
value: 18.752
- type: recall_at_100
value: 39.7
- type: recall_at_1000
value: 65.57300000000001
- type: recall_at_3
value: 9.428
- type: recall_at_5
value: 13.133000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04338850207233
- type: cos_sim_spearman
value: 78.5054651430423
- type: euclidean_pearson
value: 80.30739451228612
- type: euclidean_spearman
value: 78.48377464299097
- type: manhattan_pearson
value: 80.40795049052781
- type: manhattan_spearman
value: 78.49506205443114
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.11596224442962
- type: cos_sim_spearman
value: 76.20997388935461
- type: euclidean_pearson
value: 80.56858451349109
- type: euclidean_spearman
value: 75.92659183871186
- type: manhattan_pearson
value: 80.60246102203844
- type: manhattan_spearman
value: 76.03018971432664
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.34691640755737
- type: cos_sim_spearman
value: 82.4018369631579
- type: euclidean_pearson
value: 81.87673092245366
- type: euclidean_spearman
value: 82.3671489960678
- type: manhattan_pearson
value: 81.88222387719948
- type: manhattan_spearman
value: 82.3816590344736
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.2836092579524
- type: cos_sim_spearman
value: 78.99982781772064
- type: euclidean_pearson
value: 80.5184271010527
- type: euclidean_spearman
value: 78.89777392101904
- type: manhattan_pearson
value: 80.53585705018664
- type: manhattan_spearman
value: 78.92898405472994
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.7349907750784
- type: cos_sim_spearman
value: 87.7611234446225
- type: euclidean_pearson
value: 86.98759326731624
- type: euclidean_spearman
value: 87.58321319424618
- type: manhattan_pearson
value: 87.03483090370842
- type: manhattan_spearman
value: 87.63278333060288
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.75873694924825
- type: cos_sim_spearman
value: 83.80237999094724
- type: euclidean_pearson
value: 83.55023725861537
- type: euclidean_spearman
value: 84.12744338577744
- type: manhattan_pearson
value: 83.58816983036232
- type: manhattan_spearman
value: 84.18520748676501
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.21630882940174
- type: cos_sim_spearman
value: 87.72382883437031
- type: euclidean_pearson
value: 88.69933350930333
- type: euclidean_spearman
value: 88.24660814383081
- type: manhattan_pearson
value: 88.77331018833499
- type: manhattan_spearman
value: 88.26109989380632
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 61.11854063060489
- type: cos_sim_spearman
value: 63.14678634195072
- type: euclidean_pearson
value: 61.679090067000864
- type: euclidean_spearman
value: 62.28876589509653
- type: manhattan_pearson
value: 62.082324165511004
- type: manhattan_spearman
value: 62.56030932816679
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.00319882832645
- type: cos_sim_spearman
value: 85.94529772647257
- type: euclidean_pearson
value: 85.6661390122756
- type: euclidean_spearman
value: 85.97747815545827
- type: manhattan_pearson
value: 85.58422770541893
- type: manhattan_spearman
value: 85.9237139181532
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.16198731863916
- type: mrr
value: 94.25202702163487
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 54.761
- type: map_at_10
value: 64.396
- type: map_at_100
value: 65.07
- type: map_at_1000
value: 65.09899999999999
- type: map_at_3
value: 61.846000000000004
- type: map_at_5
value: 63.284
- type: mrr_at_1
value: 57.667
- type: mrr_at_10
value: 65.83099999999999
- type: mrr_at_100
value: 66.36800000000001
- type: mrr_at_1000
value: 66.39399999999999
- type: mrr_at_3
value: 64.056
- type: mrr_at_5
value: 65.206
- type: ndcg_at_1
value: 57.667
- type: ndcg_at_10
value: 68.854
- type: ndcg_at_100
value: 71.59100000000001
- type: ndcg_at_1000
value: 72.383
- type: ndcg_at_3
value: 64.671
- type: ndcg_at_5
value: 66.796
- type: precision_at_1
value: 57.667
- type: precision_at_10
value: 9.167
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 25.444
- type: precision_at_5
value: 16.667
- type: recall_at_1
value: 54.761
- type: recall_at_10
value: 80.9
- type: recall_at_100
value: 92.767
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 69.672
- type: recall_at_5
value: 75.083
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8079207920792
- type: cos_sim_ap
value: 94.88470927617445
- type: cos_sim_f1
value: 90.08179959100204
- type: cos_sim_precision
value: 92.15481171548117
- type: cos_sim_recall
value: 88.1
- type: dot_accuracy
value: 99.58613861386138
- type: dot_ap
value: 82.94822578881316
- type: dot_f1
value: 77.33333333333333
- type: dot_precision
value: 79.36842105263158
- type: dot_recall
value: 75.4
- type: euclidean_accuracy
value: 99.8069306930693
- type: euclidean_ap
value: 94.81367858031837
- type: euclidean_f1
value: 90.01009081735621
- type: euclidean_precision
value: 90.83503054989816
- type: euclidean_recall
value: 89.2
- type: manhattan_accuracy
value: 99.81188118811882
- type: manhattan_ap
value: 94.91405337220161
- type: manhattan_f1
value: 90.2763561924258
- type: manhattan_precision
value: 92.45283018867924
- type: manhattan_recall
value: 88.2
- type: max_accuracy
value: 99.81188118811882
- type: max_ap
value: 94.91405337220161
- type: max_f1
value: 90.2763561924258
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 58.511599500053094
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.984728147814707
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.93428193939015
- type: mrr
value: 50.916557911043206
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.562500894537145
- type: cos_sim_spearman
value: 31.162587976726307
- type: dot_pearson
value: 22.633662187735762
- type: dot_spearman
value: 22.723000282378962
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.219
- type: map_at_10
value: 1.871
- type: map_at_100
value: 10.487
- type: map_at_1000
value: 25.122
- type: map_at_3
value: 0.657
- type: map_at_5
value: 1.0699999999999998
- type: mrr_at_1
value: 84
- type: mrr_at_10
value: 89.567
- type: mrr_at_100
value: 89.748
- type: mrr_at_1000
value: 89.748
- type: mrr_at_3
value: 88.667
- type: mrr_at_5
value: 89.567
- type: ndcg_at_1
value: 80
- type: ndcg_at_10
value: 74.533
- type: ndcg_at_100
value: 55.839000000000006
- type: ndcg_at_1000
value: 49.748
- type: ndcg_at_3
value: 79.53099999999999
- type: ndcg_at_5
value: 78.245
- type: precision_at_1
value: 84
- type: precision_at_10
value: 78.4
- type: precision_at_100
value: 56.99999999999999
- type: precision_at_1000
value: 21.98
- type: precision_at_3
value: 85.333
- type: precision_at_5
value: 84.8
- type: recall_at_1
value: 0.219
- type: recall_at_10
value: 2.02
- type: recall_at_100
value: 13.555
- type: recall_at_1000
value: 46.739999999999995
- type: recall_at_3
value: 0.685
- type: recall_at_5
value: 1.13
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.5029999999999997
- type: map_at_10
value: 11.042
- type: map_at_100
value: 16.326999999999998
- type: map_at_1000
value: 17.836
- type: map_at_3
value: 6.174
- type: map_at_5
value: 7.979
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 52.617000000000004
- type: mrr_at_100
value: 53.351000000000006
- type: mrr_at_1000
value: 53.351000000000006
- type: mrr_at_3
value: 46.939
- type: mrr_at_5
value: 50.714000000000006
- type: ndcg_at_1
value: 38.775999999999996
- type: ndcg_at_10
value: 27.125
- type: ndcg_at_100
value: 35.845
- type: ndcg_at_1000
value: 47.377
- type: ndcg_at_3
value: 29.633
- type: ndcg_at_5
value: 28.378999999999998
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 24.082
- type: precision_at_100
value: 6.877999999999999
- type: precision_at_1000
value: 1.463
- type: precision_at_3
value: 29.932
- type: precision_at_5
value: 28.571
- type: recall_at_1
value: 3.5029999999999997
- type: recall_at_10
value: 17.068
- type: recall_at_100
value: 43.361
- type: recall_at_1000
value: 78.835
- type: recall_at_3
value: 6.821000000000001
- type: recall_at_5
value: 10.357
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.0954
- type: ap
value: 14.216844153511959
- type: f1
value: 54.63687418565117
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.46293152235427
- type: f1
value: 61.744177921638645
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 41.12708617788644
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.75430649102938
- type: cos_sim_ap
value: 73.34252536948081
- type: cos_sim_f1
value: 67.53758935173774
- type: cos_sim_precision
value: 63.3672525439408
- type: cos_sim_recall
value: 72.29551451187335
- type: dot_accuracy
value: 81.71305954580676
- type: dot_ap
value: 59.5532209082386
- type: dot_f1
value: 56.18466898954705
- type: dot_precision
value: 47.830923248053395
- type: dot_recall
value: 68.07387862796834
- type: euclidean_accuracy
value: 85.81987244441795
- type: euclidean_ap
value: 73.34325409809446
- type: euclidean_f1
value: 67.83451360417443
- type: euclidean_precision
value: 64.09955388588871
- type: euclidean_recall
value: 72.0316622691293
- type: manhattan_accuracy
value: 85.68277999642368
- type: manhattan_ap
value: 73.1535450121903
- type: manhattan_f1
value: 67.928237896289
- type: manhattan_precision
value: 63.56945722171113
- type: manhattan_recall
value: 72.9287598944591
- type: max_accuracy
value: 85.81987244441795
- type: max_ap
value: 73.34325409809446
- type: max_f1
value: 67.928237896289
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.90441262079403
- type: cos_sim_ap
value: 85.79331880741438
- type: cos_sim_f1
value: 78.31563529842548
- type: cos_sim_precision
value: 74.6683424102779
- type: cos_sim_recall
value: 82.33754234678165
- type: dot_accuracy
value: 84.89928978926534
- type: dot_ap
value: 75.25819218316
- type: dot_f1
value: 69.88730119720536
- type: dot_precision
value: 64.23362374959665
- type: dot_recall
value: 76.63227594702803
- type: euclidean_accuracy
value: 89.01695967710637
- type: euclidean_ap
value: 85.98986606038852
- type: euclidean_f1
value: 78.5277880014722
- type: euclidean_precision
value: 75.22211253701876
- type: euclidean_recall
value: 82.13735756082538
- type: manhattan_accuracy
value: 88.99561454573679
- type: manhattan_ap
value: 85.92262421793953
- type: manhattan_f1
value: 78.38866094740769
- type: manhattan_precision
value: 76.02373028505282
- type: manhattan_recall
value: 80.9054511857099
- type: max_accuracy
value: 89.01695967710637
- type: max_ap
value: 85.98986606038852
- type: max_f1
value: 78.5277880014722
---
# E5-small-v2
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2')
model = AutoModel.from_pretrained('intfloat/e5-small-v2')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Training Details
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
## Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-small-v2')
input_texts = [
'query: how much protein should a female eat',
'query: summit define',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
hkunlp/instructor-large | hkunlp | sentence-similarity | [
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2212.09741",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2022-12-20T05:31:06 | 2023-04-21T06:04:33 | 200,795 | 506 | ---
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
inference: false
model-index:
- name: INSTRUCTOR
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 88.13432835820896
- type: ap
value: 59.298209334395665
- type: f1
value: 83.31769058643586
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.526375
- type: ap
value: 88.16327709705504
- type: f1
value: 91.51095801287843
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.856
- type: f1
value: 45.41490917650942
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.947
- type: map_at_100
value: 48.742000000000004
- type: map_at_1000
value: 48.745
- type: map_at_3
value: 43.137
- type: map_at_5
value: 45.992
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 48.4
- type: mrr_at_100
value: 49.202
- type: mrr_at_1000
value: 49.205
- type: mrr_at_3
value: 43.551
- type: mrr_at_5
value: 46.467999999999996
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 57.045
- type: ndcg_at_100
value: 60.175
- type: ndcg_at_1000
value: 60.233000000000004
- type: ndcg_at_3
value: 47.171
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.599
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.63
- type: precision_at_5
value: 14.282
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.989
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.89
- type: recall_at_5
value: 71.408
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.1621946393635
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.56417132407894
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29539304390207
- type: mrr
value: 76.44484017060196
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.38746499431112
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.51298701298701
- type: f1
value: 77.49041754069235
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61848554098577
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.32623280148178
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.803000000000004
- type: map_at_10
value: 48.848
- type: map_at_100
value: 50.5
- type: map_at_1000
value: 50.602999999999994
- type: map_at_3
value: 45.111000000000004
- type: map_at_5
value: 47.202
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 55.593
- type: mrr_at_100
value: 56.169999999999995
- type: mrr_at_1000
value: 56.19499999999999
- type: mrr_at_3
value: 53.361999999999995
- type: mrr_at_5
value: 54.806999999999995
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.899
- type: ndcg_at_100
value: 60.958
- type: ndcg_at_1000
value: 62.302
- type: ndcg_at_3
value: 51.051
- type: ndcg_at_5
value: 53.351000000000006
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.786999999999999
- type: precision_at_100
value: 1.6580000000000001
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 24.893
- type: precision_at_5
value: 17.740000000000002
- type: recall_at_1
value: 35.803000000000004
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 89.77199999999999
- type: recall_at_1000
value: 97.67
- type: recall_at_3
value: 54.066
- type: recall_at_5
value: 60.788
- type: map_at_1
value: 33.706
- type: map_at_10
value: 44.896
- type: map_at_100
value: 46.299
- type: map_at_1000
value: 46.44
- type: map_at_3
value: 41.721000000000004
- type: map_at_5
value: 43.486000000000004
- type: mrr_at_1
value: 41.592
- type: mrr_at_10
value: 50.529
- type: mrr_at_100
value: 51.22
- type: mrr_at_1000
value: 51.258
- type: mrr_at_3
value: 48.205999999999996
- type: mrr_at_5
value: 49.528
- type: ndcg_at_1
value: 41.592
- type: ndcg_at_10
value: 50.77199999999999
- type: ndcg_at_100
value: 55.383
- type: ndcg_at_1000
value: 57.288
- type: ndcg_at_3
value: 46.324
- type: ndcg_at_5
value: 48.346000000000004
- type: precision_at_1
value: 41.592
- type: precision_at_10
value: 9.516
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.399
- type: precision_at_5
value: 15.770999999999999
- type: recall_at_1
value: 33.706
- type: recall_at_10
value: 61.353
- type: recall_at_100
value: 80.182
- type: recall_at_1000
value: 91.896
- type: recall_at_3
value: 48.204
- type: recall_at_5
value: 53.89699999999999
- type: map_at_1
value: 44.424
- type: map_at_10
value: 57.169000000000004
- type: map_at_100
value: 58.202
- type: map_at_1000
value: 58.242000000000004
- type: map_at_3
value: 53.825
- type: map_at_5
value: 55.714
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 60.489000000000004
- type: mrr_at_100
value: 61.096
- type: mrr_at_1000
value: 61.112
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.611999999999995
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 63.071999999999996
- type: ndcg_at_100
value: 66.964
- type: ndcg_at_1000
value: 67.659
- type: ndcg_at_3
value: 57.74399999999999
- type: ndcg_at_5
value: 60.367000000000004
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 10.019
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.558999999999997
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 44.424
- type: recall_at_10
value: 77.02
- type: recall_at_100
value: 93.738
- type: recall_at_1000
value: 98.451
- type: recall_at_3
value: 62.888
- type: recall_at_5
value: 69.138
- type: map_at_1
value: 26.294
- type: map_at_10
value: 34.503
- type: map_at_100
value: 35.641
- type: map_at_1000
value: 35.724000000000004
- type: map_at_3
value: 31.753999999999998
- type: map_at_5
value: 33.190999999999995
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 36.53
- type: mrr_at_100
value: 37.541000000000004
- type: mrr_at_1000
value: 37.602000000000004
- type: mrr_at_3
value: 33.917
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 39.513999999999996
- type: ndcg_at_100
value: 44.815
- type: ndcg_at_1000
value: 46.839
- type: ndcg_at_3
value: 34.02
- type: ndcg_at_5
value: 36.522
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.161999999999999
- type: precision_at_5
value: 9.966
- type: recall_at_1
value: 26.294
- type: recall_at_10
value: 53.098
- type: recall_at_100
value: 76.877
- type: recall_at_1000
value: 91.834
- type: recall_at_3
value: 38.266
- type: recall_at_5
value: 44.287
- type: map_at_1
value: 16.407
- type: map_at_10
value: 25.185999999999996
- type: map_at_100
value: 26.533
- type: map_at_1000
value: 26.657999999999998
- type: map_at_3
value: 22.201999999999998
- type: map_at_5
value: 23.923
- type: mrr_at_1
value: 20.522000000000002
- type: mrr_at_10
value: 29.522
- type: mrr_at_100
value: 30.644
- type: mrr_at_1000
value: 30.713
- type: mrr_at_3
value: 26.679000000000002
- type: mrr_at_5
value: 28.483000000000004
- type: ndcg_at_1
value: 20.522000000000002
- type: ndcg_at_10
value: 30.656
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.675
- type: ndcg_at_3
value: 25.319000000000003
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.522000000000002
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 16.407
- type: recall_at_10
value: 43.164
- type: recall_at_100
value: 69.695
- type: recall_at_1000
value: 89.41900000000001
- type: recall_at_3
value: 28.634999999999998
- type: recall_at_5
value: 35.308
- type: map_at_1
value: 30.473
- type: map_at_10
value: 41.676
- type: map_at_100
value: 43.120999999999995
- type: map_at_1000
value: 43.230000000000004
- type: map_at_3
value: 38.306000000000004
- type: map_at_5
value: 40.355999999999995
- type: mrr_at_1
value: 37.536
- type: mrr_at_10
value: 47.643
- type: mrr_at_100
value: 48.508
- type: mrr_at_1000
value: 48.551
- type: mrr_at_3
value: 45.348
- type: mrr_at_5
value: 46.744
- type: ndcg_at_1
value: 37.536
- type: ndcg_at_10
value: 47.823
- type: ndcg_at_100
value: 53.395
- type: ndcg_at_1000
value: 55.271
- type: ndcg_at_3
value: 42.768
- type: ndcg_at_5
value: 45.373000000000005
- type: precision_at_1
value: 37.536
- type: precision_at_10
value: 8.681
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.495
- type: recall_at_1
value: 30.473
- type: recall_at_10
value: 60.092999999999996
- type: recall_at_100
value: 82.733
- type: recall_at_1000
value: 94.875
- type: recall_at_3
value: 45.734
- type: recall_at_5
value: 52.691
- type: map_at_1
value: 29.976000000000003
- type: map_at_10
value: 41.097
- type: map_at_100
value: 42.547000000000004
- type: map_at_1000
value: 42.659000000000006
- type: map_at_3
value: 37.251
- type: map_at_5
value: 39.493
- type: mrr_at_1
value: 37.557
- type: mrr_at_10
value: 46.605000000000004
- type: mrr_at_100
value: 47.487
- type: mrr_at_1000
value: 47.54
- type: mrr_at_3
value: 43.721
- type: mrr_at_5
value: 45.411
- type: ndcg_at_1
value: 37.557
- type: ndcg_at_10
value: 47.449000000000005
- type: ndcg_at_100
value: 53.052
- type: ndcg_at_1000
value: 55.010999999999996
- type: ndcg_at_3
value: 41.439
- type: ndcg_at_5
value: 44.292
- type: precision_at_1
value: 37.557
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.357
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 20.091
- type: precision_at_5
value: 14.384
- type: recall_at_1
value: 29.976000000000003
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 84.245
- type: recall_at_1000
value: 96.97200000000001
- type: recall_at_3
value: 43.794
- type: recall_at_5
value: 51.778999999999996
- type: map_at_1
value: 28.099166666666665
- type: map_at_10
value: 38.1365
- type: map_at_100
value: 39.44491666666667
- type: map_at_1000
value: 39.55858333333334
- type: map_at_3
value: 35.03641666666666
- type: map_at_5
value: 36.79833333333334
- type: mrr_at_1
value: 33.39966666666667
- type: mrr_at_10
value: 42.42583333333333
- type: mrr_at_100
value: 43.28575
- type: mrr_at_1000
value: 43.33741666666667
- type: mrr_at_3
value: 39.94975
- type: mrr_at_5
value: 41.41633333333334
- type: ndcg_at_1
value: 33.39966666666667
- type: ndcg_at_10
value: 43.81741666666667
- type: ndcg_at_100
value: 49.08166666666667
- type: ndcg_at_1000
value: 51.121166666666674
- type: ndcg_at_3
value: 38.73575
- type: ndcg_at_5
value: 41.18158333333333
- type: precision_at_1
value: 33.39966666666667
- type: precision_at_10
value: 7.738916666666667
- type: precision_at_100
value: 1.2265833333333331
- type: precision_at_1000
value: 0.15983333333333336
- type: precision_at_3
value: 17.967416666666665
- type: precision_at_5
value: 12.78675
- type: recall_at_1
value: 28.099166666666665
- type: recall_at_10
value: 56.27049999999999
- type: recall_at_100
value: 78.93291666666667
- type: recall_at_1000
value: 92.81608333333334
- type: recall_at_3
value: 42.09775
- type: recall_at_5
value: 48.42533333333334
- type: map_at_1
value: 23.663
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.426
- type: map_at_1000
value: 31.519000000000002
- type: map_at_3
value: 28.069
- type: map_at_5
value: 29.256999999999998
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.107
- type: mrr_at_100
value: 34.055
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.14
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.615
- type: ndcg_at_100
value: 39.776
- type: ndcg_at_1000
value: 42.05
- type: ndcg_at_3
value: 30.322
- type: ndcg_at_5
value: 32.157000000000004
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.491
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.139000000000001
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.663
- type: recall_at_10
value: 45.035
- type: recall_at_100
value: 68.554
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 32.982
- type: recall_at_5
value: 37.688
- type: map_at_1
value: 17.403
- type: map_at_10
value: 25.197000000000003
- type: map_at_100
value: 26.355
- type: map_at_1000
value: 26.487
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.114
- type: mrr_at_1
value: 21.37
- type: mrr_at_10
value: 29.091
- type: mrr_at_100
value: 30.018
- type: mrr_at_1000
value: 30.096
- type: mrr_at_3
value: 26.887
- type: mrr_at_5
value: 28.157
- type: ndcg_at_1
value: 21.37
- type: ndcg_at_10
value: 30.026000000000003
- type: ndcg_at_100
value: 35.416
- type: ndcg_at_1000
value: 38.45
- type: ndcg_at_3
value: 25.764
- type: ndcg_at_5
value: 27.742
- type: precision_at_1
value: 21.37
- type: precision_at_10
value: 5.609
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.423
- type: precision_at_5
value: 9.009
- type: recall_at_1
value: 17.403
- type: recall_at_10
value: 40.573
- type: recall_at_100
value: 64.818
- type: recall_at_1000
value: 86.53699999999999
- type: recall_at_3
value: 28.493000000000002
- type: recall_at_5
value: 33.660000000000004
- type: map_at_1
value: 28.639
- type: map_at_10
value: 38.951
- type: map_at_100
value: 40.238
- type: map_at_1000
value: 40.327
- type: map_at_3
value: 35.842
- type: map_at_5
value: 37.617
- type: mrr_at_1
value: 33.769
- type: mrr_at_10
value: 43.088
- type: mrr_at_100
value: 44.03
- type: mrr_at_1000
value: 44.072
- type: mrr_at_3
value: 40.656
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 33.769
- type: ndcg_at_10
value: 44.676
- type: ndcg_at_100
value: 50.416000000000004
- type: ndcg_at_1000
value: 52.227999999999994
- type: ndcg_at_3
value: 39.494
- type: ndcg_at_5
value: 42.013
- type: precision_at_1
value: 33.769
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.221
- type: precision_at_5
value: 12.966
- type: recall_at_1
value: 28.639
- type: recall_at_10
value: 57.687999999999995
- type: recall_at_100
value: 82.541
- type: recall_at_1000
value: 94.896
- type: recall_at_3
value: 43.651
- type: recall_at_5
value: 49.925999999999995
- type: map_at_1
value: 29.57
- type: map_at_10
value: 40.004
- type: map_at_100
value: 41.75
- type: map_at_1000
value: 41.97
- type: map_at_3
value: 36.788
- type: map_at_5
value: 38.671
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 45.121
- type: mrr_at_100
value: 45.994
- type: mrr_at_1000
value: 46.04
- type: mrr_at_3
value: 42.227
- type: mrr_at_5
value: 43.995
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 46.392
- type: ndcg_at_100
value: 52.196
- type: ndcg_at_1000
value: 54.274
- type: ndcg_at_3
value: 41.163
- type: ndcg_at_5
value: 43.813
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.676
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 19.104
- type: precision_at_5
value: 13.913
- type: recall_at_1
value: 29.57
- type: recall_at_10
value: 58.779
- type: recall_at_100
value: 83.337
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 44.005
- type: recall_at_5
value: 50.975
- type: map_at_1
value: 20.832
- type: map_at_10
value: 29.733999999999998
- type: map_at_100
value: 30.727
- type: map_at_1000
value: 30.843999999999998
- type: map_at_3
value: 26.834999999999997
- type: map_at_5
value: 28.555999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.791999999999998
- type: mrr_at_100
value: 32.666000000000004
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 30.622
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.744
- type: ndcg_at_1000
value: 42.407000000000004
- type: ndcg_at_3
value: 29.421000000000003
- type: ndcg_at_5
value: 32.211
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.675
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.753999999999998
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 20.832
- type: recall_at_10
value: 48.795
- type: recall_at_100
value: 70.703
- type: recall_at_1000
value: 90.187
- type: recall_at_3
value: 34.455000000000005
- type: recall_at_5
value: 40.967
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.334
- type: map_at_10
value: 19.009999999999998
- type: map_at_100
value: 21.129
- type: map_at_1000
value: 21.328
- type: map_at_3
value: 15.152
- type: map_at_5
value: 17.084
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 36.099
- type: mrr_at_100
value: 37.069
- type: mrr_at_1000
value: 37.104
- type: mrr_at_3
value: 32.096000000000004
- type: mrr_at_5
value: 34.451
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 27.739000000000004
- type: ndcg_at_100
value: 35.836
- type: ndcg_at_1000
value: 39.242
- type: ndcg_at_3
value: 21.263
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 9.199
- type: precision_at_100
value: 1.791
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.2
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 10.334
- type: recall_at_10
value: 35.177
- type: recall_at_100
value: 63.009
- type: recall_at_1000
value: 81.938
- type: recall_at_3
value: 19.914
- type: recall_at_5
value: 26.077
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.212
- type: map_at_10
value: 17.386
- type: map_at_100
value: 24.234
- type: map_at_1000
value: 25.724999999999998
- type: map_at_3
value: 12.727
- type: map_at_5
value: 14.785
- type: mrr_at_1
value: 59.25
- type: mrr_at_10
value: 68.687
- type: mrr_at_100
value: 69.133
- type: mrr_at_1000
value: 69.14099999999999
- type: mrr_at_3
value: 66.917
- type: mrr_at_5
value: 67.742
- type: ndcg_at_1
value: 48.625
- type: ndcg_at_10
value: 36.675999999999995
- type: ndcg_at_100
value: 41.543
- type: ndcg_at_1000
value: 49.241
- type: ndcg_at_3
value: 41.373
- type: ndcg_at_5
value: 38.707
- type: precision_at_1
value: 59.25
- type: precision_at_10
value: 28.525
- type: precision_at_100
value: 9.027000000000001
- type: precision_at_1000
value: 1.8339999999999999
- type: precision_at_3
value: 44.833
- type: precision_at_5
value: 37.35
- type: recall_at_1
value: 8.212
- type: recall_at_10
value: 23.188
- type: recall_at_100
value: 48.613
- type: recall_at_1000
value: 73.093
- type: recall_at_3
value: 14.419
- type: recall_at_5
value: 17.798
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.725
- type: f1
value: 46.50743309855908
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.086
- type: map_at_10
value: 66.914
- type: map_at_100
value: 67.321
- type: map_at_1000
value: 67.341
- type: map_at_3
value: 64.75800000000001
- type: map_at_5
value: 66.189
- type: mrr_at_1
value: 59.28600000000001
- type: mrr_at_10
value: 71.005
- type: mrr_at_100
value: 71.304
- type: mrr_at_1000
value: 71.313
- type: mrr_at_3
value: 69.037
- type: mrr_at_5
value: 70.35
- type: ndcg_at_1
value: 59.28600000000001
- type: ndcg_at_10
value: 72.695
- type: ndcg_at_100
value: 74.432
- type: ndcg_at_1000
value: 74.868
- type: ndcg_at_3
value: 68.72200000000001
- type: ndcg_at_5
value: 71.081
- type: precision_at_1
value: 59.28600000000001
- type: precision_at_10
value: 9.499
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 27.503
- type: precision_at_5
value: 17.854999999999997
- type: recall_at_1
value: 55.086
- type: recall_at_10
value: 86.453
- type: recall_at_100
value: 94.028
- type: recall_at_1000
value: 97.052
- type: recall_at_3
value: 75.821
- type: recall_at_5
value: 81.6
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262999999999998
- type: map_at_10
value: 37.488
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.687
- type: map_at_3
value: 32.529
- type: map_at_5
value: 35.455
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.239000000000004
- type: mrr_at_100
value: 54.086
- type: mrr_at_1000
value: 54.122
- type: mrr_at_3
value: 51.235
- type: mrr_at_5
value: 52.415
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 45.446
- type: ndcg_at_100
value: 52.429
- type: ndcg_at_1000
value: 55.169000000000004
- type: ndcg_at_3
value: 41.882000000000005
- type: ndcg_at_5
value: 43.178
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.931999999999999
- type: precision_at_100
value: 2.025
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 28.652
- type: precision_at_5
value: 21.204
- type: recall_at_1
value: 22.262999999999998
- type: recall_at_10
value: 52.447
- type: recall_at_100
value: 78.045
- type: recall_at_1000
value: 94.419
- type: recall_at_3
value: 38.064
- type: recall_at_5
value: 44.769
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.519
- type: map_at_10
value: 45.831
- type: map_at_100
value: 46.815
- type: map_at_1000
value: 46.899
- type: map_at_3
value: 42.836
- type: map_at_5
value: 44.65
- type: mrr_at_1
value: 65.037
- type: mrr_at_10
value: 72.16
- type: mrr_at_100
value: 72.51100000000001
- type: mrr_at_1000
value: 72.53
- type: mrr_at_3
value: 70.682
- type: mrr_at_5
value: 71.54599999999999
- type: ndcg_at_1
value: 65.037
- type: ndcg_at_10
value: 55.17999999999999
- type: ndcg_at_100
value: 58.888
- type: ndcg_at_1000
value: 60.648
- type: ndcg_at_3
value: 50.501
- type: ndcg_at_5
value: 52.977
- type: precision_at_1
value: 65.037
- type: precision_at_10
value: 11.530999999999999
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 31.483
- type: precision_at_5
value: 20.845
- type: recall_at_1
value: 32.519
- type: recall_at_10
value: 57.657000000000004
- type: recall_at_100
value: 72.30199999999999
- type: recall_at_1000
value: 84.024
- type: recall_at_3
value: 47.225
- type: recall_at_5
value: 52.113
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.3168
- type: ap
value: 83.80165516037135
- type: f1
value: 88.29942471066407
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.724999999999998
- type: map_at_10
value: 32.736
- type: map_at_100
value: 33.938
- type: map_at_1000
value: 33.991
- type: map_at_3
value: 28.788000000000004
- type: map_at_5
value: 31.016
- type: mrr_at_1
value: 21.361
- type: mrr_at_10
value: 33.323
- type: mrr_at_100
value: 34.471000000000004
- type: mrr_at_1000
value: 34.518
- type: mrr_at_3
value: 29.453000000000003
- type: mrr_at_5
value: 31.629
- type: ndcg_at_1
value: 21.361
- type: ndcg_at_10
value: 39.649
- type: ndcg_at_100
value: 45.481
- type: ndcg_at_1000
value: 46.775
- type: ndcg_at_3
value: 31.594
- type: ndcg_at_5
value: 35.543
- type: precision_at_1
value: 21.361
- type: precision_at_10
value: 6.3740000000000006
- type: precision_at_100
value: 0.931
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.514999999999999
- type: precision_at_5
value: 10.100000000000001
- type: recall_at_1
value: 20.724999999999998
- type: recall_at_10
value: 61.034
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 97.86399999999999
- type: recall_at_3
value: 39.072
- type: recall_at_5
value: 48.53
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.8919288645691
- type: f1
value: 93.57059586398059
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.97993616051072
- type: f1
value: 48.244319183606535
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.90047074646941
- type: f1
value: 66.48999056063725
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.34566240753195
- type: f1
value: 73.54164154290658
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.21866934757011
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.000936217235534
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.68189362520352
- type: mrr
value: 32.69603637784303
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.078
- type: map_at_10
value: 12.671
- type: map_at_100
value: 16.291
- type: map_at_1000
value: 17.855999999999998
- type: map_at_3
value: 9.610000000000001
- type: map_at_5
value: 11.152
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 53.173
- type: mrr_at_100
value: 53.718999999999994
- type: mrr_at_1000
value: 53.756
- type: mrr_at_3
value: 50.980000000000004
- type: mrr_at_5
value: 52.42
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.086
- type: ndcg_at_100
value: 32.545
- type: ndcg_at_1000
value: 41.144999999999996
- type: ndcg_at_3
value: 39.434999999999995
- type: ndcg_at_5
value: 37.888
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.014999999999997
- type: precision_at_100
value: 8.594
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 37.049
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 6.078
- type: recall_at_10
value: 16.17
- type: recall_at_100
value: 34.512
- type: recall_at_1000
value: 65.447
- type: recall_at_3
value: 10.706
- type: recall_at_5
value: 13.158
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.378000000000004
- type: map_at_10
value: 42.178
- type: map_at_100
value: 43.32
- type: map_at_1000
value: 43.358000000000004
- type: map_at_3
value: 37.474000000000004
- type: map_at_5
value: 40.333000000000006
- type: mrr_at_1
value: 30.823
- type: mrr_at_10
value: 44.626
- type: mrr_at_100
value: 45.494
- type: mrr_at_1000
value: 45.519
- type: mrr_at_3
value: 40.585
- type: mrr_at_5
value: 43.146
- type: ndcg_at_1
value: 30.794
- type: ndcg_at_10
value: 50.099000000000004
- type: ndcg_at_100
value: 54.900999999999996
- type: ndcg_at_1000
value: 55.69499999999999
- type: ndcg_at_3
value: 41.238
- type: ndcg_at_5
value: 46.081
- type: precision_at_1
value: 30.794
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 18.926000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.378000000000004
- type: recall_at_10
value: 71.842
- type: recall_at_100
value: 92.565
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 49.053999999999995
- type: recall_at_5
value: 60.207
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.557
- type: map_at_10
value: 84.729
- type: map_at_100
value: 85.369
- type: map_at_1000
value: 85.382
- type: map_at_3
value: 81.72
- type: map_at_5
value: 83.613
- type: mrr_at_1
value: 81.3
- type: mrr_at_10
value: 87.488
- type: mrr_at_100
value: 87.588
- type: mrr_at_1000
value: 87.589
- type: mrr_at_3
value: 86.53
- type: mrr_at_5
value: 87.18599999999999
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 89.637
- type: ndcg_at_1000
value: 89.70700000000001
- type: ndcg_at_3
value: 85.55199999999999
- type: ndcg_at_5
value: 87.154
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.489999999999998
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.708
- type: recall_at_1
value: 70.557
- type: recall_at_10
value: 95.645
- type: recall_at_100
value: 99.693
- type: recall_at_1000
value: 99.995
- type: recall_at_3
value: 87.359
- type: recall_at_5
value: 91.89699999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.65060114776209
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.63271250680617
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.263
- type: map_at_10
value: 10.801
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.224
- type: map_at_3
value: 7.362
- type: map_at_5
value: 9.149000000000001
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 31.416
- type: mrr_at_100
value: 32.513
- type: mrr_at_1000
value: 32.58
- type: mrr_at_3
value: 28.116999999999997
- type: mrr_at_5
value: 29.976999999999997
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 18.551000000000002
- type: ndcg_at_100
value: 26.657999999999998
- type: ndcg_at_1000
value: 32.485
- type: ndcg_at_3
value: 16.834
- type: ndcg_at_5
value: 15.204999999999998
- type: precision_at_1
value: 21
- type: precision_at_10
value: 9.84
- type: precision_at_100
value: 2.16
- type: precision_at_1000
value: 0.35500000000000004
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 13.62
- type: recall_at_1
value: 4.263
- type: recall_at_10
value: 19.922
- type: recall_at_100
value: 43.808
- type: recall_at_1000
value: 72.14500000000001
- type: recall_at_3
value: 9.493
- type: recall_at_5
value: 13.767999999999999
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.27446313317233
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 76.27963301217527
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 88.18495048450949
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.91982338692046
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 89.00896818385291
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.48814644586132
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.30116926966582
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 67.74132963032342
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.87741355780479
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.0019012295875
- type: mrr
value: 94.70267024188593
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.05
- type: map_at_10
value: 59.36
- type: map_at_100
value: 59.967999999999996
- type: map_at_1000
value: 60.023
- type: map_at_3
value: 56.515
- type: map_at_5
value: 58.272999999999996
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.102000000000004
- type: mrr_at_100
value: 61.476
- type: mrr_at_1000
value: 61.523
- type: mrr_at_3
value: 58.778
- type: mrr_at_5
value: 60.128
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 64.43100000000001
- type: ndcg_at_100
value: 66.73599999999999
- type: ndcg_at_1000
value: 68.027
- type: ndcg_at_3
value: 59.279
- type: ndcg_at_5
value: 61.888
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.767
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.444000000000003
- type: precision_at_5
value: 15.667
- type: recall_at_1
value: 50.05
- type: recall_at_10
value: 78.511
- type: recall_at_100
value: 88.5
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.117
- type: recall_at_5
value: 70.867
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72178217821782
- type: cos_sim_ap
value: 93.0728601593541
- type: cos_sim_f1
value: 85.6727976766699
- type: cos_sim_precision
value: 83.02063789868667
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.72178217821782
- type: dot_ap
value: 93.07287396168348
- type: dot_f1
value: 85.6727976766699
- type: dot_precision
value: 83.02063789868667
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.72178217821782
- type: euclidean_ap
value: 93.07285657982895
- type: euclidean_f1
value: 85.6727976766699
- type: euclidean_precision
value: 83.02063789868667
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.72475247524753
- type: manhattan_ap
value: 93.02792973059809
- type: manhattan_f1
value: 85.7727737973388
- type: manhattan_precision
value: 87.84067085953879
- type: manhattan_recall
value: 83.8
- type: max_accuracy
value: 99.72475247524753
- type: max_ap
value: 93.07287396168348
- type: max_f1
value: 85.7727737973388
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.77583615550819
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.151636938606956
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.16607939471187
- type: mrr
value: 52.95172046091163
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.314646669495666
- type: cos_sim_spearman
value: 31.83562491439455
- type: dot_pearson
value: 31.314590842874157
- type: dot_spearman
value: 31.83363065810437
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.3010000000000002
- type: map_at_100
value: 7.2139999999999995
- type: map_at_1000
value: 20.179
- type: map_at_3
value: 0.528
- type: map_at_5
value: 0.8019999999999999
- type: mrr_at_1
value: 72
- type: mrr_at_10
value: 83.39999999999999
- type: mrr_at_100
value: 83.39999999999999
- type: mrr_at_1000
value: 83.39999999999999
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 83.06700000000001
- type: ndcg_at_1
value: 66
- type: ndcg_at_10
value: 58.059000000000005
- type: ndcg_at_100
value: 44.316
- type: ndcg_at_1000
value: 43.147000000000006
- type: ndcg_at_3
value: 63.815999999999995
- type: ndcg_at_5
value: 63.005
- type: precision_at_1
value: 72
- type: precision_at_10
value: 61.4
- type: precision_at_100
value: 45.62
- type: precision_at_1000
value: 19.866
- type: precision_at_3
value: 70
- type: precision_at_5
value: 68.8
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.517
- type: recall_at_100
value: 10.587
- type: recall_at_1000
value: 41.233
- type: recall_at_3
value: 0.573
- type: recall_at_5
value: 0.907
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.894
- type: map_at_10
value: 8.488999999999999
- type: map_at_100
value: 14.445
- type: map_at_1000
value: 16.078
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.019
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 39.82
- type: mrr_at_100
value: 40.752
- type: mrr_at_1000
value: 40.771
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 37.721
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 21.563
- type: ndcg_at_100
value: 33.857
- type: ndcg_at_1000
value: 46.199
- type: ndcg_at_3
value: 22.296
- type: ndcg_at_5
value: 21.770999999999997
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.142999999999999
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 1.894
- type: recall_at_10
value: 14.931
- type: recall_at_100
value: 45.524
- type: recall_at_1000
value: 83.243
- type: recall_at_3
value: 5.712
- type: recall_at_5
value: 8.386000000000001
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.049
- type: ap
value: 13.85116971310922
- type: f1
value: 54.37504302487686
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.1312959818902
- type: f1
value: 64.11413877009383
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.13103431861502
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.327889372355
- type: cos_sim_ap
value: 77.42059895975699
- type: cos_sim_f1
value: 71.02706903250873
- type: cos_sim_precision
value: 69.75324344950394
- type: cos_sim_recall
value: 72.34828496042216
- type: dot_accuracy
value: 87.327889372355
- type: dot_ap
value: 77.4209479346677
- type: dot_f1
value: 71.02706903250873
- type: dot_precision
value: 69.75324344950394
- type: dot_recall
value: 72.34828496042216
- type: euclidean_accuracy
value: 87.327889372355
- type: euclidean_ap
value: 77.42096495861037
- type: euclidean_f1
value: 71.02706903250873
- type: euclidean_precision
value: 69.75324344950394
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.31000774870358
- type: manhattan_ap
value: 77.38930750711619
- type: manhattan_f1
value: 71.07935314027831
- type: manhattan_precision
value: 67.70957726295677
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.327889372355
- type: max_ap
value: 77.42096495861037
- type: max_f1
value: 71.07935314027831
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.58939729110878
- type: cos_sim_ap
value: 87.17594155025475
- type: cos_sim_f1
value: 79.21146953405018
- type: cos_sim_precision
value: 76.8918527109307
- type: cos_sim_recall
value: 81.67539267015707
- type: dot_accuracy
value: 89.58939729110878
- type: dot_ap
value: 87.17593963273593
- type: dot_f1
value: 79.21146953405018
- type: dot_precision
value: 76.8918527109307
- type: dot_recall
value: 81.67539267015707
- type: euclidean_accuracy
value: 89.58939729110878
- type: euclidean_ap
value: 87.17592466925834
- type: euclidean_f1
value: 79.21146953405018
- type: euclidean_precision
value: 76.8918527109307
- type: euclidean_recall
value: 81.67539267015707
- type: manhattan_accuracy
value: 89.62626615438352
- type: manhattan_ap
value: 87.16589873161546
- type: manhattan_f1
value: 79.25143598295348
- type: manhattan_precision
value: 76.39494177323712
- type: manhattan_recall
value: 82.32984293193716
- type: max_accuracy
value: 89.62626615438352
- type: max_ap
value: 87.17594155025475
- type: max_f1
value: 79.25143598295348
---
# hkunlp/instructor-large
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks ([MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard))!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 12/28: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-large) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-large) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-large')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-70m-deduped | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-13T16:01:41 | 2023-07-09T16:07:33 | 191,714 | 25 | ---
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-70M-deduped
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-70M-deduped for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-70M-deduped as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-70M-deduped has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-70M-deduped will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-70M-deduped to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-70M-deduped may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-70M-deduped.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
Pythia-70M-deduped was trained on the Pile **after the dataset has been globally
deduplicated**.<br>
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
intfloat/e5-mistral-7b-instruct | intfloat | feature-extraction | [
"sentence-transformers",
"pytorch",
"safetensors",
"mistral",
"feature-extraction",
"mteb",
"transformers",
"en",
"arxiv:2401.00368",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-12-20T10:17:02 | 2024-04-23T08:03:51 | 183,947 | 502 | ---
language:
- en
license: mit
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: e5-mistral-7b-instruct
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 37.863226091673866
- type: cos_sim_spearman
value: 38.98733013335281
- type: euclidean_pearson
value: 37.51783380497874
- type: euclidean_spearman
value: 38.98733012753365
- type: manhattan_pearson
value: 37.26706888081721
- type: manhattan_spearman
value: 38.709750161903834
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 43.33924583134623
- type: cos_sim_spearman
value: 42.84316155158754
- type: euclidean_pearson
value: 45.62709879515238
- type: euclidean_spearman
value: 42.843155921732404
- type: manhattan_pearson
value: 45.4786950991229
- type: manhattan_spearman
value: 42.657334751855984
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.68656716417911
- type: ap
value: 41.71522322900398
- type: f1
value: 72.37207703532552
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.04710920770879
- type: ap
value: 83.42622221864045
- type: f1
value: 72.14388257905772
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.93103448275862
- type: ap
value: 26.039284760509513
- type: f1
value: 64.81092954450712
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.21627408993577
- type: ap
value: 24.876490553983036
- type: f1
value: 63.8773359684989
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.90679999999999
- type: ap
value: 94.32357863164454
- type: f1
value: 95.90485634708557
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.786
- type: f1
value: 55.31211995815146
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.26
- type: f1
value: 52.156230111544986
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.33
- type: f1
value: 49.195023008878145
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.3
- type: f1
value: 48.434470184108
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.68599999999999
- type: f1
value: 47.62681775202072
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.238
- type: f1
value: 45.014030559653705
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.486000000000004
- type: map_at_10
value: 53.076
- type: map_at_100
value: 53.657999999999994
- type: map_at_1000
value: 53.659
- type: map_at_3
value: 48.234
- type: map_at_5
value: 51.121
- type: mrr_at_1
value: 37.269000000000005
- type: mrr_at_10
value: 53.335
- type: mrr_at_100
value: 53.916
- type: mrr_at_1000
value: 53.918
- type: mrr_at_3
value: 48.518
- type: mrr_at_5
value: 51.406
- type: ndcg_at_1
value: 36.486000000000004
- type: ndcg_at_10
value: 61.882000000000005
- type: ndcg_at_100
value: 64.165
- type: ndcg_at_1000
value: 64.203
- type: ndcg_at_3
value: 52.049
- type: ndcg_at_5
value: 57.199
- type: precision_at_1
value: 36.486000000000004
- type: precision_at_10
value: 8.982999999999999
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.029
- type: precision_at_5
value: 15.092
- type: recall_at_1
value: 36.486000000000004
- type: recall_at_10
value: 89.82900000000001
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 63.087
- type: recall_at_5
value: 75.46199999999999
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 50.45119266859667
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 45.4958298992051
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 66.98177472838887
- type: mrr
value: 79.91854636591478
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.67086498650698
- type: cos_sim_spearman
value: 85.54773239564638
- type: euclidean_pearson
value: 86.48229161588425
- type: euclidean_spearman
value: 85.54773239564638
- type: manhattan_pearson
value: 86.67533327742343
- type: manhattan_spearman
value: 85.76099026691983
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 50.31998888922809
- type: cos_sim_spearman
value: 50.6369940530675
- type: euclidean_pearson
value: 50.055544636296055
- type: euclidean_spearman
value: 50.63699405154838
- type: manhattan_pearson
value: 50.00739378036807
- type: manhattan_spearman
value: 50.607237418676945
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.5615866388309
- type: f1
value: 99.49895615866389
- type: precision
value: 99.46764091858039
- type: recall
value: 99.5615866388309
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.19656614571869
- type: f1
value: 99.08650671362535
- type: precision
value: 99.0314769975787
- type: recall
value: 99.19656614571869
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0256321440942
- type: f1
value: 97.83743216718624
- type: precision
value: 97.74390947927492
- type: recall
value: 98.0256321440942
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.22766368264
- type: precision
value: 99.21011058451816
- type: recall
value: 99.26276987888363
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.22727272727272
- type: f1
value: 88.17411732496673
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 43.530637846246975
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 40.23505728593893
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 44.419028279451275
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 42.5820277929776
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 77.67811726152972
- type: mrr
value: 80.99003968253969
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 78.66055354534922
- type: mrr
value: 81.66119047619047
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.162333333333333
- type: map_at_10
value: 37.22291666666667
- type: map_at_100
value: 38.56733333333333
- type: map_at_1000
value: 38.684250000000006
- type: map_at_3
value: 34.22858333333333
- type: map_at_5
value: 35.852500000000006
- type: mrr_at_1
value: 32.459833333333336
- type: mrr_at_10
value: 41.65358333333333
- type: mrr_at_100
value: 42.566916666666664
- type: mrr_at_1000
value: 42.61766666666667
- type: mrr_at_3
value: 39.210499999999996
- type: mrr_at_5
value: 40.582166666666666
- type: ndcg_at_1
value: 32.459833333333336
- type: ndcg_at_10
value: 42.96758333333333
- type: ndcg_at_100
value: 48.5065
- type: ndcg_at_1000
value: 50.556583333333336
- type: ndcg_at_3
value: 38.004416666666664
- type: ndcg_at_5
value: 40.25916666666667
- type: precision_at_1
value: 32.459833333333336
- type: precision_at_10
value: 7.664583333333333
- type: precision_at_100
value: 1.2349999999999999
- type: precision_at_1000
value: 0.15966666666666668
- type: precision_at_3
value: 17.731166666666663
- type: precision_at_5
value: 12.575333333333335
- type: recall_at_1
value: 27.162333333333333
- type: recall_at_10
value: 55.44158333333334
- type: recall_at_100
value: 79.56966666666666
- type: recall_at_1000
value: 93.45224999999999
- type: recall_at_3
value: 41.433083333333336
- type: recall_at_5
value: 47.31108333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.539
- type: map_at_10
value: 28.494999999999997
- type: map_at_100
value: 30.568
- type: map_at_1000
value: 30.741000000000003
- type: map_at_3
value: 23.846999999999998
- type: map_at_5
value: 26.275
- type: mrr_at_1
value: 37.394
- type: mrr_at_10
value: 50.068
- type: mrr_at_100
value: 50.727
- type: mrr_at_1000
value: 50.751000000000005
- type: mrr_at_3
value: 46.938
- type: mrr_at_5
value: 48.818
- type: ndcg_at_1
value: 37.394
- type: ndcg_at_10
value: 38.349
- type: ndcg_at_100
value: 45.512
- type: ndcg_at_1000
value: 48.321
- type: ndcg_at_3
value: 32.172
- type: ndcg_at_5
value: 34.265
- type: precision_at_1
value: 37.394
- type: precision_at_10
value: 11.927999999999999
- type: precision_at_100
value: 1.966
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 24.126
- type: precision_at_5
value: 18.306
- type: recall_at_1
value: 16.539
- type: recall_at_10
value: 44.504
- type: recall_at_100
value: 68.605
- type: recall_at_1000
value: 84.1
- type: recall_at_3
value: 29.008
- type: recall_at_5
value: 35.58
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.482
- type: map_at_10
value: 28.622999999999998
- type: map_at_100
value: 30.262
- type: map_at_1000
value: 30.432
- type: map_at_3
value: 25.647
- type: map_at_5
value: 27.128000000000004
- type: mrr_at_1
value: 30.408
- type: mrr_at_10
value: 37.188
- type: mrr_at_100
value: 38.196000000000005
- type: mrr_at_1000
value: 38.273
- type: mrr_at_3
value: 35.067
- type: mrr_at_5
value: 36.124
- type: ndcg_at_1
value: 30.408
- type: ndcg_at_10
value: 34.215
- type: ndcg_at_100
value: 41.349999999999994
- type: ndcg_at_1000
value: 44.689
- type: ndcg_at_3
value: 30.264999999999997
- type: ndcg_at_5
value: 31.572
- type: precision_at_1
value: 30.408
- type: precision_at_10
value: 7.6770000000000005
- type: precision_at_100
value: 1.352
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 17.213
- type: precision_at_5
value: 12.198
- type: recall_at_1
value: 19.482
- type: recall_at_10
value: 42.368
- type: recall_at_100
value: 72.694
- type: recall_at_1000
value: 95.602
- type: recall_at_3
value: 30.101
- type: recall_at_5
value: 34.708
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 71.16055321707758
- type: cos_sim_ap
value: 80.21073839711723
- type: cos_sim_f1
value: 72.9740932642487
- type: cos_sim_precision
value: 65.53136050623488
- type: cos_sim_recall
value: 82.3240589198036
- type: dot_accuracy
value: 71.16055321707758
- type: dot_ap
value: 80.212299264122
- type: dot_f1
value: 72.9740932642487
- type: dot_precision
value: 65.53136050623488
- type: dot_recall
value: 82.3240589198036
- type: euclidean_accuracy
value: 71.16055321707758
- type: euclidean_ap
value: 80.21076298680417
- type: euclidean_f1
value: 72.9740932642487
- type: euclidean_precision
value: 65.53136050623488
- type: euclidean_recall
value: 82.3240589198036
- type: manhattan_accuracy
value: 70.71557426337944
- type: manhattan_ap
value: 79.93448977199749
- type: manhattan_f1
value: 72.83962726826877
- type: manhattan_precision
value: 62.7407908077053
- type: manhattan_recall
value: 86.81318681318682
- type: max_accuracy
value: 71.16055321707758
- type: max_ap
value: 80.212299264122
- type: max_f1
value: 72.9740932642487
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 60.643
- type: map_at_10
value: 69.011
- type: map_at_100
value: 69.533
- type: map_at_1000
value: 69.545
- type: map_at_3
value: 67.167
- type: map_at_5
value: 68.12700000000001
- type: mrr_at_1
value: 60.801
- type: mrr_at_10
value: 69.111
- type: mrr_at_100
value: 69.6
- type: mrr_at_1000
value: 69.611
- type: mrr_at_3
value: 67.229
- type: mrr_at_5
value: 68.214
- type: ndcg_at_1
value: 60.801
- type: ndcg_at_10
value: 73.128
- type: ndcg_at_100
value: 75.614
- type: ndcg_at_1000
value: 75.92
- type: ndcg_at_3
value: 69.261
- type: ndcg_at_5
value: 70.973
- type: precision_at_1
value: 60.801
- type: precision_at_10
value: 8.662
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 25.149
- type: precision_at_5
value: 15.953999999999999
- type: recall_at_1
value: 60.643
- type: recall_at_10
value: 85.959
- type: recall_at_100
value: 97.576
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 75.184
- type: recall_at_5
value: 79.32000000000001
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.183
- type: map_at_10
value: 23.958
- type: map_at_100
value: 34.354
- type: map_at_1000
value: 36.442
- type: map_at_3
value: 16.345000000000002
- type: map_at_5
value: 19.647000000000002
- type: mrr_at_1
value: 74.25
- type: mrr_at_10
value: 80.976
- type: mrr_at_100
value: 81.256
- type: mrr_at_1000
value: 81.262
- type: mrr_at_3
value: 79.958
- type: mrr_at_5
value: 80.37100000000001
- type: ndcg_at_1
value: 62.0
- type: ndcg_at_10
value: 48.894999999999996
- type: ndcg_at_100
value: 53.867
- type: ndcg_at_1000
value: 61.304
- type: ndcg_at_3
value: 53.688
- type: ndcg_at_5
value: 50.900999999999996
- type: precision_at_1
value: 74.25
- type: precision_at_10
value: 39.525
- type: precision_at_100
value: 12.323
- type: precision_at_1000
value: 2.539
- type: precision_at_3
value: 57.49999999999999
- type: precision_at_5
value: 49.1
- type: recall_at_1
value: 10.183
- type: recall_at_10
value: 29.296
- type: recall_at_100
value: 60.394999999999996
- type: recall_at_1000
value: 83.12
- type: recall_at_3
value: 17.495
- type: recall_at_5
value: 22.235
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.613999999999997
- type: map_at_10
value: 79.77300000000001
- type: map_at_100
value: 82.71
- type: map_at_1000
value: 82.75
- type: map_at_3
value: 55.92700000000001
- type: map_at_5
value: 70.085
- type: mrr_at_1
value: 90.7
- type: mrr_at_10
value: 93.438
- type: mrr_at_100
value: 93.504
- type: mrr_at_1000
value: 93.50699999999999
- type: mrr_at_3
value: 93.125
- type: mrr_at_5
value: 93.34
- type: ndcg_at_1
value: 90.7
- type: ndcg_at_10
value: 87.023
- type: ndcg_at_100
value: 90.068
- type: ndcg_at_1000
value: 90.43299999999999
- type: ndcg_at_3
value: 86.339
- type: ndcg_at_5
value: 85.013
- type: precision_at_1
value: 90.7
- type: precision_at_10
value: 41.339999999999996
- type: precision_at_100
value: 4.806
- type: precision_at_1000
value: 0.48900000000000005
- type: precision_at_3
value: 76.983
- type: precision_at_5
value: 64.69
- type: recall_at_1
value: 26.613999999999997
- type: recall_at_10
value: 87.681
- type: recall_at_100
value: 97.44699999999999
- type: recall_at_1000
value: 99.348
- type: recall_at_3
value: 57.809999999999995
- type: recall_at_5
value: 74.258
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 30.9
- type: map_at_10
value: 40.467
- type: map_at_100
value: 41.423
- type: map_at_1000
value: 41.463
- type: map_at_3
value: 37.25
- type: map_at_5
value: 39.31
- type: mrr_at_1
value: 30.9
- type: mrr_at_10
value: 40.467
- type: mrr_at_100
value: 41.423
- type: mrr_at_1000
value: 41.463
- type: mrr_at_3
value: 37.25
- type: mrr_at_5
value: 39.31
- type: ndcg_at_1
value: 30.9
- type: ndcg_at_10
value: 45.957
- type: ndcg_at_100
value: 50.735
- type: ndcg_at_1000
value: 51.861999999999995
- type: ndcg_at_3
value: 39.437
- type: ndcg_at_5
value: 43.146
- type: precision_at_1
value: 30.9
- type: precision_at_10
value: 6.35
- type: precision_at_100
value: 0.861
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 15.267
- type: precision_at_5
value: 10.96
- type: recall_at_1
value: 30.9
- type: recall_at_10
value: 63.5
- type: recall_at_100
value: 86.1
- type: recall_at_1000
value: 95.1
- type: recall_at_3
value: 45.800000000000004
- type: recall_at_5
value: 54.800000000000004
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.765
- type: f1
value: 45.93242203574485
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 75.138
- type: map_at_10
value: 84.21300000000001
- type: map_at_100
value: 84.43
- type: map_at_1000
value: 84.441
- type: map_at_3
value: 83.071
- type: map_at_5
value: 83.853
- type: mrr_at_1
value: 80.948
- type: mrr_at_10
value: 88.175
- type: mrr_at_100
value: 88.24
- type: mrr_at_1000
value: 88.241
- type: mrr_at_3
value: 87.516
- type: mrr_at_5
value: 87.997
- type: ndcg_at_1
value: 80.948
- type: ndcg_at_10
value: 87.84100000000001
- type: ndcg_at_100
value: 88.576
- type: ndcg_at_1000
value: 88.75699999999999
- type: ndcg_at_3
value: 86.176
- type: ndcg_at_5
value: 87.214
- type: precision_at_1
value: 80.948
- type: precision_at_10
value: 10.632
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.193
- type: precision_at_5
value: 20.663
- type: recall_at_1
value: 75.138
- type: recall_at_10
value: 94.89699999999999
- type: recall_at_100
value: 97.751
- type: recall_at_1000
value: 98.833
- type: recall_at_3
value: 90.455
- type: recall_at_5
value: 93.085
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.45
- type: map_at_10
value: 48.596000000000004
- type: map_at_100
value: 50.70400000000001
- type: map_at_1000
value: 50.83800000000001
- type: map_at_3
value: 42.795
- type: map_at_5
value: 46.085
- type: mrr_at_1
value: 56.172999999999995
- type: mrr_at_10
value: 64.35300000000001
- type: mrr_at_100
value: 64.947
- type: mrr_at_1000
value: 64.967
- type: mrr_at_3
value: 62.653999999999996
- type: mrr_at_5
value: 63.534
- type: ndcg_at_1
value: 56.172999999999995
- type: ndcg_at_10
value: 56.593
- type: ndcg_at_100
value: 62.942
- type: ndcg_at_1000
value: 64.801
- type: ndcg_at_3
value: 53.024
- type: ndcg_at_5
value: 53.986999999999995
- type: precision_at_1
value: 56.172999999999995
- type: precision_at_10
value: 15.494
- type: precision_at_100
value: 2.222
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 35.185
- type: precision_at_5
value: 25.556
- type: recall_at_1
value: 29.45
- type: recall_at_10
value: 62.882000000000005
- type: recall_at_100
value: 85.56099999999999
- type: recall_at_1000
value: 96.539
- type: recall_at_3
value: 47.911
- type: recall_at_5
value: 54.52
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.581
- type: map_at_10
value: 68.401
- type: map_at_100
value: 69.207
- type: map_at_1000
value: 69.25200000000001
- type: map_at_3
value: 64.689
- type: map_at_5
value: 67.158
- type: mrr_at_1
value: 79.163
- type: mrr_at_10
value: 85.22999999999999
- type: mrr_at_100
value: 85.386
- type: mrr_at_1000
value: 85.39099999999999
- type: mrr_at_3
value: 84.432
- type: mrr_at_5
value: 84.952
- type: ndcg_at_1
value: 79.163
- type: ndcg_at_10
value: 75.721
- type: ndcg_at_100
value: 78.411
- type: ndcg_at_1000
value: 79.23599999999999
- type: ndcg_at_3
value: 70.68799999999999
- type: ndcg_at_5
value: 73.694
- type: precision_at_1
value: 79.163
- type: precision_at_10
value: 16.134
- type: precision_at_100
value: 1.821
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 46.446
- type: precision_at_5
value: 30.242
- type: recall_at_1
value: 39.581
- type: recall_at_10
value: 80.66799999999999
- type: recall_at_100
value: 91.033
- type: recall_at_1000
value: 96.408
- type: recall_at_3
value: 69.669
- type: recall_at_5
value: 75.604
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 45.04809542131589
- type: f1
value: 37.01181779071118
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.78120000000001
- type: ap
value: 92.52931921594387
- type: f1
value: 94.77902110732532
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.430320593468394
- type: f1
value: 79.95467268178068
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.05801751913393
- type: cos_sim_spearman
value: 75.47954644971965
- type: euclidean_pearson
value: 74.27472296759713
- type: euclidean_spearman
value: 75.47954201369866
- type: manhattan_pearson
value: 74.30508190186474
- type: manhattan_spearman
value: 75.51326518159436
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 24.21110921666315
- type: mrr
value: 22.863492063492064
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 61.38400000000001
- type: map_at_10
value: 70.895
- type: map_at_100
value: 71.314
- type: map_at_1000
value: 71.331
- type: map_at_3
value: 69.016
- type: map_at_5
value: 70.179
- type: mrr_at_1
value: 63.481
- type: mrr_at_10
value: 71.543
- type: mrr_at_100
value: 71.91300000000001
- type: mrr_at_1000
value: 71.928
- type: mrr_at_3
value: 69.90899999999999
- type: mrr_at_5
value: 70.907
- type: ndcg_at_1
value: 63.481
- type: ndcg_at_10
value: 74.833
- type: ndcg_at_100
value: 76.705
- type: ndcg_at_1000
value: 77.13600000000001
- type: ndcg_at_3
value: 71.236
- type: ndcg_at_5
value: 73.199
- type: precision_at_1
value: 63.481
- type: precision_at_10
value: 9.179
- type: precision_at_100
value: 1.011
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 27.044
- type: precision_at_5
value: 17.272000000000002
- type: recall_at_1
value: 61.38400000000001
- type: recall_at_10
value: 86.318
- type: recall_at_100
value: 94.786
- type: recall_at_1000
value: 98.14500000000001
- type: recall_at_3
value: 76.717
- type: recall_at_5
value: 81.416
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.363999999999997
- type: map_at_10
value: 36.022
- type: map_at_100
value: 37.229
- type: map_at_1000
value: 37.274
- type: map_at_3
value: 32.131
- type: map_at_5
value: 34.391
- type: mrr_at_1
value: 24.069
- type: mrr_at_10
value: 36.620000000000005
- type: mrr_at_100
value: 37.769999999999996
- type: mrr_at_1000
value: 37.809
- type: mrr_at_3
value: 32.846
- type: mrr_at_5
value: 35.02
- type: ndcg_at_1
value: 24.069
- type: ndcg_at_10
value: 43.056
- type: ndcg_at_100
value: 48.754
- type: ndcg_at_1000
value: 49.829
- type: ndcg_at_3
value: 35.167
- type: ndcg_at_5
value: 39.168
- type: precision_at_1
value: 24.069
- type: precision_at_10
value: 6.762
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.957
- type: precision_at_5
value: 11.023
- type: recall_at_1
value: 23.363999999999997
- type: recall_at_10
value: 64.696
- type: recall_at_100
value: 90.795
- type: recall_at_1000
value: 98.892
- type: recall_at_3
value: 43.247
- type: recall_at_5
value: 52.86300000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.11947104423166
- type: f1
value: 95.89561841159332
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.97548605240912
- type: f1
value: 92.17133696717212
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.37224816544364
- type: f1
value: 93.19978829237863
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.28719072972127
- type: f1
value: 91.28448045979604
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.8131946934385
- type: f1
value: 88.27883019362747
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.52260397830018
- type: f1
value: 85.15528226728568
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 86.10807113543093
- type: f1
value: 70.88498219072167
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.77120315581854
- type: f1
value: 57.97153920153224
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.93995997331554
- type: f1
value: 58.839203810064866
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.801440651425
- type: f1
value: 58.68009647839332
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.90785227680172
- type: f1
value: 49.83760954655788
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.24050632911391
- type: f1
value: 52.0562553541082
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.47948890383321
- type: f1
value: 63.334877563135485
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.2871553463349
- type: f1
value: 43.17658050605427
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.174176193678555
- type: f1
value: 59.236659587042425
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.226630800269
- type: f1
value: 60.951842696956184
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.94283792871555
- type: f1
value: 61.40057652844215
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.480833893745796
- type: f1
value: 52.5298332072816
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.52858103564223
- type: f1
value: 69.3770851919204
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.09213180901143
- type: f1
value: 71.13518469365879
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.31203765971756
- type: f1
value: 66.05906970865144
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.57162071284465
- type: f1
value: 77.7866172598823
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.09414929388029
- type: f1
value: 72.5712594833695
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.20914593140553
- type: f1
value: 68.90619124909186
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.74243443174176
- type: f1
value: 64.72743141749955
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.11096166778749
- type: f1
value: 72.61849933064694
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (he)
type: mteb/amazon_massive_intent
config: he
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.22394082044384
- type: f1
value: 62.43648797607235
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hi)
type: mteb/amazon_massive_intent
config: hi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.44855413584399
- type: f1
value: 66.56851670913659
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hu)
type: mteb/amazon_massive_intent
config: hu
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.4149293880296
- type: f1
value: 66.12960877904776
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (hy)
type: mteb/amazon_massive_intent
config: hy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 56.916610625420304
- type: f1
value: 54.02534600927991
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (id)
type: mteb/amazon_massive_intent
config: id
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.71351714862138
- type: f1
value: 69.70227985126316
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (is)
type: mteb/amazon_massive_intent
config: is
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 59.91257565568257
- type: f1
value: 57.06811572144974
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (it)
type: mteb/amazon_massive_intent
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.25218560860793
- type: f1
value: 72.48057563104247
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ja)
type: mteb/amazon_massive_intent
config: ja
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.35507733691998
- type: f1
value: 73.03024649541128
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (jv)
type: mteb/amazon_massive_intent
config: jv
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 57.918628110289184
- type: f1
value: 54.75590124456177
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ka)
type: mteb/amazon_massive_intent
config: ka
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 52.548755884330866
- type: f1
value: 51.5356975360209
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (km)
type: mteb/amazon_massive_intent
config: km
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 46.44922663080027
- type: f1
value: 44.561114416830975
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (kn)
type: mteb/amazon_massive_intent
config: kn
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dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.87020847343645
- type: f1
value: 78.224240866849
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.6570275722932
- type: f1
value: 63.274871811412545
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 57.760591795561524
- type: f1
value: 56.73711528075771
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 57.26967047747142
- type: f1
value: 55.74735330863165
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.46133154001345
- type: f1
value: 71.9644168952811
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.70880968392737
- type: f1
value: 73.61543141070884
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.0437121721587
- type: f1
value: 74.83359868879921
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.05110961667788
- type: f1
value: 66.25869819274315
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.52118359112306
- type: f1
value: 75.92098546052303
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.92938802958977
- type: f1
value: 79.79833572573796
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.86617350369872
- type: f1
value: 77.42645654909516
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 44.6
- type: map_at_10
value: 50.019000000000005
- type: map_at_100
value: 50.611
- type: map_at_1000
value: 50.67
- type: map_at_3
value: 48.699999999999996
- type: map_at_5
value: 49.455
- type: mrr_at_1
value: 44.800000000000004
- type: mrr_at_10
value: 50.119
- type: mrr_at_100
value: 50.711
- type: mrr_at_1000
value: 50.77
- type: mrr_at_3
value: 48.8
- type: mrr_at_5
value: 49.555
- type: ndcg_at_1
value: 44.6
- type: ndcg_at_10
value: 52.754
- type: ndcg_at_100
value: 55.935
- type: ndcg_at_1000
value: 57.607
- type: ndcg_at_3
value: 50.012
- type: ndcg_at_5
value: 51.393
- type: precision_at_1
value: 44.6
- type: precision_at_10
value: 6.140000000000001
- type: precision_at_100
value: 0.77
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 17.933
- type: precision_at_5
value: 11.44
- type: recall_at_1
value: 44.6
- type: recall_at_10
value: 61.4
- type: recall_at_100
value: 77.0
- type: recall_at_1000
value: 90.4
- type: recall_at_3
value: 53.800000000000004
- type: recall_at_5
value: 57.199999999999996
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 38.192667527616315
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 37.44738902946689
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.59661273103955
- type: mrr
value: 33.82024242497473
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 73.31333333333335
- type: f1
value: 73.0873466527602
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.471
- type: map_at_10
value: 14.142
- type: map_at_100
value: 18.179000000000002
- type: map_at_1000
value: 19.772000000000002
- type: map_at_3
value: 9.716
- type: map_at_5
value: 11.763
- type: mrr_at_1
value: 51.393
- type: mrr_at_10
value: 58.814
- type: mrr_at_100
value: 59.330000000000005
- type: mrr_at_1000
value: 59.35
- type: mrr_at_3
value: 56.398
- type: mrr_at_5
value: 58.038999999999994
- type: ndcg_at_1
value: 49.69
- type: ndcg_at_10
value: 38.615
- type: ndcg_at_100
value: 35.268
- type: ndcg_at_1000
value: 43.745
- type: ndcg_at_3
value: 43.187
- type: ndcg_at_5
value: 41.528999999999996
- type: precision_at_1
value: 51.083999999999996
- type: precision_at_10
value: 29.474
- type: precision_at_100
value: 9.167
- type: precision_at_1000
value: 2.2089999999999996
- type: precision_at_3
value: 40.351
- type: precision_at_5
value: 36.285000000000004
- type: recall_at_1
value: 5.471
- type: recall_at_10
value: 19.242
- type: recall_at_100
value: 37.14
- type: recall_at_1000
value: 68.35900000000001
- type: recall_at_3
value: 10.896
- type: recall_at_5
value: 14.75
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.499
- type: map_at_10
value: 55.862
- type: map_at_100
value: 56.667
- type: map_at_1000
value: 56.684999999999995
- type: map_at_3
value: 51.534
- type: map_at_5
value: 54.2
- type: mrr_at_1
value: 44.351
- type: mrr_at_10
value: 58.567
- type: mrr_at_100
value: 59.099000000000004
- type: mrr_at_1000
value: 59.109
- type: mrr_at_3
value: 55.218999999999994
- type: mrr_at_5
value: 57.391999999999996
- type: ndcg_at_1
value: 44.322
- type: ndcg_at_10
value: 63.535
- type: ndcg_at_100
value: 66.654
- type: ndcg_at_1000
value: 66.991
- type: ndcg_at_3
value: 55.701
- type: ndcg_at_5
value: 60.06700000000001
- type: precision_at_1
value: 44.322
- type: precision_at_10
value: 10.026
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 24.865000000000002
- type: precision_at_5
value: 17.48
- type: recall_at_1
value: 39.499
- type: recall_at_10
value: 84.053
- type: recall_at_100
value: 97.11
- type: recall_at_1000
value: 99.493
- type: recall_at_3
value: 64.091
- type: recall_at_5
value: 74.063
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 61.18029236599891
- type: cos_sim_ap
value: 64.18398769398412
- type: cos_sim_f1
value: 67.96347757046446
- type: cos_sim_precision
value: 54.4529262086514
- type: cos_sim_recall
value: 90.3907074973601
- type: dot_accuracy
value: 61.18029236599891
- type: dot_ap
value: 64.18393484706077
- type: dot_f1
value: 67.96347757046446
- type: dot_precision
value: 54.4529262086514
- type: dot_recall
value: 90.3907074973601
- type: euclidean_accuracy
value: 61.18029236599891
- type: euclidean_ap
value: 64.18395024821486
- type: euclidean_f1
value: 67.96347757046446
- type: euclidean_precision
value: 54.4529262086514
- type: euclidean_recall
value: 90.3907074973601
- type: manhattan_accuracy
value: 61.451001624255554
- type: manhattan_ap
value: 64.38232708763513
- type: manhattan_f1
value: 68.05860805860804
- type: manhattan_precision
value: 52.10319685922602
- type: manhattan_recall
value: 98.09926082365365
- type: max_accuracy
value: 61.451001624255554
- type: max_ap
value: 64.38232708763513
- type: max_f1
value: 68.05860805860804
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 92.19000000000001
- type: ap
value: 89.73918431886767
- type: f1
value: 92.17175032574507
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 15.079320253752224
- type: cos_sim_spearman
value: 16.813772504404263
- type: euclidean_pearson
value: 19.476541162041762
- type: euclidean_spearman
value: 16.813772498098782
- type: manhattan_pearson
value: 19.497429832915277
- type: manhattan_spearman
value: 16.869600674180607
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 30.36139599797913
- type: cos_sim_spearman
value: 31.80296402851347
- type: euclidean_pearson
value: 30.10387888252793
- type: euclidean_spearman
value: 31.80297780103808
- type: manhattan_pearson
value: 30.86720382849436
- type: manhattan_spearman
value: 32.70491131366606
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.911
- type: map_at_10
value: 86.087
- type: map_at_100
value: 86.701
- type: map_at_1000
value: 86.715
- type: map_at_3
value: 83.231
- type: map_at_5
value: 85.051
- type: mrr_at_1
value: 82.75
- type: mrr_at_10
value: 88.759
- type: mrr_at_100
value: 88.844
- type: mrr_at_1000
value: 88.844
- type: mrr_at_3
value: 87.935
- type: mrr_at_5
value: 88.504
- type: ndcg_at_1
value: 82.75
- type: ndcg_at_10
value: 89.605
- type: ndcg_at_100
value: 90.664
- type: ndcg_at_1000
value: 90.733
- type: ndcg_at_3
value: 87.03
- type: ndcg_at_5
value: 88.473
- type: precision_at_1
value: 82.75
- type: precision_at_10
value: 13.575000000000001
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.153
- type: precision_at_5
value: 25.008000000000003
- type: recall_at_1
value: 71.911
- type: recall_at_10
value: 96.261
- type: recall_at_100
value: 99.72800000000001
- type: recall_at_1000
value: 99.993
- type: recall_at_3
value: 88.762
- type: recall_at_5
value: 92.949
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 57.711581165572376
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 66.48938885750297
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.7379999999999995
- type: map_at_10
value: 9.261
- type: map_at_100
value: 11.001
- type: map_at_1000
value: 11.262
- type: map_at_3
value: 6.816
- type: map_at_5
value: 8.0
- type: mrr_at_1
value: 18.4
- type: mrr_at_10
value: 28.755999999999997
- type: mrr_at_100
value: 29.892000000000003
- type: mrr_at_1000
value: 29.961
- type: mrr_at_3
value: 25.467000000000002
- type: mrr_at_5
value: 27.332
- type: ndcg_at_1
value: 18.4
- type: ndcg_at_10
value: 16.296
- type: ndcg_at_100
value: 23.52
- type: ndcg_at_1000
value: 28.504
- type: ndcg_at_3
value: 15.485
- type: ndcg_at_5
value: 13.471
- type: precision_at_1
value: 18.4
- type: precision_at_10
value: 8.469999999999999
- type: precision_at_100
value: 1.8950000000000002
- type: precision_at_1000
value: 0.309
- type: precision_at_3
value: 14.6
- type: precision_at_5
value: 11.84
- type: recall_at_1
value: 3.7379999999999995
- type: recall_at_10
value: 17.185
- type: recall_at_100
value: 38.397
- type: recall_at_1000
value: 62.798
- type: recall_at_3
value: 8.896999999999998
- type: recall_at_5
value: 12.021999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.43977757480083
- type: cos_sim_spearman
value: 82.64182475199533
- type: euclidean_pearson
value: 83.71756009999591
- type: euclidean_spearman
value: 82.64182331395057
- type: manhattan_pearson
value: 83.8028936913025
- type: manhattan_spearman
value: 82.71024597804252
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.85653060698912
- type: cos_sim_spearman
value: 79.65598885228324
- type: euclidean_pearson
value: 83.1205137628455
- type: euclidean_spearman
value: 79.65629387709038
- type: manhattan_pearson
value: 83.71108853545837
- type: manhattan_spearman
value: 80.25617619716708
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.22921688565664
- type: cos_sim_spearman
value: 88.42662103041957
- type: euclidean_pearson
value: 87.91679798473325
- type: euclidean_spearman
value: 88.42662103041957
- type: manhattan_pearson
value: 88.16927537961303
- type: manhattan_spearman
value: 88.81581680062541
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.77261424554293
- type: cos_sim_spearman
value: 84.53930146434155
- type: euclidean_pearson
value: 85.67420491389697
- type: euclidean_spearman
value: 84.53929771783851
- type: manhattan_pearson
value: 85.74306784515618
- type: manhattan_spearman
value: 84.7399304675314
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 89.86138395166455
- type: cos_sim_spearman
value: 90.42577823022054
- type: euclidean_pearson
value: 89.8787763797515
- type: euclidean_spearman
value: 90.42577823022054
- type: manhattan_pearson
value: 89.9592937492158
- type: manhattan_spearman
value: 90.63535505335524
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 86.5176674585941
- type: cos_sim_spearman
value: 87.6842917085397
- type: euclidean_pearson
value: 86.70213081520711
- type: euclidean_spearman
value: 87.6842917085397
- type: manhattan_pearson
value: 86.83702628983627
- type: manhattan_spearman
value: 87.87791000374443
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.86395454805867
- type: cos_sim_spearman
value: 83.69454595252267
- type: euclidean_pearson
value: 83.04743892608313
- type: euclidean_spearman
value: 83.69454026433006
- type: manhattan_pearson
value: 83.4032095553322
- type: manhattan_spearman
value: 84.11527379013802
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.80249894729546
- type: cos_sim_spearman
value: 81.87004960533409
- type: euclidean_pearson
value: 80.0392760044179
- type: euclidean_spearman
value: 81.87004960533409
- type: manhattan_pearson
value: 80.38096542355912
- type: manhattan_spearman
value: 82.40774679630341
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.6158201787172
- type: cos_sim_spearman
value: 77.934651044009
- type: euclidean_pearson
value: 77.7874683895269
- type: euclidean_spearman
value: 77.934651044009
- type: manhattan_pearson
value: 78.36151849193052
- type: manhattan_spearman
value: 78.52439586349938
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.04363311392207
- type: cos_sim_spearman
value: 87.30483659369973
- type: euclidean_pearson
value: 87.62634489502616
- type: euclidean_spearman
value: 87.30483659369973
- type: manhattan_pearson
value: 88.02340837141445
- type: manhattan_spearman
value: 87.55012003294
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 91.69172851958248
- type: cos_sim_spearman
value: 91.7546879482416
- type: euclidean_pearson
value: 91.84843039183963
- type: euclidean_spearman
value: 91.7546879482416
- type: manhattan_pearson
value: 91.72325753804357
- type: manhattan_spearman
value: 91.55330259513397
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 73.95572901084864
- type: cos_sim_spearman
value: 72.56217821552626
- type: euclidean_pearson
value: 74.24242980323574
- type: euclidean_spearman
value: 72.56217821552626
- type: manhattan_pearson
value: 74.57473362519922
- type: manhattan_spearman
value: 72.76048826648497
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.93329396008296
- type: cos_sim_spearman
value: 88.2406635486219
- type: euclidean_pearson
value: 87.49687343908533
- type: euclidean_spearman
value: 88.2406635486219
- type: manhattan_pearson
value: 88.14088309231084
- type: manhattan_spearman
value: 88.93314020908534
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.70124451546057
- type: cos_sim_spearman
value: 87.45988160052252
- type: euclidean_pearson
value: 88.44395505247728
- type: euclidean_spearman
value: 87.45988160052252
- type: manhattan_pearson
value: 88.69269783495425
- type: manhattan_spearman
value: 87.65383425621
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.64109149761346
- type: cos_sim_spearman
value: 88.06459637689733
- type: euclidean_pearson
value: 88.02313315797703
- type: euclidean_spearman
value: 88.06459637689733
- type: manhattan_pearson
value: 88.28328539133253
- type: manhattan_spearman
value: 88.06605708379142
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.9040028177525
- type: cos_sim_spearman
value: 89.68152202933464
- type: euclidean_pearson
value: 89.23684469601253
- type: euclidean_spearman
value: 89.68152202933464
- type: manhattan_pearson
value: 89.59504307277454
- type: manhattan_spearman
value: 89.88060100313582
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.69891585325125
- type: cos_sim_spearman
value: 88.25252785071736
- type: euclidean_pearson
value: 87.99932873748662
- type: euclidean_spearman
value: 88.25252785071736
- type: manhattan_pearson
value: 88.26959683009446
- type: manhattan_spearman
value: 88.32583227300715
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.53235909794135
- type: cos_sim_spearman
value: 66.97521740529574
- type: euclidean_pearson
value: 68.19502223613912
- type: euclidean_spearman
value: 66.97521740529574
- type: manhattan_pearson
value: 68.39070714774539
- type: manhattan_spearman
value: 67.1072812364868
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 43.715742021204775
- type: cos_sim_spearman
value: 49.12255971271453
- type: euclidean_pearson
value: 40.76848562610837
- type: euclidean_spearman
value: 49.12255971271453
- type: manhattan_pearson
value: 40.92204625614112
- type: manhattan_spearman
value: 49.23333793661129
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.35268345563588
- type: cos_sim_spearman
value: 66.99661626042061
- type: euclidean_pearson
value: 65.85589122857066
- type: euclidean_spearman
value: 66.99661626042061
- type: manhattan_pearson
value: 66.78454301512294
- type: manhattan_spearman
value: 67.17570330149233
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 33.36599908204445
- type: cos_sim_spearman
value: 39.20768331939503
- type: euclidean_pearson
value: 22.16066769530468
- type: euclidean_spearman
value: 39.20768331939503
- type: manhattan_pearson
value: 22.386053195546022
- type: manhattan_spearman
value: 39.70172817465986
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.06813956986753
- type: cos_sim_spearman
value: 68.72065117995668
- type: euclidean_pearson
value: 66.97373456344194
- type: euclidean_spearman
value: 68.72065117995668
- type: manhattan_pearson
value: 67.34907265771595
- type: manhattan_spearman
value: 68.73705769957843
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.17664865207108
- type: cos_sim_spearman
value: 54.115568323148864
- type: euclidean_pearson
value: 48.56418162879182
- type: euclidean_spearman
value: 54.115568323148864
- type: manhattan_pearson
value: 48.85951643453165
- type: manhattan_spearman
value: 54.13599784169052
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.87514136275987
- type: cos_sim_spearman
value: 60.82923573674973
- type: euclidean_pearson
value: 53.724183308215615
- type: euclidean_spearman
value: 60.82923573674973
- type: manhattan_pearson
value: 53.954305573102445
- type: manhattan_spearman
value: 60.957483900644526
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.55001413648593
- type: cos_sim_spearman
value: 63.395777040381276
- type: euclidean_pearson
value: 59.869972550293305
- type: euclidean_spearman
value: 63.395777040381276
- type: manhattan_pearson
value: 61.16195496847885
- type: manhattan_spearman
value: 63.41968682525581
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 79.13334972675852
- type: cos_sim_spearman
value: 79.86263136371802
- type: euclidean_pearson
value: 78.2433603592541
- type: euclidean_spearman
value: 79.86263136371802
- type: manhattan_pearson
value: 78.87337106318412
- type: manhattan_spearman
value: 80.31230584758441
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.559700748242356
- type: cos_sim_spearman
value: 60.92342109509558
- type: euclidean_pearson
value: 66.07256437521119
- type: euclidean_spearman
value: 60.92342109509558
- type: manhattan_pearson
value: 67.72769744612663
- type: manhattan_spearman
value: 59.64714507774168
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.93491616145891
- type: cos_sim_spearman
value: 75.84242594400156
- type: euclidean_pearson
value: 74.87279745626121
- type: euclidean_spearman
value: 75.84242594400156
- type: manhattan_pearson
value: 76.47764144677505
- type: manhattan_spearman
value: 77.08411157845183
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.75624124540954
- type: cos_sim_spearman
value: 75.8667941654703
- type: euclidean_pearson
value: 73.74314588451925
- type: euclidean_spearman
value: 75.8667941654703
- type: manhattan_pearson
value: 73.99641425871518
- type: manhattan_spearman
value: 76.1982840205817
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 75.20898141298767
- type: cos_sim_spearman
value: 73.18060375331436
- type: euclidean_pearson
value: 75.44489280944619
- type: euclidean_spearman
value: 73.18060375331436
- type: manhattan_pearson
value: 75.65451039552286
- type: manhattan_spearman
value: 72.97744006123156
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.04278252247816
- type: cos_sim_spearman
value: 71.8846446821539
- type: euclidean_pearson
value: 73.16043307050612
- type: euclidean_spearman
value: 71.8846446821539
- type: manhattan_pearson
value: 74.76905116839777
- type: manhattan_spearman
value: 72.66237093518471
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.71033173838558
- type: cos_sim_spearman
value: 75.043122881885
- type: euclidean_pearson
value: 72.77579680345087
- type: euclidean_spearman
value: 75.043122881885
- type: manhattan_pearson
value: 72.99901534854922
- type: manhattan_spearman
value: 75.15418335015957
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.75733447190482
- type: cos_sim_spearman
value: 61.38968334176681
- type: euclidean_pearson
value: 55.479231520643744
- type: euclidean_spearman
value: 61.38968334176681
- type: manhattan_pearson
value: 56.05230571465244
- type: manhattan_spearman
value: 62.69383054007398
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 41.72244325050302
- type: cos_sim_spearman
value: 54.47476909084119
- type: euclidean_pearson
value: 43.94629756436873
- type: euclidean_spearman
value: 54.47476909084119
- type: manhattan_pearson
value: 46.36533046394657
- type: manhattan_spearman
value: 54.87509243633636
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.75183711835146
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 71.84188960126669
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 73.94847166379994
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 81.78690149086131
- type: cos_sim_spearman
value: 81.81202616916873
- type: euclidean_pearson
value: 80.98792254251062
- type: euclidean_spearman
value: 81.81202616916873
- type: manhattan_pearson
value: 81.46953021346732
- type: manhattan_spearman
value: 82.34259562492315
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.68273341294419
- type: cos_sim_spearman
value: 88.59927164210958
- type: euclidean_pearson
value: 88.10745681818025
- type: euclidean_spearman
value: 88.59927164210958
- type: manhattan_pearson
value: 88.25166703784649
- type: manhattan_spearman
value: 88.85343247873482
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.3340463345719
- type: mrr
value: 96.5182611506141
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.967000000000006
- type: map_at_10
value: 71.873
- type: map_at_100
value: 72.271
- type: map_at_1000
value: 72.292
- type: map_at_3
value: 69.006
- type: map_at_5
value: 70.856
- type: mrr_at_1
value: 63.666999999999994
- type: mrr_at_10
value: 72.929
- type: mrr_at_100
value: 73.26
- type: mrr_at_1000
value: 73.282
- type: mrr_at_3
value: 71.111
- type: mrr_at_5
value: 72.328
- type: ndcg_at_1
value: 63.666999999999994
- type: ndcg_at_10
value: 76.414
- type: ndcg_at_100
value: 78.152
- type: ndcg_at_1000
value: 78.604
- type: ndcg_at_3
value: 71.841
- type: ndcg_at_5
value: 74.435
- type: precision_at_1
value: 63.666999999999994
- type: precision_at_10
value: 10.067
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 18.467
- type: recall_at_1
value: 60.967000000000006
- type: recall_at_10
value: 88.922
- type: recall_at_100
value: 96.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 77.228
- type: recall_at_5
value: 83.428
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82277227722773
- type: cos_sim_ap
value: 95.66279851444406
- type: cos_sim_f1
value: 90.9367088607595
- type: cos_sim_precision
value: 92.1025641025641
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.82277227722773
- type: dot_ap
value: 95.66279851444406
- type: dot_f1
value: 90.9367088607595
- type: dot_precision
value: 92.1025641025641
- type: dot_recall
value: 89.8
- type: euclidean_accuracy
value: 99.82277227722773
- type: euclidean_ap
value: 95.66279851444406
- type: euclidean_f1
value: 90.9367088607595
- type: euclidean_precision
value: 92.1025641025641
- type: euclidean_recall
value: 89.8
- type: manhattan_accuracy
value: 99.82673267326733
- type: manhattan_ap
value: 95.86094873177069
- type: manhattan_f1
value: 91.26788357178096
- type: manhattan_precision
value: 90.06815968841285
- type: manhattan_recall
value: 92.5
- type: max_accuracy
value: 99.82673267326733
- type: max_ap
value: 95.86094873177069
- type: max_f1
value: 91.26788357178096
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 73.09533925852372
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 45.90745648090035
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.91147686504404
- type: mrr
value: 56.03900082760377
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.46908662038217
- type: cos_sim_spearman
value: 31.40325730367437
- type: dot_pearson
value: 31.469083969291894
- type: dot_spearman
value: 31.40325730367437
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.90300783402137
- type: mrr
value: 77.06451972574179
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.82
- type: map_at_10
value: 72.32300000000001
- type: map_at_100
value: 76.198
- type: map_at_1000
value: 76.281
- type: map_at_3
value: 50.719
- type: map_at_5
value: 62.326
- type: mrr_at_1
value: 86.599
- type: mrr_at_10
value: 89.751
- type: mrr_at_100
value: 89.876
- type: mrr_at_1000
value: 89.88000000000001
- type: mrr_at_3
value: 89.151
- type: mrr_at_5
value: 89.519
- type: ndcg_at_1
value: 86.599
- type: ndcg_at_10
value: 80.676
- type: ndcg_at_100
value: 85.03
- type: ndcg_at_1000
value: 85.854
- type: ndcg_at_3
value: 82.057
- type: ndcg_at_5
value: 80.537
- type: precision_at_1
value: 86.599
- type: precision_at_10
value: 40.373
- type: precision_at_100
value: 4.95
- type: precision_at_1000
value: 0.514
- type: precision_at_3
value: 71.918
- type: precision_at_5
value: 60.246
- type: recall_at_1
value: 25.82
- type: recall_at_10
value: 79.905
- type: recall_at_100
value: 93.88499999999999
- type: recall_at_1000
value: 98.073
- type: recall_at_3
value: 52.623
- type: recall_at_5
value: 66.233
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 47.050000000000004
- type: f1
value: 45.704071498353294
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.243
- type: map_at_10
value: 2.278
- type: map_at_100
value: 14.221
- type: map_at_1000
value: 33.474
- type: map_at_3
value: 0.7270000000000001
- type: map_at_5
value: 1.183
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 97.0
- type: mrr_at_100
value: 97.0
- type: mrr_at_1000
value: 97.0
- type: mrr_at_3
value: 97.0
- type: mrr_at_5
value: 97.0
- type: ndcg_at_1
value: 90.0
- type: ndcg_at_10
value: 87.249
- type: ndcg_at_100
value: 67.876
- type: ndcg_at_1000
value: 59.205
- type: ndcg_at_3
value: 90.12299999999999
- type: ndcg_at_5
value: 89.126
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 90.8
- type: precision_at_100
value: 69.28
- type: precision_at_1000
value: 25.85
- type: precision_at_3
value: 94.667
- type: precision_at_5
value: 92.80000000000001
- type: recall_at_1
value: 0.243
- type: recall_at_10
value: 2.392
- type: recall_at_100
value: 16.982
- type: recall_at_1000
value: 55.214
- type: recall_at_3
value: 0.745
- type: recall_at_5
value: 1.2229999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.5
- type: f1
value: 67.05501804646966
- type: precision
value: 65.73261904761904
- type: recall
value: 70.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.14450867052022
- type: f1
value: 70.98265895953759
- type: precision
value: 69.26782273603082
- type: recall
value: 75.14450867052022
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 33.170731707317074
- type: f1
value: 29.92876500193573
- type: precision
value: 28.669145894755648
- type: recall
value: 33.170731707317074
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.13333333333333
- type: precision
value: 93.46666666666667
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.6
- type: f1
value: 99.46666666666665
- type: precision
value: 99.4
- type: recall
value: 99.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.39999999999999
- type: precision
value: 96.0
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.5
- type: f1
value: 92.99666666666667
- type: precision
value: 92.31666666666666
- type: recall
value: 94.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.82089552238806
- type: f1
value: 81.59203980099502
- type: precision
value: 79.60199004975124
- type: recall
value: 85.82089552238806
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.5
- type: f1
value: 75.11246031746032
- type: precision
value: 73.38734126984127
- type: recall
value: 79.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.390243902439025
- type: f1
value: 38.48896631823461
- type: precision
value: 36.57220286488579
- type: recall
value: 44.390243902439025
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.57333333333334
- type: precision
value: 86.34166666666665
- type: recall
value: 90.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.82138517618469
- type: f1
value: 85.98651854423423
- type: precision
value: 84.79257073424753
- type: recall
value: 88.82138517618469
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.04347826086956
- type: f1
value: 72.32108147606868
- type: precision
value: 70.37207357859532
- type: recall
value: 77.04347826086956
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.04347826086957
- type: f1
value: 46.88868184955141
- type: precision
value: 44.71730105643149
- type: recall
value: 53.04347826086957
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.0
- type: f1
value: 62.891813186813195
- type: precision
value: 61.037906162464985
- type: recall
value: 68.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.3
- type: f1
value: 82.82000000000001
- type: precision
value: 81.25690476190475
- type: recall
value: 86.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.87816646562122
- type: f1
value: 63.53054933272062
- type: precision
value: 61.47807816331196
- type: recall
value: 68.87816646562122
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.4
- type: f1
value: 68.99388888888889
- type: precision
value: 66.81035714285713
- type: recall
value: 74.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5
- type: f1
value: 87.93666666666667
- type: precision
value: 86.825
- type: recall
value: 90.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.7
- type: f1
value: 88.09
- type: precision
value: 86.85833333333333
- type: recall
value: 90.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.61904761904762
- type: f1
value: 62.30239247214037
- type: precision
value: 60.340702947845806
- type: recall
value: 67.61904761904762
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.81285714285714
- type: precision
value: 72.21570818070818
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.8
- type: f1
value: 89.66666666666667
- type: precision
value: 88.66666666666666
- type: recall
value: 91.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.85666666666665
- type: precision
value: 96.50833333333333
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.39999999999999
- type: f1
value: 93.98333333333333
- type: precision
value: 93.30000000000001
- type: recall
value: 95.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.0
- type: f1
value: 81.31538461538462
- type: precision
value: 79.70666666666666
- type: recall
value: 85.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.60000000000001
- type: f1
value: 89.81888888888888
- type: precision
value: 89.08583333333333
- type: recall
value: 91.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.3
- type: f1
value: 38.8623088023088
- type: precision
value: 37.03755623461505
- type: recall
value: 44.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.75
- type: precision
value: 93.05
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.1
- type: f1
value: 98.8
- type: precision
value: 98.65
- type: recall
value: 99.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.6765498652291
- type: f1
value: 63.991785393402644
- type: precision
value: 61.7343729944808
- type: recall
value: 69.6765498652291
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.79341029341029
- type: precision
value: 40.25098358431692
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 87.19023809523809
- type: precision
value: 86.12595238095237
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.72727272727273
- type: f1
value: 37.78789518562245
- type: precision
value: 36.24208471267295
- type: recall
value: 42.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.26205450733752
- type: f1
value: 70.72842833849123
- type: precision
value: 68.93256464011182
- type: recall
value: 75.26205450733752
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.96666666666668
- type: precision
value: 93.42
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.26459143968872
- type: f1
value: 72.40190419178747
- type: precision
value: 70.84954604409856
- type: recall
value: 76.26459143968872
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.82905982905983
- type: f1
value: 52.2100122100122
- type: precision
value: 49.52516619183286
- type: recall
value: 59.82905982905983
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 81.69999999999999
- type: f1
value: 77.41714285714286
- type: precision
value: 75.64833333333334
- type: recall
value: 81.69999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.45
- type: precision
value: 93.93333333333334
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 58.41121495327103
- type: f1
value: 52.73495974430554
- type: precision
value: 50.717067200712066
- type: recall
value: 58.41121495327103
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lit-eng)
type: mteb/tatoeba-bitext-mining
config: lit-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.3
- type: f1
value: 69.20371794871795
- type: precision
value: 67.6597557997558
- type: recall
value: 73.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ina-eng)
type: mteb/tatoeba-bitext-mining
config: ina-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.5
- type: f1
value: 95.51666666666667
- type: precision
value: 95.05
- type: recall
value: 96.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lfn-eng)
type: mteb/tatoeba-bitext-mining
config: lfn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.4
- type: f1
value: 73.88856643356644
- type: precision
value: 72.01373015873016
- type: recall
value: 78.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (zsm-eng)
type: mteb/tatoeba-bitext-mining
config: zsm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.3
- type: f1
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.2
- type: f1
value: 7.622184434339607
- type: precision
value: 7.287048159682417
- type: recall
value: 9.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.85016286644951
- type: f1
value: 72.83387622149837
- type: precision
value: 70.58450959102424
- type: recall
value: 77.85016286644951
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.84333333333333
- type: precision
value: 87.96666666666665
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.14
- type: precision
value: 92.49833333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.25196850393701
- type: f1
value: 80.94488188976378
- type: precision
value: 79.65879265091863
- type: recall
value: 84.25196850393701
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.89666666666666
- type: precision
value: 85.7
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.797783933518005
- type: f1
value: 37.30617360155193
- type: precision
value: 35.34933825792552
- type: recall
value: 42.797783933518005
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 94.93333333333332
- type: precision
value: 94.38333333333333
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 54.807692307692314
- type: f1
value: 49.506903353057204
- type: precision
value: 47.54807692307693
- type: recall
value: 54.807692307692314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.1
- type: f1
value: 83.61857142857143
- type: precision
value: 81.975
- type: recall
value: 87.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.10000000000001
- type: f1
value: 88.76333333333332
- type: precision
value: 87.67
- type: recall
value: 91.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.28999999999999
- type: precision
value: 90.44500000000001
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 39.97641509433962
- type: f1
value: 33.12271889998028
- type: precision
value: 30.95185381542554
- type: recall
value: 39.97641509433962
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.60000000000001
- type: f1
value: 90.69
- type: precision
value: 89.84500000000001
- type: recall
value: 92.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.07299270072993
- type: f1
value: 93.64355231143554
- type: precision
value: 92.94403892944038
- type: recall
value: 95.07299270072993
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.9
- type: f1
value: 89.61333333333333
- type: precision
value: 88.53333333333333
- type: recall
value: 91.9
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 64.68478289806511
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 57.53010296184097
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.519
- type: map_at_10
value: 10.31
- type: map_at_100
value: 16.027
- type: map_at_1000
value: 17.827
- type: map_at_3
value: 5.721
- type: map_at_5
value: 7.7829999999999995
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 52.642999999999994
- type: mrr_at_100
value: 53.366
- type: mrr_at_1000
value: 53.366
- type: mrr_at_3
value: 48.638999999999996
- type: mrr_at_5
value: 50.578
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 26.394000000000002
- type: ndcg_at_100
value: 36.41
- type: ndcg_at_1000
value: 49.206
- type: ndcg_at_3
value: 31.694
- type: ndcg_at_5
value: 29.529
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.286
- type: precision_at_1000
value: 1.5610000000000002
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.519
- type: recall_at_10
value: 17.091
- type: recall_at_100
value: 45.429
- type: recall_at_1000
value: 84.621
- type: recall_at_3
value: 7.208
- type: recall_at_5
value: 10.523
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.58659999999999
- type: ap
value: 14.735696532619
- type: f1
value: 54.23517220069903
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.723825693265425
- type: f1
value: 64.02405729449103
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.310161547491006
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.77630088812064
- type: cos_sim_ap
value: 81.61725457333809
- type: cos_sim_f1
value: 74.91373801916932
- type: cos_sim_precision
value: 72.63940520446097
- type: cos_sim_recall
value: 77.33509234828496
- type: dot_accuracy
value: 88.77630088812064
- type: dot_ap
value: 81.61725317476251
- type: dot_f1
value: 74.91373801916932
- type: dot_precision
value: 72.63940520446097
- type: dot_recall
value: 77.33509234828496
- type: euclidean_accuracy
value: 88.77630088812064
- type: euclidean_ap
value: 81.61724596869566
- type: euclidean_f1
value: 74.91373801916932
- type: euclidean_precision
value: 72.63940520446097
- type: euclidean_recall
value: 77.33509234828496
- type: manhattan_accuracy
value: 88.67497168742922
- type: manhattan_ap
value: 81.430251048948
- type: manhattan_f1
value: 74.79593118171543
- type: manhattan_precision
value: 71.3635274382938
- type: manhattan_recall
value: 78.57519788918206
- type: max_accuracy
value: 88.77630088812064
- type: max_ap
value: 81.61725457333809
- type: max_f1
value: 74.91373801916932
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.85136026700819
- type: cos_sim_ap
value: 87.74656687446567
- type: cos_sim_f1
value: 80.3221673073403
- type: cos_sim_precision
value: 76.56871640957633
- type: cos_sim_recall
value: 84.46258084385587
- type: dot_accuracy
value: 89.85136026700819
- type: dot_ap
value: 87.74656471395072
- type: dot_f1
value: 80.3221673073403
- type: dot_precision
value: 76.56871640957633
- type: dot_recall
value: 84.46258084385587
- type: euclidean_accuracy
value: 89.85136026700819
- type: euclidean_ap
value: 87.74656885754466
- type: euclidean_f1
value: 80.3221673073403
- type: euclidean_precision
value: 76.56871640957633
- type: euclidean_recall
value: 84.46258084385587
- type: manhattan_accuracy
value: 89.86300306593705
- type: manhattan_ap
value: 87.78807479093082
- type: manhattan_f1
value: 80.31663429471911
- type: manhattan_precision
value: 76.63472970137772
- type: manhattan_recall
value: 84.3701878657222
- type: max_accuracy
value: 89.86300306593705
- type: max_ap
value: 87.78807479093082
- type: max_f1
value: 80.3221673073403
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 32.4
- type: map_at_10
value: 40.961999999999996
- type: map_at_100
value: 41.660000000000004
- type: map_at_1000
value: 41.721000000000004
- type: map_at_3
value: 38.550000000000004
- type: map_at_5
value: 40.06
- type: mrr_at_1
value: 32.4
- type: mrr_at_10
value: 40.961999999999996
- type: mrr_at_100
value: 41.660000000000004
- type: mrr_at_1000
value: 41.721000000000004
- type: mrr_at_3
value: 38.550000000000004
- type: mrr_at_5
value: 40.06
- type: ndcg_at_1
value: 32.4
- type: ndcg_at_10
value: 45.388
- type: ndcg_at_100
value: 49.012
- type: ndcg_at_1000
value: 50.659
- type: ndcg_at_3
value: 40.47
- type: ndcg_at_5
value: 43.232
- type: precision_at_1
value: 32.4
- type: precision_at_10
value: 5.94
- type: precision_at_100
value: 0.769
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 15.333
- type: precision_at_5
value: 10.56
- type: recall_at_1
value: 32.4
- type: recall_at_10
value: 59.4
- type: recall_at_100
value: 76.9
- type: recall_at_1000
value: 90.0
- type: recall_at_3
value: 46.0
- type: recall_at_5
value: 52.800000000000004
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.94000000000001
- type: ap
value: 70.57373468481975
- type: f1
value: 85.26264784928323
---
## E5-mistral-7b-instruct
[Improving Text Embeddings with Large Language Models](https://arxiv.org/pdf/2401.00368.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 32 layers and the embedding size is 4096.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("intfloat/e5-mistral-7b-instruct")
# In case you want to reduce the maximum sequence length:
model.max_seq_length = 4096
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
query_embeddings = model.encode(queries, prompt_name="web_search_query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
```
Have a look at [config_sentence_transformers.json](config_sentence_transformers.json) for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `summarization_query`. Additionally, check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for prompts we used for evaluation. You can use these via e.g. `model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ")`.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct')
model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct')
max_length = 4096
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
and fine-tuned on a mixture of multilingual datasets.
As a result, it has some multilingual capability.
However, since Mistral-7B-v0.1 is mainly trained on English data, we recommend using this model for English only.
For multilingual use cases, please refer to [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large).
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## FAQ
**1. Do I need to add instructions to the query?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
The task definition should be a one-sentence instruction that describes the task.
This is a way to customize text embeddings for different scenarios through natural language instructions.
Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation.
On the other hand, there is no need to add instructions to the document side.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Where are the LoRA-only weights?**
You can find the LoRA-only weights at [https://huggingface.co/intfloat/e5-mistral-7b-instruct/tree/main/lora](https://huggingface.co/intfloat/e5-mistral-7b-instruct/tree/main/lora).
## Citation
If you find our paper or models helpful, please consider cite as follows:
```bibtex
@article{wang2023improving,
title={Improving Text Embeddings with Large Language Models},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2401.00368},
year={2023}
}
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
```
## Limitations
Using this model for inputs longer than 4096 tokens is not recommended.
This model's multilingual capability is still inferior to [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) for some cases.
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
NovaSearch/stella_en_1.5B_v5 | NovaSearch | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"arxiv:2412.19048",
"arxiv:2205.13147",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-12T15:52:09 | 2025-03-05T13:58:41 | 179,630 | 234 | ---
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
model-index:
- name: stella_en_1.5B_v5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 92.86567164179104
- type: ap
value: 72.13503907102613
- type: ap_weighted
value: 72.13503907102613
- type: f1
value: 89.5586886376355
- type: f1_weighted
value: 93.13621183004571
- type: main_score
value: 92.86567164179104
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
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type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
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dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
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- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
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type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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dataset:
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type: mteb/biorxiv-clustering-p2p
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
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dataset:
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type: mteb/biorxiv-clustering-s2s
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type: mteb/cqadupstack
config: default
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revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
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dataset:
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type: mteb/reddit-clustering
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revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
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dataset:
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split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sts12-sts
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revision: a0d554a64d88156834ff5ae9920b964011b16384
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- type: manhattan_spearman
value: 80.24881789268238
- type: pearson
value: 87.0445014940449
- type: spearman
value: 80.0880365116599
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 89.33900828959968
- type: cosine_spearman
value: 89.68256358526733
- type: euclidean_pearson
value: 89.29188708262265
- type: euclidean_spearman
value: 89.68204344658601
- type: main_score
value: 89.68256358526733
- type: manhattan_pearson
value: 89.13996588193149
- type: manhattan_spearman
value: 89.61372804425623
- type: pearson
value: 89.33900828959968
- type: spearman
value: 89.68256358526733
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 86.42029843639123
- type: cosine_spearman
value: 85.0707889220723
- type: euclidean_pearson
value: 85.75114239552562
- type: euclidean_spearman
value: 85.06858160270725
- type: main_score
value: 85.0707889220723
- type: manhattan_pearson
value: 85.86461900459038
- type: manhattan_spearman
value: 85.28671103475605
- type: pearson
value: 86.42029843639123
- type: spearman
value: 85.0707889220723
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 88.3660081271444
- type: cosine_spearman
value: 89.39375083609528
- type: euclidean_pearson
value: 89.21818482894895
- type: euclidean_spearman
value: 89.39361588875443
- type: main_score
value: 89.39375083609528
- type: manhattan_pearson
value: 89.53535068014057
- type: manhattan_spearman
value: 89.81077130567752
- type: pearson
value: 88.3660081271444
- type: spearman
value: 89.39375083609528
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 85.60708247171874
- type: cosine_spearman
value: 87.15234952832193
- type: euclidean_pearson
value: 86.21743555548137
- type: euclidean_spearman
value: 87.14450217418016
- type: main_score
value: 87.15234952832193
- type: manhattan_pearson
value: 86.2467748746084
- type: manhattan_spearman
value: 87.2197479717654
- type: pearson
value: 85.60708247171874
- type: spearman
value: 87.15234952832193
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 91.25898556808458
- type: cosine_spearman
value: 91.35372390581641
- type: euclidean_pearson
value: 91.319520321348
- type: euclidean_spearman
value: 91.30821135416925
- type: main_score
value: 91.35372390581641
- type: manhattan_pearson
value: 91.14800959939069
- type: manhattan_spearman
value: 91.09775424245629
- type: pearson
value: 91.25898556808458
- type: spearman
value: 91.35372390581641
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 67.61637111515797
- type: cosine_spearman
value: 68.10379096526697
- type: euclidean_pearson
value: 69.2652309491375
- type: euclidean_spearman
value: 68.18436357033228
- type: main_score
value: 68.10379096526697
- type: manhattan_pearson
value: 69.52531340510775
- type: manhattan_spearman
value: 68.17874790391862
- type: pearson
value: 67.61637111515797
- type: spearman
value: 68.10379096526697
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 87.81592853782297
- type: cosine_spearman
value: 88.2302550329183
- type: euclidean_pearson
value: 88.01165144519526
- type: euclidean_spearman
value: 88.23342148890097
- type: main_score
value: 88.2302550329183
- type: manhattan_pearson
value: 88.148592564938
- type: manhattan_spearman
value: 88.49226317320988
- type: pearson
value: 87.81592853782297
- type: spearman
value: 88.2302550329183
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 89.196009707431
- type: map
value: 89.196009707431
- type: mrr
value: 97.07198121413808
- type: nAUC_map_diff1
value: -14.066667940115352
- type: nAUC_map_max
value: 49.73702475027407
- type: nAUC_map_std
value: 64.0986775782592
- type: nAUC_mrr_diff1
value: 21.96846389417319
- type: nAUC_mrr_max
value: 86.38341077184032
- type: nAUC_mrr_std
value: 75.38945014727746
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 80.08999999999999
- type: map_at_1
value: 63.161
- type: map_at_10
value: 75.163
- type: map_at_100
value: 75.408
- type: map_at_1000
value: 75.409
- type: map_at_20
value: 75.332
- type: map_at_3
value: 71.839
- type: map_at_5
value: 74.32600000000001
- type: mrr_at_1
value: 66.33333333333333
- type: mrr_at_10
value: 75.95978835978836
- type: mrr_at_100
value: 76.15647881281473
- type: mrr_at_1000
value: 76.15736533763744
- type: mrr_at_20
value: 76.08557368557368
- type: mrr_at_3
value: 73.55555555555556
- type: mrr_at_5
value: 75.4888888888889
- type: nauc_map_at_1000_diff1
value: 77.31229383811176
- type: nauc_map_at_1000_max
value: 58.848319058605156
- type: nauc_map_at_1000_std
value: -14.290090263454985
- type: nauc_map_at_100_diff1
value: 77.31325400213969
- type: nauc_map_at_100_max
value: 58.848885054155275
- type: nauc_map_at_100_std
value: -14.285806618869273
- type: nauc_map_at_10_diff1
value: 77.1806705504232
- type: nauc_map_at_10_max
value: 59.02905805134415
- type: nauc_map_at_10_std
value: -14.132954900037467
- type: nauc_map_at_1_diff1
value: 81.03932970557837
- type: nauc_map_at_1_max
value: 49.02073230264529
- type: nauc_map_at_1_std
value: -22.977452975845512
- type: nauc_map_at_20_diff1
value: 77.22581364818562
- type: nauc_map_at_20_max
value: 58.90740400399768
- type: nauc_map_at_20_std
value: -14.245079150986745
- type: nauc_map_at_3_diff1
value: 76.99793243255563
- type: nauc_map_at_3_max
value: 54.9930733886623
- type: nauc_map_at_3_std
value: -19.297708446082407
- type: nauc_map_at_5_diff1
value: 77.1671608360295
- type: nauc_map_at_5_max
value: 57.27757489519526
- type: nauc_map_at_5_std
value: -15.446338357667708
- type: nauc_mrr_at_1000_diff1
value: 77.4806080821202
- type: nauc_mrr_at_1000_max
value: 60.9213776129792
- type: nauc_mrr_at_1000_std
value: -12.139599632228343
- type: nauc_mrr_at_100_diff1
value: 77.48158073865281
- type: nauc_mrr_at_100_max
value: 60.9218657185361
- type: nauc_mrr_at_100_std
value: -12.13532070453677
- type: nauc_mrr_at_10_diff1
value: 77.32428546014407
- type: nauc_mrr_at_10_max
value: 61.018407010343466
- type: nauc_mrr_at_10_std
value: -12.143193773309347
- type: nauc_mrr_at_1_diff1
value: 80.99806778887115
- type: nauc_mrr_at_1_max
value: 59.17855969530095
- type: nauc_mrr_at_1_std
value: -12.30545640831458
- type: nauc_mrr_at_20_diff1
value: 77.3811067653992
- type: nauc_mrr_at_20_max
value: 60.9648880366335
- type: nauc_mrr_at_20_std
value: -12.124066076541853
- type: nauc_mrr_at_3_diff1
value: 77.31304316321959
- type: nauc_mrr_at_3_max
value: 60.75536766404163
- type: nauc_mrr_at_3_std
value: -12.997876030849623
- type: nauc_mrr_at_5_diff1
value: 77.12952864141742
- type: nauc_mrr_at_5_max
value: 60.995943754968685
- type: nauc_mrr_at_5_std
value: -11.353447465605694
- type: nauc_ndcg_at_1000_diff1
value: 76.81788665683746
- type: nauc_ndcg_at_1000_max
value: 60.35947755262391
- type: nauc_ndcg_at_1000_std
value: -12.884942372460362
- type: nauc_ndcg_at_100_diff1
value: 76.87388230365198
- type: nauc_ndcg_at_100_max
value: 60.38813162962434
- type: nauc_ndcg_at_100_std
value: -12.64384717800478
- type: nauc_ndcg_at_10_diff1
value: 75.87713506026317
- type: nauc_ndcg_at_10_max
value: 61.39356554675667
- type: nauc_ndcg_at_10_std
value: -12.144227584144218
- type: nauc_ndcg_at_1_diff1
value: 80.99806778887115
- type: nauc_ndcg_at_1_max
value: 59.17855969530095
- type: nauc_ndcg_at_1_std
value: -12.30545640831458
- type: nauc_ndcg_at_20_diff1
value: 76.09913944506627
- type: nauc_ndcg_at_20_max
value: 61.01644448834147
- type: nauc_ndcg_at_20_std
value: -12.456209267623857
- type: nauc_ndcg_at_3_diff1
value: 75.52717946614608
- type: nauc_ndcg_at_3_max
value: 58.96433090721983
- type: nauc_ndcg_at_3_std
value: -15.849280494339556
- type: nauc_ndcg_at_5_diff1
value: 75.69026981016921
- type: nauc_ndcg_at_5_max
value: 58.924044405851326
- type: nauc_ndcg_at_5_std
value: -13.182728827923107
- type: nauc_precision_at_1000_diff1
value: -31.634022001609914
- type: nauc_precision_at_1000_max
value: 31.46271490784504
- type: nauc_precision_at_1000_std
value: 60.44801276891442
- type: nauc_precision_at_100_diff1
value: -29.722363469948103
- type: nauc_precision_at_100_max
value: 32.05464592020074
- type: nauc_precision_at_100_std
value: 60.832570595613554
- type: nauc_precision_at_10_diff1
value: -11.91731376599939
- type: nauc_precision_at_10_max
value: 45.43646553157129
- type: nauc_precision_at_10_std
value: 52.962408871791276
- type: nauc_precision_at_1_diff1
value: 80.99806778887115
- type: nauc_precision_at_1_max
value: 59.17855969530095
- type: nauc_precision_at_1_std
value: -12.30545640831458
- type: nauc_precision_at_20_diff1
value: -18.43293701721667
- type: nauc_precision_at_20_max
value: 39.53434874203934
- type: nauc_precision_at_20_std
value: 53.6291982468461
- type: nauc_precision_at_3_diff1
value: 30.84789043003892
- type: nauc_precision_at_3_max
value: 55.660727758110376
- type: nauc_precision_at_3_std
value: 17.87243920840355
- type: nauc_precision_at_5_diff1
value: 4.099395181445625
- type: nauc_precision_at_5_max
value: 50.346770968709386
- type: nauc_precision_at_5_std
value: 44.66722483255029
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_100_diff1
value: 100.0
- type: nauc_recall_at_100_max
value: 72.2222222222207
- type: nauc_recall_at_100_std
value: 86.92810457516407
- type: nauc_recall_at_10_diff1
value: 62.18887555022005
- type: nauc_recall_at_10_max
value: 75.14339068960916
- type: nauc_recall_at_10_std
value: -1.4912631719357108
- type: nauc_recall_at_1_diff1
value: 81.03932970557837
- type: nauc_recall_at_1_max
value: 49.02073230264529
- type: nauc_recall_at_1_std
value: -22.977452975845512
- type: nauc_recall_at_20_diff1
value: 59.27414444038499
- type: nauc_recall_at_20_max
value: 76.32241302318047
- type: nauc_recall_at_20_std
value: -0.8322169447488666
- type: nauc_recall_at_3_diff1
value: 69.58783002593157
- type: nauc_recall_at_3_max
value: 55.89660919896563
- type: nauc_recall_at_3_std
value: -21.183005510917862
- type: nauc_recall_at_5_diff1
value: 65.53660499878802
- type: nauc_recall_at_5_max
value: 58.218018535135805
- type: nauc_recall_at_5_std
value: -8.328952210032455
- type: ndcg_at_1
value: 66.333
- type: ndcg_at_10
value: 80.08999999999999
- type: ndcg_at_100
value: 81.24900000000001
- type: ndcg_at_1000
value: 81.28800000000001
- type: ndcg_at_20
value: 80.625
- type: ndcg_at_3
value: 74.98700000000001
- type: ndcg_at_5
value: 78.553
- type: precision_at_1
value: 66.333
- type: precision_at_10
value: 10.667
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.45
- type: precision_at_3
value: 29.555999999999997
- type: precision_at_5
value: 20.133000000000003
- type: recall_at_1
value: 63.161
- type: recall_at_10
value: 94.167
- type: recall_at_100
value: 99.667
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 96.167
- type: recall_at_3
value: 80.972
- type: recall_at_5
value: 89.90599999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.81881188118813
- type: cosine_accuracy_threshold
value: 85.55081486701965
- type: cosine_ap
value: 96.0359661816236
- type: cosine_f1
value: 90.6584992343032
- type: cosine_f1_threshold
value: 84.82859134674072
- type: cosine_precision
value: 92.59645464025026
- type: cosine_recall
value: 88.8
- type: dot_accuracy
value: 99.81881188118813
- type: dot_accuracy_threshold
value: 84.91908311843872
- type: dot_ap
value: 96.05740121094365
- type: dot_f1
value: 90.81885856079404
- type: dot_f1_threshold
value: 83.84919166564941
- type: dot_precision
value: 90.14778325123153
- type: dot_recall
value: 91.5
- type: euclidean_accuracy
value: 99.82079207920792
- type: euclidean_accuracy_threshold
value: 54.49706315994263
- type: euclidean_ap
value: 96.03223527068818
- type: euclidean_f1
value: 90.72270630445925
- type: euclidean_f1_threshold
value: 54.49706315994263
- type: euclidean_precision
value: 93.05993690851734
- type: euclidean_recall
value: 88.5
- type: main_score
value: 96.32671902439806
- type: manhattan_accuracy
value: 99.83267326732673
- type: manhattan_accuracy_threshold
value: 3818.192672729492
- type: manhattan_ap
value: 96.32671902439806
- type: manhattan_f1
value: 91.52032112393378
- type: manhattan_f1_threshold
value: 3818.192672729492
- type: manhattan_precision
value: 91.8429003021148
- type: manhattan_recall
value: 91.2
- type: max_ap
value: 96.32671902439806
- type: max_f1
value: 91.52032112393378
- type: max_precision
value: 93.05993690851734
- type: max_recall
value: 91.5
- type: similarity_accuracy
value: 99.81881188118813
- type: similarity_accuracy_threshold
value: 85.55081486701965
- type: similarity_ap
value: 96.0359661816236
- type: similarity_f1
value: 90.6584992343032
- type: similarity_f1_threshold
value: 84.82859134674072
- type: similarity_precision
value: 92.59645464025026
- type: similarity_recall
value: 88.8
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 80.28558559137414
- type: v_measure
value: 80.28558559137414
- type: v_measure_std
value: 2.795276520287584
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 49.57135582416209
- type: v_measure
value: 49.57135582416209
- type: v_measure_std
value: 1.6414135468423754
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 55.253002583598644
- type: map
value: 55.253002583598644
- type: mrr
value: 56.24172396231219
- type: nAUC_map_diff1
value: 40.00053248203427
- type: nAUC_map_max
value: 10.05441740585869
- type: nAUC_map_std
value: 8.227169286387552
- type: nAUC_mrr_diff1
value: 40.250446264233744
- type: nAUC_mrr_max
value: 10.586310195339053
- type: nAUC_mrr_std
value: 8.47326494370076
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 31.19874648747059
- type: cosine_spearman
value: 31.493550648844863
- type: dot_pearson
value: 31.157847680289407
- type: dot_spearman
value: 31.575299712180538
- type: main_score
value: 31.493550648844863
- type: pearson
value: 31.19874648747059
- type: spearman
value: 31.493550648844863
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 85.983
- type: map_at_1
value: 0.247
- type: map_at_10
value: 2.177
- type: map_at_100
value: 14.804
- type: map_at_1000
value: 37.045
- type: map_at_20
value: 4.12
- type: map_at_3
value: 0.7000000000000001
- type: map_at_5
value: 1.1320000000000001
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 98.0
- type: mrr_at_100
value: 98.0
- type: mrr_at_1000
value: 98.0
- type: mrr_at_20
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: nauc_map_at_1000_diff1
value: -0.9165125200337213
- type: nauc_map_at_1000_max
value: 40.260117798042764
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type: mteb/toxic_conversations_50k
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revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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- type: manhattan_accuracy_threshold
value: 3907.2471618652344
- type: manhattan_ap
value: 80.3538079655539
- type: manhattan_f1
value: 73.40466675261054
- type: manhattan_f1_threshold
value: 4103.794097900391
- type: manhattan_precision
value: 71.76707839677337
- type: manhattan_recall
value: 75.11873350923483
- type: max_ap
value: 80.57982723880139
- type: max_f1
value: 73.70579190158894
- type: max_precision
value: 72.51087791144101
- type: max_recall
value: 75.8839050131926
- type: similarity_accuracy
value: 88.22197055492639
- type: similarity_accuracy_threshold
value: 83.30042362213135
- type: similarity_ap
value: 80.57754959194938
- type: similarity_f1
value: 73.70579190158894
- type: similarity_f1_threshold
value: 81.04978799819946
- type: similarity_precision
value: 71.64922770303936
- type: similarity_recall
value: 75.8839050131926
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.88628866379477
- type: cosine_accuracy_threshold
value: 80.8050274848938
- type: cosine_ap
value: 87.57594591596816
- type: cosine_f1
value: 80.0812257707218
- type: cosine_f1_threshold
value: 77.990061044693
- type: cosine_precision
value: 76.93126197063205
- type: cosine_recall
value: 83.50015398829689
- type: dot_accuracy
value: 89.87852679784221
- type: dot_accuracy_threshold
value: 80.84419965744019
- type: dot_ap
value: 87.56136742222151
- type: dot_f1
value: 80.05898617511521
- type: dot_f1_threshold
value: 77.92385816574097
- type: dot_precision
value: 76.80554573106035
- type: dot_recall
value: 83.60024638127503
- type: euclidean_accuracy
value: 89.86882446540149
- type: euclidean_accuracy_threshold
value: 62.08193898200989
- type: euclidean_ap
value: 87.57517549192228
- type: euclidean_f1
value: 80.05286925872892
- type: euclidean_f1_threshold
value: 66.65036082267761
- type: euclidean_precision
value: 76.51063232507545
- type: euclidean_recall
value: 83.93902063443178
- type: main_score
value: 87.64162614197194
- type: manhattan_accuracy
value: 89.8959909962355
- type: manhattan_accuracy_threshold
value: 4176.108169555664
- type: manhattan_ap
value: 87.64162614197194
- type: manhattan_f1
value: 80.17116279069768
- type: manhattan_f1_threshold
value: 4433.153533935547
- type: manhattan_precision
value: 77.57615035644848
- type: manhattan_recall
value: 82.94579611949491
- type: max_ap
value: 87.64162614197194
- type: max_f1
value: 80.17116279069768
- type: max_precision
value: 77.57615035644848
- type: max_recall
value: 83.93902063443178
- type: similarity_accuracy
value: 89.88628866379477
- type: similarity_accuracy_threshold
value: 80.8050274848938
- type: similarity_ap
value: 87.57594591596816
- type: similarity_f1
value: 80.0812257707218
- type: similarity_f1_threshold
value: 77.990061044693
- type: similarity_precision
value: 76.93126197063205
- type: similarity_recall
value: 83.50015398829689
---
# Updates
We released a Jasper and Stella model technology report and code.(2025.1)
**Report:** https://arxiv.org/abs/2412.19048
**Codes:** https://github.com/NLPJCL/RAG-Retrieval
# Introduction
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for
their contributions!
**We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.**
Prompt of s2p task(e.g. retrieve task):
```text
Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}
```
Prompt of s2s task(e.g. semantic textual similarity task):
```text
Instruct: Retrieve semantically similar text.\nQuery: {query}
```
The models are finally trained by [MRL]((https://arxiv.org/abs/2205.13147)), so they have multiple dimensions: 512, 768,
1024, 2048, 4096, 6144 and 8192.
The higher the dimension, the better the performance.
**Generally speaking, 1024d is good enough.** The MTEB score of 1024d is only 0.001 lower than 8192d.
# Model directory structure
The model directory structure is very simple, it is a standard SentenceTransformer directory **with a series
of `2_Dense_{dims}`
folders**, where `dims` represents the final vector dimension.
For example, the `2_Dense_256` folder stores Linear weights that convert vector dimensions to 256 dimensions.
Please refer to the following chapters for specific instructions on how to use them.
# Usage
You can use `SentenceTransformers` or `transformers` library to encode text.
## Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively.
# They are defined in `config_sentence_transformers.json`
query_prompt_name = "s2p_query"
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` !
model = SentenceTransformer("dunzhang/stella_en_1.5B_v5", trust_remote_code=True).cuda()
query_embeddings = model.encode(queries, prompt_name=query_prompt_name)
doc_embeddings = model.encode(docs)
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 1024) (2, 1024)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.8179, 0.2958],
# [0.3194, 0.7854]])
```
## Transformers
```python
import os
import torch
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# The path of your model after cloning it
model_dir = "{Your MODEL_PATH}"
vector_dim = 1024
vector_linear_directory = f"2_Dense_{vector_dim}"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim)
vector_linear_dict = {
k.replace("linear.", ""): v for k, v in
torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items()
}
vector_linear.load_state_dict(vector_linear_dict)
vector_linear.cuda()
# Embed the queries
with torch.no_grad():
input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_vectors = normalize(vector_linear(query_vectors).cpu().numpy())
# Embed the documents
with torch.no_grad():
input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy())
print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)
similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8178789 0.2958377 ]
# [0.31938642 0.7853526 ]]
```
## Infinity
Usage with [Infinity, MIT Licensed Inference Server](https://github.com/michaelfeil/infinity) and Docker.
```bash
docker run --gpus all -v $PWD/data:/app/.cache \
michaelf34/infinity:0.0.69-trt-onnx \
v2 --model-id dunzhang/stella_en_1.5B_v5 --batch-size 16 --device cuda --engine torch --port 7997
```
# Citation
```
@misc{zhang2025jasperstelladistillationsota,
title={Jasper and Stella: distillation of SOTA embedding models},
author={Dun Zhang and Jiacheng Li and Ziyang Zeng and Fulong Wang},
year={2025},
eprint={2412.19048},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2412.19048},
}
```
# FAQ
Q: The details of training?
A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper)
Q: How to choose a suitable prompt for my own task?
A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training
data.
Q: How to reproduce MTEB results?
A: Please use evaluation scripts in `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `intfloat/e5-mistral-7b-instruct`
Q: Why each dimension has a linear weight?
A: MRL has multiple training methods, we choose this method which has the best performance.
Q: What is the sequence length of models?
A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the
model is trained on datasets of 512 length. This may be an optimization term.
If you have any questions, please start a discussion on community. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-multilingual-gemma2 | BAAI | feature-extraction | [
"sentence-transformers",
"safetensors",
"gemma2",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"arxiv:2402.03216",
"arxiv:2309.07597",
"license:gemma",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-25T16:55:46 | 2024-07-31T08:07:09 | 167,139 | 174 | ---
license: gemma
tags:
- feature-extraction
- sentence-similarity
- sentence-transformers
- transformers
- mteb
model-index:
- name: bge-multilingual-gemma2
results:
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: main_score
value: 38.11433513284057
- type: ndcg_at_1
value: 48.45201238390093
- type: ndcg_at_3
value: 44.451438575534574
- type: ndcg_at_5
value: 41.13929990797894
- type: ndcg_at_10
value: 38.11433513284057
- type: ndcg_at_100
value: 35.36065387898559
- type: ndcg_at_1000
value: 44.01125752781003
- type: map_at_1
value: 5.638004398054564
- type: map_at_3
value: 10.375632572339333
- type: map_at_5
value: 11.820531148202422
- type: map_at_10
value: 14.087436978063389
- type: map_at_100
value: 18.25397463114958
- type: map_at_1000
value: 19.868440221606203
- type: precision_at_1
value: 49.84520123839009
- type: precision_at_3
value: 41.89886480908153
- type: precision_at_5
value: 35.356037151702814
- type: precision_at_10
value: 28.513931888544857
- type: precision_at_100
value: 9.337461300309604
- type: precision_at_1000
value: 2.210216718266251
- type: recall_at_1
value: 5.638004398054564
- type: recall_at_3
value: 11.938154656310312
- type: recall_at_5
value: 14.06183119422843
- type: recall_at_10
value: 18.506397834147705
- type: recall_at_100
value: 35.96995569451433
- type: recall_at_1000
value: 68.31771509404795
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: main_score
value: 45.70688915742828
- type: ndcg_at_1
value: 26.002865329512893
- type: ndcg_at_3
value: 37.49665652114275
- type: ndcg_at_5
value: 41.684045067615834
- type: ndcg_at_10
value: 45.70688915742828
- type: ndcg_at_100
value: 51.08932609519671
- type: ndcg_at_1000
value: 51.98806137292924
- type: map_at_1
value: 25.35219675262655
- type: map_at_3
value: 34.39549506526583
- type: map_at_5
value: 36.74936326010824
- type: map_at_10
value: 38.44429852488596
- type: map_at_100
value: 39.60260286311527
- type: map_at_1000
value: 39.64076154054021
- type: precision_at_1
value: 26.002865329512893
- type: precision_at_3
value: 15.840496657115954
- type: precision_at_5
value: 11.647564469914684
- type: precision_at_10
value: 7.1275071633243705
- type: precision_at_100
value: 0.9782234957019871
- type: precision_at_1000
value: 0.10565902578797497
- type: recall_at_1
value: 25.35219675262655
- type: recall_at_3
value: 45.78438395415474
- type: recall_at_5
value: 55.83213944603631
- type: recall_at_10
value: 68.08500477554918
- type: recall_at_100
value: 92.55133715377269
- type: recall_at_1000
value: 99.29083094555875
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: main_score
value: 60.04205769404706
- type: ndcg_at_1
value: 59.25925925925925
- type: ndcg_at_3
value: 55.96637679199298
- type: ndcg_at_5
value: 56.937223390223956
- type: ndcg_at_10
value: 60.04205769404706
- type: ndcg_at_100
value: 66.01619664462949
- type: ndcg_at_1000
value: 67.59651529720728
- type: map_at_1
value: 31.5081163692275
- type: map_at_3
value: 45.7486689836227
- type: map_at_5
value: 48.944906602314
- type: map_at_10
value: 51.85427043799874
- type: map_at_100
value: 53.92920237379484
- type: map_at_1000
value: 54.04694438963671
- type: precision_at_1
value: 59.25925925925925
- type: precision_at_3
value: 37.44855967078195
- type: precision_at_5
value: 26.913580246913547
- type: precision_at_10
value: 16.52777777777774
- type: precision_at_100
value: 2.2962962962962754
- type: precision_at_1000
value: 0.2566358024691334
- type: recall_at_1
value: 31.5081163692275
- type: recall_at_3
value: 50.71759045138676
- type: recall_at_5
value: 57.49321152098932
- type: recall_at_10
value: 67.36356750245642
- type: recall_at_100
value: 88.67335767798735
- type: recall_at_1000
value: 97.83069725199356
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: main_score
value: 26.93150756480961
- type: ndcg_at_1
value: 30.8
- type: ndcg_at_3
value: 25.048085553386628
- type: ndcg_at_5
value: 22.351207380852305
- type: ndcg_at_10
value: 26.93150756480961
- type: ndcg_at_100
value: 37.965486832874014
- type: ndcg_at_1000
value: 43.346046425140244
- type: map_at_1
value: 6.238333333333366
- type: map_at_3
value: 11.479166666666679
- type: map_at_5
value: 14.215999999999983
- type: map_at_10
value: 16.774632936507945
- type: map_at_100
value: 20.148869158557293
- type: map_at_1000
value: 20.528644104490823
- type: precision_at_1
value: 30.8
- type: precision_at_3
value: 23.466666666666736
- type: precision_at_5
value: 19.899999999999967
- type: precision_at_10
value: 14.069999999999938
- type: precision_at_100
value: 2.9770000000000065
- type: precision_at_1000
value: 0.42569999999999486
- type: recall_at_1
value: 6.238333333333366
- type: recall_at_3
value: 14.29333333333338
- type: recall_at_5
value: 20.206666666666628
- type: recall_at_10
value: 28.573333333333224
- type: recall_at_100
value: 60.43666666666675
- type: recall_at_1000
value: 86.3649999999997
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: main_score
value: 90.38165339181239
- type: ndcg_at_1
value: 84.86348634863486
- type: ndcg_at_3
value: 88.98667069230609
- type: ndcg_at_5
value: 89.86028996734895
- type: ndcg_at_10
value: 90.38165339181239
- type: ndcg_at_100
value: 90.99655378684439
- type: ndcg_at_1000
value: 91.15536362599602
- type: map_at_1
value: 78.8556296105801
- type: map_at_3
value: 86.24061810942983
- type: map_at_5
value: 86.94776680048933
- type: map_at_10
value: 87.26956235873007
- type: map_at_100
value: 87.47986397174834
- type: map_at_1000
value: 87.4897076664281
- type: precision_at_1
value: 84.86348634863486
- type: precision_at_3
value: 34.02340234023296
- type: precision_at_5
value: 21.10411041104359
- type: precision_at_10
value: 10.828082808282083
- type: precision_at_100
value: 1.1381638163816703
- type: precision_at_1000
value: 0.11662166216622569
- type: recall_at_1
value: 78.8556296105801
- type: recall_at_3
value: 92.34465708475605
- type: recall_at_5
value: 94.58010682020583
- type: recall_at_10
value: 96.10713452297611
- type: recall_at_100
value: 98.31672452959585
- type: recall_at_1000
value: 99.25967001462051
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 77.36555747844541
- type: ndcg_at_1
value: 57.681365576102415
- type: ndcg_at_3
value: 72.01664798084765
- type: ndcg_at_5
value: 75.26345973082836
- type: ndcg_at_10
value: 77.36555747844541
- type: ndcg_at_100
value: 78.15567833673768
- type: ndcg_at_1000
value: 78.16528851292641
- type: map_at_1
value: 57.681365576102415
- type: map_at_3
value: 68.59886201991475
- type: map_at_5
value: 70.38051209103858
- type: map_at_10
value: 71.26684955632336
- type: map_at_100
value: 71.4637216600468
- type: map_at_1000
value: 71.46414501573332
- type: precision_at_1
value: 57.681365576102415
- type: precision_at_3
value: 27.287814129919084
- type: precision_at_5
value: 17.965860597439132
- type: precision_at_10
value: 9.623044096728066
- type: precision_at_100
value: 0.995732574679925
- type: precision_at_1000
value: 0.09964438122332549
- type: recall_at_1
value: 57.681365576102415
- type: recall_at_3
value: 81.86344238975818
- type: recall_at_5
value: 89.82930298719772
- type: recall_at_10
value: 96.23044096728307
- type: recall_at_100
value: 99.57325746799431
- type: recall_at_1000
value: 99.6443812233286
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 72.0465439956427
- type: ndcg_at_1
value: 58.666666666666664
- type: ndcg_at_3
value: 66.84566274610046
- type: ndcg_at_5
value: 69.46578881873717
- type: ndcg_at_10
value: 72.0465439956427
- type: ndcg_at_100
value: 74.25705461923272
- type: ndcg_at_1000
value: 74.63689058493014
- type: map_at_1
value: 55.59444444444445
- type: map_at_3
value: 63.71851851851852
- type: map_at_5
value: 65.5362962962963
- type: map_at_10
value: 66.84112433862435
- type: map_at_100
value: 67.36269426417417
- type: map_at_1000
value: 67.37568665562833
- type: precision_at_1
value: 58.666666666666664
- type: precision_at_3
value: 26.444444444444425
- type: precision_at_5
value: 17.66666666666672
- type: precision_at_10
value: 9.866666666666706
- type: precision_at_100
value: 1.0966666666666596
- type: precision_at_1000
value: 0.11266666666666675
- type: recall_at_1
value: 55.59444444444445
- type: recall_at_3
value: 72.72777777777777
- type: recall_at_5
value: 79.31666666666666
- type: recall_at_10
value: 86.75
- type: recall_at_100
value: 96.66666666666667
- type: recall_at_1000
value: 99.66666666666667
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 64.26928884606035
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_3
value: 64.18432764386345
- type: ndcg_at_5
value: 64.73235515799435
- type: ndcg_at_10
value: 64.26928884606035
- type: ndcg_at_100
value: 52.39807133285409
- type: ndcg_at_1000
value: 52.19937563361241
- type: map_at_1
value: 0.18483494997310454
- type: map_at_3
value: 0.5139705769331114
- type: map_at_5
value: 0.8245601222717243
- type: map_at_10
value: 1.5832530269558573
- type: map_at_100
value: 9.664760850102393
- type: map_at_1000
value: 25.568347406468334
- type: precision_at_1
value: 70.0
- type: precision_at_3
value: 71.33333333333333
- type: precision_at_5
value: 71.60000000000001
- type: precision_at_10
value: 70.99999999999996
- type: precision_at_100
value: 55.140000000000015
- type: precision_at_1000
value: 23.857999999999997
- type: recall_at_1
value: 0.18483494997310454
- type: recall_at_3
value: 0.5584287301859913
- type: recall_at_5
value: 0.9489025953807098
- type: recall_at_10
value: 1.9023711039425688
- type: recall_at_100
value: 13.596810701594226
- type: recall_at_1000
value: 50.92058432920189
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: main_score
value: 39.37204193531481
- type: ndcg_at_1
value: 35.11400651465798
- type: ndcg_at_3
value: 32.36672790229743
- type: ndcg_at_5
value: 34.79369234162357
- type: ndcg_at_10
value: 39.37204193531481
- type: ndcg_at_100
value: 47.544500439419124
- type: ndcg_at_1000
value: 50.305733346049855
- type: map_at_1
value: 15.516829533116216
- type: map_at_3
value: 23.73669923995656
- type: map_at_5
value: 26.43208469055373
- type: map_at_10
value: 28.912036175309773
- type: map_at_100
value: 31.413762299240894
- type: map_at_1000
value: 31.596796093997014
- type: precision_at_1
value: 35.11400651465798
- type: precision_at_3
value: 24.994571118349487
- type: precision_at_5
value: 19.231270358305956
- type: precision_at_10
value: 12.690553745928165
- type: precision_at_100
value: 2.1576547231270466
- type: precision_at_1000
value: 0.2676221498371306
- type: recall_at_1
value: 15.516829533116216
- type: recall_at_3
value: 29.994571118349512
- type: recall_at_5
value: 37.14223669923993
- type: recall_at_10
value: 47.29207383279043
- type: recall_at_100
value: 74.37133550488598
- type: recall_at_1000
value: 89.41585233441913
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: main_score
value: 83.26282954330777
- type: ndcg_at_1
value: 87.5489534098582
- type: ndcg_at_3
value: 78.7646435855166
- type: ndcg_at_5
value: 81.41629077444277
- type: ndcg_at_10
value: 83.26282954330777
- type: ndcg_at_100
value: 85.2771369900158
- type: ndcg_at_1000
value: 85.77519303747493
- type: map_at_1
value: 43.7744767049291
- type: map_at_3
value: 73.4661264911093
- type: map_at_5
value: 75.7169705154168
- type: map_at_10
value: 76.89183627536043
- type: map_at_100
value: 77.53680315727078
- type: map_at_1000
value: 77.5649311522075
- type: precision_at_1
value: 87.5489534098582
- type: precision_at_3
value: 51.74881836596788
- type: precision_at_5
value: 33.13977042539127
- type: precision_at_10
value: 17.492234976369023
- type: precision_at_100
value: 1.9030384875084312
- type: precision_at_1000
value: 0.19679945982446267
- type: recall_at_1
value: 43.7744767049291
- type: recall_at_3
value: 77.62322754895341
- type: recall_at_5
value: 82.84942606347063
- type: recall_at_10
value: 87.4611748818366
- type: recall_at_100
value: 95.15192437542201
- type: recall_at_1000
value: 98.39972991222147
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: main_score
value: 71.44670934705796
- type: ndcg_at_1
value: 54.026651216685984
- type: ndcg_at_3
value: 65.1267452491225
- type: ndcg_at_5
value: 68.6696802020747
- type: ndcg_at_10
value: 71.44670934705796
- type: ndcg_at_100
value: 73.74642927386503
- type: ndcg_at_1000
value: 73.90908268307331
- type: map_at_1
value: 48.50086906141366
- type: map_at_3
value: 61.07691193510995
- type: map_at_5
value: 63.36580243337187
- type: map_at_10
value: 64.74485498782997
- type: map_at_100
value: 65.34329174534082
- type: map_at_1000
value: 65.35107870745652
- type: precision_at_1
value: 54.026651216685984
- type: precision_at_3
value: 28.437620702974996
- type: precision_at_5
value: 19.20625724217861
- type: precision_at_10
value: 10.67207415990753
- type: precision_at_100
value: 1.1987253765932955
- type: precision_at_1000
value: 0.12143684820393259
- type: recall_at_1
value: 48.50086906141366
- type: recall_at_3
value: 73.19428350714561
- type: recall_at_5
value: 81.19689069138664
- type: recall_at_10
value: 89.04741212823485
- type: recall_at_100
value: 98.58053302433372
- type: recall_at_1000
value: 99.75376593279258
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: main_score
value: 90.03760323006117
- type: ndcg_at_1
value: 83.53
- type: ndcg_at_3
value: 87.53800795646302
- type: ndcg_at_5
value: 88.92909168525203
- type: ndcg_at_10
value: 90.03760323006117
- type: ndcg_at_100
value: 91.08558507332712
- type: ndcg_at_1000
value: 91.1430039358834
- type: map_at_1
value: 72.61760432018744
- type: map_at_3
value: 83.8457060028347
- type: map_at_5
value: 85.6228412692169
- type: map_at_10
value: 86.67700531365115
- type: map_at_100
value: 87.29851728827602
- type: map_at_1000
value: 87.31014621733333
- type: precision_at_1
value: 83.53
- type: precision_at_3
value: 38.33666666667159
- type: precision_at_5
value: 25.12599999999881
- type: precision_at_10
value: 13.629999999998683
- type: precision_at_100
value: 1.5431999999999773
- type: precision_at_1000
value: 0.15671999999997974
- type: recall_at_1
value: 72.61760432018744
- type: recall_at_3
value: 89.06736052932686
- type: recall_at_5
value: 93.09634203522849
- type: recall_at_10
value: 96.35128012894234
- type: recall_at_100
value: 99.7740237858541
- type: recall_at_1000
value: 99.99690476190477
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/webis-touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 30.2563523019649
- type: ndcg_at_1
value: 37.755102040816325
- type: ndcg_at_3
value: 34.45349994459905
- type: ndcg_at_5
value: 32.508805919063086
- type: ndcg_at_10
value: 30.2563523019649
- type: ndcg_at_100
value: 40.538336664503746
- type: ndcg_at_1000
value: 52.2066951614923
- type: map_at_1
value: 2.75537988273998
- type: map_at_3
value: 6.011397290504469
- type: map_at_5
value: 8.666495836494098
- type: map_at_10
value: 12.17701515007822
- type: map_at_100
value: 18.789086471205852
- type: map_at_1000
value: 20.42972375502502
- type: precision_at_1
value: 40.816326530612244
- type: precision_at_3
value: 35.37414965986394
- type: precision_at_5
value: 32.244897959183675
- type: precision_at_10
value: 26.93877551020408
- type: precision_at_100
value: 8.163265306122451
- type: precision_at_1000
value: 1.5979591836734703
- type: recall_at_1
value: 2.75537988273998
- type: recall_at_3
value: 7.254270324385098
- type: recall_at_5
value: 11.580137100328589
- type: recall_at_10
value: 18.745232816450553
- type: recall_at_100
value: 50.196809658622755
- type: recall_at_1000
value: 85.87317364148332
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: main_score
value: 51.36940792375597
- type: ndcg_at_1
value: 65.125
- type: ndcg_at_3
value: 55.3967569049025
- type: ndcg_at_5
value: 53.09668587926677
- type: ndcg_at_10
value: 51.36940792375597
- type: ndcg_at_100
value: 56.69623269243084
- type: ndcg_at_1000
value: 63.481061270842
- type: map_at_1
value: 10.265595545755545
- type: map_at_3
value: 16.776544233350698
- type: map_at_5
value: 20.184523605272798
- type: map_at_10
value: 24.772797659849264
- type: map_at_100
value: 36.72689012514183
- type: map_at_1000
value: 38.73869985105569
- type: precision_at_1
value: 77.5
- type: precision_at_3
value: 59.75000000000003
- type: precision_at_5
value: 52.849999999999994
- type: precision_at_10
value: 42.47499999999995
- type: precision_at_100
value: 13.614999999999993
- type: precision_at_1000
value: 2.500749999999998
- type: recall_at_1
value: 10.265595545755545
- type: recall_at_3
value: 17.819804963534246
- type: recall_at_5
value: 22.46124219601634
- type: recall_at_10
value: 30.44583516613163
- type: recall_at_100
value: 63.84118006287797
- type: recall_at_1000
value: 85.06450356093833
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: main_score
value: 47.93921415959017
- type: ndcg_at_1
value: 36.526219490536015
- type: ndcg_at_3
value: 42.35099043224295
- type: ndcg_at_5
value: 44.989685312964156
- type: ndcg_at_10
value: 47.93921415959017
- type: ndcg_at_100
value: 53.05390282389675
- type: ndcg_at_1000
value: 54.776052731794266
- type: map_at_1
value: 30.818605279548184
- type: map_at_3
value: 38.363350019087974
- type: map_at_5
value: 40.295203936887226
- type: map_at_10
value: 41.81978941662592
- type: map_at_100
value: 43.13300727554278
- type: map_at_1000
value: 43.2351061120207
- type: precision_at_1
value: 36.526219490536015
- type: precision_at_3
value: 19.550515857206346
- type: precision_at_5
value: 13.958783060831967
- type: precision_at_10
value: 8.498592395773393
- type: precision_at_100
value: 1.3024888941713948
- type: precision_at_1000
value: 0.1630253057414617
- type: recall_at_1
value: 30.818605279548184
- type: recall_at_3
value: 45.9132085981904
- type: recall_at_5
value: 52.6851323959227
- type: recall_at_10
value: 61.39718618970463
- type: recall_at_100
value: 83.30757187969981
- type: recall_at_1000
value: 94.9192024147964
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 89.47761194029852
- type: accuracy_stderr
value: 1.6502495811564162
- type: ap
value: 62.20813715457866
- type: ap_stderr
value: 3.7902166647587854
- type: f1
value: 84.91493292274734
- type: f1_stderr
value: 1.9572239640276208
- type: main_score
value: 89.47761194029852
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.89569999999999
- type: accuracy_stderr
value: 0.6886368582206464
- type: ap
value: 95.38531339207739
- type: ap_stderr
value: 0.9009257949898158
- type: f1
value: 96.8941935264779
- type: f1_stderr
value: 0.6908609132985931
- type: main_score
value: 96.89569999999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.602000000000004
- type: accuracy_stderr
value: 1.4532019818318436
- type: f1
value: 60.96100449021481
- type: f1_stderr
value: 1.8031398419765765
- type: main_score
value: 61.602000000000004
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 54.906319409992
- type: v_measure
value: 54.906319409992
- type: v_measure_std
value: 14.382682652951683
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 50.27779516565727
- type: v_measure
value: 50.27779516565727
- type: v_measure_std
value: 14.463711418590636
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.59457317979604
- type: mrr
value: 78.05214791364376
- type: main_score
value: 64.59457317979604
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cosine_pearson
value: 86.5833945335644
- type: cosine_spearman
value: 85.74472483606
- type: manhattan_pearson
value: 85.07748703871708
- type: manhattan_spearman
value: 85.1459160110718
- type: euclidean_pearson
value: 85.14704290043478
- type: euclidean_spearman
value: 85.10073425868336
- type: main_score
value: 85.74472483606
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 92.53246753246755
- type: accuracy_stderr
value: 0.5488837781559508
- type: f1
value: 92.5143182074032
- type: f1_stderr
value: 0.5657577980223147
- type: main_score
value: 92.53246753246755
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 52.64099497480452
- type: v_measure
value: 52.64099497480452
- type: v_measure_std
value: 1.081892399559334
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 49.1972734308178
- type: v_measure
value: 49.1972734308178
- type: v_measure_std
value: 0.9081245477708283
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 92.975
- type: accuracy_stderr
value: 0.5287958017987677
- type: f1
value: 89.29755895896542
- type: f1_stderr
value: 0.6485027046025079
- type: main_score
value: 92.975
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.66480000000001
- type: accuracy_stderr
value: 0.45673204398202666
- type: ap
value: 95.33843919456118
- type: ap_stderr
value: 0.6449846039754393
- type: f1
value: 96.6637668164617
- type: f1_stderr
value: 0.45793673051468287
- type: main_score
value: 96.66480000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 98.61149110807114
- type: accuracy_stderr
value: 0.469748178253266
- type: f1
value: 98.4685511007568
- type: f1_stderr
value: 0.51636776728259
- type: main_score
value: 98.61149110807114
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 95.51299589603283
- type: accuracy_stderr
value: 0.3591676911539482
- type: f1
value: 85.2464691439773
- type: f1_stderr
value: 0.9234502856695337
- type: main_score
value: 95.51299589603283
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 82.04774714189644
- type: accuracy_stderr
value: 0.7288818520309376
- type: f1
value: 79.28060657840692
- type: f1_stderr
value: 0.6872008571781982
- type: main_score
value: 82.04774714189644
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 84.40147948890383
- type: accuracy_stderr
value: 1.2939587629143627
- type: f1
value: 83.97779287582267
- type: f1_stderr
value: 0.9970599222060901
- type: main_score
value: 84.40147948890383
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: main_score
value: 45.80879120838561
- type: v_measure
value: 45.80879120838561
- type: v_measure_std
value: 1.257800489264564
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: main_score
value: 44.106849261042505
- type: v_measure
value: 44.106849261042505
- type: v_measure_std
value: 1.4347344477874981
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.794062752995345
- type: mrr
value: 32.98581714772614
- type: main_score
value: 31.794062752995345
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 56.03342473834434
- type: v_measure
value: 56.03342473834434
- type: v_measure_std
value: 5.972192613803461
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: main_score
value: 65.83156688381274
- type: v_measure
value: 65.83156688381274
- type: v_measure_std
value: 14.180225112120162
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cosine_pearson
value: 84.15759544348467
- type: cosine_spearman
value: 82.66085892322664
- type: manhattan_pearson
value: 82.27257241990692
- type: manhattan_spearman
value: 82.57752467555896
- type: euclidean_pearson
value: 82.20795646456065
- type: euclidean_spearman
value: 82.51008729416401
- type: main_score
value: 82.66085892322664
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 84.3406321391237
- type: cosine_spearman
value: 77.71091257651071
- type: manhattan_pearson
value: 81.25784268400994
- type: manhattan_spearman
value: 77.98426383345507
- type: euclidean_pearson
value: 81.25641851462917
- type: euclidean_spearman
value: 77.93254971878063
- type: main_score
value: 77.71091257651071
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 86.1528398894769
- type: cosine_spearman
value: 87.44662352358895
- type: manhattan_pearson
value: 86.92164570802663
- type: manhattan_spearman
value: 86.9132692625668
- type: euclidean_pearson
value: 87.00156426580821
- type: euclidean_spearman
value: 86.98750068631274
- type: main_score
value: 87.44662352358895
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 83.32782491176253
- type: cosine_spearman
value: 83.48313793311584
- type: manhattan_pearson
value: 82.60528063429948
- type: manhattan_spearman
value: 83.10434862310481
- type: euclidean_pearson
value: 82.68016090104034
- type: euclidean_spearman
value: 83.14418662406631
- type: main_score
value: 83.48313793311584
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 86.31535441436343
- type: cosine_spearman
value: 87.63145141246594
- type: manhattan_pearson
value: 86.95972711389149
- type: manhattan_spearman
value: 86.9849824463052
- type: euclidean_pearson
value: 86.95391575487379
- type: euclidean_spearman
value: 86.97613682266213
- type: main_score
value: 87.63145141246594
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 83.43854397443079
- type: cosine_spearman
value: 86.70176531845136
- type: manhattan_pearson
value: 85.82302317064868
- type: manhattan_spearman
value: 86.36561734213241
- type: euclidean_pearson
value: 85.80127366135169
- type: euclidean_spearman
value: 86.34803859754834
- type: main_score
value: 86.70176531845136
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cosine_pearson
value: 90.38940955877999
- type: cosine_spearman
value: 91.18282119920893
- type: manhattan_pearson
value: 91.31823663739615
- type: manhattan_spearman
value: 90.67257321731341
- type: euclidean_pearson
value: 91.30318753138528
- type: euclidean_spearman
value: 90.69044765693836
- type: main_score
value: 91.18282119920893
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cosine_pearson
value: 69.33936467780947
- type: cosine_spearman
value: 69.02345807358802
- type: manhattan_pearson
value: 70.11799452953082
- type: manhattan_spearman
value: 68.55450923481405
- type: euclidean_pearson
value: 70.10857680491809
- type: euclidean_spearman
value: 68.44610245708984
- type: main_score
value: 69.02345807358802
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 85.97288135509513
- type: cosine_spearman
value: 87.25208310840168
- type: manhattan_pearson
value: 86.3786471501451
- type: manhattan_spearman
value: 86.71177136523868
- type: euclidean_pearson
value: 86.40522339296625
- type: euclidean_spearman
value: 86.73930576508816
- type: main_score
value: 87.25208310840168
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.60324164489178
- type: mrr
value: 96.30331904841708
- type: main_score
value: 87.60324164489178
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.6920792079208
- type: cos_sim_accuracy_threshold
value: 90.36337347155474
- type: cos_sim_ap
value: 90.93952679056765
- type: cos_sim_f1
value: 83.10700706137968
- type: cos_sim_f1_threshold
value: 90.36337347155474
- type: cos_sim_precision
value: 90.96313912009512
- type: cos_sim_recall
value: 76.5
- type: dot_accuracy
value: 99.54554455445545
- type: dot_accuracy_threshold
value: 2876800.0
- type: dot_ap
value: 84.01112287735286
- type: dot_f1
value: 75.7622739018088
- type: dot_f1_threshold
value: 2820800.0
- type: dot_precision
value: 78.39572192513369
- type: dot_recall
value: 73.3
- type: euclidean_accuracy
value: 99.6930693069307
- type: euclidean_accuracy_threshold
value: 7718.054017089397
- type: euclidean_ap
value: 91.1257568881301
- type: euclidean_f1
value: 83.09022150189087
- type: euclidean_f1_threshold
value: 7817.08324628535
- type: euclidean_precision
value: 90.36427732079906
- type: euclidean_recall
value: 76.9
- type: manhattan_accuracy
value: 99.6920792079208
- type: manhattan_accuracy_threshold
value: 364735.19654273987
- type: manhattan_ap
value: 91.2326885940691
- type: manhattan_f1
value: 83.36008560727663
- type: manhattan_f1_threshold
value: 375395.8945572376
- type: manhattan_precision
value: 89.64326812428078
- type: manhattan_recall
value: 77.9
- type: max_accuracy
value: 99.6930693069307
- type: max_ap
value: 91.2326885940691
- type: max_f1
value: 83.36008560727663
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 66.2095300942637
- type: v_measure
value: 66.2095300942637
- type: v_measure_std
value: 3.214369679617631
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 45.74307000935057
- type: v_measure
value: 45.74307000935057
- type: v_measure_std
value: 1.5352466748569888
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.90337951829123
- type: mrr
value: 56.12889663441134
- type: main_score
value: 54.90337951829123
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 31.0669308484832
- type: cosine_spearman
value: 31.19637421540861
- type: dot_pearson
value: 30.62326176666765
- type: dot_spearman
value: 30.42135737502967
- type: main_score
value: 31.19637421540861
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 87.34339999999999
- type: accuracy_stderr
value: 1.838245696309393
- type: ap
value: 33.536584790435406
- type: ap_stderr
value: 2.276373512492581
- type: f1
value: 72.47307082324448
- type: f1_stderr
value: 1.9964640292072542
- type: main_score
value: 87.34339999999999
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 78.86247877758915
- type: accuracy_stderr
value: 1.1273253738982443
- type: f1
value: 79.14666244848874
- type: f1_stderr
value: 1.1532640958036497
- type: main_score
value: 78.86247877758915
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 70.44270836680788
- type: v_measure
value: 70.44270836680788
- type: v_measure_std
value: 1.5185423698266132
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.74512725755498
- type: cos_sim_accuracy_threshold
value: 82.34941560483547
- type: cos_sim_ap
value: 79.6389274210382
- type: cos_sim_f1
value: 71.76319176319176
- type: cos_sim_f1_threshold
value: 80.1523829249257
- type: cos_sim_precision
value: 70.0502512562814
- type: cos_sim_recall
value: 73.56200527704485
- type: dot_accuracy
value: 85.13441020444657
- type: dot_accuracy_threshold
value: 2220800.0
- type: dot_ap
value: 71.67080150823449
- type: dot_f1
value: 66.18984119287187
- type: dot_f1_threshold
value: 2086400.0
- type: dot_precision
value: 61.224489795918366
- type: dot_recall
value: 72.0316622691293
- type: euclidean_accuracy
value: 87.69148238660071
- type: euclidean_accuracy_threshold
value: 9221.50036619459
- type: euclidean_ap
value: 79.65326151280289
- type: euclidean_f1
value: 71.7903489983621
- type: euclidean_f1_threshold
value: 10313.528386219872
- type: euclidean_precision
value: 68.70026525198939
- type: euclidean_recall
value: 75.17150395778364
- type: manhattan_accuracy
value: 87.74512725755498
- type: manhattan_accuracy_threshold
value: 444289.1119837761
- type: manhattan_ap
value: 79.67744645365104
- type: manhattan_f1
value: 71.94423699278066
- type: manhattan_f1_threshold
value: 491676.24004781246
- type: manhattan_precision
value: 68.0961357210179
- type: manhattan_recall
value: 76.2532981530343
- type: max_accuracy
value: 87.74512725755498
- type: max_ap
value: 79.67744645365104
- type: max_f1
value: 71.94423699278066
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.5544688943222
- type: cos_sim_accuracy_threshold
value: 81.58909533293946
- type: cos_sim_ap
value: 86.95174990178396
- type: cos_sim_f1
value: 79.1543756145526
- type: cos_sim_f1_threshold
value: 80.08573448087095
- type: cos_sim_precision
value: 77.78355879292404
- type: cos_sim_recall
value: 80.5743763473976
- type: dot_accuracy
value: 88.60752124810804
- type: dot_accuracy_threshold
value: 2136000.0
- type: dot_ap
value: 84.26724775947629
- type: dot_f1
value: 77.67666146985243
- type: dot_f1_threshold
value: 2064000.0
- type: dot_precision
value: 73.40505721921468
- type: dot_recall
value: 82.47613181398214
- type: euclidean_accuracy
value: 89.5370046959289
- type: euclidean_accuracy_threshold
value: 9750.113991666478
- type: euclidean_ap
value: 86.99393092403776
- type: euclidean_f1
value: 79.07167337207571
- type: euclidean_f1_threshold
value: 10338.095928500366
- type: euclidean_precision
value: 76.59497690531177
- type: euclidean_recall
value: 81.71388974437943
- type: manhattan_accuracy
value: 89.57581402569178
- type: manhattan_accuracy_threshold
value: 463812.92815208435
- type: manhattan_ap
value: 87.00849868076658
- type: manhattan_f1
value: 79.08583576933297
- type: manhattan_f1_threshold
value: 482453.35128605366
- type: manhattan_precision
value: 78.00494270950348
- type: manhattan_recall
value: 80.19710502001848
- type: max_accuracy
value: 89.57581402569178
- type: max_ap
value: 87.00849868076658
- type: max_f1
value: 79.1543756145526
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cosine_pearson
value: 45.108559635369325
- type: cosine_spearman
value: 47.172833128216176
- type: manhattan_pearson
value: 45.75443077564791
- type: manhattan_spearman
value: 47.13974146235398
- type: euclidean_pearson
value: 45.78921257223492
- type: euclidean_spearman
value: 47.177095238278625
- type: main_score
value: 47.172833128216176
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 48.304409578388466
- type: cosine_spearman
value: 50.75006977697012
- type: manhattan_pearson
value: 52.688818756177035
- type: manhattan_spearman
value: 50.739214155741095
- type: euclidean_pearson
value: 52.71788557204978
- type: euclidean_spearman
value: 50.77895730336448
- type: main_score
value: 50.75006977697012
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 54.339999999999996
- type: accuracy_stderr
value: 1.6518837731511269
- type: f1
value: 53.37316538790502
- type: f1_stderr
value: 1.6112926272861336
- type: main_score
value: 54.339999999999996
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cosine_pearson
value: 59.62831218167518
- type: cosine_spearman
value: 62.02213472473759
- type: manhattan_pearson
value: 61.122261197018176
- type: manhattan_spearman
value: 62.208780520694454
- type: euclidean_pearson
value: 61.17827629627213
- type: euclidean_spearman
value: 62.266859648664244
- type: main_score
value: 62.02213472473759
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 54.64518394835408
- type: v_measure
value: 54.64518394835408
- type: v_measure_std
value: 1.2745946640208072
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 63.68323477729556
- type: v_measure
value: 63.68323477729556
- type: v_measure_std
value: 1.740918833098302
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 84.61500884703916
- type: mrr
value: 87.01424603174604
- type: main_score
value: 84.61500884703916
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 85.60137988993483
- type: mrr
value: 87.96857142857142
- type: main_score
value: 85.60137988993483
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 24.191
- type: map_at_10
value: 35.819
- type: map_at_100
value: 37.639
- type: map_at_1000
value: 37.775
- type: map_at_3
value: 32.045
- type: map_at_5
value: 34.008
- type: mrr_at_1
value: 36.684
- type: mrr_at_10
value: 44.769
- type: mrr_at_100
value: 45.754
- type: mrr_at_1000
value: 45.809
- type: mrr_at_3
value: 42.465
- type: mrr_at_5
value: 43.696
- type: ndcg_at_1
value: 36.834
- type: ndcg_at_10
value: 42.208
- type: ndcg_at_100
value: 49.507
- type: ndcg_at_1000
value: 51.834
- type: ndcg_at_3
value: 37.416
- type: ndcg_at_5
value: 39.152
- type: precision_at_1
value: 36.834
- type: precision_at_10
value: 9.357
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 21.08
- type: precision_at_5
value: 15.068999999999999
- type: recall_at_1
value: 24.191
- type: recall_at_10
value: 52.078
- type: recall_at_100
value: 82.548
- type: recall_at_1000
value: 98.017
- type: recall_at_3
value: 37.484
- type: recall_at_5
value: 43.187
- type: main_score
value: 42.208
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 81.98436560432953
- type: cos_sim_accuracy_threshold
value: 67.33228049687503
- type: cos_sim_ap
value: 90.13312662430796
- type: cos_sim_f1
value: 83.2163938077737
- type: cos_sim_f1_threshold
value: 64.44945196171463
- type: cos_sim_precision
value: 79.45555082943429
- type: cos_sim_recall
value: 87.350946925415
- type: dot_accuracy
value: 80.50511124473843
- type: dot_accuracy_threshold
value: 1736000.0
- type: dot_ap
value: 88.76136186445322
- type: dot_f1
value: 81.75838631878973
- type: dot_f1_threshold
value: 1681600.0
- type: dot_precision
value: 76.96594427244582
- type: dot_recall
value: 87.18728080430208
- type: euclidean_accuracy
value: 82.21286831028262
- type: euclidean_accuracy_threshold
value: 13240.938473272565
- type: euclidean_ap
value: 90.14863232280865
- type: euclidean_f1
value: 83.277292086976
- type: euclidean_f1_threshold
value: 13667.852165734186
- type: euclidean_precision
value: 79.97847147470398
- type: euclidean_recall
value: 86.85994856207621
- type: manhattan_accuracy
value: 82.21286831028262
- type: manhattan_accuracy_threshold
value: 629412.1389746666
- type: manhattan_ap
value: 90.03868533208357
- type: manhattan_f1
value: 83.15683870248579
- type: manhattan_f1_threshold
value: 649621.3114321232
- type: manhattan_precision
value: 79.46314443971026
- type: manhattan_recall
value: 87.21066167874679
- type: max_accuracy
value: 82.21286831028262
- type: max_ap
value: 90.14863232280865
- type: max_f1
value: 83.277292086976
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 65.595
- type: map_at_10
value: 73.717
- type: map_at_100
value: 74.134
- type: map_at_1000
value: 74.143
- type: map_at_3
value: 71.97
- type: map_at_5
value: 73.11800000000001
- type: mrr_at_1
value: 65.648
- type: mrr_at_10
value: 73.618
- type: mrr_at_100
value: 74.02499999999999
- type: mrr_at_1000
value: 74.033
- type: mrr_at_3
value: 71.865
- type: mrr_at_5
value: 73.04
- type: ndcg_at_1
value: 65.753
- type: ndcg_at_10
value: 77.458
- type: ndcg_at_100
value: 79.46
- type: ndcg_at_1000
value: 79.666
- type: ndcg_at_3
value: 73.988
- type: ndcg_at_5
value: 76.038
- type: precision_at_1
value: 65.753
- type: precision_at_10
value: 8.999
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.765
- type: precision_at_5
value: 17.092
- type: recall_at_1
value: 65.595
- type: recall_at_10
value: 89.041
- type: recall_at_100
value: 98.31400000000001
- type: recall_at_1000
value: 99.895
- type: recall_at_3
value: 79.768
- type: recall_at_5
value: 84.66799999999999
- type: main_score
value: 77.458
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 27.248
- type: map_at_10
value: 84.303
- type: map_at_100
value: 86.866
- type: map_at_1000
value: 86.888
- type: map_at_3
value: 58.658
- type: map_at_5
value: 74.265
- type: mrr_at_1
value: 92.2
- type: mrr_at_10
value: 94.733
- type: mrr_at_100
value: 94.767
- type: mrr_at_1000
value: 94.768
- type: mrr_at_3
value: 94.492
- type: mrr_at_5
value: 94.627
- type: ndcg_at_1
value: 92.2
- type: ndcg_at_10
value: 90.462
- type: ndcg_at_100
value: 92.562
- type: ndcg_at_1000
value: 92.757
- type: ndcg_at_3
value: 89.44800000000001
- type: ndcg_at_5
value: 88.683
- type: precision_at_1
value: 92.2
- type: precision_at_10
value: 42.980000000000004
- type: precision_at_100
value: 4.851
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 80.233
- type: precision_at_5
value: 67.95
- type: recall_at_1
value: 27.248
- type: recall_at_10
value: 91.46600000000001
- type: recall_at_100
value: 98.566
- type: recall_at_1000
value: 99.557
- type: recall_at_3
value: 60.671
- type: recall_at_5
value: 78.363
- type: main_score
value: 90.462
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 54.7
- type: map_at_10
value: 64.574
- type: map_at_100
value: 65.144
- type: map_at_1000
value: 65.156
- type: map_at_3
value: 62.333000000000006
- type: map_at_5
value: 63.63799999999999
- type: mrr_at_1
value: 54.7
- type: mrr_at_10
value: 64.603
- type: mrr_at_100
value: 65.172
- type: mrr_at_1000
value: 65.184
- type: mrr_at_3
value: 62.383
- type: mrr_at_5
value: 63.683
- type: ndcg_at_1
value: 54.7
- type: ndcg_at_10
value: 69.298
- type: ndcg_at_100
value: 71.81
- type: ndcg_at_1000
value: 72.117
- type: ndcg_at_3
value: 64.72099999999999
- type: ndcg_at_5
value: 67.071
- type: precision_at_1
value: 54.7
- type: precision_at_10
value: 8.41
- type: precision_at_100
value: 0.9530000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.867
- type: precision_at_5
value: 15.459999999999999
- type: recall_at_1
value: 54.7
- type: recall_at_10
value: 84.1
- type: recall_at_100
value: 95.3
- type: recall_at_1000
value: 97.7
- type: recall_at_3
value: 71.6
- type: recall_at_5
value: 77.3
- type: main_score
value: 69.298
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 49.942285494420936
- type: accuracy_stderr
value: 0.9218275144833329
- type: f1
value: 41.32381790374152
- type: f1_stderr
value: 0.8291507105327707
- type: main_score
value: 49.942285494420936
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 88.91181988742964
- type: accuracy_stderr
value: 1.952391767940518
- type: ap
value: 60.18509628974178
- type: ap_stderr
value: 4.273060966573582
- type: f1
value: 84.02722221827027
- type: f1_stderr
value: 2.238197243395083
- type: main_score
value: 88.91181988742964
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cosine_pearson
value: 68.32691294171383
- type: cosine_spearman
value: 75.95458618586729
- type: manhattan_pearson
value: 74.37198807732018
- type: manhattan_spearman
value: 75.99352157963375
- type: euclidean_pearson
value: 74.36294627886716
- type: euclidean_spearman
value: 75.98632511635132
- type: main_score
value: 75.95458618586729
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 35.4327533126161
- type: mrr
value: 34.61507936507937
- type: main_score
value: 35.4327533126161
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 72.652
- type: map_at_10
value: 81.396
- type: map_at_100
value: 81.597
- type: map_at_1000
value: 81.60300000000001
- type: map_at_3
value: 79.757
- type: map_at_5
value: 80.798
- type: mrr_at_1
value: 75.01400000000001
- type: mrr_at_10
value: 81.842
- type: mrr_at_100
value: 82.025
- type: mrr_at_1000
value: 82.03099999999999
- type: mrr_at_3
value: 80.45400000000001
- type: mrr_at_5
value: 81.345
- type: ndcg_at_1
value: 74.98599999999999
- type: ndcg_at_10
value: 84.70100000000001
- type: ndcg_at_100
value: 85.568
- type: ndcg_at_1000
value: 85.721
- type: ndcg_at_3
value: 81.64099999999999
- type: ndcg_at_5
value: 83.375
- type: precision_at_1
value: 74.98599999999999
- type: precision_at_10
value: 10.049
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.458000000000002
- type: precision_at_5
value: 19.206
- type: recall_at_1
value: 72.652
- type: recall_at_10
value: 94.40899999999999
- type: recall_at_100
value: 98.241
- type: recall_at_1000
value: 99.42
- type: recall_at_3
value: 86.354
- type: recall_at_5
value: 90.472
- type: main_score
value: 84.70100000000001
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 78.19098856758575
- type: accuracy_stderr
value: 0.6325028678427684
- type: f1
value: 74.80611425574001
- type: f1_stderr
value: 0.9021806207904779
- type: main_score
value: 78.19098856758575
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 82.58238063214526
- type: accuracy_stderr
value: 1.0999970213165273
- type: f1
value: 81.94734854057064
- type: f1_stderr
value: 1.248633855872851
- type: main_score
value: 82.58238063214526
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 53.7
- type: map_at_10
value: 59.184000000000005
- type: map_at_100
value: 59.754
- type: map_at_1000
value: 59.8
- type: map_at_3
value: 57.833
- type: map_at_5
value: 58.548
- type: mrr_at_1
value: 54.0
- type: mrr_at_10
value: 59.352000000000004
- type: mrr_at_100
value: 59.926
- type: mrr_at_1000
value: 59.971
- type: mrr_at_3
value: 57.99999999999999
- type: mrr_at_5
value: 58.714999999999996
- type: ndcg_at_1
value: 53.7
- type: ndcg_at_10
value: 62.022
- type: ndcg_at_100
value: 65.038
- type: ndcg_at_1000
value: 66.366
- type: ndcg_at_3
value: 59.209
- type: ndcg_at_5
value: 60.51299999999999
- type: precision_at_1
value: 53.7
- type: precision_at_10
value: 7.1
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 21.067
- type: precision_at_5
value: 13.28
- type: recall_at_1
value: 53.7
- type: recall_at_10
value: 71.0
- type: recall_at_100
value: 85.6
- type: recall_at_1000
value: 96.3
- type: recall_at_3
value: 63.2
- type: recall_at_5
value: 66.4
- type: main_score
value: 62.022
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 78.91333333333334
- type: accuracy_stderr
value: 1.0834307648494321
- type: f1
value: 78.881433228092
- type: f1_stderr
value: 1.122457277013712
- type: main_score
value: 78.91333333333334
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 76.39415268002165
- type: cos_sim_accuracy_threshold
value: 68.98242139321592
- type: cos_sim_ap
value: 83.20687440058073
- type: cos_sim_f1
value: 78.4351145038168
- type: cos_sim_f1_threshold
value: 65.47409929698304
- type: cos_sim_precision
value: 71.54046997389034
- type: cos_sim_recall
value: 86.80042238648363
- type: dot_accuracy
value: 74.60747157552788
- type: dot_accuracy_threshold
value: 1737600.0
- type: dot_ap
value: 79.78938545919723
- type: dot_f1
value: 76.92307692307692
- type: dot_f1_threshold
value: 1652800.0
- type: dot_precision
value: 67.90622473726758
- type: dot_recall
value: 88.70116156283
- type: euclidean_accuracy
value: 76.34001082837032
- type: euclidean_accuracy_threshold
value: 12597.299662420446
- type: euclidean_ap
value: 83.60222701792158
- type: euclidean_f1
value: 78.77947295423024
- type: euclidean_f1_threshold
value: 13639.653702639469
- type: euclidean_precision
value: 70.06578947368422
- type: euclidean_recall
value: 89.96832101372756
- type: manhattan_accuracy
value: 76.23172712506768
- type: manhattan_accuracy_threshold
value: 587601.2824743986
- type: manhattan_ap
value: 83.51813426548178
- type: manhattan_f1
value: 78.6654135338346
- type: manhattan_f1_threshold
value: 639711.1931562424
- type: manhattan_precision
value: 70.87214225232854
- type: manhattan_recall
value: 88.3843717001056
- type: max_accuracy
value: 76.39415268002165
- type: max_ap
value: 83.60222701792158
- type: max_f1
value: 78.77947295423024
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.59
- type: accuracy_stderr
value: 0.8971621926942733
- type: ap
value: 93.01229797205905
- type: ap_stderr
value: 1.0519542956523058
- type: f1
value: 94.58077736915268
- type: f1_stderr
value: 0.8954928292768671
- type: main_score
value: 94.59
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cosine_pearson
value: 24.341872875292857
- type: cosine_spearman
value: 30.570037022875436
- type: manhattan_pearson
value: 31.41015320258418
- type: manhattan_spearman
value: 30.604526098895114
- type: euclidean_pearson
value: 31.400038084432175
- type: euclidean_spearman
value: 30.61062265273698
- type: main_score
value: 30.570037022875436
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cosine_pearson
value: 36.61757468091905
- type: cosine_spearman
value: 38.981417359835504
- type: manhattan_pearson
value: 37.971127169578764
- type: manhattan_spearman
value: 39.55028286687854
- type: euclidean_pearson
value: 37.96983777648438
- type: euclidean_spearman
value: 39.542856511171784
- type: main_score
value: 38.981417359835504
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cosine_pearson
value: 68.29834902017382
- type: cosine_spearman
value: 68.6823378297782
- type: manhattan_pearson
value: 68.47336169904406
- type: manhattan_spearman
value: 69.08033223619941
- type: euclidean_pearson
value: 68.38785956191622
- type: euclidean_spearman
value: 68.97973814449657
- type: main_score
value: 68.6823378297782
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cosine_pearson
value: 80.60572958563593
- type: cosine_spearman
value: 80.87063761195603
- type: manhattan_pearson
value: 79.30174059269083
- type: manhattan_spearman
value: 80.02203618135883
- type: euclidean_pearson
value: 79.3314553444783
- type: euclidean_spearman
value: 80.04556415585255
- type: main_score
value: 80.87063761195603
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.47921173708028
- type: mrr
value: 77.9396513739777
- type: main_score
value: 67.47921173708028
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.021
- type: map_at_10
value: 79.149
- type: map_at_100
value: 82.613
- type: map_at_1000
value: 82.67099999999999
- type: map_at_3
value: 55.665
- type: map_at_5
value: 68.46900000000001
- type: mrr_at_1
value: 91.106
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value: 93.372
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value: 93.445
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value: 90.012
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value: 86.283
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value: 98.547
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value: 71.628
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value: 86.259
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type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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value: 50.255
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value: 0.9341868121526873
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value: 48.65080322457893
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value: 0.9391547591179161
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value: 50.255
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type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
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value: 64.32076022871308
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value: 64.32076022871308
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value: 0.7190996709617924
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type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
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value: 54.57080911705562
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value: 54.57080911705562
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value: 1.5185826402845883
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type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
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type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
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value: 89.26
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value: 89.26
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type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
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type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
metrics:
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dataset:
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type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
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value: 55.186
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value: 55.186
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dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
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value: 14.31173939909079
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value: 6.50215473066801
- type: nauc_recall_at_5_max
value: 18.889080152631095
- type: nauc_recall_at_5_std
value: 12.221319698087767
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value: 28.524
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value: 33.476
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value: 36.012
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value: 30.820999999999998
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value: 22.721
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value: 26.596999999999998
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value: 17.116999999999997
- type: precision_at_10
value: 4.234
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value: 0.658
- type: precision_at_1000
value: 0.086
- type: precision_at_20
value: 2.568
- type: precision_at_3
value: 9.009
- type: precision_at_5
value: 7.297
- type: recall_at_1
value: 17.116999999999997
- type: recall_at_10
value: 42.342
- type: recall_at_100
value: 65.766
- type: recall_at_1000
value: 86.036
- type: recall_at_20
value: 51.351
- type: recall_at_3
value: 27.027
- type: recall_at_5
value: 36.486000000000004
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: main_score
value: 28.18744772954557
- type: v_measure
value: 28.18744772954557
- type: v_measure_std
value: 3.239838057506439
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (fr)
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: main_score
value: 47.75009059283003
- type: v_measure
value: 47.75009059283003
- type: v_measure_std
value: 2.009277732690298
- type: main_score
value: 47.46091989113078
- type: v_measure
value: 47.46091989113078
- type: v_measure_std
value: 2.604802270948194
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.20325712496086
- type: f1
value: 97.05991090368462
- type: f1_weighted
value: 97.20748006323807
- type: main_score
value: 97.20325712496086
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 93.07234575634199
- type: f1
value: 76.54521288506878
- type: f1_weighted
value: 93.6903586431893
- type: main_score
value: 93.07234575634199
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 82.48815165876778
- type: f1
value: 78.71164464238117
- type: f1_weighted
value: 82.38927389376973
- type: main_score
value: 82.48815165876778
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: main_score
value: 73.85712952800003
- type: v_measure
value: 73.85712952800003
- type: v_measure_std
value: 22.471668299794416
- type: main_score
value: 67.23960512566751
- type: v_measure
value: 67.23960512566751
- type: v_measure_std
value: 24.65079601360142
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 79.59986550100874
- type: f1
value: 76.0439154517916
- type: f1_weighted
value: 79.48538292013761
- type: main_score
value: 79.59986550100874
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 82.182246133154
- type: f1
value: 81.68006668655397
- type: f1_weighted
value: 81.94775072858566
- type: main_score
value: 82.182246133154
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
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value: 62.532
- type: map_at_1
value: 45.823
- type: map_at_10
value: 57.174
- type: map_at_100
value: 57.735
- type: map_at_1000
value: 57.767
- type: map_at_20
value: 57.53
- type: map_at_3
value: 54.716
- type: map_at_5
value: 56.227000000000004
- type: mrr_at_1
value: 45.82309582309582
- type: mrr_at_10
value: 57.17958217958217
- type: mrr_at_100
value: 57.744059413627866
- type: mrr_at_1000
value: 57.776651992832605
- type: mrr_at_20
value: 57.53890924556554
- type: mrr_at_3
value: 54.716079716079676
- type: mrr_at_5
value: 56.227136227136256
- type: nauc_map_at_1000_diff1
value: 39.48401851944296
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value: 3.9173787361040913
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value: 3.9506714061766557
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value: 39.31548009319837
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value: 3.782911249250981
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value: 44.190649439568766
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value: 31.017419446234317
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value: 0.5544388561183956
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value: 3.934276303386171
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value: 40.30871768246873
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value: 2.9847330185694556
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value: 4.006374171904806
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
value: 7.864444241838433
- type: nauc_precision_at_5_diff1
value: 29.494641243672138
- type: nauc_precision_at_5_max
value: 47.326071718857484
- type: nauc_precision_at_5_std
value: 12.273738036245172
- type: nauc_recall_at_1000_diff1
value: -13.365474882756335
- type: nauc_recall_at_1000_max
value: 50.68568417959348
- type: nauc_recall_at_1000_std
value: 37.8471451290128
- type: nauc_recall_at_100_diff1
value: 12.08144320748251
- type: nauc_recall_at_100_max
value: 43.675613561914986
- type: nauc_recall_at_100_std
value: 44.645239877595564
- type: nauc_recall_at_10_diff1
value: 23.203582041832526
- type: nauc_recall_at_10_max
value: 46.9370613928509
- type: nauc_recall_at_10_std
value: 17.36243956517297
- type: nauc_recall_at_1_diff1
value: 44.190649439568766
- type: nauc_recall_at_1_max
value: 31.017419446234317
- type: nauc_recall_at_1_std
value: 0.5544388561183956
- type: nauc_recall_at_20_diff1
value: 22.42836999246212
- type: nauc_recall_at_20_max
value: 46.29381413041773
- type: nauc_recall_at_20_std
value: 26.12660940192268
- type: nauc_recall_at_3_diff1
value: 34.50301870470248
- type: nauc_recall_at_3_max
value: 45.19477535801611
- type: nauc_recall_at_3_std
value: 7.8644442418384335
- type: nauc_recall_at_5_diff1
value: 29.494641243672216
- type: nauc_recall_at_5_max
value: 47.32607171885759
- type: nauc_recall_at_5_std
value: 12.273738036245142
- type: ndcg_at_1
value: 45.823
- type: ndcg_at_10
value: 62.532
- type: ndcg_at_100
value: 65.298
- type: ndcg_at_1000
value: 66.214
- type: ndcg_at_20
value: 63.82600000000001
- type: ndcg_at_3
value: 57.528999999999996
- type: ndcg_at_5
value: 60.24
- type: precision_at_1
value: 45.823
- type: precision_at_10
value: 7.928
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.22
- type: precision_at_3
value: 21.881
- type: precision_at_5
value: 14.438999999999998
- type: recall_at_1
value: 45.823
- type: recall_at_10
value: 79.279
- type: recall_at_100
value: 92.301
- type: recall_at_1000
value: 99.631
- type: recall_at_20
value: 84.398
- type: recall_at_3
value: 65.643
- type: recall_at_5
value: 72.195
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 99.90069513406156
- type: cosine_accuracy_threshold
value: 54.45001207375879
- type: cosine_ap
value: 100.0
- type: cosine_f1
value: 99.95032290114257
- type: cosine_f1_threshold
value: 54.45001207375879
- type: cosine_precision
value: 100.0
- type: cosine_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_accuracy_threshold
value: 1312800.0
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_f1_threshold
value: 1312800.0
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_accuracy_threshold
value: 15150.791732002876
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_f1_threshold
value: 15150.791732002876
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: main_score
value: 100.0
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_accuracy_threshold
value: 717903.2791554928
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_f1_threshold
value: 717903.2791554928
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
- type: max_precision
value: 100.0
- type: max_recall
value: 99.90069513406156
- type: similarity_accuracy
value: 99.90069513406156
- type: similarity_accuracy_threshold
value: 54.45001207375879
- type: similarity_ap
value: 100.0
- type: similarity_f1
value: 99.95032290114257
- type: similarity_f1_threshold
value: 54.45001207375879
- type: similarity_precision
value: 100.0
- type: similarity_recall
value: 99.90069513406156
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 67.95
- type: cosine_accuracy_threshold
value: 97.36901285947026
- type: cosine_ap
value: 70.14158727060726
- type: cosine_f1
value: 65.38108356290174
- type: cosine_f1_threshold
value: 94.90683744884689
- type: cosine_precision
value: 55.84313725490196
- type: cosine_recall
value: 78.8482834994463
- type: dot_accuracy
value: 60.5
- type: dot_accuracy_threshold
value: 2606400.0
- type: dot_ap
value: 57.0114505567262
- type: dot_f1
value: 63.29394387001477
- type: dot_f1_threshold
value: 2345600.0
- type: dot_precision
value: 47.4792243767313
- type: dot_recall
value: 94.90586932447398
- type: euclidean_accuracy
value: 68.05
- type: euclidean_accuracy_threshold
value: 3824.99743197985
- type: euclidean_ap
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- type: euclidean_f1
value: 65.21939953810623
- type: euclidean_f1_threshold
value: 5187.47968966464
- type: euclidean_precision
value: 55.942947702060216
- type: euclidean_recall
value: 78.18383167220377
- type: main_score
value: 70.14158727060726
- type: manhattan_accuracy
value: 68.05
- type: manhattan_accuracy_threshold
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- type: manhattan_ap
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value: 65.2854511970534
- type: manhattan_f1_threshold
value: 246807.1710705757
- type: manhattan_precision
value: 55.87076438140268
- type: manhattan_recall
value: 78.51605758582502
- type: max_ap
value: 70.14158727060726
- type: max_f1
value: 65.38108356290174
- type: max_precision
value: 55.942947702060216
- type: max_recall
value: 94.90586932447398
- type: similarity_accuracy
value: 67.95
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value: 97.36901285947026
- type: similarity_ap
value: 70.14158727060726
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value: 65.38108356290174
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value: 94.90683744884689
- type: similarity_precision
value: 55.84313725490196
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value: 78.8482834994463
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
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value: 79.79861486027
- type: cosine_spearman
value: 79.3918786992987
- type: euclidean_pearson
value: 77.73226212475764
- type: euclidean_spearman
value: 79.08856888397014
- type: main_score
value: 79.3918786992987
- type: manhattan_pearson
value: 77.8002206650809
- type: manhattan_spearman
value: 79.15284532531264
- type: pearson
value: 79.79861486027
- type: spearman
value: 79.3918786992987
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 83.32314025534286
- type: cosine_spearman
value: 83.2806004701507
- type: euclidean_pearson
value: 81.88040500817269
- type: euclidean_spearman
value: 82.73179823676206
- type: main_score
value: 83.2806004701507
- type: manhattan_pearson
value: 82.0438174605579
- type: manhattan_spearman
value: 83.0253049811576
- type: pearson
value: 83.32314025534286
- type: spearman
value: 83.2806004701507
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
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value: 84.56723075054445
- type: cosine_spearman
value: 85.08759191551403
- type: euclidean_pearson
value: 83.186096744725
- type: euclidean_spearman
value: 84.36958569816491
- type: main_score
value: 85.08759191551403
- type: manhattan_pearson
value: 83.1405072165467
- type: manhattan_spearman
value: 84.34227830781155
- type: pearson
value: 84.56723075054445
- type: spearman
value: 85.08759191551403
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
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value: 31.921764332449115
- type: cosine_spearman
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- type: dot_pearson
value: 31.585578707631406
- type: dot_spearman
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- type: main_score
value: 31.260442997631806
- type: pearson
value: 31.921764332449115
- type: spearman
value: 31.260442997631806
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
metrics:
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value: 91.83333333333333
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- type: mrr
value: 92.0
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value: 17.48914092664096
- task:
type: Retrieval
dataset:
name: MTEB SyntecRetrieval
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
metrics:
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- type: recall_at_1
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- type: recall_at_20
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- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fra-fra
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
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type: PL-MTEB/allegro-reviews
config: default
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revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
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type: clarin-knext/arguana-pl
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revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
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dataset:
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type: PL-MTEB/cbd
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split: test
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type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
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- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
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value: 1.127
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- type: recall_at_1
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- type: recall_at_100
value: 92.837
- type: recall_at_1000
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- type: recall_at_3
value: 57.484
- type: recall_at_5
value: 66.476
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 67.24297712134376
- type: accuracy_stderr
value: 4.77558207347837
- type: ap
value: 77.38171975466854
- type: ap_stderr
value: 2.5801970175320394
- type: f1
value: 65.21823897814332
- type: f1_stderr
value: 4.317111734308895
- type: main_score
value: 67.24297712134376
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
metrics:
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value: 97.95918367346938
- type: cosine_accuracy_threshold
value: 59.87724328133361
- type: cosine_ap
value: 99.24498625606927
- type: cosine_f1
value: 96.6867469879518
- type: cosine_f1_threshold
value: 59.87724328133361
- type: cosine_precision
value: 95.53571428571429
- type: cosine_recall
value: 97.86585365853658
- type: dot_accuracy
value: 98.51576994434137
- type: dot_accuracy_threshold
value: 1574400.0
- type: dot_ap
value: 99.28566232682996
- type: dot_f1
value: 97.57575757575758
- type: dot_f1_threshold
value: 1564800.0
- type: dot_precision
value: 96.98795180722891
- type: dot_recall
value: 98.17073170731707
- type: euclidean_accuracy
value: 97.6808905380334
- type: euclidean_accuracy_threshold
value: 14418.957939643331
- type: euclidean_ap
value: 99.0876340868033
- type: euclidean_f1
value: 96.24060150375941
- type: euclidean_f1_threshold
value: 14442.183182634264
- type: euclidean_precision
value: 94.95548961424333
- type: euclidean_recall
value: 97.5609756097561
- type: main_score
value: 99.28566232682996
- type: manhattan_accuracy
value: 97.86641929499072
- type: manhattan_accuracy_threshold
value: 681802.1857857704
- type: manhattan_ap
value: 99.08465290287205
- type: manhattan_f1
value: 96.52042360060513
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value: 681802.1857857704
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value: 95.7957957957958
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value: 97.2560975609756
- type: max_ap
value: 99.28566232682996
- type: max_f1
value: 97.57575757575758
- type: max_precision
value: 96.98795180722891
- type: max_recall
value: 98.17073170731707
- type: similarity_accuracy
value: 97.95918367346938
- type: similarity_accuracy_threshold
value: 59.87724328133361
- type: similarity_ap
value: 99.24498625606927
- type: similarity_f1
value: 96.6867469879518
- type: similarity_f1_threshold
value: 59.87724328133361
- type: similarity_precision
value: 95.53571428571429
- type: similarity_recall
value: 97.86585365853658
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: d90724373c70959f17d2331ad51fb60c71176b03
metrics:
- type: accuracy
value: 90.41551246537396
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value: 89.15361039614409
- type: f1_weighted
value: 90.69893050097603
- type: main_score
value: 90.41551246537396
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
metrics:
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value: 77.77327935222672
- type: f1
value: 61.238079022455636
- type: f1_weighted
value: 80.58753601509183
- type: main_score
value: 77.77327935222672
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
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value: 87.2
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value: 83.69773167092553
- type: cos_sim_ap
value: 95.43345251568122
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value: 89.82785602503913
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value: 81.2116503074739
- type: cos_sim_precision
value: 85.16320474777447
- type: cos_sim_recall
value: 95.03311258278146
- type: dot_accuracy
value: 85.9
- type: dot_accuracy_threshold
value: 2177600.0
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value: 2163200.0
- type: dot_precision
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- type: euclidean_accuracy
value: 87.5
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- type: manhattan_accuracy
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- type: max_accuracy
value: 87.5
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value: 95.43345251568122
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value: 89.92747784045125
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
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type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
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dataset:
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type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
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type: mteb/sts22-crosslingual-sts
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dataset:
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type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
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- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: clarin-knext/trec-covid-pl
config: default
split: test
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
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---
<h1 align="center">FlagEmbedding</h1>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
**BGE-Multilingual-Gemma2** is a LLM-based multilingual embedding model. It is trained on a diverse range of languages and tasks based on [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b). BGE-Multilingual-Gemma2 primarily demonstrates the following advancements:
- Diverse training data: The model's training data spans a broad range of languages, including English, Chinese, Japanese, Korean, French, and more.Additionally, the data covers a variety of task types, such as retrieval, classification, and clustering.
- Outstanding performance: The model exhibits state-of-the-art (SOTA) results on multilingual benchmarks like MIRACL, MTEB-pl, and MTEB-fr. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench.
## 📑 Open-source Plan
- [x] Checkpoint
- [ ] Training Data
We will release the training data of **BGE-Multilingual-Gemma2** in the future.
## Usage
### Using FlagEmbedding
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
```python
from FlagEmbedding import FlagLLMModel
queries = ["how much protein should a female eat", "summit define"]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
model = FlagLLMModel('BAAI/bge-multilingual-gemma2',
query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[ 0.559 0.01654 ]
# [-0.002575 0.4998 ]]
```
By default, FlagLLMModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
import torch
# Load the model, optionally in float16 precision for faster inference
model = SentenceTransformer("BAAI/bge-multilingual-gemma2", model_kwargs={"torch_dtype": torch.float16})
# Prepare a prompt given an instruction
instruction = 'Given a web search query, retrieve relevant passages that answer the query.'
prompt = f'<instruct>{instruction}\n<query>'
# Prepare queries and documents
queries = [
'how much protein should a female eat',
'summit define',
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# Compute the query and document embeddings
query_embeddings = model.encode(queries, prompt=prompt)
document_embeddings = model.encode(documents)
# Compute the cosine similarity between the query and document embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.5591, 0.0164],
# [-0.0026, 0.4993]], dtype=torch.float16)
```
### Using HuggingFace Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'<instruct>{task_description}\n<query>{query}'
task = 'Given a web search query, retrieve relevant passages that answer the query.'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instructions for documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-multilingual-gemma2')
model = AutoModel.from_pretrained('BAAI/bge-multilingual-gemma2')
model.eval()
max_length = 4096
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt', pad_to_multiple_of=8)
with torch.no_grad():
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[55.92064666748047, 1.6549524068832397], [-0.2698777914047241, 49.95653533935547]]
```
## Evaluation
`bge-multilingual-gemma2` exhibits **state-of-the-art (SOTA) results on benchmarks like MIRACL, MTEB-pl, and MTEB-fr**. It also achieves excellent performance on other major evaluations, including MTEB, C-MTEB and AIR-Bench.
- [**MIRACL**](https://github.com/project-miracl/miracl)
nDCG@10:
<img src="./imgs/[email protected]" alt="MIRACL-nDCG@10" style="zoom:200%;" />
Recall@100:
<img src="./imgs/[email protected]" alt="MIRACL-Recall@100" style="zoom:200%;" />
- [**MTEB-fr/pl**](https://huggingface.co/spaces/mteb/leaderboard)
<img src="./imgs/MTEB_FR_PL.png" alt="MTEB-fr/pl" style="zoom:200%;" />
- [**MTEB**](https://huggingface.co/spaces/mteb/leaderboard)
<img src="./imgs/MTEB.png" alt="MTEB" style="zoom:200%;" />
- [**BEIR**](https://huggingface.co/spaces/mteb/leaderboard)
<img src="./imgs/BEIR.png" alt="BEIR" style="zoom:200%;" />
- [**C-MTEB**](https://huggingface.co/spaces/mteb/leaderboard)
<img src="./imgs/C-MTEB.png" alt="C-MTEB" style="zoom:200%;" />
- [**AIR-Bench**](https://huggingface.co/spaces/AIR-Bench/leaderboard)
Long-Doc (en, Recall@10):
<img src="./imgs/AIR-Bench_Long-Doc_en.png" alt="AIR-Bench_Long-Doc" style="zoom:200%;" />
QA (en&zh, nDCG@10):
<img src="./imgs/AIR-Bench_QA_en_zh.png" alt="AIR-Bench_QA" style="zoom:200%;" />
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
| :----------------------------------------------------------- | :-----------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | - | A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. |
| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | - | A LLM-based dense retriever with in-context learning capabilities can fully leverage the model's potential based on a few shot examples(4096 tokens) | Provide instructions and few-shot examples freely based on the given task. |
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
avsolatorio/NoInstruct-small-Embedding-v0 | avsolatorio | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"transformers",
"en",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-01T16:21:05 | 2024-05-04T02:11:03 | 163,145 | 22 | ---
language:
- en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
- transformers
model-index:
- name: NoInstruct-small-Embedding-v0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.76119402985074
- type: ap
value: 39.03628777559392
- type: f1
value: 69.85860402259618
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.29920000000001
- type: ap
value: 90.03479490717608
- type: f1
value: 93.28554395248467
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.98799999999999
- type: f1
value: 49.46151232451642
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 31.935000000000002
- type: map_at_10
value: 48.791000000000004
- type: map_at_100
value: 49.619
- type: map_at_1000
value: 49.623
- type: map_at_3
value: 44.334
- type: map_at_5
value: 46.908
- type: mrr_at_1
value: 32.93
- type: mrr_at_10
value: 49.158
- type: mrr_at_100
value: 50.00599999999999
- type: mrr_at_1000
value: 50.01
- type: mrr_at_3
value: 44.618
- type: mrr_at_5
value: 47.325
- type: ndcg_at_1
value: 31.935000000000002
- type: ndcg_at_10
value: 57.593
- type: ndcg_at_100
value: 60.841
- type: ndcg_at_1000
value: 60.924
- type: ndcg_at_3
value: 48.416
- type: ndcg_at_5
value: 53.05
- type: precision_at_1
value: 31.935000000000002
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.081
- type: precision_at_5
value: 14.296000000000001
- type: recall_at_1
value: 31.935000000000002
- type: recall_at_10
value: 85.491
- type: recall_at_100
value: 99.004
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.242
- type: recall_at_5
value: 71.479
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.78438534940855
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.12916178519471
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.125361608299855
- type: mrr
value: 74.92525172580574
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.64322910336641
- type: cos_sim_spearman
value: 87.20138453306345
- type: euclidean_pearson
value: 87.08547818178234
- type: euclidean_spearman
value: 87.17066094143931
- type: manhattan_pearson
value: 87.30053110771618
- type: manhattan_spearman
value: 86.86824441211934
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.3961038961039
- type: f1
value: 86.3669961645295
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.40291404289857
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.102356817746816
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 31.013
- type: map_at_10
value: 42.681999999999995
- type: map_at_100
value: 44.24
- type: map_at_1000
value: 44.372
- type: map_at_3
value: 39.181
- type: map_at_5
value: 41.071999999999996
- type: mrr_at_1
value: 38.196999999999996
- type: mrr_at_10
value: 48.604
- type: mrr_at_100
value: 49.315
- type: mrr_at_1000
value: 49.363
- type: mrr_at_3
value: 45.756
- type: mrr_at_5
value: 47.43
- type: ndcg_at_1
value: 38.196999999999996
- type: ndcg_at_10
value: 49.344
- type: ndcg_at_100
value: 54.662
- type: ndcg_at_1000
value: 56.665
- type: ndcg_at_3
value: 44.146
- type: ndcg_at_5
value: 46.514
- type: precision_at_1
value: 38.196999999999996
- type: precision_at_10
value: 9.571
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.364
- type: precision_at_5
value: 15.336
- type: recall_at_1
value: 31.013
- type: recall_at_10
value: 61.934999999999995
- type: recall_at_100
value: 83.923
- type: recall_at_1000
value: 96.601
- type: recall_at_3
value: 46.86
- type: recall_at_5
value: 53.620000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 29.84
- type: map_at_10
value: 39.335
- type: map_at_100
value: 40.647
- type: map_at_1000
value: 40.778
- type: map_at_3
value: 36.556
- type: map_at_5
value: 38.048
- type: mrr_at_1
value: 36.815
- type: mrr_at_10
value: 45.175
- type: mrr_at_100
value: 45.907
- type: mrr_at_1000
value: 45.946999999999996
- type: mrr_at_3
value: 42.909000000000006
- type: mrr_at_5
value: 44.227
- type: ndcg_at_1
value: 36.815
- type: ndcg_at_10
value: 44.783
- type: ndcg_at_100
value: 49.551
- type: ndcg_at_1000
value: 51.612
- type: ndcg_at_3
value: 40.697
- type: ndcg_at_5
value: 42.558
- type: precision_at_1
value: 36.815
- type: precision_at_10
value: 8.363
- type: precision_at_100
value: 1.385
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 19.342000000000002
- type: precision_at_5
value: 13.706999999999999
- type: recall_at_1
value: 29.84
- type: recall_at_10
value: 54.164
- type: recall_at_100
value: 74.36
- type: recall_at_1000
value: 87.484
- type: recall_at_3
value: 42.306
- type: recall_at_5
value: 47.371
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.231
- type: map_at_10
value: 51.44800000000001
- type: map_at_100
value: 52.574
- type: map_at_1000
value: 52.629999999999995
- type: map_at_3
value: 48.077
- type: map_at_5
value: 50.019000000000005
- type: mrr_at_1
value: 44.89
- type: mrr_at_10
value: 54.803000000000004
- type: mrr_at_100
value: 55.556000000000004
- type: mrr_at_1000
value: 55.584
- type: mrr_at_3
value: 52.32
- type: mrr_at_5
value: 53.846000000000004
- type: ndcg_at_1
value: 44.89
- type: ndcg_at_10
value: 57.228
- type: ndcg_at_100
value: 61.57
- type: ndcg_at_1000
value: 62.613
- type: ndcg_at_3
value: 51.727000000000004
- type: ndcg_at_5
value: 54.496
- type: precision_at_1
value: 44.89
- type: precision_at_10
value: 9.266
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 23.051
- type: precision_at_5
value: 15.987000000000002
- type: recall_at_1
value: 39.231
- type: recall_at_10
value: 70.82000000000001
- type: recall_at_100
value: 89.446
- type: recall_at_1000
value: 96.665
- type: recall_at_3
value: 56.40500000000001
- type: recall_at_5
value: 62.993
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 25.296000000000003
- type: map_at_10
value: 34.021
- type: map_at_100
value: 35.158
- type: map_at_1000
value: 35.233
- type: map_at_3
value: 31.424999999999997
- type: map_at_5
value: 33.046
- type: mrr_at_1
value: 27.232
- type: mrr_at_10
value: 36.103
- type: mrr_at_100
value: 37.076
- type: mrr_at_1000
value: 37.135
- type: mrr_at_3
value: 33.635
- type: mrr_at_5
value: 35.211
- type: ndcg_at_1
value: 27.232
- type: ndcg_at_10
value: 38.878
- type: ndcg_at_100
value: 44.284
- type: ndcg_at_1000
value: 46.268
- type: ndcg_at_3
value: 33.94
- type: ndcg_at_5
value: 36.687
- type: precision_at_1
value: 27.232
- type: precision_at_10
value: 5.921
- type: precision_at_100
value: 0.907
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.426
- type: precision_at_5
value: 10.215
- type: recall_at_1
value: 25.296000000000003
- type: recall_at_10
value: 51.708
- type: recall_at_100
value: 76.36699999999999
- type: recall_at_1000
value: 91.306
- type: recall_at_3
value: 38.651
- type: recall_at_5
value: 45.201
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 16.24
- type: map_at_10
value: 24.696
- type: map_at_100
value: 25.945
- type: map_at_1000
value: 26.069
- type: map_at_3
value: 22.542
- type: map_at_5
value: 23.526
- type: mrr_at_1
value: 20.149
- type: mrr_at_10
value: 29.584
- type: mrr_at_100
value: 30.548
- type: mrr_at_1000
value: 30.618000000000002
- type: mrr_at_3
value: 27.301
- type: mrr_at_5
value: 28.563
- type: ndcg_at_1
value: 20.149
- type: ndcg_at_10
value: 30.029
- type: ndcg_at_100
value: 35.812
- type: ndcg_at_1000
value: 38.755
- type: ndcg_at_3
value: 26.008
- type: ndcg_at_5
value: 27.517000000000003
- type: precision_at_1
value: 20.149
- type: precision_at_10
value: 5.647
- type: precision_at_100
value: 0.968
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 12.934999999999999
- type: precision_at_5
value: 8.955
- type: recall_at_1
value: 16.24
- type: recall_at_10
value: 41.464
- type: recall_at_100
value: 66.781
- type: recall_at_1000
value: 87.85300000000001
- type: recall_at_3
value: 29.822
- type: recall_at_5
value: 34.096
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 29.044999999999998
- type: map_at_10
value: 39.568999999999996
- type: map_at_100
value: 40.831
- type: map_at_1000
value: 40.948
- type: map_at_3
value: 36.495
- type: map_at_5
value: 38.21
- type: mrr_at_1
value: 35.611
- type: mrr_at_10
value: 45.175
- type: mrr_at_100
value: 45.974
- type: mrr_at_1000
value: 46.025
- type: mrr_at_3
value: 42.765
- type: mrr_at_5
value: 44.151
- type: ndcg_at_1
value: 35.611
- type: ndcg_at_10
value: 45.556999999999995
- type: ndcg_at_100
value: 50.86000000000001
- type: ndcg_at_1000
value: 52.983000000000004
- type: ndcg_at_3
value: 40.881
- type: ndcg_at_5
value: 43.035000000000004
- type: precision_at_1
value: 35.611
- type: precision_at_10
value: 8.306
- type: precision_at_100
value: 1.276
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 19.57
- type: precision_at_5
value: 13.725000000000001
- type: recall_at_1
value: 29.044999999999998
- type: recall_at_10
value: 57.513999999999996
- type: recall_at_100
value: 80.152
- type: recall_at_1000
value: 93.982
- type: recall_at_3
value: 44.121
- type: recall_at_5
value: 50.007000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 22.349
- type: map_at_10
value: 33.434000000000005
- type: map_at_100
value: 34.8
- type: map_at_1000
value: 34.919
- type: map_at_3
value: 30.348000000000003
- type: map_at_5
value: 31.917
- type: mrr_at_1
value: 28.195999999999998
- type: mrr_at_10
value: 38.557
- type: mrr_at_100
value: 39.550999999999995
- type: mrr_at_1000
value: 39.607
- type: mrr_at_3
value: 36.035000000000004
- type: mrr_at_5
value: 37.364999999999995
- type: ndcg_at_1
value: 28.195999999999998
- type: ndcg_at_10
value: 39.656000000000006
- type: ndcg_at_100
value: 45.507999999999996
- type: ndcg_at_1000
value: 47.848
- type: ndcg_at_3
value: 34.609
- type: ndcg_at_5
value: 36.65
- type: precision_at_1
value: 28.195999999999998
- type: precision_at_10
value: 7.534000000000001
- type: precision_at_100
value: 1.217
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.085
- type: precision_at_5
value: 12.169
- type: recall_at_1
value: 22.349
- type: recall_at_10
value: 53.127
- type: recall_at_100
value: 77.884
- type: recall_at_1000
value: 93.705
- type: recall_at_3
value: 38.611000000000004
- type: recall_at_5
value: 44.182
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.215749999999996
- type: map_at_10
value: 34.332750000000004
- type: map_at_100
value: 35.58683333333333
- type: map_at_1000
value: 35.70458333333333
- type: map_at_3
value: 31.55441666666667
- type: map_at_5
value: 33.100833333333334
- type: mrr_at_1
value: 29.697250000000004
- type: mrr_at_10
value: 38.372249999999994
- type: mrr_at_100
value: 39.26708333333334
- type: mrr_at_1000
value: 39.3265
- type: mrr_at_3
value: 35.946083333333334
- type: mrr_at_5
value: 37.336999999999996
- type: ndcg_at_1
value: 29.697250000000004
- type: ndcg_at_10
value: 39.64575
- type: ndcg_at_100
value: 44.996833333333335
- type: ndcg_at_1000
value: 47.314499999999995
- type: ndcg_at_3
value: 34.93383333333334
- type: ndcg_at_5
value: 37.15291666666667
- type: precision_at_1
value: 29.697250000000004
- type: precision_at_10
value: 6.98825
- type: precision_at_100
value: 1.138
- type: precision_at_1000
value: 0.15283333333333332
- type: precision_at_3
value: 16.115583333333333
- type: precision_at_5
value: 11.460916666666666
- type: recall_at_1
value: 25.215749999999996
- type: recall_at_10
value: 51.261250000000004
- type: recall_at_100
value: 74.67258333333334
- type: recall_at_1000
value: 90.72033333333334
- type: recall_at_3
value: 38.1795
- type: recall_at_5
value: 43.90658333333334
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.352
- type: map_at_10
value: 30.576999999999998
- type: map_at_100
value: 31.545
- type: map_at_1000
value: 31.642
- type: map_at_3
value: 28.605000000000004
- type: map_at_5
value: 29.828
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.151
- type: mrr_at_100
value: 33.973
- type: mrr_at_1000
value: 34.044999999999995
- type: mrr_at_3
value: 31.135
- type: mrr_at_5
value: 32.262
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.307
- type: ndcg_at_100
value: 39.079
- type: ndcg_at_1000
value: 41.548
- type: ndcg_at_3
value: 30.581000000000003
- type: ndcg_at_5
value: 32.541
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.244999999999999
- type: precision_at_100
value: 0.831
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.781
- type: precision_at_5
value: 9.017999999999999
- type: recall_at_1
value: 24.352
- type: recall_at_10
value: 43.126999999999995
- type: recall_at_100
value: 64.845
- type: recall_at_1000
value: 83.244
- type: recall_at_3
value: 33.308
- type: recall_at_5
value: 37.984
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 16.592000000000002
- type: map_at_10
value: 23.29
- type: map_at_100
value: 24.423000000000002
- type: map_at_1000
value: 24.554000000000002
- type: map_at_3
value: 20.958
- type: map_at_5
value: 22.267
- type: mrr_at_1
value: 20.061999999999998
- type: mrr_at_10
value: 26.973999999999997
- type: mrr_at_100
value: 27.944999999999997
- type: mrr_at_1000
value: 28.023999999999997
- type: mrr_at_3
value: 24.839
- type: mrr_at_5
value: 26.033
- type: ndcg_at_1
value: 20.061999999999998
- type: ndcg_at_10
value: 27.682000000000002
- type: ndcg_at_100
value: 33.196
- type: ndcg_at_1000
value: 36.246
- type: ndcg_at_3
value: 23.559
- type: ndcg_at_5
value: 25.507
- type: precision_at_1
value: 20.061999999999998
- type: precision_at_10
value: 5.086
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 11.046
- type: precision_at_5
value: 8.149000000000001
- type: recall_at_1
value: 16.592000000000002
- type: recall_at_10
value: 37.181999999999995
- type: recall_at_100
value: 62.224999999999994
- type: recall_at_1000
value: 84.072
- type: recall_at_3
value: 25.776
- type: recall_at_5
value: 30.680000000000003
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 26.035999999999998
- type: map_at_10
value: 34.447
- type: map_at_100
value: 35.697
- type: map_at_1000
value: 35.802
- type: map_at_3
value: 31.64
- type: map_at_5
value: 33.056999999999995
- type: mrr_at_1
value: 29.851
- type: mrr_at_10
value: 38.143
- type: mrr_at_100
value: 39.113
- type: mrr_at_1000
value: 39.175
- type: mrr_at_3
value: 35.665
- type: mrr_at_5
value: 36.901
- type: ndcg_at_1
value: 29.851
- type: ndcg_at_10
value: 39.554
- type: ndcg_at_100
value: 45.091
- type: ndcg_at_1000
value: 47.504000000000005
- type: ndcg_at_3
value: 34.414
- type: ndcg_at_5
value: 36.508
- type: precision_at_1
value: 29.851
- type: precision_at_10
value: 6.614000000000001
- type: precision_at_100
value: 1.051
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.329999999999998
- type: precision_at_5
value: 10.671999999999999
- type: recall_at_1
value: 26.035999999999998
- type: recall_at_10
value: 51.396
- type: recall_at_100
value: 75.09
- type: recall_at_1000
value: 91.904
- type: recall_at_3
value: 37.378
- type: recall_at_5
value: 42.69
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 23.211000000000002
- type: map_at_10
value: 32.231
- type: map_at_100
value: 33.772999999999996
- type: map_at_1000
value: 33.982
- type: map_at_3
value: 29.128
- type: map_at_5
value: 31.002999999999997
- type: mrr_at_1
value: 27.668
- type: mrr_at_10
value: 36.388
- type: mrr_at_100
value: 37.384
- type: mrr_at_1000
value: 37.44
- type: mrr_at_3
value: 33.762
- type: mrr_at_5
value: 35.234
- type: ndcg_at_1
value: 27.668
- type: ndcg_at_10
value: 38.043
- type: ndcg_at_100
value: 44.21
- type: ndcg_at_1000
value: 46.748
- type: ndcg_at_3
value: 32.981
- type: ndcg_at_5
value: 35.58
- type: precision_at_1
value: 27.668
- type: precision_at_10
value: 7.352
- type: precision_at_100
value: 1.5
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 15.613
- type: precision_at_5
value: 11.501999999999999
- type: recall_at_1
value: 23.211000000000002
- type: recall_at_10
value: 49.851
- type: recall_at_100
value: 77.596
- type: recall_at_1000
value: 93.683
- type: recall_at_3
value: 35.403
- type: recall_at_5
value: 42.485
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 19.384
- type: map_at_10
value: 26.262999999999998
- type: map_at_100
value: 27.409
- type: map_at_1000
value: 27.526
- type: map_at_3
value: 23.698
- type: map_at_5
value: 25.217
- type: mrr_at_1
value: 20.702
- type: mrr_at_10
value: 27.810000000000002
- type: mrr_at_100
value: 28.863
- type: mrr_at_1000
value: 28.955
- type: mrr_at_3
value: 25.230999999999998
- type: mrr_at_5
value: 26.821
- type: ndcg_at_1
value: 20.702
- type: ndcg_at_10
value: 30.688
- type: ndcg_at_100
value: 36.138999999999996
- type: ndcg_at_1000
value: 38.984
- type: ndcg_at_3
value: 25.663000000000004
- type: ndcg_at_5
value: 28.242
- type: precision_at_1
value: 20.702
- type: precision_at_10
value: 4.954
- type: precision_at_100
value: 0.823
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 10.844
- type: precision_at_5
value: 8.096
- type: recall_at_1
value: 19.384
- type: recall_at_10
value: 42.847
- type: recall_at_100
value: 67.402
- type: recall_at_1000
value: 88.145
- type: recall_at_3
value: 29.513
- type: recall_at_5
value: 35.57
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 14.915000000000001
- type: map_at_10
value: 25.846999999999998
- type: map_at_100
value: 27.741
- type: map_at_1000
value: 27.921000000000003
- type: map_at_3
value: 21.718
- type: map_at_5
value: 23.948
- type: mrr_at_1
value: 33.941
- type: mrr_at_10
value: 46.897
- type: mrr_at_100
value: 47.63
- type: mrr_at_1000
value: 47.658
- type: mrr_at_3
value: 43.919999999999995
- type: mrr_at_5
value: 45.783
- type: ndcg_at_1
value: 33.941
- type: ndcg_at_10
value: 35.202
- type: ndcg_at_100
value: 42.132
- type: ndcg_at_1000
value: 45.190999999999995
- type: ndcg_at_3
value: 29.68
- type: ndcg_at_5
value: 31.631999999999998
- type: precision_at_1
value: 33.941
- type: precision_at_10
value: 10.906
- type: precision_at_100
value: 1.8339999999999999
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 22.606
- type: precision_at_5
value: 17.081
- type: recall_at_1
value: 14.915000000000001
- type: recall_at_10
value: 40.737
- type: recall_at_100
value: 64.42
- type: recall_at_1000
value: 81.435
- type: recall_at_3
value: 26.767000000000003
- type: recall_at_5
value: 32.895
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.665000000000001
- type: map_at_10
value: 19.087
- type: map_at_100
value: 26.555
- type: map_at_1000
value: 28.105999999999998
- type: map_at_3
value: 13.858999999999998
- type: map_at_5
value: 16.083
- type: mrr_at_1
value: 68.5
- type: mrr_at_10
value: 76.725
- type: mrr_at_100
value: 76.974
- type: mrr_at_1000
value: 76.981
- type: mrr_at_3
value: 75.583
- type: mrr_at_5
value: 76.208
- type: ndcg_at_1
value: 55.875
- type: ndcg_at_10
value: 41.018
- type: ndcg_at_100
value: 44.982
- type: ndcg_at_1000
value: 52.43
- type: ndcg_at_3
value: 46.534
- type: ndcg_at_5
value: 43.083
- type: precision_at_1
value: 68.5
- type: precision_at_10
value: 32.35
- type: precision_at_100
value: 10.078
- type: precision_at_1000
value: 1.957
- type: precision_at_3
value: 50.083
- type: precision_at_5
value: 41.3
- type: recall_at_1
value: 8.665000000000001
- type: recall_at_10
value: 24.596999999999998
- type: recall_at_100
value: 50.612
- type: recall_at_1000
value: 74.24
- type: recall_at_3
value: 15.337
- type: recall_at_5
value: 18.796
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 55.06500000000001
- type: f1
value: 49.827367590822035
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 76.059
- type: map_at_10
value: 83.625
- type: map_at_100
value: 83.845
- type: map_at_1000
value: 83.858
- type: map_at_3
value: 82.67099999999999
- type: map_at_5
value: 83.223
- type: mrr_at_1
value: 82.013
- type: mrr_at_10
value: 88.44800000000001
- type: mrr_at_100
value: 88.535
- type: mrr_at_1000
value: 88.537
- type: mrr_at_3
value: 87.854
- type: mrr_at_5
value: 88.221
- type: ndcg_at_1
value: 82.013
- type: ndcg_at_10
value: 87.128
- type: ndcg_at_100
value: 87.922
- type: ndcg_at_1000
value: 88.166
- type: ndcg_at_3
value: 85.648
- type: ndcg_at_5
value: 86.366
- type: precision_at_1
value: 82.013
- type: precision_at_10
value: 10.32
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.408
- type: precision_at_5
value: 19.973
- type: recall_at_1
value: 76.059
- type: recall_at_10
value: 93.229
- type: recall_at_100
value: 96.387
- type: recall_at_1000
value: 97.916
- type: recall_at_3
value: 89.025
- type: recall_at_5
value: 90.96300000000001
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 20.479
- type: map_at_10
value: 33.109
- type: map_at_100
value: 34.803
- type: map_at_1000
value: 35.003
- type: map_at_3
value: 28.967
- type: map_at_5
value: 31.385
- type: mrr_at_1
value: 40.278000000000006
- type: mrr_at_10
value: 48.929
- type: mrr_at_100
value: 49.655
- type: mrr_at_1000
value: 49.691
- type: mrr_at_3
value: 46.605000000000004
- type: mrr_at_5
value: 48.056
- type: ndcg_at_1
value: 40.278000000000006
- type: ndcg_at_10
value: 40.649
- type: ndcg_at_100
value: 47.027
- type: ndcg_at_1000
value: 50.249
- type: ndcg_at_3
value: 37.364000000000004
- type: ndcg_at_5
value: 38.494
- type: precision_at_1
value: 40.278000000000006
- type: precision_at_10
value: 11.327
- type: precision_at_100
value: 1.802
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 25.102999999999998
- type: precision_at_5
value: 18.457
- type: recall_at_1
value: 20.479
- type: recall_at_10
value: 46.594
- type: recall_at_100
value: 71.101
- type: recall_at_1000
value: 90.31099999999999
- type: recall_at_3
value: 33.378
- type: recall_at_5
value: 39.587
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 36.59
- type: map_at_10
value: 58.178
- type: map_at_100
value: 59.095
- type: map_at_1000
value: 59.16400000000001
- type: map_at_3
value: 54.907
- type: map_at_5
value: 56.89999999999999
- type: mrr_at_1
value: 73.18
- type: mrr_at_10
value: 79.935
- type: mrr_at_100
value: 80.16799999999999
- type: mrr_at_1000
value: 80.17800000000001
- type: mrr_at_3
value: 78.776
- type: mrr_at_5
value: 79.522
- type: ndcg_at_1
value: 73.18
- type: ndcg_at_10
value: 66.538
- type: ndcg_at_100
value: 69.78
- type: ndcg_at_1000
value: 71.102
- type: ndcg_at_3
value: 61.739
- type: ndcg_at_5
value: 64.35600000000001
- type: precision_at_1
value: 73.18
- type: precision_at_10
value: 14.035
- type: precision_at_100
value: 1.657
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 39.684999999999995
- type: precision_at_5
value: 25.885
- type: recall_at_1
value: 36.59
- type: recall_at_10
value: 70.176
- type: recall_at_100
value: 82.836
- type: recall_at_1000
value: 91.526
- type: recall_at_3
value: 59.526999999999994
- type: recall_at_5
value: 64.713
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.1472
- type: ap
value: 85.73994227076815
- type: f1
value: 90.1271700788608
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.689
- type: map_at_10
value: 33.518
- type: map_at_100
value: 34.715
- type: map_at_1000
value: 34.766000000000005
- type: map_at_3
value: 29.781000000000002
- type: map_at_5
value: 31.838
- type: mrr_at_1
value: 22.249
- type: mrr_at_10
value: 34.085
- type: mrr_at_100
value: 35.223
- type: mrr_at_1000
value: 35.266999999999996
- type: mrr_at_3
value: 30.398999999999997
- type: mrr_at_5
value: 32.437
- type: ndcg_at_1
value: 22.249
- type: ndcg_at_10
value: 40.227000000000004
- type: ndcg_at_100
value: 45.961999999999996
- type: ndcg_at_1000
value: 47.248000000000005
- type: ndcg_at_3
value: 32.566
- type: ndcg_at_5
value: 36.229
- type: precision_at_1
value: 22.249
- type: precision_at_10
value: 6.358
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.83
- type: precision_at_5
value: 10.145999999999999
- type: recall_at_1
value: 21.689
- type: recall_at_10
value: 60.92999999999999
- type: recall_at_100
value: 87.40599999999999
- type: recall_at_1000
value: 97.283
- type: recall_at_3
value: 40.01
- type: recall_at_5
value: 48.776
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.28727770177838
- type: f1
value: 95.02577308660041
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.5736434108527
- type: f1
value: 61.2451202054398
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.01210490921318
- type: f1
value: 73.70188053982473
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.33422999327504
- type: f1
value: 79.48369022509658
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.70891567267726
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.15203494451706
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.919517862194173
- type: mrr
value: 33.15466289140483
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.992
- type: map_at_10
value: 13.197000000000001
- type: map_at_100
value: 16.907
- type: map_at_1000
value: 18.44
- type: map_at_3
value: 9.631
- type: map_at_5
value: 11.243
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 53.321
- type: mrr_at_100
value: 53.903
- type: mrr_at_1000
value: 53.952999999999996
- type: mrr_at_3
value: 51.393
- type: mrr_at_5
value: 52.708999999999996
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.921
- type: ndcg_at_100
value: 32.384
- type: ndcg_at_1000
value: 41.260000000000005
- type: ndcg_at_3
value: 40.186
- type: ndcg_at_5
value: 37.89
- type: precision_at_1
value: 44.272
- type: precision_at_10
value: 26.006
- type: precision_at_100
value: 8.44
- type: precision_at_1000
value: 2.136
- type: precision_at_3
value: 37.977
- type: precision_at_5
value: 32.755
- type: recall_at_1
value: 5.992
- type: recall_at_10
value: 17.01
- type: recall_at_100
value: 33.080999999999996
- type: recall_at_1000
value: 65.054
- type: recall_at_3
value: 10.528
- type: recall_at_5
value: 13.233
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 28.871999999999996
- type: map_at_10
value: 43.286
- type: map_at_100
value: 44.432
- type: map_at_1000
value: 44.464999999999996
- type: map_at_3
value: 38.856
- type: map_at_5
value: 41.514
- type: mrr_at_1
value: 32.619
- type: mrr_at_10
value: 45.75
- type: mrr_at_100
value: 46.622
- type: mrr_at_1000
value: 46.646
- type: mrr_at_3
value: 41.985
- type: mrr_at_5
value: 44.277
- type: ndcg_at_1
value: 32.59
- type: ndcg_at_10
value: 50.895999999999994
- type: ndcg_at_100
value: 55.711999999999996
- type: ndcg_at_1000
value: 56.48800000000001
- type: ndcg_at_3
value: 42.504999999999995
- type: ndcg_at_5
value: 46.969
- type: precision_at_1
value: 32.59
- type: precision_at_10
value: 8.543000000000001
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 19.448
- type: precision_at_5
value: 14.218
- type: recall_at_1
value: 28.871999999999996
- type: recall_at_10
value: 71.748
- type: recall_at_100
value: 92.55499999999999
- type: recall_at_1000
value: 98.327
- type: recall_at_3
value: 49.944
- type: recall_at_5
value: 60.291
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 70.664
- type: map_at_10
value: 84.681
- type: map_at_100
value: 85.289
- type: map_at_1000
value: 85.306
- type: map_at_3
value: 81.719
- type: map_at_5
value: 83.601
- type: mrr_at_1
value: 81.35
- type: mrr_at_10
value: 87.591
- type: mrr_at_100
value: 87.691
- type: mrr_at_1000
value: 87.693
- type: mrr_at_3
value: 86.675
- type: mrr_at_5
value: 87.29299999999999
- type: ndcg_at_1
value: 81.33
- type: ndcg_at_10
value: 88.411
- type: ndcg_at_100
value: 89.579
- type: ndcg_at_1000
value: 89.687
- type: ndcg_at_3
value: 85.613
- type: ndcg_at_5
value: 87.17
- type: precision_at_1
value: 81.33
- type: precision_at_10
value: 13.422
- type: precision_at_100
value: 1.5270000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.463
- type: precision_at_5
value: 24.646
- type: recall_at_1
value: 70.664
- type: recall_at_10
value: 95.54
- type: recall_at_100
value: 99.496
- type: recall_at_1000
value: 99.978
- type: recall_at_3
value: 87.481
- type: recall_at_5
value: 91.88499999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.40341814991112
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 61.231318481346655
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.833
- type: map_at_10
value: 13.149
- type: map_at_100
value: 15.578
- type: map_at_1000
value: 15.963
- type: map_at_3
value: 9.269
- type: map_at_5
value: 11.182
- type: mrr_at_1
value: 23.9
- type: mrr_at_10
value: 35.978
- type: mrr_at_100
value: 37.076
- type: mrr_at_1000
value: 37.126
- type: mrr_at_3
value: 32.333
- type: mrr_at_5
value: 34.413
- type: ndcg_at_1
value: 23.9
- type: ndcg_at_10
value: 21.823
- type: ndcg_at_100
value: 30.833
- type: ndcg_at_1000
value: 36.991
- type: ndcg_at_3
value: 20.465
- type: ndcg_at_5
value: 17.965999999999998
- type: precision_at_1
value: 23.9
- type: precision_at_10
value: 11.49
- type: precision_at_100
value: 2.444
- type: precision_at_1000
value: 0.392
- type: precision_at_3
value: 19.3
- type: precision_at_5
value: 15.959999999999999
- type: recall_at_1
value: 4.833
- type: recall_at_10
value: 23.294999999999998
- type: recall_at_100
value: 49.63
- type: recall_at_1000
value: 79.49199999999999
- type: recall_at_3
value: 11.732
- type: recall_at_5
value: 16.167
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 85.62938108735759
- type: cos_sim_spearman
value: 80.30777094408789
- type: euclidean_pearson
value: 82.94516686659536
- type: euclidean_spearman
value: 80.34489663248169
- type: manhattan_pearson
value: 82.85830094736245
- type: manhattan_spearman
value: 80.24902623215449
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.23777464247604
- type: cos_sim_spearman
value: 75.75714864112797
- type: euclidean_pearson
value: 82.33806918604493
- type: euclidean_spearman
value: 75.45282124387357
- type: manhattan_pearson
value: 82.32555620660538
- type: manhattan_spearman
value: 75.49228731684082
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.88151620954451
- type: cos_sim_spearman
value: 86.08377598473446
- type: euclidean_pearson
value: 85.36958329369413
- type: euclidean_spearman
value: 86.10274219670679
- type: manhattan_pearson
value: 85.25873897594711
- type: manhattan_spearman
value: 85.98096461661584
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.29360558735978
- type: cos_sim_spearman
value: 82.28284203795577
- type: euclidean_pearson
value: 83.81636655536633
- type: euclidean_spearman
value: 82.24340438530236
- type: manhattan_pearson
value: 83.83914453428608
- type: manhattan_spearman
value: 82.28391354080694
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.47344180426744
- type: cos_sim_spearman
value: 88.90045649789438
- type: euclidean_pearson
value: 88.43020815961273
- type: euclidean_spearman
value: 89.0087449011776
- type: manhattan_pearson
value: 88.37601826505525
- type: manhattan_spearman
value: 88.96756360690617
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.35997025304613
- type: cos_sim_spearman
value: 85.18237675717147
- type: euclidean_pearson
value: 84.46478196990202
- type: euclidean_spearman
value: 85.27748677712205
- type: manhattan_pearson
value: 84.29342543953123
- type: manhattan_spearman
value: 85.10579612516567
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.56668329596836
- type: cos_sim_spearman
value: 88.72837234129177
- type: euclidean_pearson
value: 89.39395650897828
- type: euclidean_spearman
value: 88.82001247906778
- type: manhattan_pearson
value: 89.41735354368878
- type: manhattan_spearman
value: 88.95159141850039
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 67.466167902991
- type: cos_sim_spearman
value: 68.54466147197274
- type: euclidean_pearson
value: 69.35551179564695
- type: euclidean_spearman
value: 68.75455717749132
- type: manhattan_pearson
value: 69.42432368208264
- type: manhattan_spearman
value: 68.83203709670562
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.33241300373689
- type: cos_sim_spearman
value: 86.97909372129874
- type: euclidean_pearson
value: 86.99526113559924
- type: euclidean_spearman
value: 87.02644372623219
- type: manhattan_pearson
value: 86.78744182759846
- type: manhattan_spearman
value: 86.8886180198196
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.18374413668717
- type: mrr
value: 95.93213068703264
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 58.31699999999999
- type: map_at_10
value: 67.691
- type: map_at_100
value: 68.201
- type: map_at_1000
value: 68.232
- type: map_at_3
value: 64.47800000000001
- type: map_at_5
value: 66.51
- type: mrr_at_1
value: 61.0
- type: mrr_at_10
value: 68.621
- type: mrr_at_100
value: 68.973
- type: mrr_at_1000
value: 69.002
- type: mrr_at_3
value: 66.111
- type: mrr_at_5
value: 67.578
- type: ndcg_at_1
value: 61.0
- type: ndcg_at_10
value: 72.219
- type: ndcg_at_100
value: 74.397
- type: ndcg_at_1000
value: 75.021
- type: ndcg_at_3
value: 66.747
- type: ndcg_at_5
value: 69.609
- type: precision_at_1
value: 61.0
- type: precision_at_10
value: 9.6
- type: precision_at_100
value: 1.08
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.667
- type: precision_at_5
value: 17.267
- type: recall_at_1
value: 58.31699999999999
- type: recall_at_10
value: 85.233
- type: recall_at_100
value: 95.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.589
- type: recall_at_5
value: 77.628
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83267326732673
- type: cos_sim_ap
value: 96.13707107038228
- type: cos_sim_f1
value: 91.48830263812842
- type: cos_sim_precision
value: 91.0802775024777
- type: cos_sim_recall
value: 91.9
- type: dot_accuracy
value: 99.83069306930693
- type: dot_ap
value: 96.21199069147254
- type: dot_f1
value: 91.36295556665004
- type: dot_precision
value: 91.22632103688933
- type: dot_recall
value: 91.5
- type: euclidean_accuracy
value: 99.83267326732673
- type: euclidean_ap
value: 96.08957801367436
- type: euclidean_f1
value: 91.33004926108374
- type: euclidean_precision
value: 90.0
- type: euclidean_recall
value: 92.7
- type: manhattan_accuracy
value: 99.83564356435643
- type: manhattan_ap
value: 96.10534946461945
- type: manhattan_f1
value: 91.74950298210736
- type: manhattan_precision
value: 91.20553359683794
- type: manhattan_recall
value: 92.30000000000001
- type: max_accuracy
value: 99.83564356435643
- type: max_ap
value: 96.21199069147254
- type: max_f1
value: 91.74950298210736
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 62.045718843534736
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.6501777041092
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.963913408053955
- type: mrr
value: 53.87972423818012
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.44195730764998
- type: cos_sim_spearman
value: 30.59626288679397
- type: dot_pearson
value: 30.22974492404086
- type: dot_spearman
value: 29.345245972906497
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.24
- type: map_at_10
value: 2.01
- type: map_at_100
value: 11.928999999999998
- type: map_at_1000
value: 29.034
- type: map_at_3
value: 0.679
- type: map_at_5
value: 1.064
- type: mrr_at_1
value: 92.0
- type: mrr_at_10
value: 96.0
- type: mrr_at_100
value: 96.0
- type: mrr_at_1000
value: 96.0
- type: mrr_at_3
value: 96.0
- type: mrr_at_5
value: 96.0
- type: ndcg_at_1
value: 87.0
- type: ndcg_at_10
value: 80.118
- type: ndcg_at_100
value: 60.753
- type: ndcg_at_1000
value: 54.632999999999996
- type: ndcg_at_3
value: 83.073
- type: ndcg_at_5
value: 80.733
- type: precision_at_1
value: 92.0
- type: precision_at_10
value: 84.8
- type: precision_at_100
value: 62.019999999999996
- type: precision_at_1000
value: 24.028
- type: precision_at_3
value: 87.333
- type: precision_at_5
value: 85.2
- type: recall_at_1
value: 0.24
- type: recall_at_10
value: 2.205
- type: recall_at_100
value: 15.068000000000001
- type: recall_at_1000
value: 51.796
- type: recall_at_3
value: 0.698
- type: recall_at_5
value: 1.1199999999999999
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.066
- type: map_at_10
value: 9.219
- type: map_at_100
value: 15.387
- type: map_at_1000
value: 16.957
- type: map_at_3
value: 5.146
- type: map_at_5
value: 6.6739999999999995
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 50.844
- type: mrr_at_100
value: 51.664
- type: mrr_at_1000
value: 51.664
- type: mrr_at_3
value: 46.259
- type: mrr_at_5
value: 49.116
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_10
value: 23.477
- type: ndcg_at_100
value: 36.268
- type: ndcg_at_1000
value: 47.946
- type: ndcg_at_3
value: 25.832
- type: ndcg_at_5
value: 24.235
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 20.204
- type: precision_at_100
value: 7.611999999999999
- type: precision_at_1000
value: 1.543
- type: precision_at_3
value: 25.169999999999998
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 3.066
- type: recall_at_10
value: 14.985999999999999
- type: recall_at_100
value: 47.902
- type: recall_at_1000
value: 83.56400000000001
- type: recall_at_3
value: 5.755
- type: recall_at_5
value: 8.741999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 69.437
- type: ap
value: 12.844066827082706
- type: f1
value: 52.74974809872495
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.26768534238823
- type: f1
value: 61.65100187399282
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.860968711078804
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.7423854085951
- type: cos_sim_ap
value: 73.47560303339571
- type: cos_sim_f1
value: 67.372778183589
- type: cos_sim_precision
value: 62.54520795660036
- type: cos_sim_recall
value: 73.00791556728232
- type: dot_accuracy
value: 85.36091077069798
- type: dot_ap
value: 72.42521572307255
- type: dot_f1
value: 66.90576304724215
- type: dot_precision
value: 62.96554934823091
- type: dot_recall
value: 71.37203166226914
- type: euclidean_accuracy
value: 85.76026703224653
- type: euclidean_ap
value: 73.44852563860128
- type: euclidean_f1
value: 67.3
- type: euclidean_precision
value: 63.94299287410926
- type: euclidean_recall
value: 71.02902374670185
- type: manhattan_accuracy
value: 85.7423854085951
- type: manhattan_ap
value: 73.2635034755551
- type: manhattan_f1
value: 67.3180263800684
- type: manhattan_precision
value: 62.66484765802638
- type: manhattan_recall
value: 72.71767810026385
- type: max_accuracy
value: 85.76026703224653
- type: max_ap
value: 73.47560303339571
- type: max_f1
value: 67.372778183589
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.67543757519307
- type: cos_sim_ap
value: 85.35516518531304
- type: cos_sim_f1
value: 77.58197635511934
- type: cos_sim_precision
value: 75.01078360891445
- type: cos_sim_recall
value: 80.33569448721897
- type: dot_accuracy
value: 87.61400240617844
- type: dot_ap
value: 83.0774968268665
- type: dot_f1
value: 75.68229012162561
- type: dot_precision
value: 72.99713876967095
- type: dot_recall
value: 78.57252848783493
- type: euclidean_accuracy
value: 88.73753250281368
- type: euclidean_ap
value: 85.48043564821317
- type: euclidean_f1
value: 77.75975862719216
- type: euclidean_precision
value: 76.21054187920456
- type: euclidean_recall
value: 79.37326763166
- type: manhattan_accuracy
value: 88.75111576823068
- type: manhattan_ap
value: 85.44993439423668
- type: manhattan_f1
value: 77.6861329994845
- type: manhattan_precision
value: 74.44601270289344
- type: manhattan_recall
value: 81.22112719433323
- type: max_accuracy
value: 88.75111576823068
- type: max_ap
value: 85.48043564821317
- type: max_f1
value: 77.75975862719216
---
<h1 align="center">NoInstruct small Embedding v0</h1>
*NoInstruct Embedding: Asymmetric Pooling is All You Need*
This model has improved retrieval performance compared to the [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) model.
One of the things that the `GIST` family of models fell short on is the performance on retrieval tasks. We propose a method that produces improved retrieval performance while maintaining independence on crafting arbitrary instructions, a trending paradigm in embedding models for retrieval tasks, when encoding a query.
Technical details of the model will be published shortly.
# Usage
```Python
from typing import Union
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("avsolatorio/NoInstruct-small-Embedding-v0")
tokenizer = AutoTokenizer.from_pretrained("avsolatorio/NoInstruct-small-Embedding-v0")
def get_embedding(text: Union[str, list[str]], mode: str = "sentence"):
model.eval()
assert mode in ("query", "sentence"), f"mode={mode} was passed but only `query` and `sentence` are the supported modes."
if isinstance(text, str):
text = [text]
inp = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
output = model(**inp)
# The model is optimized to use the mean pooling for queries,
# while the sentence / document embedding uses the [CLS] representation.
if mode == "query":
vectors = output.last_hidden_state * inp["attention_mask"].unsqueeze(2)
vectors = vectors.sum(dim=1) / inp["attention_mask"].sum(dim=-1).view(-1, 1)
else:
vectors = output.last_hidden_state[:, 0, :]
return vectors
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = get_embedding(texts, mode="sentence")
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
# Test the retrieval performance.
query = get_embedding("Which sentence talks about concept on jobs?", mode="query")
scores = F.cosine_similarity(query, embeddings, dim=-1)
print(scores.cpu().numpy())
```
Support for the Sentence Transformers library will follow soon.
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Snowflake/snowflake-arctic-embed-m-v2.0 | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"gte",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"custom_code",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"pa",
"pl",
"pt",
"qu",
"ro",
"ru",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"vi",
"yo",
"zh",
"arxiv:2412.04506",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-11-08T16:52:25 | 2024-12-14T00:20:05 | 161,691 | 63 | ---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-arctic-embed-m-v2.0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.6867
- type: f1
value: 55.0373
- type: f1_weighted
value: 73.07430000000001
- type: ap
value: 18.077399999999997
- type: ap_weighted
value: 18.077399999999997
- type: main_score
value: 66.6867
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.194
- type: f1
value: 60.854299999999995
- type: f1_weighted
value: 69.57339999999999
- type: ap
value: 30.279099999999996
- type: ap_weighted
value: 30.279099999999996
- type: main_score
value: 66.194
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 70.3589
- type: f1
value: 70.0409
- type: f1_weighted
value: 70.0409
- type: ap
value: 64.81949999999999
- type: ap_weighted
value: 64.81949999999999
- type: main_score
value: 70.3589
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 33.766
- type: f1
value: 33.3656
- type: f1_weighted
value: 33.3656
- type: main_score
value: 33.766
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_1
value: 33.144
- type: ndcg_at_3
value: 47.909
- type: ndcg_at_5
value: 52.932
- type: ndcg_at_10
value: 58.011
- type: ndcg_at_20
value: 60.168
- type: ndcg_at_100
value: 60.928000000000004
- type: ndcg_at_1000
value: 61.046
- type: map_at_1
value: 33.144
- type: map_at_3
value: 44.156
- type: map_at_5
value: 46.951
- type: map_at_10
value: 49.071999999999996
- type: map_at_20
value: 49.692
- type: map_at_100
value: 49.809
- type: map_at_1000
value: 49.815
- type: recall_at_1
value: 33.144
- type: recall_at_3
value: 58.819
- type: recall_at_5
value: 70.982
- type: recall_at_10
value: 86.558
- type: recall_at_20
value: 94.879
- type: recall_at_100
value: 98.791
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.144
- type: precision_at_3
value: 19.606
- type: precision_at_5
value: 14.196
- type: precision_at_10
value: 8.656
- type: precision_at_20
value: 4.744000000000001
- type: precision_at_100
value: 0.988
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 33.4993
- type: mrr_at_3
value: 44.393100000000004
- type: mrr_at_5
value: 47.131299999999996
- type: mrr_at_10
value: 49.264599999999994
- type: mrr_at_20
value: 49.8707
- type: mrr_at_100
value: 49.987700000000004
- type: mrr_at_1000
value: 49.993700000000004
- type: nauc_ndcg_at_1_max
value: -10.8287
- type: nauc_ndcg_at_1_std
value: -17.1177
- type: nauc_ndcg_at_1_diff1
value: 14.4508
- type: nauc_ndcg_at_3_max
value: -7.7004
- type: nauc_ndcg_at_3_std
value: -16.6705
- type: nauc_ndcg_at_3_diff1
value: 10.0448
- type: nauc_ndcg_at_5_max
value: -7.0436
- type: nauc_ndcg_at_5_std
value: -15.8744
- type: nauc_ndcg_at_5_diff1
value: 9.1132
- type: nauc_ndcg_at_10_max
value: -7.4729
- type: nauc_ndcg_at_10_std
value: -14.9349
- type: nauc_ndcg_at_10_diff1
value: 8.527700000000001
- type: nauc_ndcg_at_20_max
value: -6.997000000000001
- type: nauc_ndcg_at_20_std
value: -14.688399999999998
- type: nauc_ndcg_at_20_diff1
value: 9.7605
- type: nauc_ndcg_at_100_max
value: -7.5599
- type: nauc_ndcg_at_100_std
value: -15.0565
- type: nauc_ndcg_at_100_diff1
value: 10.2688
- type: nauc_ndcg_at_1000_max
value: -7.675800000000001
- type: nauc_ndcg_at_1000_std
value: -15.223500000000001
- type: nauc_ndcg_at_1000_diff1
value: 10.32
- type: nauc_map_at_1_max
value: -10.8287
- type: nauc_map_at_1_std
value: -17.1177
- type: nauc_map_at_1_diff1
value: 14.4508
- type: nauc_map_at_3_max
value: -8.5473
- type: nauc_map_at_3_std
value: -16.6674
- type: nauc_map_at_3_diff1
value: 11.1004
- type: nauc_map_at_5_max
value: -8.1927
- type: nauc_map_at_5_std
value: -16.2275
- type: nauc_map_at_5_diff1
value: 10.678600000000001
- type: nauc_map_at_10_max
value: -8.3855
- type: nauc_map_at_10_std
value: -15.8309
- type: nauc_map_at_10_diff1
value: 10.5414
- type: nauc_map_at_20_max
value: -8.277700000000001
- type: nauc_map_at_20_std
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type: mteb/arxiv-clustering-p2p
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dataset:
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type: mteb/mtop_domain
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split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: mteb/mtop_intent
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split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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dataset:
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type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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type: mteb/medrxiv-clustering-p2p
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split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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split: test
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revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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- type: nauc_recall_at_100_diff1
value: 73.5857
- type: nauc_recall_at_1000_max
value: 48.8898
- type: nauc_recall_at_1000_std
value: 54.583800000000004
- type: nauc_recall_at_1000_diff1
value: 64.0609
- type: nauc_precision_at_1_max
value: 37.866499999999995
- type: nauc_precision_at_1_std
value: -40.9317
- type: nauc_precision_at_1_diff1
value: 78.09089999999999
- type: nauc_precision_at_3_max
value: 8.2308
- type: nauc_precision_at_3_std
value: 5.0732
- type: nauc_precision_at_3_diff1
value: -19.919
- type: nauc_precision_at_5_max
value: 3.0249
- type: nauc_precision_at_5_std
value: 16.7897
- type: nauc_precision_at_5_diff1
value: -32.0086
- type: nauc_precision_at_10_max
value: -0.5459999999999999
- type: nauc_precision_at_10_std
value: 27.1262
- type: nauc_precision_at_10_diff1
value: -38.8076
- type: nauc_precision_at_20_max
value: -2.7663
- type: nauc_precision_at_20_std
value: 34.1696
- type: nauc_precision_at_20_diff1
value: -42.1088
- type: nauc_precision_at_100_max
value: -5.0689
- type: nauc_precision_at_100_std
value: 40.023599999999995
- type: nauc_precision_at_100_diff1
value: -43.8996
- type: nauc_precision_at_1000_max
value: -5.1495
- type: nauc_precision_at_1000_std
value: 41.4194
- type: nauc_precision_at_1000_diff1
value: -44.219
- type: nauc_mrr_at_1_max
value: 37.7695
- type: nauc_mrr_at_1_std
value: -41.0563
- type: nauc_mrr_at_1_diff1
value: 78.1854
- type: nauc_mrr_at_3_max
value: 38.3824
- type: nauc_mrr_at_3_std
value: -43.7797
- type: nauc_mrr_at_3_diff1
value: 77.0796
- type: nauc_mrr_at_5_max
value: 38.5156
- type: nauc_mrr_at_5_std
value: -43.8092
- type: nauc_mrr_at_5_diff1
value: 77.31710000000001
- type: nauc_mrr_at_10_max
value: 38.523
- type: nauc_mrr_at_10_std
value: -43.5039
- type: nauc_mrr_at_10_diff1
value: 77.375
- type: nauc_mrr_at_20_max
value: 38.4635
- type: nauc_mrr_at_20_std
value: -43.3619
- type: nauc_mrr_at_20_diff1
value: 77.3565
- type: nauc_mrr_at_100_max
value: 38.4502
- type: nauc_mrr_at_100_std
value: -43.3315
- type: nauc_mrr_at_100_diff1
value: 77.3584
- type: nauc_mrr_at_1000_max
value: 38.449
- type: nauc_mrr_at_1000_std
value: -43.3339
- type: nauc_mrr_at_1000_diff1
value: 77.3584
- type: main_score
value: 88.735
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 49.1271
- type: v_measure_std
value: 4.5517
- type: main_score
value: 49.1271
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 61.0626
- type: v_measure_std
value: 12.6364
- type: main_score
value: 61.0626
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: ndcg_at_1
value: 23.7
- type: ndcg_at_3
value: 19.346
- type: ndcg_at_5
value: 17.044999999999998
- type: ndcg_at_10
value: 20.347
- type: ndcg_at_20
value: 23.237
- type: ndcg_at_100
value: 27.923
- type: ndcg_at_1000
value: 32.891999999999996
- type: map_at_1
value: 4.813
- type: map_at_3
value: 8.688
- type: map_at_5
value: 10.41
- type: map_at_10
value: 12.107999999999999
- type: map_at_20
value: 13.187
- type: map_at_100
value: 14.113000000000001
- type: map_at_1000
value: 14.383000000000001
- type: recall_at_1
value: 4.813
- type: recall_at_3
value: 11.022
- type: recall_at_5
value: 15.242
- type: recall_at_10
value: 21.308
- type: recall_at_20
value: 28.1
- type: recall_at_100
value: 43.335
- type: recall_at_1000
value: 67.672
- type: precision_at_1
value: 23.7
- type: precision_at_3
value: 18.099999999999998
- type: precision_at_5
value: 15.0
- type: precision_at_10
value: 10.48
- type: precision_at_20
value: 6.909999999999999
- type: precision_at_100
value: 2.133
- type: precision_at_1000
value: 0.333
- type: mrr_at_1
value: 23.7
- type: mrr_at_3
value: 31.35
- type: mrr_at_5
value: 33.650000000000006
- type: mrr_at_10
value: 34.9399
- type: mrr_at_20
value: 35.5429
- type: mrr_at_100
value: 35.9342
- type: mrr_at_1000
value: 35.9943
- type: nauc_ndcg_at_1_max
value: 20.214499999999997
- type: nauc_ndcg_at_1_std
value: 7.2459999999999996
- type: nauc_ndcg_at_1_diff1
value: 26.8353
- type: nauc_ndcg_at_3_max
value: 23.3459
- type: nauc_ndcg_at_3_std
value: 10.9732
- type: nauc_ndcg_at_3_diff1
value: 21.0618
- type: nauc_ndcg_at_5_max
value: 24.5147
- type: nauc_ndcg_at_5_std
value: 13.309000000000001
- type: nauc_ndcg_at_5_diff1
value: 20.0975
- type: nauc_ndcg_at_10_max
value: 27.0937
- type: nauc_ndcg_at_10_std
value: 16.4516
- type: nauc_ndcg_at_10_diff1
value: 19.9585
- type: nauc_ndcg_at_20_max
value: 28.503600000000002
- type: nauc_ndcg_at_20_std
value: 19.1956
- type: nauc_ndcg_at_20_diff1
value: 19.508200000000002
- type: nauc_ndcg_at_100_max
value: 30.7317
- type: nauc_ndcg_at_100_std
value: 23.2169
- type: nauc_ndcg_at_100_diff1
value: 19.7085
- type: nauc_ndcg_at_1000_max
value: 30.3307
- type: nauc_ndcg_at_1000_std
value: 24.7664
- type: nauc_ndcg_at_1000_diff1
value: 19.0469
- type: nauc_map_at_1_max
value: 20.3702
- type: nauc_map_at_1_std
value: 7.219200000000001
- type: nauc_map_at_1_diff1
value: 27.0193
- type: nauc_map_at_3_max
value: 23.0558
- type: nauc_map_at_3_std
value: 9.411999999999999
- type: nauc_map_at_3_diff1
value: 21.3691
- type: nauc_map_at_5_max
value: 23.763
- type: nauc_map_at_5_std
value: 11.228
- type: nauc_map_at_5_diff1
value: 20.4299
- type: nauc_map_at_10_max
value: 25.6655
- type: nauc_map_at_10_std
value: 14.0481
- type: nauc_map_at_10_diff1
value: 19.7937
- type: nauc_map_at_20_max
value: 26.5994
- type: nauc_map_at_20_std
value: 15.820400000000001
- type: nauc_map_at_20_diff1
value: 19.476499999999998
- type: nauc_map_at_100_max
value: 27.4895
- type: nauc_map_at_100_std
value: 17.262
- type: nauc_map_at_100_diff1
value: 19.4661
- type: nauc_map_at_1000_max
value: 27.5301
- type: nauc_map_at_1000_std
value: 17.4927
- type: nauc_map_at_1000_diff1
value: 19.4691
- type: nauc_recall_at_1_max
value: 20.3702
- type: nauc_recall_at_1_std
value: 7.219200000000001
- type: nauc_recall_at_1_diff1
value: 27.0193
- type: nauc_recall_at_3_max
value: 23.6476
- type: nauc_recall_at_3_std
value: 11.9176
- type: nauc_recall_at_3_diff1
value: 18.1657
- type: nauc_recall_at_5_max
value: 24.8053
- type: nauc_recall_at_5_std
value: 15.5205
- type: nauc_recall_at_5_diff1
value: 16.4924
- type: nauc_recall_at_10_max
value: 27.9864
- type: nauc_recall_at_10_std
value: 20.1496
- type: nauc_recall_at_10_diff1
value: 16.0154
- type: nauc_recall_at_20_max
value: 29.0157
- type: nauc_recall_at_20_std
value: 24.374100000000002
- type: nauc_recall_at_20_diff1
value: 14.174800000000001
- type: nauc_recall_at_100_max
value: 31.245299999999997
- type: nauc_recall_at_100_std
value: 32.161699999999996
- type: nauc_recall_at_100_diff1
value: 12.9714
- type: nauc_recall_at_1000_max
value: 25.6486
- type: nauc_recall_at_1000_std
value: 37.1526
- type: nauc_recall_at_1000_diff1
value: 6.0907
- type: nauc_precision_at_1_max
value: 20.214499999999997
- type: nauc_precision_at_1_std
value: 7.2459999999999996
- type: nauc_precision_at_1_diff1
value: 26.8353
- type: nauc_precision_at_3_max
value: 23.8245
- type: nauc_precision_at_3_std
value: 12.2589
- type: nauc_precision_at_3_diff1
value: 18.192800000000002
- type: nauc_precision_at_5_max
value: 25.3681
- type: nauc_precision_at_5_std
value: 15.947700000000001
- type: nauc_precision_at_5_diff1
value: 16.6931
- type: nauc_precision_at_10_max
value: 28.2682
- type: nauc_precision_at_10_std
value: 20.2673
- type: nauc_precision_at_10_diff1
value: 15.8977
- type: nauc_precision_at_20_max
value: 29.3989
- type: nauc_precision_at_20_std
value: 24.5769
- type: nauc_precision_at_20_diff1
value: 14.1994
- type: nauc_precision_at_100_max
value: 31.418000000000003
- type: nauc_precision_at_100_std
value: 32.0978
- type: nauc_precision_at_100_diff1
value: 12.768199999999998
- type: nauc_precision_at_1000_max
value: 25.501099999999997
- type: nauc_precision_at_1000_std
value: 36.477399999999996
- type: nauc_precision_at_1000_diff1
value: 5.5335
- type: nauc_mrr_at_1_max
value: 20.214499999999997
- type: nauc_mrr_at_1_std
value: 7.2459999999999996
- type: nauc_mrr_at_1_diff1
value: 26.8353
- type: nauc_mrr_at_3_max
value: 22.7925
- type: nauc_mrr_at_3_std
value: 10.6945
- type: nauc_mrr_at_3_diff1
value: 23.6308
- type: nauc_mrr_at_5_max
value: 23.427799999999998
- type: nauc_mrr_at_5_std
value: 11.8634
- type: nauc_mrr_at_5_diff1
value: 23.0875
- type: nauc_mrr_at_10_max
value: 24.0918
- type: nauc_mrr_at_10_std
value: 12.4753
- type: nauc_mrr_at_10_diff1
value: 23.352999999999998
- type: nauc_mrr_at_20_max
value: 24.078
- type: nauc_mrr_at_20_std
value: 12.5849
- type: nauc_mrr_at_20_diff1
value: 23.3351
- type: nauc_mrr_at_100_max
value: 24.0858
- type: nauc_mrr_at_100_std
value: 12.5772
- type: nauc_mrr_at_100_diff1
value: 23.4778
- type: nauc_mrr_at_1000_max
value: 24.058799999999998
- type: nauc_mrr_at_1000_std
value: 12.549
- type: nauc_mrr_at_1000_diff1
value: 23.4713
- type: main_score
value: 20.347
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 75.7747
- type: spearman
value: 71.3142
- type: cosine_pearson
value: 75.7747
- type: cosine_spearman
value: 71.3142
- type: manhattan_pearson
value: 73.8759
- type: manhattan_spearman
value: 71.1003
- type: euclidean_pearson
value: 74.088
- type: euclidean_spearman
value: 71.3142
- type: main_score
value: 71.3142
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 72.5903
- type: spearman
value: 70.6581
- type: cosine_pearson
value: 72.5903
- type: cosine_spearman
value: 70.6581
- type: manhattan_pearson
value: 69.2077
- type: manhattan_spearman
value: 70.4521
- type: euclidean_pearson
value: 69.41720000000001
- type: euclidean_spearman
value: 70.6581
- type: main_score
value: 70.6581
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 73.1686
- type: spearman
value: 77.4225
- type: cosine_pearson
value: 73.1686
- type: cosine_spearman
value: 77.4225
- type: manhattan_pearson
value: 76.2481
- type: manhattan_spearman
value: 77.325
- type: euclidean_pearson
value: 76.3568
- type: euclidean_spearman
value: 77.4225
- type: main_score
value: 77.4225
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 74.46340000000001
- type: spearman
value: 72.9162
- type: cosine_pearson
value: 74.46340000000001
- type: cosine_spearman
value: 72.9162
- type: manhattan_pearson
value: 73.8079
- type: manhattan_spearman
value: 72.8704
- type: euclidean_pearson
value: 73.8244
- type: euclidean_spearman
value: 72.9162
- type: main_score
value: 72.9162
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 80.1161
- type: spearman
value: 81.83200000000001
- type: cosine_pearson
value: 80.1161
- type: cosine_spearman
value: 81.83200000000001
- type: manhattan_pearson
value: 81.573
- type: manhattan_spearman
value: 81.807
- type: euclidean_pearson
value: 81.59490000000001
- type: euclidean_spearman
value: 81.83200000000001
- type: main_score
value: 81.83200000000001
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 78.8244
- type: spearman
value: 81.2262
- type: cosine_pearson
value: 78.8244
- type: cosine_spearman
value: 81.2262
- type: manhattan_pearson
value: 80.6177
- type: manhattan_spearman
value: 81.1361
- type: euclidean_pearson
value: 80.7347
- type: euclidean_spearman
value: 81.2262
- type: main_score
value: 81.2262
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 67.9751
- type: spearman
value: 68.92099999999999
- type: cosine_pearson
value: 67.9751
- type: cosine_spearman
value: 68.92099999999999
- type: manhattan_pearson
value: 68.9355
- type: manhattan_spearman
value: 68.777
- type: euclidean_pearson
value: 69.11410000000001
- type: euclidean_spearman
value: 68.92099999999999
- type: main_score
value: 68.92099999999999
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 72.08449999999999
- type: spearman
value: 74.6931
- type: cosine_pearson
value: 72.08449999999999
- type: cosine_spearman
value: 74.6931
- type: manhattan_pearson
value: 73.52
- type: manhattan_spearman
value: 74.7097
- type: euclidean_pearson
value: 73.62180000000001
- type: euclidean_spearman
value: 74.6931
- type: main_score
value: 74.6931
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 80.528
- type: spearman
value: 84.10459999999999
- type: cosine_pearson
value: 80.528
- type: cosine_spearman
value: 84.10459999999999
- type: manhattan_pearson
value: 83.1537
- type: manhattan_spearman
value: 84.0952
- type: euclidean_pearson
value: 83.337
- type: euclidean_spearman
value: 84.10459999999999
- type: main_score
value: 84.10459999999999
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 49.641400000000004
- type: spearman
value: 48.9413
- type: cosine_pearson
value: 49.641400000000004
- type: cosine_spearman
value: 48.9413
- type: manhattan_pearson
value: 51.434000000000005
- type: manhattan_spearman
value: 49.1595
- type: euclidean_pearson
value: 50.867799999999995
- type: euclidean_spearman
value: 48.9413
- type: main_score
value: 48.9413
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 71.2577
- type: spearman
value: 73.82419999999999
- type: cosine_pearson
value: 71.2577
- type: cosine_spearman
value: 73.82419999999999
- type: manhattan_pearson
value: 71.9329
- type: manhattan_spearman
value: 73.4651
- type: euclidean_pearson
value: 72.2771
- type: euclidean_spearman
value: 73.82419999999999
- type: main_score
value: 73.82419999999999
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 64.1562
- type: spearman
value: 64.8766
- type: cosine_pearson
value: 64.1562
- type: cosine_spearman
value: 64.8766
- type: manhattan_pearson
value: 64.16579999999999
- type: manhattan_spearman
value: 64.1931
- type: euclidean_pearson
value: 64.6169
- type: euclidean_spearman
value: 64.8766
- type: main_score
value: 64.8766
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 42.257400000000004
- type: spearman
value: 43.2176
- type: cosine_pearson
value: 42.257400000000004
- type: cosine_spearman
value: 43.2176
- type: manhattan_pearson
value: 43.5359
- type: manhattan_spearman
value: 42.4143
- type: euclidean_pearson
value: 43.6717
- type: euclidean_spearman
value: 43.2176
- type: main_score
value: 43.2176
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 74.0088
- type: spearman
value: 75.8687
- type: cosine_pearson
value: 74.0088
- type: cosine_spearman
value: 75.8687
- type: manhattan_pearson
value: 74.8505
- type: manhattan_spearman
value: 75.6101
- type: euclidean_pearson
value: 75.1303
- type: euclidean_spearman
value: 75.8687
- type: main_score
value: 75.8687
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 68.0842
- type: spearman
value: 69.4346
- type: cosine_pearson
value: 68.0842
- type: cosine_spearman
value: 69.4346
- type: manhattan_pearson
value: 69.9982
- type: manhattan_spearman
value: 69.8952
- type: euclidean_pearson
value: 69.6375
- type: euclidean_spearman
value: 69.4346
- type: main_score
value: 69.4346
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 76.3695
- type: spearman
value: 78.88730000000001
- type: cosine_pearson
value: 76.3695
- type: cosine_spearman
value: 78.88730000000001
- type: manhattan_pearson
value: 79.0721
- type: manhattan_spearman
value: 79.1151
- type: euclidean_pearson
value: 78.783
- type: euclidean_spearman
value: 78.88730000000001
- type: main_score
value: 78.88730000000001
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 60.59139999999999
- type: spearman
value: 52.692099999999996
- type: cosine_pearson
value: 60.59139999999999
- type: cosine_spearman
value: 52.692099999999996
- type: manhattan_pearson
value: 64.66499999999999
- type: manhattan_spearman
value: 53.09009999999999
- type: euclidean_pearson
value: 64.5541
- type: euclidean_spearman
value: 52.692099999999996
- type: main_score
value: 52.692099999999996
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 77.8405
- type: spearman
value: 76.6188
- type: cosine_pearson
value: 77.8405
- type: cosine_spearman
value: 76.6188
- type: manhattan_pearson
value: 76.6598
- type: manhattan_spearman
value: 76.3583
- type: euclidean_pearson
value: 77.1442
- type: euclidean_spearman
value: 76.6188
- type: main_score
value: 76.6188
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 69.8017
- type: spearman
value: 68.7734
- type: cosine_pearson
value: 69.8017
- type: cosine_spearman
value: 68.7734
- type: manhattan_pearson
value: 70.6884
- type: manhattan_spearman
value: 68.2974
- type: euclidean_pearson
value: 70.7968
- type: euclidean_spearman
value: 68.7734
- type: main_score
value: 68.7734
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 73.3293
- type: spearman
value: 76.00919999999999
- type: cosine_pearson
value: 73.3293
- type: cosine_spearman
value: 76.00919999999999
- type: manhattan_pearson
value: 75.0184
- type: manhattan_spearman
value: 75.8014
- type: euclidean_pearson
value: 75.2638
- type: euclidean_spearman
value: 76.00919999999999
- type: main_score
value: 76.00919999999999
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 77.3669
- type: mrr
value: 93.5985
- type: nAUC_map_max
value: 50.2355
- type: nAUC_map_std
value: 65.5401
- type: nAUC_map_diff1
value: 9.6333
- type: nAUC_mrr_max
value: 76.5201
- type: nAUC_mrr_std
value: 74.7401
- type: nAUC_mrr_diff1
value: 53.170899999999996
- type: main_score
value: 77.3669
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 61.0
- type: ndcg_at_3
value: 67.589
- type: ndcg_at_5
value: 68.948
- type: ndcg_at_10
value: 71.8
- type: ndcg_at_20
value: 72.595
- type: ndcg_at_100
value: 74.138
- type: ndcg_at_1000
value: 74.83800000000001
- type: map_at_1
value: 57.74399999999999
- type: map_at_3
value: 64.866
- type: map_at_5
value: 66.018
- type: map_at_10
value: 67.535
- type: map_at_20
value: 67.77
- type: map_at_100
value: 68.011
- type: map_at_1000
value: 68.042
- type: recall_at_1
value: 57.74399999999999
- type: recall_at_3
value: 71.906
- type: recall_at_5
value: 75.344
- type: recall_at_10
value: 83.2
- type: recall_at_20
value: 86.26700000000001
- type: recall_at_100
value: 94.333
- type: recall_at_1000
value: 99.667
- type: precision_at_1
value: 61.0
- type: precision_at_3
value: 26.111
- type: precision_at_5
value: 16.8
- type: precision_at_10
value: 9.5
- type: precision_at_20
value: 4.933
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 61.0
- type: mrr_at_3
value: 67.4444
- type: mrr_at_5
value: 68.0778
- type: mrr_at_10
value: 69.0483
- type: mrr_at_20
value: 69.2333
- type: mrr_at_100
value: 69.4403
- type: mrr_at_1000
value: 69.4708
- type: nauc_ndcg_at_1_max
value: 53.481500000000004
- type: nauc_ndcg_at_1_std
value: 8.227
- type: nauc_ndcg_at_1_diff1
value: 72.0771
- type: nauc_ndcg_at_3_max
value: 57.0147
- type: nauc_ndcg_at_3_std
value: 5.2435
- type: nauc_ndcg_at_3_diff1
value: 68.8841
- type: nauc_ndcg_at_5_max
value: 57.4675
- type: nauc_ndcg_at_5_std
value: 8.4709
- type: nauc_ndcg_at_5_diff1
value: 67.2977
- type: nauc_ndcg_at_10_max
value: 60.3957
- type: nauc_ndcg_at_10_std
value: 11.3174
- type: nauc_ndcg_at_10_diff1
value: 67.8332
- type: nauc_ndcg_at_20_max
value: 60.3607
- type: nauc_ndcg_at_20_std
value: 11.9948
- type: nauc_ndcg_at_20_diff1
value: 68.1122
- type: nauc_ndcg_at_100_max
value: 59.5293
- type: nauc_ndcg_at_100_std
value: 11.697799999999999
- type: nauc_ndcg_at_100_diff1
value: 68.453
- type: nauc_ndcg_at_1000_max
value: 58.8931
- type: nauc_ndcg_at_1000_std
value: 10.876199999999999
- type: nauc_ndcg_at_1000_diff1
value: 68.5746
- type: nauc_map_at_1_max
value: 49.762299999999996
- type: nauc_map_at_1_std
value: -0.2785
- type: nauc_map_at_1_diff1
value: 71.9072
- type: nauc_map_at_3_max
value: 54.108599999999996
- type: nauc_map_at_3_std
value: 2.0995
- type: nauc_map_at_3_diff1
value: 69.3459
- type: nauc_map_at_5_max
value: 55.257
- type: nauc_map_at_5_std
value: 5.5776
- type: nauc_map_at_5_diff1
value: 68.3314
- type: nauc_map_at_10_max
value: 57.1506
- type: nauc_map_at_10_std
value: 7.4561
- type: nauc_map_at_10_diff1
value: 68.8482
- type: nauc_map_at_20_max
value: 57.126200000000004
- type: nauc_map_at_20_std
value: 7.6833
- type: nauc_map_at_20_diff1
value: 68.9132
- type: nauc_map_at_100_max
value: 56.9874
- type: nauc_map_at_100_std
value: 7.7405
- type: nauc_map_at_100_diff1
value: 68.9371
- type: nauc_map_at_1000_max
value: 56.959199999999996
- type: nauc_map_at_1000_std
value: 7.709499999999999
- type: nauc_map_at_1000_diff1
value: 68.9444
- type: nauc_recall_at_1_max
value: 49.762299999999996
- type: nauc_recall_at_1_std
value: -0.2785
- type: nauc_recall_at_1_diff1
value: 71.9072
- type: nauc_recall_at_3_max
value: 58.22580000000001
- type: nauc_recall_at_3_std
value: 2.3135
- type: nauc_recall_at_3_diff1
value: 65.5868
- type: nauc_recall_at_5_max
value: 60.4096
- type: nauc_recall_at_5_std
value: 11.7662
- type: nauc_recall_at_5_diff1
value: 61.5815
- type: nauc_recall_at_10_max
value: 72.74629999999999
- type: nauc_recall_at_10_std
value: 22.148
- type: nauc_recall_at_10_diff1
value: 62.2401
- type: nauc_recall_at_20_max
value: 74.9625
- type: nauc_recall_at_20_std
value: 28.1358
- type: nauc_recall_at_20_diff1
value: 63.240700000000004
- type: nauc_recall_at_100_max
value: 79.15910000000001
- type: nauc_recall_at_100_std
value: 39.4162
- type: nauc_recall_at_100_diff1
value: 65.733
- type: nauc_recall_at_1000_max
value: 100.0
- type: nauc_recall_at_1000_std
value: 72.2222
- type: nauc_recall_at_1000_diff1
value: 72.2222
- type: nauc_precision_at_1_max
value: 53.481500000000004
- type: nauc_precision_at_1_std
value: 8.227
- type: nauc_precision_at_1_diff1
value: 72.0771
- type: nauc_precision_at_3_max
value: 55.675799999999995
- type: nauc_precision_at_3_std
value: 23.9615
- type: nauc_precision_at_3_diff1
value: 48.1199
- type: nauc_precision_at_5_max
value: 50.503299999999996
- type: nauc_precision_at_5_std
value: 36.9259
- type: nauc_precision_at_5_diff1
value: 31.769399999999997
- type: nauc_precision_at_10_max
value: 45.4878
- type: nauc_precision_at_10_std
value: 44.0469
- type: nauc_precision_at_10_diff1
value: 16.666900000000002
- type: nauc_precision_at_20_max
value: 40.2908
- type: nauc_precision_at_20_std
value: 47.330600000000004
- type: nauc_precision_at_20_diff1
value: 11.0043
- type: nauc_precision_at_100_max
value: 27.4643
- type: nauc_precision_at_100_std
value: 53.0014
- type: nauc_precision_at_100_diff1
value: -4.8238
- type: nauc_precision_at_1000_max
value: 15.755099999999999
- type: nauc_precision_at_1000_std
value: 56.634499999999996
- type: nauc_precision_at_1000_diff1
value: -21.124100000000002
- type: nauc_mrr_at_1_max
value: 53.481500000000004
- type: nauc_mrr_at_1_std
value: 8.227
- type: nauc_mrr_at_1_diff1
value: 72.0771
- type: nauc_mrr_at_3_max
value: 57.6662
- type: nauc_mrr_at_3_std
value: 9.2816
- type: nauc_mrr_at_3_diff1
value: 69.8276
- type: nauc_mrr_at_5_max
value: 57.6565
- type: nauc_mrr_at_5_std
value: 10.422099999999999
- type: nauc_mrr_at_5_diff1
value: 69.0964
- type: nauc_mrr_at_10_max
value: 58.000099999999996
- type: nauc_mrr_at_10_std
value: 10.957600000000001
- type: nauc_mrr_at_10_diff1
value: 69.0098
- type: nauc_mrr_at_20_max
value: 58.0066
- type: nauc_mrr_at_20_std
value: 11.0139
- type: nauc_mrr_at_20_diff1
value: 69.1278
- type: nauc_mrr_at_100_max
value: 57.9072
- type: nauc_mrr_at_100_std
value: 10.9621
- type: nauc_mrr_at_100_diff1
value: 69.1925
- type: nauc_mrr_at_1000_max
value: 57.87949999999999
- type: nauc_mrr_at_1000_std
value: 10.934199999999999
- type: nauc_mrr_at_1000_diff1
value: 69.2004
- type: main_score
value: 71.8
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.8248
- type: similarity_accuracy_threshold
value: 74.6155
- type: similarity_f1
value: 91.12780000000001
- type: similarity_f1_threshold
value: 74.2422
- type: similarity_precision
value: 91.3568
- type: similarity_recall
value: 90.9
- type: similarity_ap
value: 96.00319999999999
- type: cosine_accuracy
value: 99.8248
- type: cosine_accuracy_threshold
value: 74.6155
- type: cosine_f1
value: 91.12780000000001
- type: cosine_f1_threshold
value: 74.2422
- type: cosine_precision
value: 91.3568
- type: cosine_recall
value: 90.9
- type: cosine_ap
value: 96.00319999999999
- type: manhattan_accuracy
value: 99.8257
- type: manhattan_accuracy_threshold
value: 1574.1653
- type: manhattan_f1
value: 91.1531
- type: manhattan_f1_threshold
value: 1595.7924
- type: manhattan_precision
value: 90.6126
- type: manhattan_recall
value: 91.7
- type: manhattan_ap
value: 95.9848
- type: euclidean_accuracy
value: 99.8248
- type: euclidean_accuracy_threshold
value: 71.2523
- type: euclidean_f1
value: 91.12780000000001
- type: euclidean_f1_threshold
value: 71.7744
- type: euclidean_precision
value: 91.3568
- type: euclidean_recall
value: 90.9
- type: euclidean_ap
value: 96.00319999999999
- type: dot_accuracy
value: 99.8248
- type: dot_accuracy_threshold
value: 74.6155
- type: dot_f1
value: 91.12780000000001
- type: dot_f1_threshold
value: 74.2422
- type: dot_precision
value: 91.3568
- type: dot_recall
value: 90.9
- type: dot_ap
value: 96.00319999999999
- type: max_accuracy
value: 99.8257
- type: max_f1
value: 91.1531
- type: max_precision
value: 91.3568
- type: max_recall
value: 91.7
- type: max_ap
value: 96.00319999999999
- type: main_score
value: 96.00319999999999
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 61.3985
- type: v_measure_std
value: 5.2151000000000005
- type: main_score
value: 61.3985
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.1433
- type: v_measure_std
value: 1.5853
- type: main_score
value: 36.1433
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.47580000000001
- type: mrr
value: 51.221399999999996
- type: nAUC_map_max
value: 10.1311
- type: nAUC_map_std
value: 6.239999999999999
- type: nAUC_map_diff1
value: 36.3486
- type: nAUC_mrr_max
value: 10.9306
- type: nAUC_mrr_std
value: 6.7909
- type: nAUC_mrr_diff1
value: 36.5536
- type: main_score
value: 50.47580000000001
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: pearson
value: 29.8474
- type: spearman
value: 29.391099999999998
- type: cosine_spearman
value: 29.391099999999998
- type: cosine_pearson
value: 29.8474
- type: dot_spearman
value: 29.391099999999998
- type: dot_pearson
value: 29.8474
- type: main_score
value: 29.391099999999998
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID (default)
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: ndcg_at_1
value: 85.0
- type: ndcg_at_3
value: 84.58099999999999
- type: ndcg_at_5
value: 83.573
- type: ndcg_at_10
value: 80.285
- type: ndcg_at_20
value: 77.469
- type: ndcg_at_100
value: 63.524
- type: ndcg_at_1000
value: 56.839
- type: map_at_1
value: 0.22799999999999998
- type: map_at_3
value: 0.656
- type: map_at_5
value: 1.078
- type: map_at_10
value: 2.0389999999999997
- type: map_at_20
value: 3.7670000000000003
- type: map_at_100
value: 12.8
- type: map_at_1000
value: 31.575999999999997
- type: recall_at_1
value: 0.22799999999999998
- type: recall_at_3
value: 0.695
- type: recall_at_5
value: 1.151
- type: recall_at_10
value: 2.215
- type: recall_at_20
value: 4.232
- type: recall_at_100
value: 15.828000000000001
- type: recall_at_1000
value: 53.516
- type: precision_at_1
value: 90.0
- type: precision_at_3
value: 89.333
- type: precision_at_5
value: 88.8
- type: precision_at_10
value: 84.6
- type: precision_at_20
value: 81.6
- type: precision_at_100
value: 65.64
- type: precision_at_1000
value: 25.380000000000003
- type: mrr_at_1
value: 90.0
- type: mrr_at_3
value: 94.6667
- type: mrr_at_5
value: 94.6667
- type: mrr_at_10
value: 94.6667
- type: mrr_at_20
value: 94.6667
- type: mrr_at_100
value: 94.6667
- type: mrr_at_1000
value: 94.6667
- type: nauc_ndcg_at_1_max
value: -5.4637
- type: nauc_ndcg_at_1_std
value: 14.5981
- type: nauc_ndcg_at_1_diff1
value: 13.6414
- type: nauc_ndcg_at_3_max
value: 10.9521
- type: nauc_ndcg_at_3_std
value: 39.8204
- type: nauc_ndcg_at_3_diff1
value: -13.839799999999999
- type: nauc_ndcg_at_5_max
value: 20.9664
- type: nauc_ndcg_at_5_std
value: 50.876999999999995
- type: nauc_ndcg_at_5_diff1
value: -15.3559
- type: nauc_ndcg_at_10_max
value: 34.053
- type: nauc_ndcg_at_10_std
value: 59.1102
- type: nauc_ndcg_at_10_diff1
value: -23.3868
- type: nauc_ndcg_at_20_max
value: 39.5081
- type: nauc_ndcg_at_20_std
value: 70.287
- type: nauc_ndcg_at_20_diff1
value: -36.7999
- type: nauc_ndcg_at_100_max
value: 38.8671
- type: nauc_ndcg_at_100_std
value: 80.5875
- type: nauc_ndcg_at_100_diff1
value: -28.766599999999997
- type: nauc_ndcg_at_1000_max
value: 45.4017
- type: nauc_ndcg_at_1000_std
value: 73.1799
- type: nauc_ndcg_at_1000_diff1
value: -13.5374
- type: nauc_map_at_1_max
value: -15.7901
- type: nauc_map_at_1_std
value: -14.5481
- type: nauc_map_at_1_diff1
value: 35.3307
- type: nauc_map_at_3_max
value: -4.8114
- type: nauc_map_at_3_std
value: -8.3704
- type: nauc_map_at_3_diff1
value: 26.2918
- type: nauc_map_at_5_max
value: -0.9780000000000001
- type: nauc_map_at_5_std
value: -3.4821
- type: nauc_map_at_5_diff1
value: 25.469
- type: nauc_map_at_10_max
value: 4.2075000000000005
- type: nauc_map_at_10_std
value: 1.5897999999999999
- type: nauc_map_at_10_diff1
value: 20.0578
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- type: nauc_precision_at_3_std
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type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: 19.354599999999998
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value: -26.519900000000003
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value: 20.288
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value: -33.218599999999995
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value: -23.857400000000002
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value: 15.978200000000001
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value: -31.7904
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value: -19.169900000000002
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value: 17.762700000000002
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value: -30.44
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value: -20.2867
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value: 18.895500000000002
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value: -30.5404
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value: -20.5699
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value: 18.7046
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value: -30.5404
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value: -20.5699
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value: 18.7046
- type: main_score
value: 29.767
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 64.8096
- type: f1
value: 49.844300000000004
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value: 72.5251
- type: ap
value: 11.7519
- type: ap_weighted
value: 11.7519
- type: main_score
value: 64.8096
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.1692
- type: f1
value: 58.4408
- type: f1_weighted
value: 57.565599999999996
- type: main_score
value: 58.1692
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 39.293
- type: v_measure_std
value: 1.5684
- type: main_score
value: 39.293
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
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value: 83.29260000000001
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value: 78.2732
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value: 60.656600000000005
- type: similarity_f1_threshold
value: 73.4961
- type: similarity_precision
value: 59.007
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value: 62.4011
- type: similarity_ap
value: 64.7501
- type: cosine_accuracy
value: 83.29260000000001
- type: cosine_accuracy_threshold
value: 78.2732
- type: cosine_f1
value: 60.656600000000005
- type: cosine_f1_threshold
value: 73.4961
- type: cosine_precision
value: 59.007
- type: cosine_recall
value: 62.4011
- type: cosine_ap
value: 64.7501
- type: manhattan_accuracy
value: 83.2986
- type: manhattan_accuracy_threshold
value: 1476.7148
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value: 60.7459
- type: manhattan_f1_threshold
value: 1607.9180000000001
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value: 59.0581
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value: 62.53300000000001
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value: 64.76859999999999
- type: euclidean_accuracy
value: 83.29260000000001
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value: 65.9194
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value: 60.656600000000005
- type: euclidean_f1_threshold
value: 72.8065
- type: euclidean_precision
value: 59.007
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value: 62.4011
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value: 64.7501
- type: dot_accuracy
value: 83.29260000000001
- type: dot_accuracy_threshold
value: 78.2731
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value: 60.656600000000005
- type: dot_f1_threshold
value: 73.4961
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value: 59.007
- type: dot_recall
value: 62.4011
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value: 64.7501
- type: max_accuracy
value: 83.2986
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value: 60.7459
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value: 59.0581
- type: max_recall
value: 62.53300000000001
- type: max_ap
value: 64.76859999999999
- type: main_score
value: 64.76859999999999
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 89.0247
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value: 69.271
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value: 78.24419999999999
- type: similarity_f1_threshold
value: 66.2183
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value: 76.616
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value: 79.943
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value: 85.9494
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value: 89.0247
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value: 69.271
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value: 78.24419999999999
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value: 66.2183
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value: 76.616
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value: 79.943
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value: 85.9494
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value: 89.0267
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value: 1750.3544000000002
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value: 78.2188
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value: 1837.7304
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value: 75.1472
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value: 81.5522
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value: 85.9496
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value: 89.0247
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value: 78.3951
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value: 78.24419999999999
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value: 82.197
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value: 76.616
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value: 79.943
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value: 85.9494
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value: 89.0247
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value: 69.271
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value: 78.24419999999999
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value: 66.2183
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value: 76.616
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value: 79.943
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value: 85.9494
- type: max_accuracy
value: 89.0267
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value: 78.24419999999999
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value: 76.616
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value: 81.5522
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value: 85.9496
- type: main_score
value: 85.9496
---
<h1 align="center">Snowflake's Arctic-embed-m-v2.0</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=d5cb84e7-4b3a-4d82-85a1-19ec3721c447" />
## News
- 12/11/2024: Release of [Technical Report](https://arxiv.org/abs/2412.04506)
- 12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind.
## Models
Snowflake arctic-embed-m-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency.
Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English.
Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.
Key Features:
1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
2. Inference efficiency: Its 113m non-embedding parameters inference is fast and efficient for any scale.
3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
4. Long Context Support: arctic-embed-m-v2.0 builds on [GTE-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) which can support a context window of up to 8192 via the use of RoPE.
### Quality Benchmarks
Unlike most other open-source models, Arctic-embed-m-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF).
You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed.
| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **snowflake-arctic-m-v2.0** | 305M | 113M | 768 | **55.4** | 55.2 | **51.7** | **53.9** |
| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
| me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
| gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
Aside from high-quality retrieval, arctic delivers embeddings that are easily compressible. By leveraging vector truncation via MRL to decrease vector size by 3x with about 3% degradation in quality.
Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.
| Model | | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance |
|---|---|:---:|:---:|:---:|:---:|:---:|---|---|---|
| snowflake-arctic-m-v2.0 | 768 | 55.4 | N/A | 55.2 | N/A | 51.7 | N/A | 53.9 | N/A |
| snowflake-arctic-m-v2.0 | 256 | 54.4 | -1.81% | 54.0 | -2.17% | 50.6 | -2.13% | 52.3 | -3.06% |
## Usage
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-m-v2.0'
model = SentenceTransformer(model_name, trust_remote_code=True)
# Define the queries and documents
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
# Compute embeddings: use `prompt_name="query"` to encode queries!
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute cosine similarity scores
scores = model.similarity(query_embeddings, document_embeddings)
# Output the results
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Huggingface Transformers
You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
model_name = 'Snowflake/snowflake-arctic-embed-m-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False, trust_remote_code=True)
model.eval()
query_prefix = 'query: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Huggingface Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model for retrieval, as follows:
```js
import { pipeline, dot } from '@huggingface/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0');
// Generate sentence embeddings
const sentences = [
'query: what is snowflake?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.32719788157046004, 0.06960141111667434]
```
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
cointegrated/rubert-base-cased-nli-threeway | cointegrated | zero-shot-classification | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"text-classification",
"rubert",
"russian",
"nli",
"rte",
"zero-shot-classification",
"ru",
"dataset:cointegrated/nli-rus-translated-v2021",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2024-04-05T09:31:57 | 159,831 | 33 | ---
datasets:
- cointegrated/nli-rus-translated-v2021
language: ru
pipeline_tag: zero-shot-classification
tags:
- rubert
- russian
- nli
- rte
- zero-shot-classification
widget:
- text: Я хочу поехать в Австралию
candidate_labels: спорт,путешествия,музыка,кино,книги,наука,политика
hypothesis_template: Тема текста - {}.
---
# RuBERT for NLI (natural language inference)
This is the [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral.
## Usage
How to run the model for NLI:
```python
# !pip install transformers sentencepiece --quiet
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_checkpoint = 'cointegrated/rubert-base-cased-nli-threeway'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)
if torch.cuda.is_available():
model.cuda()
text1 = 'Сократ - человек, а все люди смертны.'
text2 = 'Сократ никогда не умрёт.'
with torch.inference_mode():
out = model(**tokenizer(text1, text2, return_tensors='pt').to(model.device))
proba = torch.softmax(out.logits, -1).cpu().numpy()[0]
print({v: proba[k] for k, v in model.config.id2label.items()})
# {'entailment': 0.009525929, 'contradiction': 0.9332064, 'neutral': 0.05726764}
```
You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis:
```python
def predict_zero_shot(text, label_texts, model, tokenizer, label='entailment', normalize=True):
label_texts
tokens = tokenizer([text] * len(label_texts), label_texts, truncation=True, return_tensors='pt', padding=True)
with torch.inference_mode():
result = torch.softmax(model(**tokens.to(model.device)).logits, -1)
proba = result[:, model.config.label2id[label]].cpu().numpy()
if normalize:
proba /= sum(proba)
return proba
classes = ['Я доволен', 'Я недоволен']
predict_zero_shot('Какая гадость эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.05609814, 0.9439019 ], dtype=float32)
predict_zero_shot('Какая вкусная эта ваша заливная рыба!', classes, model, tokenizer)
# array([0.9059292 , 0.09407079], dtype=float32)
```
Alternatively, you can use [Huggingface pipelines](https://huggingface.co/transformers/main_classes/pipelines.html) for inference.
## Sources
The model has been trained on a series of NLI datasets automatically translated to Russian from English.
Most datasets were taken [from the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets):
[JOCI](https://github.com/sheng-z/JOCI),
[MNLI](https://cims.nyu.edu/~sbowman/multinli/),
[MPE](https://aclanthology.org/I17-1011/),
[SICK](http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf),
[SNLI](https://nlp.stanford.edu/projects/snli/).
Some datasets obtained from the original sources:
[ANLI](https://github.com/facebookresearch/anli),
[NLI-style FEVER](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md),
[IMPPRES](https://github.com/facebookresearch/Imppres).
## Performance
The table below shows ROC AUC (one class vs rest) for five models on the corresponding *dev* sets:
- [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli): a small BERT predicting entailment vs not_entailment
- [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway): a base-sized BERT predicting entailment vs not_entailment
- [threeway](https://huggingface.co/cointegrated/rubert-base-cased-nli-threeway) (**this model**): a base-sized BERT predicting entailment vs contradiction vs neutral
- [vicgalle-xlm](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli): a large multilingual NLI model
- [facebook-bart](https://huggingface.co/facebook/bart-large-mnli): a large multilingual NLI model
|model |add_one_rte|anli_r1|anli_r2|anli_r3|copa|fever|help|iie |imppres|joci|mnli |monli|mpe |scitail|sick|snli|terra|total |
|------------------------|-----------|-------|-------|-------|----|-----|----|-----|-------|----|-----|-----|----|-------|----|----|-----|------|
|n_observations |387 |1000 |1000 |1200 |200 |20474|3355|31232|7661 |939 |19647|269 |1000|2126 |500 |9831|307 |101128|
|tiny/entailment |0.77 |0.59 |0.52 |0.53 |0.53|0.90 |0.81|0.78 |0.93 |0.81|0.82 |0.91 |0.81|0.78 |0.93|0.95|0.67 |0.77 |
|twoway/entailment |0.89 |0.73 |0.61 |0.62 |0.58|0.96 |0.92|0.87 |0.99 |0.90|0.90 |0.99 |0.91|0.96 |0.97|0.97|0.87 |0.86 |
|threeway/entailment |0.91 |0.75 |0.61 |0.61 |0.57|0.96 |0.56|0.61 |0.99 |0.90|0.91 |0.67 |0.92|0.84 |0.98|0.98|0.90 |0.80 |
|vicgalle-xlm/entailment |0.88 |0.79 |0.63 |0.66 |0.57|0.93 |0.56|0.62 |0.77 |0.80|0.90 |0.70 |0.83|0.84 |0.91|0.93|0.93 |0.78 |
|facebook-bart/entailment|0.51 |0.41 |0.43 |0.47 |0.50|0.74 |0.55|0.57 |0.60 |0.63|0.70 |0.52 |0.56|0.68 |0.67|0.72|0.64 |0.58 |
|threeway/contradiction | |0.71 |0.64 |0.61 | |0.97 | | |1.00 |0.77|0.92 | |0.89| |0.99|0.98| |0.85 |
|threeway/neutral | |0.79 |0.70 |0.62 | |0.91 | | |0.99 |0.68|0.86 | |0.79| |0.96|0.96| |0.83 |
For evaluation (and for training of the [tiny](https://huggingface.co/cointegrated/rubert-tiny-bilingual-nli) and [twoway](https://huggingface.co/cointegrated/rubert-base-cased-nli-twoway) models), some extra datasets were used:
[Add-one RTE](https://cs.brown.edu/people/epavlick/papers/ans.pdf),
[CoPA](https://people.ict.usc.edu/~gordon/copa.html),
[IIE](https://aclanthology.org/I17-1100), and
[SCITAIL](https://allenai.org/data/scitail) taken from [the repo of Felipe Salvatore](https://github.com/felipessalvatore/NLI_datasets) and translatted,
[HELP](https://github.com/verypluming/HELP) and [MoNLI](https://github.com/atticusg/MoNLI) taken from the original sources and translated,
and Russian [TERRa](https://russiansuperglue.com/ru/tasks/task_info/TERRa).
| [
"TEXT_CLASSIFICATION"
] | [
"SCITAIL"
] |
EleutherAI/pythia-160m | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-08T19:25:46 | 2023-07-09T15:52:09 | 134,206 | 30 | ---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-160M
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-160M for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-160M as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-160M has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-160M will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-160M to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-160M may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-160M.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-160M.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
nomic-ai/modernbert-embed-base | nomic-ai | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"modernbert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers.js",
"en",
"arxiv:2402.01613",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-12-29T23:51:30 | 2025-01-24T15:31:46 | 132,008 | 199 | ---
base_model:
- answerdotai/ModernBERT-base
- nomic-ai/modernbert-embed-unsupervised
language:
- en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- transformers.js
base_model_relation: finetune
model-index:
- name: binarize_False
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: None
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.13432835820896
- type: ap
value: 42.190424731303246
- type: f1
value: 72.34446401534811
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: None
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.093825
- type: ap
value: 90.03727505544286
- type: f1
value: 93.0874055138833
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: None
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.428000000000004
- type: f1
value: 47.74311520203536
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: None
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 23.898
- type: map_at_10
value: 39.775
- type: map_at_100
value: 40.827000000000005
- type: map_at_1000
value: 40.837
- type: map_at_20
value: 40.604
- type: map_at_3
value: 34.519
- type: map_at_5
value: 37.307
- type: mrr_at_1
value: 24.395
- type: mrr_at_10
value: 39.963
- type: mrr_at_100
value: 41.014
- type: mrr_at_1000
value: 41.024
- type: mrr_at_20
value: 40.791
- type: mrr_at_3
value: 34.732
- type: mrr_at_5
value: 37.480999999999995
- type: ndcg_at_1
value: 23.898
- type: ndcg_at_10
value: 48.962
- type: ndcg_at_100
value: 53.386
- type: ndcg_at_1000
value: 53.634
- type: ndcg_at_20
value: 51.898999999999994
- type: ndcg_at_3
value: 38.034
- type: ndcg_at_5
value: 43.036
- type: precision_at_1
value: 23.898
- type: precision_at_10
value: 7.852
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.4990000000000006
- type: precision_at_3
value: 16.073999999999998
- type: precision_at_5
value: 12.063
- type: recall_at_1
value: 23.898
- type: recall_at_10
value: 78.521
- type: recall_at_100
value: 97.724
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 89.972
- type: recall_at_3
value: 48.222
- type: recall_at_5
value: 60.313
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: None
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.69067314293749
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- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: None
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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dataset:
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type: None
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
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value: 62.33195643912506
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dataset:
name: MTEB BIOSSES
type: None
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 77.98611431591628
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dataset:
name: MTEB Banking77Classification
type: None
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
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value: 85.25324675324676
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value: 85.19854235582687
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dataset:
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type: None
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: None
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 33.28787287895752
- type: v_measures
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- 0.33383133843449825
- 0.35355224888017306
- 0.3235019092705102
- 0.34053753555843735
- 0.32485572754337366
- 0.3149662563474906
- 0.3326837187664875
- 0.3229632335470733
- 0.33078383561261365
- 0.35111148393509534
- 0.33383133843449825
- 0.35355224888017306
- 0.3235019092705102
- 0.34053753555843735
- 0.32485572754337366
- 0.3149662563474906
- 0.3326837187664875
- 0.3229632335470733
- 0.33078383561261365
- 0.35111148393509534
- 0.33383133843449825
- 0.35355224888017306
- 0.3235019092705102
- 0.34053753555843735
- 0.32485572754337366
- 0.3149662563474906
- 0.3326837187664875
- 0.3229632335470733
- 0.33078383561261365
- 0.35111148393509534
- 0.33383133843449825
- 0.35355224888017306
- 0.3235019092705102
- 0.34053753555843735
- 0.32485572754337366
- 0.3149662563474906
- 0.3326837187664875
- 0.3229632335470733
- 0.33078383561261365
- 0.35111148393509534
- 0.33383133843449825
- 0.35355224888017306
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 32.677
- type: map_at_10
value: 43.739
- type: map_at_100
value: 45.152
- type: map_at_1000
value: 45.279
- type: map_at_20
value: 44.553
- type: map_at_3
value: 40.321
- type: map_at_5
value: 42.201
- type: mrr_at_1
value: 40.2
- type: mrr_at_10
value: 49.755
- type: mrr_at_100
value: 50.468
- type: mrr_at_1000
value: 50.513
- type: mrr_at_20
value: 50.192
- type: mrr_at_3
value: 47.163
- type: mrr_at_5
value: 48.686
- type: ndcg_at_1
value: 40.2
- type: ndcg_at_10
value: 49.963
- type: ndcg_at_100
value: 54.978
- type: ndcg_at_1000
value: 56.979
- type: ndcg_at_20
value: 51.983000000000004
- type: ndcg_at_3
value: 45.086999999999996
- type: ndcg_at_5
value: 47.309
- type: precision_at_1
value: 40.2
- type: precision_at_10
value: 9.328
- type: precision_at_100
value: 1.443
- type: precision_at_1000
value: 0.19
- type: precision_at_20
value: 5.558
- type: precision_at_3
value: 21.364
- type: precision_at_5
value: 15.222
- type: recall_at_1
value: 32.677
- type: recall_at_10
value: 61.71
- type: recall_at_100
value: 82.431
- type: recall_at_1000
value: 94.896
- type: recall_at_20
value: 68.73700000000001
- type: recall_at_3
value: 47.431
- type: recall_at_5
value: 53.739000000000004
- type: map_at_1
value: 27.734166666666667
- type: map_at_10
value: 36.858
- type: map_at_100
value: 38.043833333333325
- type: map_at_1000
value: 38.15541666666667
- type: map_at_20
value: 37.521249999999995
- type: map_at_3
value: 34.07658333333333
- type: map_at_5
value: 35.62683333333333
- type: mrr_at_1
value: 32.676249999999996
- type: mrr_at_10
value: 40.999
- type: mrr_at_100
value: 41.835
- type: mrr_at_1000
value: 41.8895
- type: mrr_at_20
value: 41.4865
- type: mrr_at_3
value: 38.645
- type: mrr_at_5
value: 39.99725000000001
- type: ndcg_at_1
value: 32.676249999999996
- type: ndcg_at_10
value: 42.08016666666666
- type: ndcg_at_100
value: 47.082750000000004
- type: ndcg_at_1000
value: 49.276583333333335
- type: ndcg_at_20
value: 44.04808333333334
- type: ndcg_at_3
value: 37.43375
- type: ndcg_at_5
value: 39.623000000000005
- type: precision_at_1
value: 32.676249999999996
- type: precision_at_10
value: 7.271
- type: precision_at_100
value: 1.1458333333333333
- type: precision_at_1000
value: 0.152
- type: precision_at_20
value: 4.282916666666667
- type: precision_at_3
value: 17.061416666666666
- type: precision_at_5
value: 12.05466666666667
- type: recall_at_1
value: 27.734166666666667
- type: recall_at_10
value: 53.33574999999999
- type: recall_at_100
value: 75.16275
- type: recall_at_1000
value: 90.34891666666665
- type: recall_at_20
value: 60.4935
- type: recall_at_3
value: 40.377916666666664
- type: recall_at_5
value: 46.0195
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 32.71
- type: map_at_10
value: 43.297000000000004
- type: map_at_100
value: 44.607
- type: map_at_1000
value: 44.729
- type: map_at_20
value: 44.013999999999996
- type: map_at_3
value: 40.213
- type: map_at_5
value: 42.004000000000005
- type: mrr_at_1
value: 40.892
- type: mrr_at_10
value: 49.394
- type: mrr_at_100
value: 50.005
- type: mrr_at_1000
value: 50.043000000000006
- type: mrr_at_20
value: 49.764
- type: mrr_at_3
value: 47.134
- type: mrr_at_5
value: 48.522
- type: ndcg_at_1
value: 40.892
- type: ndcg_at_10
value: 49.047000000000004
- type: ndcg_at_100
value: 53.266999999999996
- type: ndcg_at_1000
value: 55.096999999999994
- type: ndcg_at_20
value: 50.707
- type: ndcg_at_3
value: 44.896
- type: ndcg_at_5
value: 46.983000000000004
- type: precision_at_1
value: 40.892
- type: precision_at_10
value: 9.293
- type: precision_at_100
value: 1.473
- type: precision_at_1000
value: 0.192
- type: precision_at_20
value: 5.446
- type: precision_at_3
value: 21.592
- type: precision_at_5
value: 15.540999999999999
- type: recall_at_1
value: 32.71
- type: recall_at_10
value: 58.592999999999996
- type: recall_at_100
value: 76.242
- type: recall_at_1000
value: 87.717
- type: recall_at_20
value: 64.646
- type: recall_at_3
value: 46.253
- type: recall_at_5
value: 51.946999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 41.644999999999996
- type: map_at_10
value: 53.825
- type: map_at_100
value: 54.82
- type: map_at_1000
value: 54.87499999999999
- type: map_at_20
value: 54.43
- type: map_at_3
value: 50.705
- type: map_at_5
value: 52.501
- type: mrr_at_1
value: 47.524
- type: mrr_at_10
value: 57.260999999999996
- type: mrr_at_100
value: 57.902
- type: mrr_at_1000
value: 57.931999999999995
- type: mrr_at_20
value: 57.689
- type: mrr_at_3
value: 55.089
- type: mrr_at_5
value: 56.38999999999999
- type: ndcg_at_1
value: 47.524
- type: ndcg_at_10
value: 59.41499999999999
- type: ndcg_at_100
value: 63.258
- type: ndcg_at_1000
value: 64.376
- type: ndcg_at_20
value: 61.149
- type: ndcg_at_3
value: 54.381
- type: ndcg_at_5
value: 56.89999999999999
- type: precision_at_1
value: 47.524
- type: precision_at_10
value: 9.386
- type: precision_at_100
value: 1.221
- type: precision_at_1000
value: 0.136
- type: precision_at_20
value: 5.223
- type: precision_at_3
value: 24.096
- type: precision_at_5
value: 16.364
- type: recall_at_1
value: 41.644999999999996
- type: recall_at_10
value: 72.386
- type: recall_at_100
value: 88.794
- type: recall_at_1000
value: 96.75399999999999
- type: recall_at_20
value: 78.74
- type: recall_at_3
value: 59.028000000000006
- type: recall_at_5
value: 65.197
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 28.648
- type: map_at_10
value: 36.388999999999996
- type: map_at_100
value: 37.372
- type: map_at_1000
value: 37.457
- type: map_at_20
value: 36.912
- type: map_at_3
value: 34.076
- type: map_at_5
value: 35.415
- type: mrr_at_1
value: 30.508000000000003
- type: mrr_at_10
value: 38.132
- type: mrr_at_100
value: 39.04
- type: mrr_at_1000
value: 39.106
- type: mrr_at_20
value: 38.643
- type: mrr_at_3
value: 35.876000000000005
- type: mrr_at_5
value: 37.208999999999996
- type: ndcg_at_1
value: 30.508000000000003
- type: ndcg_at_10
value: 40.762
- type: ndcg_at_100
value: 45.732
- type: ndcg_at_1000
value: 47.799
- type: ndcg_at_20
value: 42.591
- type: ndcg_at_3
value: 36.266999999999996
- type: ndcg_at_5
value: 38.58
- type: precision_at_1
value: 30.508000000000003
- type: precision_at_10
value: 6.010999999999999
- type: precision_at_100
value: 0.897
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_20
value: 3.412
- type: precision_at_3
value: 14.991
- type: precision_at_5
value: 10.328
- type: recall_at_1
value: 28.648
- type: recall_at_10
value: 52.342999999999996
- type: recall_at_100
value: 75.268
- type: recall_at_1000
value: 90.641
- type: recall_at_20
value: 59.303
- type: recall_at_3
value: 40.447
- type: recall_at_5
value: 46.117000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 18.476
- type: map_at_10
value: 27.148
- type: map_at_100
value: 28.317999999999998
- type: map_at_1000
value: 28.427999999999997
- type: map_at_20
value: 27.764
- type: map_at_3
value: 24.801000000000002
- type: map_at_5
value: 26.133
- type: mrr_at_1
value: 22.886
- type: mrr_at_10
value: 31.741000000000003
- type: mrr_at_100
value: 32.708
- type: mrr_at_1000
value: 32.769
- type: mrr_at_20
value: 32.296
- type: mrr_at_3
value: 29.498
- type: mrr_at_5
value: 30.773
- type: ndcg_at_1
value: 22.886
- type: ndcg_at_10
value: 32.265
- type: ndcg_at_100
value: 37.829
- type: ndcg_at_1000
value: 40.558
- type: ndcg_at_20
value: 34.372
- type: ndcg_at_3
value: 28.105000000000004
- type: ndcg_at_5
value: 30.04
- type: precision_at_1
value: 22.886
- type: precision_at_10
value: 5.808
- type: precision_at_100
value: 0.985
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_20
value: 3.495
- type: precision_at_3
value: 13.639999999999999
- type: precision_at_5
value: 9.577
- type: recall_at_1
value: 18.476
- type: recall_at_10
value: 43.442
- type: recall_at_100
value: 67.376
- type: recall_at_1000
value: 86.874
- type: recall_at_20
value: 51.038
- type: recall_at_3
value: 31.785999999999998
- type: recall_at_5
value: 36.858999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 29.098000000000003
- type: map_at_10
value: 38.97
- type: map_at_100
value: 40.293
- type: map_at_1000
value: 40.397
- type: map_at_20
value: 39.778999999999996
- type: map_at_3
value: 35.723
- type: map_at_5
value: 37.519999999999996
- type: mrr_at_1
value: 35.515
- type: mrr_at_10
value: 44.55
- type: mrr_at_100
value: 45.37
- type: mrr_at_1000
value: 45.412
- type: mrr_at_20
value: 45.054
- type: mrr_at_3
value: 41.835
- type: mrr_at_5
value: 43.356
- type: ndcg_at_1
value: 35.515
- type: ndcg_at_10
value: 44.91
- type: ndcg_at_100
value: 50.27700000000001
- type: ndcg_at_1000
value: 52.215
- type: ndcg_at_20
value: 47.235
- type: ndcg_at_3
value: 39.505
- type: ndcg_at_5
value: 42.016
- type: precision_at_1
value: 35.515
- type: precision_at_10
value: 8.152
- type: precision_at_100
value: 1.262
- type: precision_at_1000
value: 0.16
- type: precision_at_20
value: 4.851
- type: precision_at_3
value: 18.447
- type: precision_at_5
value: 13.321
- type: recall_at_1
value: 29.098000000000003
- type: recall_at_10
value: 57.115
- type: recall_at_100
value: 79.467
- type: recall_at_1000
value: 92.162
- type: recall_at_20
value: 65.161
- type: recall_at_3
value: 42.254000000000005
- type: recall_at_5
value: 48.415
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 27.372000000000003
- type: map_at_10
value: 37.781
- type: map_at_100
value: 39.128
- type: map_at_1000
value: 39.238
- type: map_at_20
value: 38.592
- type: map_at_3
value: 34.782999999999994
- type: map_at_5
value: 36.466
- type: mrr_at_1
value: 33.904
- type: mrr_at_10
value: 43.15
- type: mrr_at_100
value: 44.049
- type: mrr_at_1000
value: 44.107
- type: mrr_at_20
value: 43.721
- type: mrr_at_3
value: 40.677
- type: mrr_at_5
value: 42.19
- type: ndcg_at_1
value: 33.904
- type: ndcg_at_10
value: 43.527
- type: ndcg_at_100
value: 49.004999999999995
- type: ndcg_at_1000
value: 51.276999999999994
- type: ndcg_at_20
value: 45.988
- type: ndcg_at_3
value: 38.824999999999996
- type: ndcg_at_5
value: 41.04
- type: precision_at_1
value: 33.904
- type: precision_at_10
value: 7.854
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.16
- type: precision_at_20
value: 4.692
- type: precision_at_3
value: 18.531
- type: precision_at_5
value: 13.150999999999998
- type: recall_at_1
value: 27.372000000000003
- type: recall_at_10
value: 55.245999999999995
- type: recall_at_100
value: 78.278
- type: recall_at_1000
value: 93.718
- type: recall_at_20
value: 64.095
- type: recall_at_3
value: 41.665
- type: recall_at_5
value: 47.632000000000005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 25.653
- type: map_at_10
value: 32.151
- type: map_at_100
value: 33.152
- type: map_at_1000
value: 33.243
- type: map_at_20
value: 32.717
- type: map_at_3
value: 30.287
- type: map_at_5
value: 31.25
- type: mrr_at_1
value: 28.988000000000003
- type: mrr_at_10
value: 35.131
- type: mrr_at_100
value: 36.002
- type: mrr_at_1000
value: 36.069
- type: mrr_at_20
value: 35.61
- type: mrr_at_3
value: 33.308
- type: mrr_at_5
value: 34.259
- type: ndcg_at_1
value: 28.988000000000003
- type: ndcg_at_10
value: 35.988
- type: ndcg_at_100
value: 40.764
- type: ndcg_at_1000
value: 43.112
- type: ndcg_at_20
value: 37.852999999999994
- type: ndcg_at_3
value: 32.562000000000005
- type: ndcg_at_5
value: 33.983000000000004
- type: precision_at_1
value: 28.988000000000003
- type: precision_at_10
value: 5.475
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_20
value: 3.229
- type: precision_at_3
value: 13.905999999999999
- type: precision_at_5
value: 9.386999999999999
- type: recall_at_1
value: 25.653
- type: recall_at_10
value: 44.962
- type: recall_at_100
value: 66.405
- type: recall_at_1000
value: 83.88799999999999
- type: recall_at_20
value: 51.79899999999999
- type: recall_at_3
value: 35.144999999999996
- type: recall_at_5
value: 38.814
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 17.825
- type: map_at_10
value: 25.592
- type: map_at_100
value: 26.613999999999997
- type: map_at_1000
value: 26.734
- type: map_at_20
value: 26.115
- type: map_at_3
value: 23.119
- type: map_at_5
value: 24.54
- type: mrr_at_1
value: 21.335
- type: mrr_at_10
value: 29.165000000000003
- type: mrr_at_100
value: 30.049
- type: mrr_at_1000
value: 30.121
- type: mrr_at_20
value: 29.639
- type: mrr_at_3
value: 26.863999999999997
- type: mrr_at_5
value: 28.185
- type: ndcg_at_1
value: 21.335
- type: ndcg_at_10
value: 30.357
- type: ndcg_at_100
value: 35.410000000000004
- type: ndcg_at_1000
value: 38.24
- type: ndcg_at_20
value: 32.08
- type: ndcg_at_3
value: 25.95
- type: ndcg_at_5
value: 28.081
- type: precision_at_1
value: 21.335
- type: precision_at_10
value: 5.506
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.135
- type: precision_at_20
value: 3.2550000000000003
- type: precision_at_3
value: 12.239
- type: precision_at_5
value: 8.885
- type: recall_at_1
value: 17.825
- type: recall_at_10
value: 41.105999999999995
- type: recall_at_100
value: 64.17
- type: recall_at_1000
value: 84.19200000000001
- type: recall_at_20
value: 47.497
- type: recall_at_3
value: 28.862
- type: recall_at_5
value: 34.348
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 29.435
- type: map_at_10
value: 38.261
- type: map_at_100
value: 39.242
- type: map_at_1000
value: 39.347
- type: map_at_20
value: 38.742
- type: map_at_3
value: 35.457
- type: map_at_5
value: 37.043
- type: mrr_at_1
value: 34.235
- type: mrr_at_10
value: 42.24
- type: mrr_at_100
value: 42.988
- type: mrr_at_1000
value: 43.043
- type: mrr_at_20
value: 42.613
- type: mrr_at_3
value: 39.832
- type: mrr_at_5
value: 41.227000000000004
- type: ndcg_at_1
value: 34.235
- type: ndcg_at_10
value: 43.384
- type: ndcg_at_100
value: 48.14
- type: ndcg_at_1000
value: 50.414
- type: ndcg_at_20
value: 44.913
- type: ndcg_at_3
value: 38.454
- type: ndcg_at_5
value: 40.776
- type: precision_at_1
value: 34.235
- type: precision_at_10
value: 7.164
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_20
value: 4.021
- type: precision_at_3
value: 17.226
- type: precision_at_5
value: 12.071
- type: recall_at_1
value: 29.435
- type: recall_at_10
value: 54.93900000000001
- type: recall_at_100
value: 76.176
- type: recall_at_1000
value: 91.989
- type: recall_at_20
value: 60.451
- type: recall_at_3
value: 41.332
- type: recall_at_5
value: 47.316
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 25.605
- type: map_at_10
value: 34.162
- type: map_at_100
value: 35.827999999999996
- type: map_at_1000
value: 36.04
- type: map_at_20
value: 35.016000000000005
- type: map_at_3
value: 30.984
- type: map_at_5
value: 32.717
- type: mrr_at_1
value: 30.435000000000002
- type: mrr_at_10
value: 38.681
- type: mrr_at_100
value: 39.656000000000006
- type: mrr_at_1000
value: 39.71
- type: mrr_at_20
value: 39.208999999999996
- type: mrr_at_3
value: 35.903
- type: mrr_at_5
value: 37.454
- type: ndcg_at_1
value: 30.435000000000002
- type: ndcg_at_10
value: 39.916000000000004
- type: ndcg_at_100
value: 45.958
- type: ndcg_at_1000
value: 48.449999999999996
- type: ndcg_at_20
value: 42.085
- type: ndcg_at_3
value: 34.696
- type: ndcg_at_5
value: 37.147000000000006
- type: precision_at_1
value: 30.435000000000002
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.547
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_20
value: 4.941
- type: precision_at_3
value: 16.073999999999998
- type: precision_at_5
value: 11.937000000000001
- type: recall_at_1
value: 25.605
- type: recall_at_10
value: 50.654999999999994
- type: recall_at_100
value: 77.609
- type: recall_at_1000
value: 93.518
- type: recall_at_20
value: 58.845000000000006
- type: recall_at_3
value: 36.272
- type: recall_at_5
value: 42.596000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 23.666
- type: map_at_10
value: 30.980999999999998
- type: map_at_100
value: 32.0
- type: map_at_1000
value: 32.098
- type: map_at_20
value: 31.621
- type: map_at_3
value: 28.449999999999996
- type: map_at_5
value: 29.731999999999996
- type: mrr_at_1
value: 25.692999999999998
- type: mrr_at_10
value: 32.788000000000004
- type: mrr_at_100
value: 33.783
- type: mrr_at_1000
value: 33.849000000000004
- type: mrr_at_20
value: 33.408
- type: mrr_at_3
value: 30.561
- type: mrr_at_5
value: 31.716
- type: ndcg_at_1
value: 25.692999999999998
- type: ndcg_at_10
value: 35.428
- type: ndcg_at_100
value: 40.375
- type: ndcg_at_1000
value: 42.802
- type: ndcg_at_20
value: 37.621
- type: ndcg_at_3
value: 30.476999999999997
- type: ndcg_at_5
value: 32.621
- type: precision_at_1
value: 25.692999999999998
- type: precision_at_10
value: 5.508
- type: precision_at_100
value: 0.848
- type: precision_at_1000
value: 0.116
- type: precision_at_20
value: 3.272
- type: precision_at_3
value: 12.631
- type: precision_at_5
value: 8.872
- type: recall_at_1
value: 23.666
- type: recall_at_10
value: 47.532000000000004
- type: recall_at_100
value: 69.73700000000001
- type: recall_at_1000
value: 87.83800000000001
- type: recall_at_20
value: 55.61000000000001
- type: recall_at_3
value: 34.06
- type: recall_at_5
value: 39.254
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: None
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.337
- type: map_at_10
value: 26.488
- type: map_at_100
value: 28.415000000000003
- type: map_at_1000
value: 28.584
- type: map_at_20
value: 27.557
- type: map_at_3
value: 22.665
- type: map_at_5
value: 24.542
- type: mrr_at_1
value: 36.417
- type: mrr_at_10
value: 48.001
- type: mrr_at_100
value: 48.784
- type: mrr_at_1000
value: 48.809000000000005
- type: mrr_at_20
value: 48.507
- type: mrr_at_3
value: 45.103
- type: mrr_at_5
value: 46.843
- type: ndcg_at_1
value: 36.417
- type: ndcg_at_10
value: 35.67
- type: ndcg_at_100
value: 42.716
- type: ndcg_at_1000
value: 45.639
- type: ndcg_at_20
value: 38.471
- type: ndcg_at_3
value: 30.444
- type: ndcg_at_5
value: 32.004
- type: precision_at_1
value: 36.417
- type: precision_at_10
value: 10.73
- type: precision_at_100
value: 1.833
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_20
value: 6.596
- type: precision_at_3
value: 22.302
- type: precision_at_5
value: 16.521
- type: recall_at_1
value: 16.337
- type: recall_at_10
value: 40.671
- type: recall_at_100
value: 64.55300000000001
- type: recall_at_1000
value: 80.934
- type: recall_at_20
value: 48.381
- type: recall_at_3
value: 27.279999999999998
- type: recall_at_5
value: 32.621
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: None
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.056000000000001
- type: map_at_10
value: 19.419
- type: map_at_100
value: 27.069
- type: map_at_1000
value: 28.666000000000004
- type: map_at_20
value: 22.434
- type: map_at_3
value: 13.895
- type: map_at_5
value: 16.121
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 75.804
- type: mrr_at_100
value: 76.117
- type: mrr_at_1000
value: 76.125
- type: mrr_at_20
value: 76.009
- type: mrr_at_3
value: 74.375
- type: mrr_at_5
value: 75.4
- type: ndcg_at_1
value: 57.49999999999999
- type: ndcg_at_10
value: 41.495
- type: ndcg_at_100
value: 45.208
- type: ndcg_at_1000
value: 52.221
- type: ndcg_at_20
value: 40.617999999999995
- type: ndcg_at_3
value: 46.592
- type: ndcg_at_5
value: 43.559
- type: precision_at_1
value: 69.0
- type: precision_at_10
value: 32.574999999999996
- type: precision_at_100
value: 10.205
- type: precision_at_1000
value: 2.036
- type: precision_at_20
value: 24.687
- type: precision_at_3
value: 49.75
- type: precision_at_5
value: 42.0
- type: recall_at_1
value: 9.056000000000001
- type: recall_at_10
value: 24.866
- type: recall_at_100
value: 50.097
- type: recall_at_1000
value: 72.038
- type: recall_at_20
value: 31.858999999999998
- type: recall_at_3
value: 15.096000000000002
- type: recall_at_5
value: 18.548000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: None
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 48.259999999999984
- type: f1
value: 43.1498589523159
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: None
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 74.798
- type: map_at_10
value: 83.454
- type: map_at_100
value: 83.623
- type: map_at_1000
value: 83.635
- type: map_at_20
value: 83.55
- type: map_at_3
value: 82.392
- type: map_at_5
value: 83.167
- type: mrr_at_1
value: 80.708
- type: mrr_at_10
value: 88.377
- type: mrr_at_100
value: 88.411
- type: mrr_at_1000
value: 88.411
- type: mrr_at_20
value: 88.402
- type: mrr_at_3
value: 87.646
- type: mrr_at_5
value: 88.232
- type: ndcg_at_1
value: 80.708
- type: ndcg_at_10
value: 87.35199999999999
- type: ndcg_at_100
value: 87.91600000000001
- type: ndcg_at_1000
value: 88.12299999999999
- type: ndcg_at_20
value: 87.593
- type: ndcg_at_3
value: 85.738
- type: ndcg_at_5
value: 86.845
- type: precision_at_1
value: 80.708
- type: precision_at_10
value: 10.432
- type: precision_at_100
value: 1.091
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.296
- type: precision_at_3
value: 32.778
- type: precision_at_5
value: 20.399
- type: recall_at_1
value: 74.798
- type: recall_at_10
value: 94.459
- type: recall_at_100
value: 96.614
- type: recall_at_1000
value: 97.868
- type: recall_at_20
value: 95.254
- type: recall_at_3
value: 90.144
- type: recall_at_5
value: 92.965
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: None
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 20.008
- type: map_at_10
value: 32.731
- type: map_at_100
value: 34.467999999999996
- type: map_at_1000
value: 34.643
- type: map_at_20
value: 33.717000000000006
- type: map_at_3
value: 28.427999999999997
- type: map_at_5
value: 30.788
- type: mrr_at_1
value: 40.586
- type: mrr_at_10
value: 49.056
- type: mrr_at_100
value: 49.887
- type: mrr_at_1000
value: 49.929
- type: mrr_at_20
value: 49.552
- type: mrr_at_3
value: 46.785
- type: mrr_at_5
value: 48.004000000000005
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 40.589999999999996
- type: ndcg_at_100
value: 47.03
- type: ndcg_at_1000
value: 49.994
- type: ndcg_at_20
value: 43.229
- type: ndcg_at_3
value: 37.061
- type: ndcg_at_5
value: 37.992
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.219
- type: precision_at_100
value: 1.781
- type: precision_at_1000
value: 0.232
- type: precision_at_20
value: 6.705
- type: precision_at_3
value: 24.743000000000002
- type: precision_at_5
value: 18.086
- type: recall_at_1
value: 20.008
- type: recall_at_10
value: 47.412
- type: recall_at_100
value: 71.274
- type: recall_at_1000
value: 88.898
- type: recall_at_20
value: 55.706999999999994
- type: recall_at_3
value: 33.346
- type: recall_at_5
value: 39.112
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: None
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 41.789
- type: map_at_10
value: 57.898
- type: map_at_100
value: 58.632
- type: map_at_1000
value: 58.693
- type: map_at_20
value: 58.314
- type: map_at_3
value: 55.236
- type: map_at_5
value: 56.852999999999994
- type: mrr_at_1
value: 83.57900000000001
- type: mrr_at_10
value: 87.631
- type: mrr_at_100
value: 87.764
- type: mrr_at_1000
value: 87.77000000000001
- type: mrr_at_20
value: 87.70700000000001
- type: mrr_at_3
value: 87.02499999999999
- type: mrr_at_5
value: 87.34100000000001
- type: ndcg_at_1
value: 83.57900000000001
- type: ndcg_at_10
value: 67.11399999999999
- type: ndcg_at_100
value: 69.686
- type: ndcg_at_1000
value: 70.926
- type: ndcg_at_20
value: 68.119
- type: ndcg_at_3
value: 63.402
- type: ndcg_at_5
value: 65.354
- type: precision_at_1
value: 83.57900000000001
- type: precision_at_10
value: 13.333
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_20
value: 6.988999999999999
- type: precision_at_3
value: 38.929
- type: precision_at_5
value: 24.897
- type: recall_at_1
value: 41.789
- type: recall_at_10
value: 66.664
- type: recall_at_100
value: 76.833
- type: recall_at_1000
value: 85.14500000000001
- type: recall_at_20
value: 69.892
- type: recall_at_3
value: 58.392999999999994
- type: recall_at_5
value: 62.242
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: None
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.6108
- type: ap
value: 81.63890253106925
- type: f1
value: 86.54585789538082
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: None
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.407
- type: map_at_10
value: 34.603
- type: map_at_100
value: 35.808
- type: map_at_1000
value: 35.855
- type: map_at_20
value: 35.368
- type: map_at_3
value: 30.764000000000003
- type: map_at_5
value: 32.964
- type: mrr_at_1
value: 23.009
- type: mrr_at_10
value: 35.136
- type: mrr_at_100
value: 36.284
- type: mrr_at_1000
value: 36.325
- type: mrr_at_20
value: 35.869
- type: mrr_at_3
value: 31.351000000000003
- type: mrr_at_5
value: 33.54
- type: ndcg_at_1
value: 23.009
- type: ndcg_at_10
value: 41.471999999999994
- type: ndcg_at_100
value: 47.211999999999996
- type: ndcg_at_1000
value: 48.361
- type: ndcg_at_20
value: 44.169000000000004
- type: ndcg_at_3
value: 33.646
- type: ndcg_at_5
value: 37.580000000000005
- type: precision_at_1
value: 23.009
- type: precision_at_10
value: 6.54
- type: precision_at_100
value: 0.941
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.832
- type: precision_at_3
value: 14.283999999999999
- type: precision_at_5
value: 10.564
- type: recall_at_1
value: 22.407
- type: recall_at_10
value: 62.678999999999995
- type: recall_at_100
value: 89.09700000000001
- type: recall_at_1000
value: 97.822
- type: recall_at_20
value: 73.116
- type: recall_at_3
value: 41.4
- type: recall_at_5
value: 50.855
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: None
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.94573643410853
- type: f1
value: 92.73148878666994
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: None
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.86137710898313
- type: f1
value: 60.360562463738724
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: None
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.83322125084062
- type: f1
value: 71.61864304680206
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: None
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.50504371217215
- type: f1
value: 77.52039268347185
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: None
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.346952648910225
- type: v_measures
value:
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
- 0.36636134409497156
- 0.3538734420417993
- 0.3394557315590024
- 0.3246964225451952
- 0.33269208719245646
- 0.3355911472371345
- 0.32978655133380147
- 0.3275090874657499
- 0.3752583186941529
- 0.3494711327267592
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- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: None
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.19992734583148
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- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: None
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.62309561205373
- type: mrr
value: 31.707879717902554
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: None
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.668
- type: map_at_10
value: 12.225999999999999
- type: map_at_100
value: 15.122
- type: map_at_1000
value: 16.422
- type: map_at_20
value: 13.361999999999998
- type: map_at_3
value: 9.083
- type: map_at_5
value: 10.5
- type: mrr_at_1
value: 46.44
- type: mrr_at_10
value: 53.553
- type: mrr_at_100
value: 54.15
- type: mrr_at_1000
value: 54.193000000000005
- type: mrr_at_20
value: 53.837
- type: mrr_at_3
value: 51.702999999999996
- type: mrr_at_5
value: 52.647
- type: ndcg_at_1
value: 44.272
- type: ndcg_at_10
value: 33.395
- type: ndcg_at_100
value: 29.976999999999997
- type: ndcg_at_1000
value: 38.388
- type: ndcg_at_20
value: 30.606
- type: ndcg_at_3
value: 39.212
- type: ndcg_at_5
value: 36.611
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 24.334
- type: precision_at_100
value: 7.553999999999999
- type: precision_at_1000
value: 1.994
- type: precision_at_20
value: 17.678
- type: precision_at_3
value: 36.326
- type: precision_at_5
value: 31.330999999999996
- type: recall_at_1
value: 5.668
- type: recall_at_10
value: 15.837000000000002
- type: recall_at_100
value: 29.845
- type: recall_at_1000
value: 60.563
- type: recall_at_20
value: 18.587999999999997
- type: recall_at_3
value: 10.096
- type: recall_at_5
value: 12.261
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: None
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 39.335
- type: map_at_10
value: 54.932
- type: map_at_100
value: 55.742000000000004
- type: map_at_1000
value: 55.766000000000005
- type: map_at_20
value: 55.504
- type: map_at_3
value: 50.904
- type: map_at_5
value: 53.388999999999996
- type: mrr_at_1
value: 44.003
- type: mrr_at_10
value: 57.419
- type: mrr_at_100
value: 57.963
- type: mrr_at_1000
value: 57.981
- type: mrr_at_20
value: 57.80499999999999
- type: mrr_at_3
value: 54.30199999999999
- type: mrr_at_5
value: 56.257000000000005
- type: ndcg_at_1
value: 43.974999999999994
- type: ndcg_at_10
value: 62.153999999999996
- type: ndcg_at_100
value: 65.326
- type: ndcg_at_1000
value: 65.862
- type: ndcg_at_20
value: 63.922999999999995
- type: ndcg_at_3
value: 54.834
- type: ndcg_at_5
value: 58.857000000000006
- type: precision_at_1
value: 43.974999999999994
- type: precision_at_10
value: 9.722
- type: precision_at_100
value: 1.153
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 5.3
- type: precision_at_3
value: 24.392
- type: precision_at_5
value: 16.993
- type: recall_at_1
value: 39.335
- type: recall_at_10
value: 81.501
- type: recall_at_100
value: 94.851
- type: recall_at_1000
value: 98.817
- type: recall_at_20
value: 87.968
- type: recall_at_3
value: 62.795
- type: recall_at_5
value: 71.985
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: None
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.222
- type: map_at_10
value: 85.193
- type: map_at_100
value: 85.802
- type: map_at_1000
value: 85.81800000000001
- type: map_at_20
value: 85.587
- type: map_at_3
value: 82.253
- type: map_at_5
value: 84.142
- type: mrr_at_1
value: 82.04
- type: mrr_at_10
value: 88.101
- type: mrr_at_100
value: 88.196
- type: mrr_at_1000
value: 88.196
- type: mrr_at_20
value: 88.175
- type: mrr_at_3
value: 87.145
- type: mrr_at_5
value: 87.825
- type: ndcg_at_1
value: 82.04
- type: ndcg_at_10
value: 88.849
- type: ndcg_at_100
value: 89.992
- type: ndcg_at_1000
value: 90.089
- type: ndcg_at_20
value: 89.468
- type: ndcg_at_3
value: 86.06899999999999
- type: ndcg_at_5
value: 87.669
- type: precision_at_1
value: 82.04
- type: precision_at_10
value: 13.447000000000001
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.116
- type: precision_at_3
value: 37.617
- type: precision_at_5
value: 24.776
- type: recall_at_1
value: 71.222
- type: recall_at_10
value: 95.73899999999999
- type: recall_at_100
value: 99.572
- type: recall_at_1000
value: 99.988
- type: recall_at_20
value: 97.725
- type: recall_at_3
value: 87.742
- type: recall_at_5
value: 92.23400000000001
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: None
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.502005725283524
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- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: None
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 63.14989421688691
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- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: None
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.4830000000000005
- type: map_at_10
value: 11.04
- type: map_at_100
value: 12.764000000000001
- type: map_at_1000
value: 13.04
- type: map_at_20
value: 11.953
- type: map_at_3
value: 8.125
- type: map_at_5
value: 9.565999999999999
- type: mrr_at_1
value: 22.1
- type: mrr_at_10
value: 32.494
- type: mrr_at_100
value: 33.525
- type: mrr_at_1000
value: 33.596
- type: mrr_at_20
value: 33.089
- type: mrr_at_3
value: 29.416999999999998
- type: mrr_at_5
value: 31.267
- type: ndcg_at_1
value: 22.1
- type: ndcg_at_10
value: 18.587
- type: ndcg_at_100
value: 25.482
- type: ndcg_at_1000
value: 30.581999999999997
- type: ndcg_at_20
value: 21.077
- type: ndcg_at_3
value: 18.165
- type: ndcg_at_5
value: 15.676000000000002
- type: precision_at_1
value: 22.1
- type: precision_at_10
value: 9.48
- type: precision_at_100
value: 1.942
- type: precision_at_1000
value: 0.316
- type: precision_at_20
value: 6.175
- type: precision_at_3
value: 17.033
- type: precision_at_5
value: 13.719999999999999
- type: recall_at_1
value: 4.4830000000000005
- type: recall_at_10
value: 19.208
- type: recall_at_100
value: 39.417
- type: recall_at_1000
value: 64.235
- type: recall_at_20
value: 25.057000000000002
- type: recall_at_3
value: 10.348
- type: recall_at_5
value: 13.893
- task:
type: STS
dataset:
name: MTEB SICK-R
type: None
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 83.50181312649208
- type: cos_sim_spearman
value: 79.92900705478993
- type: euclidean_pearson
value: 81.13482128094503
- type: euclidean_spearman
value: 79.92732266864367
- type: manhattan_pearson
value: 81.06702121654993
- type: manhattan_spearman
value: 79.86983106619135
- task:
type: STS
dataset:
name: MTEB STS12
type: None
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.85431681906961
- type: cos_sim_spearman
value: 77.61671419416626
- type: euclidean_pearson
value: 81.30538320520961
- type: euclidean_spearman
value: 77.62096481461272
- type: manhattan_pearson
value: 81.2306021173407
- type: manhattan_spearman
value: 77.58386300715222
- task:
type: STS
dataset:
name: MTEB STS13
type: None
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.98057702322754
- type: cos_sim_spearman
value: 86.13305071688859
- type: euclidean_pearson
value: 85.70903555966376
- type: euclidean_spearman
value: 86.13150222328171
- type: manhattan_pearson
value: 85.69380834788831
- type: manhattan_spearman
value: 86.10784739081191
- task:
type: STS
dataset:
name: MTEB STS14
type: None
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.43368314724589
- type: cos_sim_spearman
value: 81.26767916144169
- type: euclidean_pearson
value: 83.23234690932492
- type: euclidean_spearman
value: 81.2671726214706
- type: manhattan_pearson
value: 83.2381239261109
- type: manhattan_spearman
value: 81.27674961470714
- task:
type: STS
dataset:
name: MTEB STS15
type: None
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.8637546411748
- type: cos_sim_spearman
value: 88.25330888676139
- type: euclidean_pearson
value: 87.81194589390417
- type: euclidean_spearman
value: 88.25258669625579
- type: manhattan_pearson
value: 87.8131866998459
- type: manhattan_spearman
value: 88.26523268929576
- task:
type: STS
dataset:
name: MTEB STS16
type: None
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.83129743147286
- type: cos_sim_spearman
value: 85.73732687732624
- type: euclidean_pearson
value: 85.18051277328075
- type: euclidean_spearman
value: 85.73565846174445
- type: manhattan_pearson
value: 85.179029651079
- type: manhattan_spearman
value: 85.75709685404729
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: None
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.04715794253148
- type: cos_sim_spearman
value: 87.61577496386343
- type: euclidean_pearson
value: 88.34713614361046
- type: euclidean_spearman
value: 87.56541901567275
- type: manhattan_pearson
value: 88.26010824585985
- type: manhattan_spearman
value: 87.35211736948182
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: None
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 62.36160793264433
- type: cos_sim_spearman
value: 66.07767480051893
- type: euclidean_pearson
value: 66.4716471304865
- type: euclidean_spearman
value: 66.03999286501872
- type: manhattan_pearson
value: 66.46197824372902
- type: manhattan_spearman
value: 65.82936468127227
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: None
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.27768996785856
- type: cos_sim_spearman
value: 86.96704639052885
- type: euclidean_pearson
value: 86.48753189555983
- type: euclidean_spearman
value: 86.96981285751171
- type: manhattan_pearson
value: 86.49262465015401
- type: manhattan_spearman
value: 86.95378609580054
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: None
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.52012853393428
- type: mrr
value: 94.70817671798063
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: None
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 55.344
- type: map_at_10
value: 64.82900000000001
- type: map_at_100
value: 65.42
- type: map_at_1000
value: 65.443
- type: map_at_20
value: 65.2
- type: map_at_3
value: 61.8
- type: map_at_5
value: 63.510999999999996
- type: mrr_at_1
value: 58.333
- type: mrr_at_10
value: 66.24600000000001
- type: mrr_at_100
value: 66.742
- type: mrr_at_1000
value: 66.762
- type: mrr_at_20
value: 66.549
- type: mrr_at_3
value: 64.056
- type: mrr_at_5
value: 65.372
- type: ndcg_at_1
value: 58.333
- type: ndcg_at_10
value: 69.626
- type: ndcg_at_100
value: 72.236
- type: ndcg_at_1000
value: 72.872
- type: ndcg_at_20
value: 70.864
- type: ndcg_at_3
value: 64.50399999999999
- type: ndcg_at_5
value: 67.07600000000001
- type: precision_at_1
value: 58.333
- type: precision_at_10
value: 9.4
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 4.983
- type: precision_at_3
value: 25.222
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 55.344
- type: recall_at_10
value: 82.789
- type: recall_at_100
value: 94.6
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 87.533
- type: recall_at_3
value: 69.18299999999999
- type: recall_at_5
value: 75.622
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: None
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.69405940594059
- type: cos_sim_ap
value: 92.03642221694545
- type: cos_sim_f1
value: 84.06395048994327
- type: cos_sim_precision
value: 86.79446219382322
- type: cos_sim_recall
value: 81.5
- type: dot_accuracy
value: 99.6930693069307
- type: dot_ap
value: 91.9971441434875
- type: dot_f1
value: 83.8006230529595
- type: dot_precision
value: 87.14902807775377
- type: dot_recall
value: 80.7
- type: euclidean_accuracy
value: 99.69504950495049
- type: euclidean_ap
value: 92.03626548389335
- type: euclidean_f1
value: 84.10732714138285
- type: euclidean_precision
value: 86.88699360341151
- type: euclidean_recall
value: 81.5
- type: manhattan_accuracy
value: 99.69504950495049
- type: manhattan_ap
value: 92.02049659660081
- type: manhattan_f1
value: 84.34959349593495
- type: manhattan_precision
value: 85.74380165289256
- type: manhattan_recall
value: 83.0
- type: max_accuracy
value: 99.69504950495049
- type: max_ap
value: 92.03642221694545
- type: max_f1
value: 84.34959349593495
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: None
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 67.04916654680977
- type: v_measures
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- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: None
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: None
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 51.19282080158746
- type: mrr
value: 51.871100713012474
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: None
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.437664703708485
- type: cos_sim_spearman
value: 31.391119208581575
- type: dot_pearson
value: 31.19925970504054
- type: dot_spearman
value: 31.38087224016694
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: None
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.249
- type: map_at_10
value: 2.163
- type: map_at_100
value: 13.242999999999999
- type: map_at_1000
value: 30.866
- type: map_at_20
value: 3.9539999999999997
- type: map_at_3
value: 0.718
- type: map_at_5
value: 1.169
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 98.0
- type: mrr_at_100
value: 98.0
- type: mrr_at_1000
value: 98.0
- type: mrr_at_20
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: ndcg_at_1
value: 92.0
- type: ndcg_at_10
value: 84.147
- type: ndcg_at_100
value: 65.143
- type: ndcg_at_1000
value: 56.038
- type: ndcg_at_20
value: 80.869
- type: ndcg_at_3
value: 89.11200000000001
- type: ndcg_at_5
value: 87.199
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 87.8
- type: precision_at_100
value: 66.72
- type: precision_at_1000
value: 24.684
- type: precision_at_20
value: 84.3
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 91.2
- type: recall_at_1
value: 0.249
- type: recall_at_10
value: 2.284
- type: recall_at_100
value: 16.025
- type: recall_at_1000
value: 52.068999999999996
- type: recall_at_20
value: 4.3180000000000005
- type: recall_at_3
value: 0.738
- type: recall_at_5
value: 1.212
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: None
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.4520000000000004
- type: map_at_10
value: 13.045000000000002
- type: map_at_100
value: 19.442
- type: map_at_1000
value: 21.09
- type: map_at_20
value: 15.667
- type: map_at_3
value: 7.409000000000001
- type: map_at_5
value: 9.73
- type: mrr_at_1
value: 46.939
- type: mrr_at_10
value: 60.295
- type: mrr_at_100
value: 60.904
- type: mrr_at_1000
value: 60.919000000000004
- type: mrr_at_20
value: 60.77
- type: mrr_at_3
value: 58.50300000000001
- type: mrr_at_5
value: 59.014
- type: ndcg_at_1
value: 44.897999999999996
- type: ndcg_at_10
value: 31.911
- type: ndcg_at_100
value: 41.945
- type: ndcg_at_1000
value: 53.181999999999995
- type: ndcg_at_20
value: 31.505
- type: ndcg_at_3
value: 39.745000000000005
- type: ndcg_at_5
value: 35.528999999999996
- type: precision_at_1
value: 46.939
- type: precision_at_10
value: 26.531
- type: precision_at_100
value: 8.163
- type: precision_at_1000
value: 1.559
- type: precision_at_20
value: 19.387999999999998
- type: precision_at_3
value: 40.136
- type: precision_at_5
value: 33.878
- type: recall_at_1
value: 3.4520000000000004
- type: recall_at_10
value: 18.899
- type: recall_at_100
value: 50.207
- type: recall_at_1000
value: 83.871
- type: recall_at_20
value: 26.756999999999998
- type: recall_at_3
value: 8.729000000000001
- type: recall_at_5
value: 12.084999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: None
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.4560546875
- type: ap
value: 12.720403845355294
- type: f1
value: 51.76062666567839
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: None
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.36276174306734
- type: f1
value: 62.69956906934332
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: None
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.473492910233965
- type: v_measures
value:
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- 0.514824404624281
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- 0.4702417082210781
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- 0.5001897357021557
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- 0.49932879417585735
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- 0.48457750518082765
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- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
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- 0.5129628835129124
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- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
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- 0.5001897357021557
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- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
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- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
- 0.48457750518082765
- 0.5020774116970983
- 0.5001897357021557
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- 0.4763216048226018
- 0.49932879417585735
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- 0.49853262011854643
- 0.48457750518082765
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- 0.4763216048226018
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- 0.514824404624281
- 0.48829262296803855
- 0.49853262011854643
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- 0.4702417082210781
- 0.4763216048226018
- 0.49932879417585735
- 0.5129628835129124
- 0.514824404624281
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: None
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.75430649102938
- type: cos_sim_ap
value: 73.62842656477649
- type: cos_sim_f1
value: 67.76023680315738
- type: cos_sim_precision
value: 63.61741547012506
- type: cos_sim_recall
value: 72.4802110817942
- type: dot_accuracy
value: 85.7423854085951
- type: dot_ap
value: 73.59147637253723
- type: dot_f1
value: 67.69498693867396
- type: dot_precision
value: 64.03859731701577
- type: dot_recall
value: 71.79419525065963
- type: euclidean_accuracy
value: 85.7423854085951
- type: euclidean_ap
value: 73.6288990409654
- type: euclidean_f1
value: 67.80415430267064
- type: euclidean_precision
value: 63.79711493718009
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 85.69470107885796
- type: manhattan_ap
value: 73.49219614602531
- type: manhattan_f1
value: 67.60809797550613
- type: manhattan_precision
value: 64.22127255460589
- type: manhattan_recall
value: 71.37203166226914
- type: max_accuracy
value: 85.75430649102938
- type: max_ap
value: 73.6288990409654
- type: max_f1
value: 67.80415430267064
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: None
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.08293553770326
- type: cos_sim_ap
value: 86.21246419992926
- type: cos_sim_f1
value: 78.49922526377924
- type: cos_sim_precision
value: 75.35769939084857
- type: cos_sim_recall
value: 81.9140745303357
- type: dot_accuracy
value: 89.08681647067955
- type: dot_ap
value: 86.19733517196862
- type: dot_f1
value: 78.51132446157838
- type: dot_precision
value: 75.70233755093287
- type: dot_recall
value: 81.53680320295658
- type: euclidean_accuracy
value: 89.07517367175069
- type: euclidean_ap
value: 86.21198725320203
- type: euclidean_f1
value: 78.49867139061116
- type: euclidean_precision
value: 75.38276155372839
- type: euclidean_recall
value: 81.88327687095781
- type: manhattan_accuracy
value: 89.0538285403811
- type: manhattan_ap
value: 86.17785515765131
- type: manhattan_f1
value: 78.48184098593084
- type: manhattan_precision
value: 74.34396308285694
- type: manhattan_recall
value: 83.10748383122882
- type: max_accuracy
value: 89.08681647067955
- type: max_ap
value: 86.21246419992926
- type: max_f1
value: 78.51132446157838
---
# ModernBERT Embed
[](https://huggingface.co/nomic-ai/modernbert-embed-base)
ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), bringing the new advances of ModernBERT to embeddings!
Trained on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets, `modernbert-embed` also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.
## Performance
| Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
| nomic-embed-text-v1 | 768 | 62.4 | 74.1 | 43.9 | **85.2** | 55.7 | 52.8 | 82.1 | 30.1 |
| nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | **53.01** | **81.94** | 30.4 |
| modernbert-embed-base | 768 | **62.62** | **74.31** | **44.98** | 83.96 | **56.42** | 52.89 | 81.78 | **31.39** |
| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 |
| modernbert-embed-base | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
## Usage
You can use these models directly with the latest transformers release and requires installing `transformers>=4.48.0`:
```bash
pip install transformers>=4.48.0
```
Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/modernbert-embed-base")
query_embeddings = model.encode([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 768) (1, 768)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.7214],
# [0.3260]])
```
<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)
query_embeddings = model.encode([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
])
doc_embeddings = model.encode([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
])
print(query_embeddings.shape, doc_embeddings.shape)
# (2, 256) (1, 256)
similarities = model.similarity(query_embeddings, doc_embeddings)
print(similarities)
# tensor([[0.7759],
# [0.3419]])
```
Note the small differences compared to the full 768-dimensional similarities.
</details>
### Transformers
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
queries_outputs = model(**encoded_queries)
documents_outputs = model(**encoded_documents)
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
print(query_embeddings.shape, doc_embeddings.shape)
# torch.Size([2, 768]) torch.Size([1, 768])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.7214],
# [0.3260]])
```
<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
tokenizer = AutoTokenizer.from_pretrained(".")
model = AutoModel.from_pretrained(".")
truncate_dim = 256
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
queries_outputs = model(**encoded_queries)
documents_outputs = model(**encoded_documents)
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
query_embeddings = query_embeddings[:, :truncate_dim]
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
doc_embeddings = doc_embeddings[:, :truncate_dim]
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
print(query_embeddings.shape, doc_embeddings.shape)
# torch.Size([2, 256]) torch.Size([1, 256])
similarities = query_embeddings @ doc_embeddings.T
print(similarities)
# tensor([[0.7759],
# [0.3419]])
```
Note the small differences compared to the full 768-dimensional similarities.
</details>
### Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
Then, you can compute embeddings as follows:
```javascript
import { pipeline, matmul } from '@huggingface/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline(
"feature-extraction",
"nomic-ai/modernbert-embed-base",
{ dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
);
// Embed queries and documents
const query_embeddings = await extractor([
"search_query: What is TSNE?",
"search_query: Who is Laurens van der Maaten?",
], { pooling: "mean", normalize: true },
);
const doc_embeddings = await extractor([
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
], { pooling: "mean", normalize: true },
);
// Compute similarity scores
const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
```
## Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
## Join the Nomic Community
- Nomic: [https://nomic.ai](https://nomic.ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
## Citation
If you find the model, dataset, or training code useful, please cite our work
```bibtex
@misc{nussbaum2024nomic,
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
year={2024},
eprint={2402.01613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
microsoft/Phi-3-mini-128k-instruct | microsoft | text-generation | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-22T16:26:23 | 2025-03-02T22:28:37 | 128,236 | 1,637 | ---
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
🎉**Phi-4**: [[multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-multimodal-instruct-onnx)];
[[mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) | [onnx](https://huggingface.co/microsoft/Phi-4-mini-instruct-onnx)]
## Model Summary
The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets.
This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties.
The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support.
After initial training, the model underwent a post-training process that involved supervised fine-tuning and direct preference optimization to enhance its ability to follow instructions and adhere to safety measures.
When evaluated against benchmarks that test common sense, language understanding, mathematics, coding, long-term context, and logical reasoning, the Phi-3 Mini-128K-Instruct demonstrated robust and state-of-the-art performance among models with fewer than 13 billion parameters.
Resources and Technical Documentation:
🏡 [Phi-3 Portal](https://azure.microsoft.com/en-us/products/phi-3) <br>
📰 [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024) <br>
📖 [Phi-3 Technical Report](https://aka.ms/phi3-tech-report) <br>
🛠️ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai) <br>
👩🍳 [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook) <br>
🖥️ [Try It](https://aka.ms/try-phi3)
| | Short Context | Long Context |
| :- | :- | :- |
| Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
| Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
| Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
| Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
## Intended Uses
**Primary use cases**
The model is intended for commercial and research use in English. The model provides uses for applications which require:
1) Memory/compute constrained environments
2) Latency bound scenarios
3) Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## Release Notes
This is an update over the original instruction-tuned Phi-3-mini release based on valuable customer feedback.
The model used additional post-training data leading to substantial gains on long-context understanding, instruction following, and structure output.
We also improve multi-turn conversation quality, explicitly support <|system|> tag, and significantly improve reasoning capability.
We believe most use cases will benefit from this release, but we encourage users to test in their particular AI applications.
We appreciate the enthusiastic adoption of the Phi-3 model family, and continue to welcome all feedback from the community.
These tables below highlights improvements on instruction following, structure output, reasoning, and long-context understanding of the new release on our public and internal benchmark datasets.
| Benchmarks | Original | June 2024 Update |
| :- | :- | :- |
| Instruction Extra Hard | 5.7 | 5.9 |
| Instruction Hard | 5.0 | 5.2 |
| JSON Structure Output | 1.9 | 60.1 |
| XML Structure Output | 47.8 | 52.9 |
| GPQA | 25.9 | 29.7 |
| MMLU | 68.1 | 69.7 |
| **Average** | **25.7** | **37.3** |
RULER: a retrieval-based benchmark for long context understanding
| Model | 4K | 8K | 16K | 32K | 64K | 128K | Average |
| :-------------------| :------| :------| :------| :------| :------| :------| :---------|
| Original | 86.7 | 78.1 | 75.6 | 70.3 | 58.9 | 43.3 | **68.8** |
| June 2024 Update | 92.4 | 91.1 | 90.8 | 87.9 | 79.8 | 65.6 | **84.6** |
RepoQA: a benchmark for long context code understanding
| Model | Python | C++ | Rust | Java | TypeScript | Average |
| :-------------------| :--------| :-----| :------| :------| :------------| :---------|
| Original | 27 | 29 | 40 | 33 | 33 | **32.4** |
| June 2024 Update | 85 | 63 | 72 | 93 | 72 | **77** |
Notes: if users would like to check out the previous version, use the git commit id **bb5bf1e4001277a606e11debca0ef80323e5f824**. For the model conversion, e.g. GGUF and other formats, we invite the community to experiment with various approaches and share your valuable feedback. Let's innovate together!
## How to Use
Phi-3 Mini-128K-Instruct has been integrated in the development version (4.41.3) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
Examples of required packages:
```
flash_attn==2.5.8
torch==2.3.1
accelerate==0.31.0
transformers==4.41.2
```
Phi-3 Mini-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/try-phi3)
### Tokenizer
Phi-3 Mini-128K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
### Chat Format
Given the nature of the training data, the Phi-3 Mini-128K-Instruct model is best suited for prompts using the chat format as follows.
You can provide the prompt as a question with a generic template as follow:
```markdown
<|system|>
You are a helpful assistant.<|end|>
<|user|>
Question?<|end|>
<|assistant|>
```
For example:
```markdown
<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
```
where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following:
```markdown
<|system|>
You are a helpful travel assistant.<|end|>
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-128k-instruct",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
Notes: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_
## Responsible AI Considerations
Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
## Training
### Model
* Architecture: Phi-3 Mini-128K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
* Inputs: Text. It is best suited for prompts using chat format.
* Context length: 128K tokens
* GPUs: 512 H100-80G
* Training time: 10 days
* Training data: 4.9T tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between May and June 2024
* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.
* Release dates: June, 2024.
### Datasets
Our training data includes a wide variety of sources, totaling 4.9 trillion tokens, and is a combination of
1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;
2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);
3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge. As an example, the result of a game in premier league in a particular day might be good training data for frontier models, but we need to remove such information to leave more model capacity for reasoning for the small size models. More details about data can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
### Fine-tuning
A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/sample_finetune.py).
## Benchmarks
We report the results under completion format for Phi-3-Mini-128K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5.
All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.
As is now standard, we use few-shot prompts to evaluate the models, at temperature 0.
The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
The number of k–shot examples is listed per-benchmark.
| Category | Benchmark | Phi-3-Mini-128K-Ins | Gemma-7B | Mistral-7B | Mixtral-8x7B | Llama-3-8B-Ins | GPT3.5-Turbo-1106 |
| :----------| :-----------| :---------------------| :----------| :------------| :--------------| :----------------| :-------------------|
| Popular aggregated benchmark | AGI Eval <br>5-shot| 39.5 | 42.1 | 35.1 | 45.2 | 42 | 48.4 |
| | MMLU <br>5-shot | 69.7 | 63.6 | 61.7 | 70.5 | 66.5 | 71.4 |
| | BigBench Hard <br>3-shot | 72.1 | 59.6 | 57.3 | 69.7 | 51.5 | 68.3 |
| Language Understanding | ANLI <br>7-shot | 52.3 | 48.7 | 47.1 | 55.2 | 57.3 | 58.1 |
| | HellaSwag <br>5-shot | 70.5 | 49.8 | 58.5 | 70.4 | 71.1 | 78.8 |
| Reasoning | ARC Challenge <br>10-shot | 85.5 | 78.3 | 78.6 | 87.3 | 82.8 | 87.4 |
| | BoolQ <br>0-shot | 77.1 | 66 | 72.2 | 76.6 | 80.9 | 79.1 |
| | MedQA <br>2-shot | 56.4 | 49.6 | 50 | 62.2 | 60.5 | 63.4 |
| | OpenBookQA <br>10-shot | 78.8 | 78.6 | 79.8 | 85.8 | 82.6 | 86 |
| | PIQA <br>5-shot | 80.1 | 78.1 | 77.7 | 86 | 75.7 | 86.6 |
| | GPQA <br>0-shot | 29.7 | 2.9 | 15 | 6.9 | 32.4 | 29.9 |
| | Social IQA <br>5-shot | 74.7 | 65.5 | 74.6 | 75.9 | 73.9 | 68.3 |
| | TruthfulQA (MC2) <br>10-shot | 64.8 | 52.1 | 53 | 60.1 | 63.2 | 67.7 |
| | WinoGrande <br>5-shot | 71.0 | 55.6 | 54.2 | 62 | 65 | 68.8 |
| Factual Knowledge | TriviaQA <br>5-shot | 57.8 | 72.3 | 75.2 | 82.2 | 67.7 | 85.8 |
| Math | GSM8K CoTT <br>8-shot | 85.3 | 59.8 | 46.4 | 64.7 | 77.4 | 78.1 |
| Code Generation | HumanEval <br>0-shot | 60.4 | 34.1 | 28.0 | 37.8 | 60.4 | 62.2 |
| | MBPP <br>3-shot | 70.0 | 51.5 | 50.8 | 60.2 | 67.7 | 77.8 |
| **Average** | | **66.4** | **56.0** | **56.4** | **64.4** | **65.5** | **70.3** |
**Long Context**: Phi-3 Mini-128K-Instruct supports 128K context length, therefore the model is capable of several long context tasks including long document/meeting summarization, long document QA.
| Benchmark | Phi-3 Mini-128K-Instruct | Mistral-7B | Mixtral 8x7B | LLaMA-3-8B-Instruct |
| :---------------| :--------------------------|:------------|:--------------|:---------------------|
| GovReport | 25.3 | 4.9 | 20.3 | 10.3 |
| QMSum | 21.9 | 15.5 | 20.6 | 2.9 |
| Qasper | 41.6 | 23.5 | 26.6 | 8.1 |
| SQuALITY | 24.1 | 14.7 | 16.2 | 25 |
| SummScreenFD | 16.8 | 9.3 | 11.3 | 5.1 |
| **Average** | **25.9** | **13.6** | **19.0** | **10.3** |
We take a closer look at different categories across 100 public benchmark datasets at the table below:
| Category | Phi-3-Mini-128K-Instruct | Gemma-7B | Mistral-7B | Mixtral 8x7B | Llama-3-8B-Instruct | GPT-3.5-Turbo |
|:----------|:--------------------------|:----------|:------------|:--------------|:---------------------|:---------------|
| Popular aggregated benchmark | 60.6 | 59.4 | 56.5 | 66.2 | 59.9 | 67.0 |
| Reasoning | 69.4 | 60.3 | 62.8 | 68.1 | 69.6 | 71.7 |
| Language understanding | 57.5 | 57.6 | 52.5 | 66.1 | 63.2 | 67.7 |
| Code generation | 61.0 | 45.6 | 42.9 | 52.7 | 56.4 | 70.4 |
| Math | 51.6 | 35.8 | 25.4 | 40.3 | 41.1 | 52.8 |
| Factual knowledge | 35.8 | 46.7 | 49.8 | 58.6 | 43.1 | 63.4 |
| Multilingual | 56.4 | 66.5 | 57.4 | 66.7 | 66.6 | 71.0 |
| Robustness | 61.1 | 38.4 | 40.6 | 51.0 | 64.5 | 69.3 |
Overall, the model with only 3.8B-param achieves a similar level of language understanding and reasoning ability as much larger models. However, it is still fundamentally limited by its size for certain tasks. The model simply does not have the capacity to store too much world knowledge, which can be seen for example with low performance on TriviaQA. However, we believe such weakness can be resolved by augmenting Phi-3-Mini with a search engine.
## Cross Platform Support
[ONNX runtime](https://onnxruntime.ai/blogs/accelerating-phi-3) now supports Phi-3 mini models across platforms and hardware.
Optimized phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML GPU acceleration is supported for Windows desktops GPUs (AMD, Intel, and NVIDIA).
Along with DML, ONNX Runtime provides cross platform support for Phi3 mini across a range of devices CPU, GPU, and mobile.
Here are some of the optimized configurations we have added:
1. ONNX models for int4 DML: Quantized to int4 via AWQ
2. ONNX model for fp16 CUDA
3. ONNX model for int4 CUDA: Quantized to int4 via RTN
4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3 Mini-128K-Instruct model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
If you want to run the model on:
* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager"
* Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [128K](https://aka.ms/phi3-mini-128k-instruct-onnx)
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-128k/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
| [
"SUMMARIZATION"
] | [
"MEDQA"
] |
shibing624/text2vec-base-multilingual | shibing624 | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"text2vec",
"mteb",
"zh",
"en",
"de",
"fr",
"it",
"nl",
"pt",
"pl",
"ru",
"dataset:shibing624/nli-zh-all",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-06-22T06:28:12 | 2024-07-31T09:01:11 | 125,023 | 53 | ---
datasets:
- shibing624/nli-zh-all
language:
- zh
- en
- de
- fr
- it
- nl
- pt
- pl
- ru
library_name: sentence-transformers
license: apache-2.0
metrics:
- spearmanr
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- text2vec
- mteb
model-index:
- name: text2vec-base-multilingual
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 70.97014925373134
- type: ap
value: 33.95151328318672
- type: f1
value: 65.14740155705596
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 68.69379014989293
- type: ap
value: 79.68277579733802
- type: f1
value: 66.54960052336921
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 70.90704647676162
- type: ap
value: 20.747518928580437
- type: f1
value: 58.64365465884924
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 61.605995717344754
- type: ap
value: 14.135974879487028
- type: f1
value: 49.980224800472136
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 66.103375
- type: ap
value: 61.10087197664471
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revision: None
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- type: map_at_100
value: 79.44600000000001
- type: map_at_1000
value: 79.469
- type: map_at_3
value: 75.693
- type: map_at_5
value: 77.537
- type: mrr_at_1
value: 75.24
- type: mrr_at_10
value: 82.304
- type: mrr_at_100
value: 82.485
- type: mrr_at_1000
value: 82.489
- type: mrr_at_3
value: 81.002
- type: mrr_at_5
value: 81.817
- type: ndcg_at_1
value: 75.26
- type: ndcg_at_10
value: 83.07
- type: ndcg_at_100
value: 84.829
- type: ndcg_at_1000
value: 85.087
- type: ndcg_at_3
value: 79.67699999999999
- type: ndcg_at_5
value: 81.42
- type: precision_at_1
value: 75.26
- type: precision_at_10
value: 12.697
- type: precision_at_100
value: 1.4829999999999999
- type: precision_at_1000
value: 0.154
- type: precision_at_3
value: 34.849999999999994
- type: precision_at_5
value: 23.054
- type: recall_at_1
value: 65.319
- type: recall_at_10
value: 91.551
- type: recall_at_100
value: 98.053
- type: recall_at_1000
value: 99.516
- type: recall_at_3
value: 81.819
- type: recall_at_5
value: 86.66199999999999
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 31.249791587189996
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 43.302922383029816
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.80670811345861
- type: cos_sim_spearman
value: 79.97373018384307
- type: euclidean_pearson
value: 83.40205934125837
- type: euclidean_spearman
value: 79.73331008251854
- type: manhattan_pearson
value: 83.3320983393412
- type: manhattan_spearman
value: 79.677919746045
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.3816087627948
- type: cos_sim_spearman
value: 80.91314664846955
- type: euclidean_pearson
value: 85.10603071031096
- type: euclidean_spearman
value: 79.42663939501841
- type: manhattan_pearson
value: 85.16096376014066
- type: manhattan_spearman
value: 79.51936545543191
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.44665329940209
- type: cos_sim_spearman
value: 82.86479010707745
- type: euclidean_pearson
value: 84.06719627734672
- type: euclidean_spearman
value: 84.9356099976297
- type: manhattan_pearson
value: 84.10370009572624
- type: manhattan_spearman
value: 84.96828040546536
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.05704260568437
- type: cos_sim_spearman
value: 87.36399473803172
- type: euclidean_pearson
value: 86.8895170159388
- type: euclidean_spearman
value: 87.16246440866921
- type: manhattan_pearson
value: 86.80814774538997
- type: manhattan_spearman
value: 87.09320142699522
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.97825118945852
- type: cos_sim_spearman
value: 88.31438033558268
- type: euclidean_pearson
value: 87.05174694758092
- type: euclidean_spearman
value: 87.80659468392355
- type: manhattan_pearson
value: 86.98831322198717
- type: manhattan_spearman
value: 87.72820615049285
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 78.68745420126719
- type: cos_sim_spearman
value: 81.6058424699445
- type: euclidean_pearson
value: 81.16540133861879
- type: euclidean_spearman
value: 81.86377535458067
- type: manhattan_pearson
value: 81.13813317937021
- type: manhattan_spearman
value: 81.87079962857256
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 68.06192660936868
- type: cos_sim_spearman
value: 68.2376353514075
- type: euclidean_pearson
value: 60.68326946956215
- type: euclidean_spearman
value: 59.19352349785952
- type: manhattan_pearson
value: 60.6592944683418
- type: manhattan_spearman
value: 59.167534419270865
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.78098264855684
- type: cos_sim_spearman
value: 78.02670452969812
- type: euclidean_pearson
value: 77.26694463661255
- type: euclidean_spearman
value: 77.47007626009587
- type: manhattan_pearson
value: 77.25070088632027
- type: manhattan_spearman
value: 77.36368265830724
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.45418506379532
- type: cos_sim_spearman
value: 78.60412019902428
- type: euclidean_pearson
value: 79.90303710850512
- type: euclidean_spearman
value: 78.67123625004957
- type: manhattan_pearson
value: 80.09189580897753
- type: manhattan_spearman
value: 79.02484481441483
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 82.35556731232779
- type: cos_sim_spearman
value: 81.48249735354844
- type: euclidean_pearson
value: 81.66748026636621
- type: euclidean_spearman
value: 80.35571574338547
- type: manhattan_pearson
value: 81.38214732806365
- type: manhattan_spearman
value: 79.9018202958774
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.4527703176897
- type: cos_sim_spearman
value: 85.81084095829584
- type: euclidean_pearson
value: 86.43489162324457
- type: euclidean_spearman
value: 85.27110976093296
- type: manhattan_pearson
value: 86.43674259444512
- type: manhattan_spearman
value: 85.05719308026032
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.00411240034492
- type: cos_sim_spearman
value: 76.33887356560854
- type: euclidean_pearson
value: 76.81730660019446
- type: euclidean_spearman
value: 75.04432185451306
- type: manhattan_pearson
value: 77.22298813168995
- type: manhattan_spearman
value: 75.56420330256725
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.1447136836213
- type: cos_sim_spearman
value: 81.80823850788917
- type: euclidean_pearson
value: 80.84505734814422
- type: euclidean_spearman
value: 81.714168092736
- type: manhattan_pearson
value: 80.84713816174187
- type: manhattan_spearman
value: 81.61267814749516
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.01257457052873
- type: cos_sim_spearman
value: 87.91146458004216
- type: euclidean_pearson
value: 88.36771859717994
- type: euclidean_spearman
value: 87.73182474597515
- type: manhattan_pearson
value: 88.26551451003671
- type: manhattan_spearman
value: 87.71675151388992
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 79.20121618382373
- type: cos_sim_spearman
value: 78.05794691968603
- type: euclidean_pearson
value: 79.93819925682054
- type: euclidean_spearman
value: 78.00586118701553
- type: manhattan_pearson
value: 80.05598625820885
- type: manhattan_spearman
value: 78.04802948866832
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.51743373871778
- type: cos_sim_spearman
value: 80.98266651818703
- type: euclidean_pearson
value: 81.11875722505269
- type: euclidean_spearman
value: 79.45188413284538
- type: manhattan_pearson
value: 80.7988457619225
- type: manhattan_spearman
value: 79.49643569311485
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.78679924046351
- type: cos_sim_spearman
value: 80.9986574147117
- type: euclidean_pearson
value: 82.09130079135713
- type: euclidean_spearman
value: 80.66215667390159
- type: manhattan_pearson
value: 82.0328610549654
- type: manhattan_spearman
value: 80.31047226932408
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.08082172994642
- type: cos_sim_spearman
value: 62.9940530222459
- type: euclidean_pearson
value: 58.47927303460365
- type: euclidean_spearman
value: 60.8440317609258
- type: manhattan_pearson
value: 58.32438211697841
- type: manhattan_spearman
value: 60.69642636776064
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 33.83985707464123
- type: cos_sim_spearman
value: 46.89093209603036
- type: euclidean_pearson
value: 34.63602187576556
- type: euclidean_spearman
value: 46.31087228200712
- type: manhattan_pearson
value: 34.66899391543166
- type: manhattan_spearman
value: 46.33049538425276
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.61315965767736
- type: cos_sim_spearman
value: 58.9434266730386
- type: euclidean_pearson
value: 50.35885602217862
- type: euclidean_spearman
value: 58.238679883286025
- type: manhattan_pearson
value: 53.01732044381151
- type: manhattan_spearman
value: 58.10482351761412
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 26.771738440430177
- type: cos_sim_spearman
value: 34.807259227816054
- type: euclidean_pearson
value: 17.82657835823811
- type: euclidean_spearman
value: 34.27912898498941
- type: manhattan_pearson
value: 19.121527758886312
- type: manhattan_spearman
value: 34.4940050226265
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 52.8354704676683
- type: cos_sim_spearman
value: 57.28629534815841
- type: euclidean_pearson
value: 54.10329332004385
- type: euclidean_spearman
value: 58.15030615859976
- type: manhattan_pearson
value: 55.42372087433115
- type: manhattan_spearman
value: 57.52270736584036
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 31.01976557986924
- type: cos_sim_spearman
value: 54.506959483927616
- type: euclidean_pearson
value: 36.917863022119086
- type: euclidean_spearman
value: 53.750194241538566
- type: manhattan_pearson
value: 37.200177833241085
- type: manhattan_spearman
value: 53.507659188082535
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 46.38635647225934
- type: cos_sim_spearman
value: 54.50892732637536
- type: euclidean_pearson
value: 40.8331015184763
- type: euclidean_spearman
value: 53.142903182230924
- type: manhattan_pearson
value: 43.07655692906317
- type: manhattan_spearman
value: 53.5833474125901
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.52525456662916
- type: cos_sim_spearman
value: 63.23975489531082
- type: euclidean_pearson
value: 58.989191722317514
- type: euclidean_spearman
value: 62.536326639863894
- type: manhattan_pearson
value: 61.32982866201855
- type: manhattan_spearman
value: 63.068262822520516
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.63798684577696
- type: cos_sim_spearman
value: 74.09937723367189
- type: euclidean_pearson
value: 63.77494904383906
- type: euclidean_spearman
value: 71.15932571292481
- type: manhattan_pearson
value: 63.69646122775205
- type: manhattan_spearman
value: 70.54960698541632
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 36.50262468726711
- type: cos_sim_spearman
value: 45.00322499674274
- type: euclidean_pearson
value: 32.58759216581778
- type: euclidean_spearman
value: 40.13720951315429
- type: manhattan_pearson
value: 34.88422299605277
- type: manhattan_spearman
value: 40.63516862200963
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.498552617040275
- type: cos_sim_spearman
value: 67.71358426124443
- type: euclidean_pearson
value: 57.16474781778287
- type: euclidean_spearman
value: 65.721515493531
- type: manhattan_pearson
value: 59.25227610738926
- type: manhattan_spearman
value: 65.89743680340739
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.97978814727984
- type: cos_sim_spearman
value: 65.85821395092104
- type: euclidean_pearson
value: 59.11117270978519
- type: euclidean_spearman
value: 64.50062069934965
- type: manhattan_pearson
value: 59.4436213778161
- type: manhattan_spearman
value: 64.4003273074382
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.00873192515712
- type: cos_sim_spearman
value: 60.167708809138745
- type: euclidean_pearson
value: 56.91950637760252
- type: euclidean_spearman
value: 58.50593399441014
- type: manhattan_pearson
value: 58.683747352584994
- type: manhattan_spearman
value: 59.38110066799761
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.26020658151187
- type: cos_sim_spearman
value: 61.29236187204147
- type: euclidean_pearson
value: 55.993896804147056
- type: euclidean_spearman
value: 58.654928232615354
- type: manhattan_pearson
value: 56.612492816099426
- type: manhattan_spearman
value: 58.65144067094258
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 49.13817835368122
- type: cos_sim_spearman
value: 50.78524216975442
- type: euclidean_pearson
value: 46.56046454501862
- type: euclidean_spearman
value: 50.3935060082369
- type: manhattan_pearson
value: 48.0232348418531
- type: manhattan_spearman
value: 50.79528358464199
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 44.274388638585286
- type: cos_sim_spearman
value: 49.43124017389838
- type: euclidean_pearson
value: 42.45909582681174
- type: euclidean_spearman
value: 49.661383797129055
- type: manhattan_pearson
value: 42.5771970142383
- type: manhattan_spearman
value: 50.14423414390715
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 26.119500839749776
- type: cos_sim_spearman
value: 39.324070169024424
- type: euclidean_pearson
value: 35.83247077201831
- type: euclidean_spearman
value: 42.61903924348457
- type: manhattan_pearson
value: 35.50415034487894
- type: manhattan_spearman
value: 41.87998075949351
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.62575835691209
- type: cos_sim_spearman
value: 73.24670207647144
- type: euclidean_pearson
value: 78.07793323914657
- type: euclidean_spearman
value: 73.24670207647144
- type: manhattan_pearson
value: 77.51429306378206
- type: manhattan_spearman
value: 73.24670207647144
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.09375596849891
- type: cos_sim_spearman
value: 86.44881302053585
- type: euclidean_pearson
value: 84.71259163967213
- type: euclidean_spearman
value: 85.63661992344069
- type: manhattan_pearson
value: 84.64466537502614
- type: manhattan_spearman
value: 85.53769949940238
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 70.2056154684549
- type: mrr
value: 89.52703161036494
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.57623762376238
- type: cos_sim_ap
value: 83.53051588811371
- type: cos_sim_f1
value: 77.72704211060375
- type: cos_sim_precision
value: 78.88774459320288
- type: cos_sim_recall
value: 76.6
- type: dot_accuracy
value: 99.06435643564356
- type: dot_ap
value: 27.003124923857463
- type: dot_f1
value: 34.125269978401725
- type: dot_precision
value: 37.08920187793427
- type: dot_recall
value: 31.6
- type: euclidean_accuracy
value: 99.61485148514852
- type: euclidean_ap
value: 85.47332647001774
- type: euclidean_f1
value: 80.0808897876643
- type: euclidean_precision
value: 80.98159509202453
- type: euclidean_recall
value: 79.2
- type: manhattan_accuracy
value: 99.61683168316831
- type: manhattan_ap
value: 85.41969859598552
- type: manhattan_f1
value: 79.77755308392315
- type: manhattan_precision
value: 80.67484662576688
- type: manhattan_recall
value: 78.9
- type: max_accuracy
value: 99.61683168316831
- type: max_ap
value: 85.47332647001774
- type: max_f1
value: 80.0808897876643
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 34.35688940053467
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 30.64427069276576
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 44.89500754900078
- type: mrr
value: 45.33215558950853
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.653069624224084
- type: cos_sim_spearman
value: 30.10187112430319
- type: dot_pearson
value: 28.966278202103666
- type: dot_spearman
value: 28.342234095507767
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 65.96839999999999
- type: ap
value: 11.846327590186444
- type: f1
value: 50.518102944693574
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 55.220713073005086
- type: f1
value: 55.47856175692088
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 31.581473892235877
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.94093103653812
- type: cos_sim_ap
value: 62.48963249213361
- type: cos_sim_f1
value: 58.9541137429912
- type: cos_sim_precision
value: 52.05091937765205
- type: cos_sim_recall
value: 67.96833773087072
- type: dot_accuracy
value: 78.24998509864696
- type: dot_ap
value: 40.82371294480071
- type: dot_f1
value: 44.711163153786096
- type: dot_precision
value: 35.475379374419326
- type: dot_recall
value: 60.4485488126649
- type: euclidean_accuracy
value: 83.13166835548668
- type: euclidean_ap
value: 63.459878609769774
- type: euclidean_f1
value: 60.337199569532466
- type: euclidean_precision
value: 55.171659741963694
- type: euclidean_recall
value: 66.56992084432719
- type: manhattan_accuracy
value: 83.00649698992669
- type: manhattan_ap
value: 63.263161177904905
- type: manhattan_f1
value: 60.17122874713614
- type: manhattan_precision
value: 55.40750610703975
- type: manhattan_recall
value: 65.8311345646438
- type: max_accuracy
value: 83.13166835548668
- type: max_ap
value: 63.459878609769774
- type: max_f1
value: 60.337199569532466
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.80416812201653
- type: cos_sim_ap
value: 83.45540469219863
- type: cos_sim_f1
value: 75.58836427422892
- type: cos_sim_precision
value: 71.93934335002783
- type: cos_sim_recall
value: 79.62734832152756
- type: dot_accuracy
value: 83.04226336011176
- type: dot_ap
value: 70.63007268018524
- type: dot_f1
value: 65.35980325765405
- type: dot_precision
value: 60.84677151768532
- type: dot_recall
value: 70.59593470896212
- type: euclidean_accuracy
value: 87.60430007373773
- type: euclidean_ap
value: 83.10068502536592
- type: euclidean_f1
value: 75.02510506936439
- type: euclidean_precision
value: 72.56637168141593
- type: euclidean_recall
value: 77.65629812134279
- type: manhattan_accuracy
value: 87.60041914076145
- type: manhattan_ap
value: 83.05480769911229
- type: manhattan_f1
value: 74.98522895125554
- type: manhattan_precision
value: 72.04797047970479
- type: manhattan_recall
value: 78.17215891592238
- type: max_accuracy
value: 87.80416812201653
- type: max_ap
value: 83.45540469219863
- type: max_f1
value: 75.58836427422892
---
# shibing624/text2vec-base-multilingual
This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-multilingual.
It maps sentences to a 384 dimensional dense vector space and can be used for tasks
like sentence embeddings, text matching or semantic search.
- training dataset: https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset
- base model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
- max_seq_length: 256
- best epoch: 4
- sentence embedding dim: 384
## Evaluation
For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
## Languages
Available languages are: de, en, es, fr, it, nl, pl, pt, ru, zh
### Release Models
- 本项目release模型的中文匹配评测结果：
| Arch | BaseModel | Model | ATEC | BQ | LCQMC | PAWSX | STS-B | SOHU-dd | SOHU-dc | Avg | QPS |
|:-----------|:-------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|:-------:|:---------:|:-----:|
| Word2Vec | word2vec | [w2v-light-tencent-chinese](https://ai.tencent.com/ailab/nlp/en/download.html) | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 55.04 | 20.70 | 35.03 | 23769 |
| SBERT | xlm-roberta-base | [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 63.01 | 52.28 | 46.46 | 3138 |
| Instructor | hfl/chinese-roberta-wwm-ext | [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) | 41.27 | 63.81 | 74.87 | 12.20 | 76.96 | 75.83 | 60.55 | 57.93 | 2980 |
| CoSENT | hfl/chinese-macbert-base | [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | 70.27 | 50.42 | 51.61 | 3008 |
| CoSENT | hfl/chinese-lert-large | [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 32.61 | 44.59 | 69.30 | 14.51 | 79.44 | 73.01 | 59.04 | 53.12 | 2092 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence) | 43.37 | 61.43 | 73.48 | 38.90 | 78.25 | 70.60 | 53.08 | 59.87 | 3089 |
| CoSENT | nghuyong/ernie-3.0-base-zh | [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase) | 44.89 | 63.58 | 74.24 | 40.90 | 78.93 | 76.70 | 63.30 | **63.08** | 3066 |
| CoSENT | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual) | 32.39 | 50.33 | 65.64 | 32.56 | 74.45 | 68.88 | 51.17 | 53.67 | 4004 |
说明：
- 结果评测指标：spearman系数
- `shibing624/text2vec-base-chinese`模型，是用CoSENT方法训练，基于`hfl/chinese-macbert-base`在中文STS-B数据训练得到，并在中文STS-B测试集评估达到较好效果，运行[examples/training_sup_text_matching_model.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model.py)代码可训练模型，模型文件已经上传HF model hub，中文通用语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-sentence`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)训练得到，并在中文各NLI测试集评估达到较好效果，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2s(句子vs句子)语义匹配任务推荐使用
- `shibing624/text2vec-base-chinese-paraphrase`模型，是用CoSENT方法训练，基于`nghuyong/ernie-3.0-base-zh`用人工挑选后的中文STS数据集[shibing624/nli-zh-all/text2vec-base-chinese-paraphrase-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-paraphrase-dataset)，数据集相对于[shibing624/nli-zh-all/text2vec-base-chinese-sentence-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-chinese-sentence-dataset)加入了s2p(sentence to paraphrase)数据，强化了其长文本的表征能力，并在中文各NLI测试集评估达到SOTA，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，中文s2p(句子vs段落)语义匹配任务推荐使用
- `shibing624/text2vec-base-multilingual`模型，是用CoSENT方法训练，基于`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`用人工挑选后的多语言STS数据集[shibing624/nli-zh-all/text2vec-base-multilingual-dataset](https://huggingface.co/datasets/shibing624/nli-zh-all/tree/main/text2vec-base-multilingual-dataset)训练得到，并在中英文测试集评估相对于原模型效果有提升，运行[examples/training_sup_text_matching_model_jsonl_data.py](https://github.com/shibing624/text2vec/blob/master/examples/training_sup_text_matching_model_jsonl_data.py)代码可训练模型，模型文件已经上传HF model hub，多语言语义匹配任务推荐使用
- `w2v-light-tencent-chinese`是腾讯词向量的Word2Vec模型，CPU加载使用，适用于中文字面匹配任务和缺少数据的冷启动情况
- QPS的GPU测试环境是Tesla V100，显存32GB
模型训练实验报告：[实验报告](https://github.com/shibing624/text2vec/blob/master/docs/model_report.md)
## Usage (text2vec)
Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
```
pip install -U text2vec
```
Then you can use the model like this:
```python
from text2vec import SentenceModel
sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
model = SentenceModel('shibing624/text2vec-base-multilingual')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this:
First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
Install transformers:
```
pip install transformers
```
Then load model and predict:
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('shibing624/text2vec-base-multilingual')
model = AutoModel.from_pretrained('shibing624/text2vec-base-multilingual')
sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Usage (sentence-transformers)
[sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
Install sentence-transformers:
```
pip install -U sentence-transformers
```
Then load model and predict:
```python
from sentence_transformers import SentenceTransformer
m = SentenceTransformer("shibing624/text2vec-base-multilingual")
sentences = ['如何更换花呗绑定银行卡', 'How to replace the Huabei bundled bank card']
sentence_embeddings = m.encode(sentences)
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Full Model Architecture
```
CoSENT(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_mean_tokens': True})
)
```
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) model.
Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each
possible sentence pairs from the batch.
We then apply the rank loss by comparing with true pairs and false pairs.
## Citing & Authors
This model was trained by [text2vec](https://github.com/shibing624/text2vec).
If you find this model helpful, feel free to cite:
```bibtex
@software{text2vec,
author = {Ming Xu},
title = {text2vec: A Tool for Text to Vector},
year = {2023},
url = {https://github.com/shibing624/text2vec},
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES"
] |
BAAI/bge-base-zh-v1.5 | BAAI | feature-extraction | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"zh",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T05:21:53 | 2023-10-12T03:35:51 | 122,857 | 78 | ---
language:
- zh
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
avsolatorio/GIST-large-Embedding-v0 | avsolatorio | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2402.16829",
"arxiv:2212.09741",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-14T18:26:25 | 2024-02-28T00:34:23 | 112,672 | 14 | ---
language:
- en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
model-index:
- name: GIST-large-Embedding-v0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.5820895522388
- type: ap
value: 38.32190121241783
- type: f1
value: 69.44777155231054
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.40514999999998
- type: ap
value: 90.2011565132406
- type: f1
value: 93.39486246843605
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.05999999999999
- type: f1
value: 48.58702718571088
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.407000000000004
- type: map_at_10
value: 54.822
- type: map_at_100
value: 55.387
- type: map_at_1000
value: 55.388999999999996
- type: map_at_3
value: 50.308
- type: map_at_5
value: 53.199
- type: mrr_at_1
value: 39.900000000000006
- type: mrr_at_10
value: 55.385
- type: mrr_at_100
value: 55.936
- type: mrr_at_1000
value: 55.93900000000001
- type: mrr_at_3
value: 50.853
- type: mrr_at_5
value: 53.738
- type: ndcg_at_1
value: 38.407000000000004
- type: ndcg_at_10
value: 63.38
- type: ndcg_at_100
value: 65.52900000000001
- type: ndcg_at_1000
value: 65.58800000000001
- type: ndcg_at_3
value: 54.26
- type: ndcg_at_5
value: 59.488
- type: precision_at_1
value: 38.407000000000004
- type: precision_at_10
value: 9.04
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.906
- type: precision_at_5
value: 15.690000000000001
- type: recall_at_1
value: 38.407000000000004
- type: recall_at_10
value: 90.398
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 65.718
- type: recall_at_5
value: 78.45
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.49766333679089
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.57731111438094
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.70120072857361
- type: mrr
value: 77.86714593501297
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 90.73821860690765
- type: cos_sim_spearman
value: 89.17070651383446
- type: euclidean_pearson
value: 88.28303958293029
- type: euclidean_spearman
value: 88.81889126856979
- type: manhattan_pearson
value: 88.09080621828731
- type: manhattan_spearman
value: 88.55924679817751
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.10064935064933
- type: f1
value: 88.08460758973867
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.338228337929976
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.179156232378226
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.440999999999995
- type: map_at_10
value: 45.495000000000005
- type: map_at_100
value: 47.132000000000005
- type: map_at_1000
value: 47.253
- type: map_at_3
value: 41.766
- type: map_at_5
value: 43.873
- type: mrr_at_1
value: 40.772999999999996
- type: mrr_at_10
value: 51.627
- type: mrr_at_100
value: 52.364
- type: mrr_at_1000
value: 52.397000000000006
- type: mrr_at_3
value: 48.951
- type: mrr_at_5
value: 50.746
- type: ndcg_at_1
value: 40.772999999999996
- type: ndcg_at_10
value: 52.306
- type: ndcg_at_100
value: 57.753
- type: ndcg_at_1000
value: 59.36900000000001
- type: ndcg_at_3
value: 47.177
- type: ndcg_at_5
value: 49.71
- type: precision_at_1
value: 40.772999999999996
- type: precision_at_10
value: 10.129000000000001
- type: precision_at_100
value: 1.617
- type: precision_at_1000
value: 0.208
- type: precision_at_3
value: 22.985
- type: precision_at_5
value: 16.652
- type: recall_at_1
value: 33.440999999999995
- type: recall_at_10
value: 65.121
- type: recall_at_100
value: 87.55199999999999
- type: recall_at_1000
value: 97.41300000000001
- type: recall_at_3
value: 49.958999999999996
- type: recall_at_5
value: 57.14900000000001
- type: map_at_1
value: 32.126
- type: map_at_10
value: 42.856
- type: map_at_100
value: 44.134
- type: map_at_1000
value: 44.274
- type: map_at_3
value: 39.594
- type: map_at_5
value: 41.504999999999995
- type: mrr_at_1
value: 40.127
- type: mrr_at_10
value: 48.736000000000004
- type: mrr_at_100
value: 49.303999999999995
- type: mrr_at_1000
value: 49.356
- type: mrr_at_3
value: 46.263
- type: mrr_at_5
value: 47.878
- type: ndcg_at_1
value: 40.127
- type: ndcg_at_10
value: 48.695
- type: ndcg_at_100
value: 52.846000000000004
- type: ndcg_at_1000
value: 54.964
- type: ndcg_at_3
value: 44.275
- type: ndcg_at_5
value: 46.54
- type: precision_at_1
value: 40.127
- type: precision_at_10
value: 9.229
- type: precision_at_100
value: 1.473
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 21.444
- type: precision_at_5
value: 15.389
- type: recall_at_1
value: 32.126
- type: recall_at_10
value: 58.971
- type: recall_at_100
value: 76.115
- type: recall_at_1000
value: 89.556
- type: recall_at_3
value: 45.891
- type: recall_at_5
value: 52.242
- type: map_at_1
value: 41.312
- type: map_at_10
value: 54.510000000000005
- type: map_at_100
value: 55.544000000000004
- type: map_at_1000
value: 55.593
- type: map_at_3
value: 50.859
- type: map_at_5
value: 52.839999999999996
- type: mrr_at_1
value: 47.147
- type: mrr_at_10
value: 57.678
- type: mrr_at_100
value: 58.287
- type: mrr_at_1000
value: 58.312
- type: mrr_at_3
value: 55.025999999999996
- type: mrr_at_5
value: 56.55
- type: ndcg_at_1
value: 47.147
- type: ndcg_at_10
value: 60.672000000000004
- type: ndcg_at_100
value: 64.411
- type: ndcg_at_1000
value: 65.35499999999999
- type: ndcg_at_3
value: 54.643
- type: ndcg_at_5
value: 57.461
- type: precision_at_1
value: 47.147
- type: precision_at_10
value: 9.881
- type: precision_at_100
value: 1.27
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 24.556
- type: precision_at_5
value: 16.814999999999998
- type: recall_at_1
value: 41.312
- type: recall_at_10
value: 75.62299999999999
- type: recall_at_100
value: 91.388
- type: recall_at_1000
value: 98.08
- type: recall_at_3
value: 59.40299999999999
- type: recall_at_5
value: 66.43900000000001
- type: map_at_1
value: 27.609
- type: map_at_10
value: 37.614
- type: map_at_100
value: 38.584
- type: map_at_1000
value: 38.652
- type: map_at_3
value: 34.731
- type: map_at_5
value: 36.308
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 39.829
- type: mrr_at_100
value: 40.659
- type: mrr_at_1000
value: 40.709
- type: mrr_at_3
value: 37.269000000000005
- type: mrr_at_5
value: 38.625
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 43.082
- type: ndcg_at_100
value: 47.857
- type: ndcg_at_1000
value: 49.612
- type: ndcg_at_3
value: 37.578
- type: ndcg_at_5
value: 40.135
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.678000000000001
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 16.045
- type: precision_at_5
value: 11.073
- type: recall_at_1
value: 27.609
- type: recall_at_10
value: 57.718
- type: recall_at_100
value: 79.768
- type: recall_at_1000
value: 92.868
- type: recall_at_3
value: 42.876
- type: recall_at_5
value: 49.104
- type: map_at_1
value: 18.071
- type: map_at_10
value: 27.471
- type: map_at_100
value: 28.71
- type: map_at_1000
value: 28.833
- type: map_at_3
value: 24.698
- type: map_at_5
value: 26.461000000000002
- type: mrr_at_1
value: 22.387999999999998
- type: mrr_at_10
value: 32.522
- type: mrr_at_100
value: 33.393
- type: mrr_at_1000
value: 33.455
- type: mrr_at_3
value: 29.830000000000002
- type: mrr_at_5
value: 31.472
- type: ndcg_at_1
value: 22.387999999999998
- type: ndcg_at_10
value: 33.278999999999996
- type: ndcg_at_100
value: 39.043
- type: ndcg_at_1000
value: 41.763
- type: ndcg_at_3
value: 28.310999999999996
- type: ndcg_at_5
value: 31.007
- type: precision_at_1
value: 22.387999999999998
- type: precision_at_10
value: 6.157
- type: precision_at_100
value: 1.042
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 13.972000000000001
- type: precision_at_5
value: 10.274
- type: recall_at_1
value: 18.071
- type: recall_at_10
value: 46.025
- type: recall_at_100
value: 71.153
- type: recall_at_1000
value: 90.232
- type: recall_at_3
value: 32.311
- type: recall_at_5
value: 39.296
- type: map_at_1
value: 30.813000000000002
- type: map_at_10
value: 42.594
- type: map_at_100
value: 43.949
- type: map_at_1000
value: 44.052
- type: map_at_3
value: 39.1
- type: map_at_5
value: 41.111
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 48.06
- type: mrr_at_100
value: 48.91
- type: mrr_at_1000
value: 48.946
- type: mrr_at_3
value: 45.509
- type: mrr_at_5
value: 47.073
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 48.882
- type: ndcg_at_100
value: 54.330999999999996
- type: ndcg_at_1000
value: 56.120999999999995
- type: ndcg_at_3
value: 43.529
- type: ndcg_at_5
value: 46.217999999999996
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.845
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 20.757
- type: precision_at_5
value: 14.802999999999999
- type: recall_at_1
value: 30.813000000000002
- type: recall_at_10
value: 61.895999999999994
- type: recall_at_100
value: 84.513
- type: recall_at_1000
value: 95.817
- type: recall_at_3
value: 47.099000000000004
- type: recall_at_5
value: 54.031
- type: map_at_1
value: 25.735999999999997
- type: map_at_10
value: 36.799
- type: map_at_100
value: 38.246
- type: map_at_1000
value: 38.353
- type: map_at_3
value: 33.133
- type: map_at_5
value: 34.954
- type: mrr_at_1
value: 31.849
- type: mrr_at_10
value: 41.928
- type: mrr_at_100
value: 42.846000000000004
- type: mrr_at_1000
value: 42.894
- type: mrr_at_3
value: 39.117000000000004
- type: mrr_at_5
value: 40.521
- type: ndcg_at_1
value: 31.849
- type: ndcg_at_10
value: 43.143
- type: ndcg_at_100
value: 48.963
- type: ndcg_at_1000
value: 51.041000000000004
- type: ndcg_at_3
value: 37.218
- type: ndcg_at_5
value: 39.542
- type: precision_at_1
value: 31.849
- type: precision_at_10
value: 8.231
- type: precision_at_100
value: 1.277
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 18.037
- type: precision_at_5
value: 12.945
- type: recall_at_1
value: 25.735999999999997
- type: recall_at_10
value: 56.735
- type: recall_at_100
value: 81.04
- type: recall_at_1000
value: 94.845
- type: recall_at_3
value: 40.239999999999995
- type: recall_at_5
value: 46.378
- type: map_at_1
value: 27.580333333333336
- type: map_at_10
value: 37.70558333333334
- type: map_at_100
value: 38.94941666666667
- type: map_at_1000
value: 39.062083333333334
- type: map_at_3
value: 34.63333333333334
- type: map_at_5
value: 36.35241666666666
- type: mrr_at_1
value: 32.64866666666667
- type: mrr_at_10
value: 42.018499999999996
- type: mrr_at_100
value: 42.83391666666666
- type: mrr_at_1000
value: 42.884166666666665
- type: mrr_at_3
value: 39.476499999999994
- type: mrr_at_5
value: 40.96983333333334
- type: ndcg_at_1
value: 32.64866666666667
- type: ndcg_at_10
value: 43.43866666666667
- type: ndcg_at_100
value: 48.569833333333335
- type: ndcg_at_1000
value: 50.6495
- type: ndcg_at_3
value: 38.327166666666656
- type: ndcg_at_5
value: 40.76941666666667
- type: precision_at_1
value: 32.64866666666667
- type: precision_at_10
value: 7.652333333333332
- type: precision_at_100
value: 1.2066666666666666
- type: precision_at_1000
value: 0.15841666666666668
- type: precision_at_3
value: 17.75108333333333
- type: precision_at_5
value: 12.641916666666669
- type: recall_at_1
value: 27.580333333333336
- type: recall_at_10
value: 56.02591666666667
- type: recall_at_100
value: 78.317
- type: recall_at_1000
value: 92.52608333333332
- type: recall_at_3
value: 41.84283333333333
- type: recall_at_5
value: 48.105666666666664
- type: map_at_1
value: 27.876
- type: map_at_10
value: 34.521
- type: map_at_100
value: 35.581
- type: map_at_1000
value: 35.674
- type: map_at_3
value: 32.501000000000005
- type: map_at_5
value: 33.602
- type: mrr_at_1
value: 31.441999999999997
- type: mrr_at_10
value: 37.669999999999995
- type: mrr_at_100
value: 38.523
- type: mrr_at_1000
value: 38.59
- type: mrr_at_3
value: 35.762
- type: mrr_at_5
value: 36.812
- type: ndcg_at_1
value: 31.441999999999997
- type: ndcg_at_10
value: 38.46
- type: ndcg_at_100
value: 43.479
- type: ndcg_at_1000
value: 45.858
- type: ndcg_at_3
value: 34.668
- type: ndcg_at_5
value: 36.416
- type: precision_at_1
value: 31.441999999999997
- type: precision_at_10
value: 5.782
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 14.417
- type: precision_at_5
value: 9.876999999999999
- type: recall_at_1
value: 27.876
- type: recall_at_10
value: 47.556
- type: recall_at_100
value: 70.39699999999999
- type: recall_at_1000
value: 87.969
- type: recall_at_3
value: 37.226
- type: recall_at_5
value: 41.43
- type: map_at_1
value: 18.854000000000003
- type: map_at_10
value: 26.632
- type: map_at_100
value: 27.849
- type: map_at_1000
value: 27.977
- type: map_at_3
value: 24.089
- type: map_at_5
value: 25.477
- type: mrr_at_1
value: 22.987
- type: mrr_at_10
value: 30.781999999999996
- type: mrr_at_100
value: 31.746000000000002
- type: mrr_at_1000
value: 31.818
- type: mrr_at_3
value: 28.43
- type: mrr_at_5
value: 29.791
- type: ndcg_at_1
value: 22.987
- type: ndcg_at_10
value: 31.585
- type: ndcg_at_100
value: 37.32
- type: ndcg_at_1000
value: 40.072
- type: ndcg_at_3
value: 27.058
- type: ndcg_at_5
value: 29.137999999999998
- type: precision_at_1
value: 22.987
- type: precision_at_10
value: 5.76
- type: precision_at_100
value: 1.018
- type: precision_at_1000
value: 0.14400000000000002
- type: precision_at_3
value: 12.767000000000001
- type: precision_at_5
value: 9.257
- type: recall_at_1
value: 18.854000000000003
- type: recall_at_10
value: 42.349
- type: recall_at_100
value: 68.15299999999999
- type: recall_at_1000
value: 87.44
- type: recall_at_3
value: 29.715999999999998
- type: recall_at_5
value: 35.085
- type: map_at_1
value: 28.094
- type: map_at_10
value: 38.22
- type: map_at_100
value: 39.352
- type: map_at_1000
value: 39.452
- type: map_at_3
value: 35.339
- type: map_at_5
value: 36.78
- type: mrr_at_1
value: 33.022
- type: mrr_at_10
value: 42.466
- type: mrr_at_100
value: 43.3
- type: mrr_at_1000
value: 43.356
- type: mrr_at_3
value: 40.159
- type: mrr_at_5
value: 41.272999999999996
- type: ndcg_at_1
value: 33.022
- type: ndcg_at_10
value: 43.976
- type: ndcg_at_100
value: 49.008
- type: ndcg_at_1000
value: 51.154999999999994
- type: ndcg_at_3
value: 38.891
- type: ndcg_at_5
value: 40.897
- type: precision_at_1
value: 33.022
- type: precision_at_10
value: 7.396999999999999
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.724
- type: precision_at_5
value: 12.239
- type: recall_at_1
value: 28.094
- type: recall_at_10
value: 57.162
- type: recall_at_100
value: 78.636
- type: recall_at_1000
value: 93.376
- type: recall_at_3
value: 43.328
- type: recall_at_5
value: 48.252
- type: map_at_1
value: 24.937
- type: map_at_10
value: 34.82
- type: map_at_100
value: 36.405
- type: map_at_1000
value: 36.626
- type: map_at_3
value: 31.548
- type: map_at_5
value: 33.355000000000004
- type: mrr_at_1
value: 30.435000000000002
- type: mrr_at_10
value: 39.946
- type: mrr_at_100
value: 40.873
- type: mrr_at_1000
value: 40.910000000000004
- type: mrr_at_3
value: 37.088
- type: mrr_at_5
value: 38.808
- type: ndcg_at_1
value: 30.435000000000002
- type: ndcg_at_10
value: 41.25
- type: ndcg_at_100
value: 47.229
- type: ndcg_at_1000
value: 49.395
- type: ndcg_at_3
value: 35.801
- type: ndcg_at_5
value: 38.457
- type: precision_at_1
value: 30.435000000000002
- type: precision_at_10
value: 8.083
- type: precision_at_100
value: 1.601
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 17.061999999999998
- type: precision_at_5
value: 12.767000000000001
- type: recall_at_1
value: 24.937
- type: recall_at_10
value: 53.905
- type: recall_at_100
value: 80.607
- type: recall_at_1000
value: 93.728
- type: recall_at_3
value: 38.446000000000005
- type: recall_at_5
value: 45.188
- type: map_at_1
value: 22.095000000000002
- type: map_at_10
value: 30.935000000000002
- type: map_at_100
value: 31.907000000000004
- type: map_at_1000
value: 32.006
- type: map_at_3
value: 28.242
- type: map_at_5
value: 29.963
- type: mrr_at_1
value: 23.845
- type: mrr_at_10
value: 32.978
- type: mrr_at_100
value: 33.802
- type: mrr_at_1000
value: 33.867000000000004
- type: mrr_at_3
value: 30.314000000000004
- type: mrr_at_5
value: 32.089
- type: ndcg_at_1
value: 23.845
- type: ndcg_at_10
value: 35.934
- type: ndcg_at_100
value: 40.598
- type: ndcg_at_1000
value: 43.089
- type: ndcg_at_3
value: 30.776999999999997
- type: ndcg_at_5
value: 33.711999999999996
- type: precision_at_1
value: 23.845
- type: precision_at_10
value: 5.656
- type: precision_at_100
value: 0.861
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 13.247
- type: precision_at_5
value: 9.612
- type: recall_at_1
value: 22.095000000000002
- type: recall_at_10
value: 49.25
- type: recall_at_100
value: 70.482
- type: recall_at_1000
value: 88.98899999999999
- type: recall_at_3
value: 35.619
- type: recall_at_5
value: 42.674
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.154
- type: map_at_10
value: 24.654999999999998
- type: map_at_100
value: 26.723999999999997
- type: map_at_1000
value: 26.912000000000003
- type: map_at_3
value: 20.4
- type: map_at_5
value: 22.477
- type: mrr_at_1
value: 32.117000000000004
- type: mrr_at_10
value: 44.590999999999994
- type: mrr_at_100
value: 45.425
- type: mrr_at_1000
value: 45.456
- type: mrr_at_3
value: 41.281
- type: mrr_at_5
value: 43.219
- type: ndcg_at_1
value: 32.117000000000004
- type: ndcg_at_10
value: 33.994
- type: ndcg_at_100
value: 41.438
- type: ndcg_at_1000
value: 44.611000000000004
- type: ndcg_at_3
value: 27.816000000000003
- type: ndcg_at_5
value: 29.816
- type: precision_at_1
value: 32.117000000000004
- type: precision_at_10
value: 10.756
- type: precision_at_100
value: 1.8679999999999999
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 20.803
- type: precision_at_5
value: 15.987000000000002
- type: recall_at_1
value: 14.154
- type: recall_at_10
value: 40.489999999999995
- type: recall_at_100
value: 65.635
- type: recall_at_1000
value: 83.276
- type: recall_at_3
value: 25.241000000000003
- type: recall_at_5
value: 31.211
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.332
- type: map_at_10
value: 20.462
- type: map_at_100
value: 29.473
- type: map_at_1000
value: 31.215
- type: map_at_3
value: 14.466999999999999
- type: map_at_5
value: 16.922
- type: mrr_at_1
value: 69.5
- type: mrr_at_10
value: 77.039
- type: mrr_at_100
value: 77.265
- type: mrr_at_1000
value: 77.271
- type: mrr_at_3
value: 75.5
- type: mrr_at_5
value: 76.4
- type: ndcg_at_1
value: 57.125
- type: ndcg_at_10
value: 42.958
- type: ndcg_at_100
value: 48.396
- type: ndcg_at_1000
value: 55.897
- type: ndcg_at_3
value: 47.188
- type: ndcg_at_5
value: 44.376
- type: precision_at_1
value: 69.5
- type: precision_at_10
value: 34.5
- type: precision_at_100
value: 11.18
- type: precision_at_1000
value: 2.13
- type: precision_at_3
value: 51.083
- type: precision_at_5
value: 43.1
- type: recall_at_1
value: 9.332
- type: recall_at_10
value: 26.422
- type: recall_at_100
value: 56.098000000000006
- type: recall_at_1000
value: 79.66
- type: recall_at_3
value: 15.703
- type: recall_at_5
value: 19.644000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 54.72
- type: f1
value: 49.67819606587526
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.97
- type: map_at_10
value: 82.956
- type: map_at_100
value: 83.193
- type: map_at_1000
value: 83.208
- type: map_at_3
value: 81.837
- type: map_at_5
value: 82.57
- type: mrr_at_1
value: 80.783
- type: mrr_at_10
value: 87.546
- type: mrr_at_100
value: 87.627
- type: mrr_at_1000
value: 87.63
- type: mrr_at_3
value: 86.79400000000001
- type: mrr_at_5
value: 87.32799999999999
- type: ndcg_at_1
value: 80.783
- type: ndcg_at_10
value: 86.54899999999999
- type: ndcg_at_100
value: 87.355
- type: ndcg_at_1000
value: 87.629
- type: ndcg_at_3
value: 84.82
- type: ndcg_at_5
value: 85.83800000000001
- type: precision_at_1
value: 80.783
- type: precision_at_10
value: 10.327
- type: precision_at_100
value: 1.094
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.218
- type: precision_at_5
value: 20.012
- type: recall_at_1
value: 74.97
- type: recall_at_10
value: 93.072
- type: recall_at_100
value: 96.218
- type: recall_at_1000
value: 97.991
- type: recall_at_3
value: 88.357
- type: recall_at_5
value: 90.983
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.12
- type: map_at_10
value: 35.908
- type: map_at_100
value: 37.895
- type: map_at_1000
value: 38.068000000000005
- type: map_at_3
value: 31.189
- type: map_at_5
value: 33.908
- type: mrr_at_1
value: 42.901
- type: mrr_at_10
value: 52.578
- type: mrr_at_100
value: 53.308
- type: mrr_at_1000
value: 53.342
- type: mrr_at_3
value: 50.385999999999996
- type: mrr_at_5
value: 51.62799999999999
- type: ndcg_at_1
value: 42.901
- type: ndcg_at_10
value: 44.302
- type: ndcg_at_100
value: 51.132999999999996
- type: ndcg_at_1000
value: 53.848
- type: ndcg_at_3
value: 40.464
- type: ndcg_at_5
value: 41.743
- type: precision_at_1
value: 42.901
- type: precision_at_10
value: 12.423
- type: precision_at_100
value: 1.968
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 27.622999999999998
- type: precision_at_5
value: 20.278
- type: recall_at_1
value: 21.12
- type: recall_at_10
value: 52.091
- type: recall_at_100
value: 77.062
- type: recall_at_1000
value: 93.082
- type: recall_at_3
value: 37.223
- type: recall_at_5
value: 43.826
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.940000000000005
- type: map_at_10
value: 62.239999999999995
- type: map_at_100
value: 63.141000000000005
- type: map_at_1000
value: 63.205999999999996
- type: map_at_3
value: 58.738
- type: map_at_5
value: 60.924
- type: mrr_at_1
value: 77.88000000000001
- type: mrr_at_10
value: 83.7
- type: mrr_at_100
value: 83.882
- type: mrr_at_1000
value: 83.889
- type: mrr_at_3
value: 82.748
- type: mrr_at_5
value: 83.381
- type: ndcg_at_1
value: 77.88000000000001
- type: ndcg_at_10
value: 70.462
- type: ndcg_at_100
value: 73.564
- type: ndcg_at_1000
value: 74.78099999999999
- type: ndcg_at_3
value: 65.524
- type: ndcg_at_5
value: 68.282
- type: precision_at_1
value: 77.88000000000001
- type: precision_at_10
value: 14.81
- type: precision_at_100
value: 1.7229999999999999
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 42.083999999999996
- type: precision_at_5
value: 27.43
- type: recall_at_1
value: 38.940000000000005
- type: recall_at_10
value: 74.051
- type: recall_at_100
value: 86.158
- type: recall_at_1000
value: 94.146
- type: recall_at_3
value: 63.126000000000005
- type: recall_at_5
value: 68.575
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.23440000000001
- type: ap
value: 87.33490392265892
- type: f1
value: 91.21374626021836
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.137999999999998
- type: map_at_10
value: 34.471000000000004
- type: map_at_100
value: 35.634
- type: map_at_1000
value: 35.685
- type: map_at_3
value: 30.587999999999997
- type: map_at_5
value: 32.812999999999995
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 35.092
- type: mrr_at_100
value: 36.193999999999996
- type: mrr_at_1000
value: 36.238
- type: mrr_at_3
value: 31.28
- type: mrr_at_5
value: 33.498
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 41.388999999999996
- type: ndcg_at_100
value: 46.967999999999996
- type: ndcg_at_1000
value: 48.178
- type: ndcg_at_3
value: 33.503
- type: ndcg_at_5
value: 37.484
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 6.54
- type: precision_at_100
value: 0.9339999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.249999999999998
- type: precision_at_5
value: 10.562000000000001
- type: recall_at_1
value: 22.137999999999998
- type: recall_at_10
value: 62.629999999999995
- type: recall_at_100
value: 88.375
- type: recall_at_1000
value: 97.529
- type: recall_at_3
value: 41.245
- type: recall_at_5
value: 50.808
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.25079799361606
- type: f1
value: 95.00726023695032
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 78.23757409940721
- type: f1
value: 58.534958803195714
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.20040349697378
- type: f1
value: 74.31261149784696
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.35104236718227
- type: f1
value: 79.7373049864316
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.478828180753126
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.25696147904426
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.82488548405117
- type: mrr
value: 34.066706809031096
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.557
- type: map_at_10
value: 15.055
- type: map_at_100
value: 19.575
- type: map_at_1000
value: 21.267
- type: map_at_3
value: 10.86
- type: map_at_5
value: 12.83
- type: mrr_at_1
value: 50.464
- type: mrr_at_10
value: 59.050999999999995
- type: mrr_at_100
value: 59.436
- type: mrr_at_1000
value: 59.476
- type: mrr_at_3
value: 56.811
- type: mrr_at_5
value: 58.08
- type: ndcg_at_1
value: 47.988
- type: ndcg_at_10
value: 38.645
- type: ndcg_at_100
value: 36.339
- type: ndcg_at_1000
value: 45.279
- type: ndcg_at_3
value: 43.35
- type: ndcg_at_5
value: 41.564
- type: precision_at_1
value: 49.845
- type: precision_at_10
value: 28.544999999999998
- type: precision_at_100
value: 9.322
- type: precision_at_1000
value: 2.258
- type: precision_at_3
value: 40.144000000000005
- type: precision_at_5
value: 35.913000000000004
- type: recall_at_1
value: 6.557
- type: recall_at_10
value: 19.5
- type: recall_at_100
value: 37.153999999999996
- type: recall_at_1000
value: 69.581
- type: recall_at_3
value: 12.133
- type: recall_at_5
value: 15.43
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.740000000000002
- type: map_at_10
value: 48.150999999999996
- type: map_at_100
value: 49.125
- type: map_at_1000
value: 49.149
- type: map_at_3
value: 43.645
- type: map_at_5
value: 46.417
- type: mrr_at_1
value: 35.892
- type: mrr_at_10
value: 50.524
- type: mrr_at_100
value: 51.232
- type: mrr_at_1000
value: 51.24999999999999
- type: mrr_at_3
value: 46.852
- type: mrr_at_5
value: 49.146
- type: ndcg_at_1
value: 35.892
- type: ndcg_at_10
value: 56.08800000000001
- type: ndcg_at_100
value: 60.077000000000005
- type: ndcg_at_1000
value: 60.632
- type: ndcg_at_3
value: 47.765
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 35.892
- type: precision_at_10
value: 9.296
- type: precision_at_100
value: 1.154
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 21.92
- type: precision_at_5
value: 15.781999999999998
- type: recall_at_1
value: 31.740000000000002
- type: recall_at_10
value: 77.725
- type: recall_at_100
value: 94.841
- type: recall_at_1000
value: 99.003
- type: recall_at_3
value: 56.407
- type: recall_at_5
value: 66.848
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.429
- type: map_at_10
value: 85.42699999999999
- type: map_at_100
value: 86.063
- type: map_at_1000
value: 86.077
- type: map_at_3
value: 82.573
- type: map_at_5
value: 84.371
- type: mrr_at_1
value: 82.34
- type: mrr_at_10
value: 88.247
- type: mrr_at_100
value: 88.357
- type: mrr_at_1000
value: 88.357
- type: mrr_at_3
value: 87.38
- type: mrr_at_5
value: 87.981
- type: ndcg_at_1
value: 82.34
- type: ndcg_at_10
value: 88.979
- type: ndcg_at_100
value: 90.18599999999999
- type: ndcg_at_1000
value: 90.254
- type: ndcg_at_3
value: 86.378
- type: ndcg_at_5
value: 87.821
- type: precision_at_1
value: 82.34
- type: precision_at_10
value: 13.482
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.852999999999994
- type: precision_at_5
value: 24.798000000000002
- type: recall_at_1
value: 71.429
- type: recall_at_10
value: 95.64099999999999
- type: recall_at_100
value: 99.723
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 88.011
- type: recall_at_5
value: 92.246
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 60.62148584103299
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.2923987272903
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.128
- type: map_at_10
value: 14.63
- type: map_at_100
value: 17.285
- type: map_at_1000
value: 17.676
- type: map_at_3
value: 9.993
- type: map_at_5
value: 12.286999999999999
- type: mrr_at_1
value: 25.4
- type: mrr_at_10
value: 38.423
- type: mrr_at_100
value: 39.497
- type: mrr_at_1000
value: 39.531
- type: mrr_at_3
value: 34.9
- type: mrr_at_5
value: 37.01
- type: ndcg_at_1
value: 25.4
- type: ndcg_at_10
value: 24.062
- type: ndcg_at_100
value: 33.823
- type: ndcg_at_1000
value: 39.663
- type: ndcg_at_3
value: 22.246
- type: ndcg_at_5
value: 19.761
- type: precision_at_1
value: 25.4
- type: precision_at_10
value: 12.85
- type: precision_at_100
value: 2.71
- type: precision_at_1000
value: 0.41000000000000003
- type: precision_at_3
value: 21.4
- type: precision_at_5
value: 17.86
- type: recall_at_1
value: 5.128
- type: recall_at_10
value: 26.06
- type: recall_at_100
value: 54.993
- type: recall_at_1000
value: 83.165
- type: recall_at_3
value: 13.003
- type: recall_at_5
value: 18.117
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 87.5466779326323
- type: cos_sim_spearman
value: 82.79782085421951
- type: euclidean_pearson
value: 84.76929982677339
- type: euclidean_spearman
value: 82.51802536005597
- type: manhattan_pearson
value: 84.76736312526177
- type: manhattan_spearman
value: 82.50799656335593
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.40486308108694
- type: cos_sim_spearman
value: 77.12670500926937
- type: euclidean_pearson
value: 85.23836845503847
- type: euclidean_spearman
value: 78.41475117006176
- type: manhattan_pearson
value: 85.24302039610805
- type: manhattan_spearman
value: 78.4053162562707
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.83570289087565
- type: cos_sim_spearman
value: 89.28563503553643
- type: euclidean_pearson
value: 87.77516003996445
- type: euclidean_spearman
value: 88.8656149534085
- type: manhattan_pearson
value: 87.75568872417946
- type: manhattan_spearman
value: 88.80445489340585
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.776406555485
- type: cos_sim_spearman
value: 83.8288465070091
- type: euclidean_pearson
value: 85.37827999808123
- type: euclidean_spearman
value: 84.11079529992739
- type: manhattan_pearson
value: 85.35336495689121
- type: manhattan_spearman
value: 84.08618492649347
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.57644404820684
- type: cos_sim_spearman
value: 89.69728364350713
- type: euclidean_pearson
value: 88.28202320389443
- type: euclidean_spearman
value: 88.9560567319321
- type: manhattan_pearson
value: 88.29461100044172
- type: manhattan_spearman
value: 88.96030920678558
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.05211938460621
- type: cos_sim_spearman
value: 86.43413865667489
- type: euclidean_pearson
value: 85.62760689259562
- type: euclidean_spearman
value: 86.28867831982394
- type: manhattan_pearson
value: 85.60828879163458
- type: manhattan_spearman
value: 86.27823731462473
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.00254140466377
- type: cos_sim_spearman
value: 89.66118745178284
- type: euclidean_pearson
value: 89.46985446236553
- type: euclidean_spearman
value: 88.92649032371526
- type: manhattan_pearson
value: 89.49600028180247
- type: manhattan_spearman
value: 88.86948431519099
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 68.93578321067938
- type: cos_sim_spearman
value: 69.60639595839257
- type: euclidean_pearson
value: 70.33485090574897
- type: euclidean_spearman
value: 69.03380379185452
- type: manhattan_pearson
value: 70.42097254943839
- type: manhattan_spearman
value: 69.25296348304255
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.29588700755069
- type: cos_sim_spearman
value: 88.30389489193672
- type: euclidean_pearson
value: 87.60349838180346
- type: euclidean_spearman
value: 87.91041868311692
- type: manhattan_pearson
value: 87.59373630607907
- type: manhattan_spearman
value: 87.88690174001724
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.8030655700857
- type: mrr
value: 96.3950637234951
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.028000000000006
- type: map_at_10
value: 69.855
- type: map_at_100
value: 70.257
- type: map_at_1000
value: 70.283
- type: map_at_3
value: 66.769
- type: map_at_5
value: 68.679
- type: mrr_at_1
value: 62.666999999999994
- type: mrr_at_10
value: 70.717
- type: mrr_at_100
value: 71.00800000000001
- type: mrr_at_1000
value: 71.033
- type: mrr_at_3
value: 68.389
- type: mrr_at_5
value: 69.939
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_10
value: 74.715
- type: ndcg_at_100
value: 76.364
- type: ndcg_at_1000
value: 76.89399999999999
- type: ndcg_at_3
value: 69.383
- type: ndcg_at_5
value: 72.322
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_10
value: 10.067
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.111
- type: precision_at_5
value: 18.267
- type: recall_at_1
value: 60.028000000000006
- type: recall_at_10
value: 88.822
- type: recall_at_100
value: 96.167
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 74.367
- type: recall_at_5
value: 81.661
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84554455445544
- type: cos_sim_ap
value: 96.54482863244152
- type: cos_sim_f1
value: 92.13709677419355
- type: cos_sim_precision
value: 92.88617886178862
- type: cos_sim_recall
value: 91.4
- type: dot_accuracy
value: 99.76039603960396
- type: dot_ap
value: 93.20115278887057
- type: dot_f1
value: 87.92079207920793
- type: dot_precision
value: 87.05882352941177
- type: dot_recall
value: 88.8
- type: euclidean_accuracy
value: 99.84950495049505
- type: euclidean_ap
value: 96.53268343961348
- type: euclidean_f1
value: 92.23697650663942
- type: euclidean_precision
value: 94.258872651357
- type: euclidean_recall
value: 90.3
- type: manhattan_accuracy
value: 99.85346534653465
- type: manhattan_ap
value: 96.54495433438355
- type: manhattan_f1
value: 92.51012145748987
- type: manhattan_precision
value: 93.64754098360656
- type: manhattan_recall
value: 91.4
- type: max_accuracy
value: 99.85346534653465
- type: max_ap
value: 96.54495433438355
- type: max_f1
value: 92.51012145748987
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.46940443952006
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.396194493841584
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.881717673695555
- type: mrr
value: 55.73439224174519
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.438177268254087
- type: cos_sim_spearman
value: 30.96177698848688
- type: dot_pearson
value: 30.513850376431435
- type: dot_spearman
value: 29.932421046509706
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.727
- type: map_at_100
value: 9.881
- type: map_at_1000
value: 24.245
- type: map_at_3
value: 0.615
- type: map_at_5
value: 0.966
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 87.333
- type: mrr_at_100
value: 87.333
- type: mrr_at_1000
value: 87.333
- type: mrr_at_3
value: 86.333
- type: mrr_at_5
value: 87.333
- type: ndcg_at_1
value: 74.0
- type: ndcg_at_10
value: 69.12700000000001
- type: ndcg_at_100
value: 53.893
- type: ndcg_at_1000
value: 49.639
- type: ndcg_at_3
value: 74.654
- type: ndcg_at_5
value: 73.232
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 72.8
- type: precision_at_100
value: 55.42
- type: precision_at_1000
value: 21.73
- type: precision_at_3
value: 79.333
- type: precision_at_5
value: 77.2
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.9709999999999999
- type: recall_at_100
value: 13.555
- type: recall_at_1000
value: 46.961999999999996
- type: recall_at_3
value: 0.66
- type: recall_at_5
value: 1.052
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.456
- type: map_at_10
value: 9.426
- type: map_at_100
value: 16.066
- type: map_at_1000
value: 17.652
- type: map_at_3
value: 5.2459999999999996
- type: map_at_5
value: 6.5360000000000005
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 47.666
- type: mrr_at_100
value: 48.681999999999995
- type: mrr_at_1000
value: 48.681999999999995
- type: mrr_at_3
value: 43.878
- type: mrr_at_5
value: 46.224
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 23.454
- type: ndcg_at_100
value: 36.616
- type: ndcg_at_1000
value: 48.596000000000004
- type: ndcg_at_3
value: 28.267999999999997
- type: ndcg_at_5
value: 25.630999999999997
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 20.204
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.5709999999999997
- type: precision_at_3
value: 29.252
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.456
- type: recall_at_10
value: 14.951
- type: recall_at_100
value: 48.399
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 6.1370000000000005
- type: recall_at_5
value: 8.671
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.86240000000001
- type: ap
value: 14.678570078747494
- type: f1
value: 55.295967793934445
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.17374080362195
- type: f1
value: 59.54410874861454
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 51.91227822485289
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.12523097097217
- type: cos_sim_ap
value: 77.59606075943269
- type: cos_sim_f1
value: 71.11395646606915
- type: cos_sim_precision
value: 69.07960199004975
- type: cos_sim_recall
value: 73.27176781002639
- type: dot_accuracy
value: 84.68736961316088
- type: dot_ap
value: 68.47167450741459
- type: dot_f1
value: 64.42152354914874
- type: dot_precision
value: 60.887949260042284
- type: dot_recall
value: 68.3905013192612
- type: euclidean_accuracy
value: 86.88084878106932
- type: euclidean_ap
value: 77.27351204978599
- type: euclidean_f1
value: 70.99179716629381
- type: euclidean_precision
value: 67.10526315789474
- type: euclidean_recall
value: 75.35620052770449
- type: manhattan_accuracy
value: 86.83316445133218
- type: manhattan_ap
value: 77.21835357308716
- type: manhattan_f1
value: 71.05587004676349
- type: manhattan_precision
value: 66.58210332103322
- type: manhattan_recall
value: 76.17414248021109
- type: max_accuracy
value: 87.12523097097217
- type: max_ap
value: 77.59606075943269
- type: max_f1
value: 71.11395646606915
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.97232894787906
- type: cos_sim_ap
value: 85.9613736469497
- type: cos_sim_f1
value: 78.40216655382532
- type: cos_sim_precision
value: 72.97512437810946
- type: cos_sim_recall
value: 84.70126270403449
- type: dot_accuracy
value: 88.04866689952264
- type: dot_ap
value: 83.15465089499936
- type: dot_f1
value: 76.32698287879329
- type: dot_precision
value: 71.23223697378077
- type: dot_recall
value: 82.20665229442562
- type: euclidean_accuracy
value: 88.67543757519307
- type: euclidean_ap
value: 85.4524355531532
- type: euclidean_f1
value: 77.78729106950081
- type: euclidean_precision
value: 75.3009009009009
- type: euclidean_recall
value: 80.44348629504158
- type: manhattan_accuracy
value: 88.65991384328792
- type: manhattan_ap
value: 85.43109069046837
- type: manhattan_f1
value: 77.72639551396425
- type: manhattan_precision
value: 73.73402417962004
- type: manhattan_recall
value: 82.17585463504774
- type: max_accuracy
value: 88.97232894787906
- type: max_ap
value: 85.9613736469497
- type: max_f1
value: 78.40216655382532
---
<h1 align="center">GIST Large Embedding v0</h1>
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
The model is fine-tuned on top of the [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
# Data
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
# Usage
The model can be easily loaded using the Sentence Transformers library.
```Python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-large-Embedding-v0", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
```
# Training Parameters
Below are the training parameters used to fine-tune the model:
```
Epochs = 40
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 16
Checkpoint step = 171000
Contrastive loss temperature = 0.01
```
# Evaluation
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
# Citation
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
```
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gte-Qwen2-7B-instruct | Alibaba-NLP | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"custom_code",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-15T11:24:21 | 2025-01-11T08:10:51 | 110,385 | 348 | ---
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 91.31343283582089
- type: ap
value: 67.64251402604096
- type: f1
value: 87.53372530755692
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.497825
- type: ap
value: 96.30329547047529
- type: f1
value: 97.49769793778039
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 62.564
- type: f1
value: 60.975777935041066
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 36.486000000000004
- type: map_at_10
value: 54.842
- type: map_at_100
value: 55.206999999999994
- type: map_at_1000
value: 55.206999999999994
- type: map_at_3
value: 49.893
- type: map_at_5
value: 53.105000000000004
- type: mrr_at_1
value: 37.34
- type: mrr_at_10
value: 55.143
- type: mrr_at_100
value: 55.509
- type: mrr_at_1000
value: 55.509
- type: mrr_at_3
value: 50.212999999999994
- type: mrr_at_5
value: 53.432
- type: ndcg_at_1
value: 36.486000000000004
- type: ndcg_at_10
value: 64.273
- type: ndcg_at_100
value: 65.66199999999999
- type: ndcg_at_1000
value: 65.66199999999999
- type: ndcg_at_3
value: 54.352999999999994
- type: ndcg_at_5
value: 60.131
- type: precision_at_1
value: 36.486000000000004
- type: precision_at_10
value: 9.395000000000001
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.428
- type: precision_at_5
value: 16.259
- type: recall_at_1
value: 36.486000000000004
- type: recall_at_10
value: 93.95400000000001
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 67.283
- type: recall_at_5
value: 81.294
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 56.461169803700564
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 51.73600434466286
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.57827065898053
- type: mrr
value: 79.08136569493911
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.53324575999243
- type: cos_sim_spearman
value: 81.37173362822374
- type: euclidean_pearson
value: 82.19243335103444
- type: euclidean_spearman
value: 81.33679307304334
- type: manhattan_pearson
value: 82.38752665975699
- type: manhattan_spearman
value: 81.31510583189689
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.56818181818181
- type: f1
value: 87.25826722019875
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 50.09239610327673
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 46.64733054606282
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.997
- type: map_at_10
value: 48.176
- type: map_at_100
value: 49.82
- type: map_at_1000
value: 49.924
- type: map_at_3
value: 43.626
- type: map_at_5
value: 46.275
- type: mrr_at_1
value: 42.059999999999995
- type: mrr_at_10
value: 53.726
- type: mrr_at_100
value: 54.398
- type: mrr_at_1000
value: 54.416
- type: mrr_at_3
value: 50.714999999999996
- type: mrr_at_5
value: 52.639
- type: ndcg_at_1
value: 42.059999999999995
- type: ndcg_at_10
value: 55.574999999999996
- type: ndcg_at_100
value: 60.744
- type: ndcg_at_1000
value: 61.85699999999999
- type: ndcg_at_3
value: 49.363
- type: ndcg_at_5
value: 52.44
- type: precision_at_1
value: 42.059999999999995
- type: precision_at_10
value: 11.101999999999999
- type: precision_at_100
value: 1.73
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 24.464
- type: precision_at_5
value: 18.026
- type: recall_at_1
value: 33.997
- type: recall_at_10
value: 70.35900000000001
- type: recall_at_100
value: 91.642
- type: recall_at_1000
value: 97.977
- type: recall_at_3
value: 52.76
- type: recall_at_5
value: 61.148
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 35.884
- type: map_at_10
value: 48.14
- type: map_at_100
value: 49.5
- type: map_at_1000
value: 49.63
- type: map_at_3
value: 44.646
- type: map_at_5
value: 46.617999999999995
- type: mrr_at_1
value: 44.458999999999996
- type: mrr_at_10
value: 53.751000000000005
- type: mrr_at_100
value: 54.37800000000001
- type: mrr_at_1000
value: 54.415
- type: mrr_at_3
value: 51.815
- type: mrr_at_5
value: 52.882
- type: ndcg_at_1
value: 44.458999999999996
- type: ndcg_at_10
value: 54.157
- type: ndcg_at_100
value: 58.362
- type: ndcg_at_1000
value: 60.178
- type: ndcg_at_3
value: 49.661
- type: ndcg_at_5
value: 51.74999999999999
- type: precision_at_1
value: 44.458999999999996
- type: precision_at_10
value: 10.248
- type: precision_at_100
value: 1.5890000000000002
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 23.928
- type: precision_at_5
value: 16.878999999999998
- type: recall_at_1
value: 35.884
- type: recall_at_10
value: 64.798
- type: recall_at_100
value: 82.345
- type: recall_at_1000
value: 93.267
- type: recall_at_3
value: 51.847
- type: recall_at_5
value: 57.601
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 39.383
- type: map_at_10
value: 53.714
- type: map_at_100
value: 54.838
- type: map_at_1000
value: 54.87800000000001
- type: map_at_3
value: 50.114999999999995
- type: map_at_5
value: 52.153000000000006
- type: mrr_at_1
value: 45.016
- type: mrr_at_10
value: 56.732000000000006
- type: mrr_at_100
value: 57.411
- type: mrr_at_1000
value: 57.431
- type: mrr_at_3
value: 54.044000000000004
- type: mrr_at_5
value: 55.639
- type: ndcg_at_1
value: 45.016
- type: ndcg_at_10
value: 60.228
- type: ndcg_at_100
value: 64.277
- type: ndcg_at_1000
value: 65.07
- type: ndcg_at_3
value: 54.124
- type: ndcg_at_5
value: 57.147000000000006
- type: precision_at_1
value: 45.016
- type: precision_at_10
value: 9.937
- type: precision_at_100
value: 1.288
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 24.471999999999998
- type: precision_at_5
value: 16.991
- type: recall_at_1
value: 39.383
- type: recall_at_10
value: 76.175
- type: recall_at_100
value: 93.02
- type: recall_at_1000
value: 98.60900000000001
- type: recall_at_3
value: 60.265
- type: recall_at_5
value: 67.46600000000001
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.426000000000002
- type: map_at_10
value: 37.397000000000006
- type: map_at_100
value: 38.61
- type: map_at_1000
value: 38.678000000000004
- type: map_at_3
value: 34.150999999999996
- type: map_at_5
value: 36.137
- type: mrr_at_1
value: 29.944
- type: mrr_at_10
value: 39.654
- type: mrr_at_100
value: 40.638000000000005
- type: mrr_at_1000
value: 40.691
- type: mrr_at_3
value: 36.817
- type: mrr_at_5
value: 38.524
- type: ndcg_at_1
value: 29.944
- type: ndcg_at_10
value: 43.094
- type: ndcg_at_100
value: 48.789
- type: ndcg_at_1000
value: 50.339999999999996
- type: ndcg_at_3
value: 36.984
- type: ndcg_at_5
value: 40.248
- type: precision_at_1
value: 29.944
- type: precision_at_10
value: 6.78
- type: precision_at_100
value: 1.024
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 15.895000000000001
- type: precision_at_5
value: 11.39
- type: recall_at_1
value: 27.426000000000002
- type: recall_at_10
value: 58.464000000000006
- type: recall_at_100
value: 84.193
- type: recall_at_1000
value: 95.52000000000001
- type: recall_at_3
value: 42.172
- type: recall_at_5
value: 50.101
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 19.721
- type: map_at_10
value: 31.604
- type: map_at_100
value: 32.972
- type: map_at_1000
value: 33.077
- type: map_at_3
value: 27.218999999999998
- type: map_at_5
value: 29.53
- type: mrr_at_1
value: 25.0
- type: mrr_at_10
value: 35.843
- type: mrr_at_100
value: 36.785000000000004
- type: mrr_at_1000
value: 36.842000000000006
- type: mrr_at_3
value: 32.193
- type: mrr_at_5
value: 34.264
- type: ndcg_at_1
value: 25.0
- type: ndcg_at_10
value: 38.606
- type: ndcg_at_100
value: 44.272
- type: ndcg_at_1000
value: 46.527
- type: ndcg_at_3
value: 30.985000000000003
- type: ndcg_at_5
value: 34.43
- type: precision_at_1
value: 25.0
- type: precision_at_10
value: 7.811
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.15
- type: precision_at_3
value: 15.423
- type: precision_at_5
value: 11.791
- type: recall_at_1
value: 19.721
- type: recall_at_10
value: 55.625
- type: recall_at_100
value: 79.34400000000001
- type: recall_at_1000
value: 95.208
- type: recall_at_3
value: 35.19
- type: recall_at_5
value: 43.626
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 33.784
- type: map_at_10
value: 47.522
- type: map_at_100
value: 48.949999999999996
- type: map_at_1000
value: 49.038
- type: map_at_3
value: 43.284
- type: map_at_5
value: 45.629
- type: mrr_at_1
value: 41.482
- type: mrr_at_10
value: 52.830999999999996
- type: mrr_at_100
value: 53.559999999999995
- type: mrr_at_1000
value: 53.588
- type: mrr_at_3
value: 50.016000000000005
- type: mrr_at_5
value: 51.614000000000004
- type: ndcg_at_1
value: 41.482
- type: ndcg_at_10
value: 54.569
- type: ndcg_at_100
value: 59.675999999999995
- type: ndcg_at_1000
value: 60.989000000000004
- type: ndcg_at_3
value: 48.187000000000005
- type: ndcg_at_5
value: 51.183
- type: precision_at_1
value: 41.482
- type: precision_at_10
value: 10.221
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.17500000000000002
- type: precision_at_3
value: 23.548
- type: precision_at_5
value: 16.805
- type: recall_at_1
value: 33.784
- type: recall_at_10
value: 69.798
- type: recall_at_100
value: 90.098
- type: recall_at_1000
value: 98.176
- type: recall_at_3
value: 52.127
- type: recall_at_5
value: 59.861
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.038999999999998
- type: map_at_10
value: 41.904
- type: map_at_100
value: 43.36
- type: map_at_1000
value: 43.453
- type: map_at_3
value: 37.785999999999994
- type: map_at_5
value: 40.105000000000004
- type: mrr_at_1
value: 35.046
- type: mrr_at_10
value: 46.926
- type: mrr_at_100
value: 47.815000000000005
- type: mrr_at_1000
value: 47.849000000000004
- type: mrr_at_3
value: 44.273
- type: mrr_at_5
value: 45.774
- type: ndcg_at_1
value: 35.046
- type: ndcg_at_10
value: 48.937000000000005
- type: ndcg_at_100
value: 54.544000000000004
- type: ndcg_at_1000
value: 56.069
- type: ndcg_at_3
value: 42.858000000000004
- type: ndcg_at_5
value: 45.644
- type: precision_at_1
value: 35.046
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 21.346999999999998
- type: precision_at_5
value: 15.342
- type: recall_at_1
value: 28.038999999999998
- type: recall_at_10
value: 64.59700000000001
- type: recall_at_100
value: 87.735
- type: recall_at_1000
value: 97.41300000000001
- type: recall_at_3
value: 47.368
- type: recall_at_5
value: 54.93900000000001
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.17291666666667
- type: map_at_10
value: 40.025749999999995
- type: map_at_100
value: 41.39208333333333
- type: map_at_1000
value: 41.499249999999996
- type: map_at_3
value: 36.347
- type: map_at_5
value: 38.41391666666667
- type: mrr_at_1
value: 33.65925
- type: mrr_at_10
value: 44.085499999999996
- type: mrr_at_100
value: 44.94116666666667
- type: mrr_at_1000
value: 44.9855
- type: mrr_at_3
value: 41.2815
- type: mrr_at_5
value: 42.91491666666666
- type: ndcg_at_1
value: 33.65925
- type: ndcg_at_10
value: 46.430833333333325
- type: ndcg_at_100
value: 51.761
- type: ndcg_at_1000
value: 53.50899999999999
- type: ndcg_at_3
value: 40.45133333333333
- type: ndcg_at_5
value: 43.31483333333334
- type: precision_at_1
value: 33.65925
- type: precision_at_10
value: 8.4995
- type: precision_at_100
value: 1.3210000000000004
- type: precision_at_1000
value: 0.16591666666666666
- type: precision_at_3
value: 19.165083333333335
- type: precision_at_5
value: 13.81816666666667
- type: recall_at_1
value: 28.17291666666667
- type: recall_at_10
value: 61.12624999999999
- type: recall_at_100
value: 83.97266666666667
- type: recall_at_1000
value: 95.66550000000001
- type: recall_at_3
value: 44.661249999999995
- type: recall_at_5
value: 51.983333333333334
- type: map_at_1
value: 17.936
- type: map_at_10
value: 27.399
- type: map_at_100
value: 28.632
- type: map_at_1000
value: 28.738000000000003
- type: map_at_3
value: 24.456
- type: map_at_5
value: 26.06
- type: mrr_at_1
value: 19.224
- type: mrr_at_10
value: 28.998
- type: mrr_at_100
value: 30.11
- type: mrr_at_1000
value: 30.177
- type: mrr_at_3
value: 26.247999999999998
- type: mrr_at_5
value: 27.708
- type: ndcg_at_1
value: 19.224
- type: ndcg_at_10
value: 32.911
- type: ndcg_at_100
value: 38.873999999999995
- type: ndcg_at_1000
value: 41.277
- type: ndcg_at_3
value: 27.142
- type: ndcg_at_5
value: 29.755
- type: precision_at_1
value: 19.224
- type: precision_at_10
value: 5.6930000000000005
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 12.138
- type: precision_at_5
value: 8.909
- type: recall_at_1
value: 17.936
- type: recall_at_10
value: 48.096
- type: recall_at_100
value: 75.389
- type: recall_at_1000
value: 92.803
- type: recall_at_3
value: 32.812999999999995
- type: recall_at_5
value: 38.851
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.681
- type: map_at_10
value: 34.892
- type: map_at_100
value: 35.996
- type: map_at_1000
value: 36.083
- type: map_at_3
value: 31.491999999999997
- type: map_at_5
value: 33.632
- type: mrr_at_1
value: 28.528
- type: mrr_at_10
value: 37.694
- type: mrr_at_100
value: 38.613
- type: mrr_at_1000
value: 38.668
- type: mrr_at_3
value: 34.714
- type: mrr_at_5
value: 36.616
- type: ndcg_at_1
value: 28.528
- type: ndcg_at_10
value: 40.703
- type: ndcg_at_100
value: 45.993
- type: ndcg_at_1000
value: 47.847
- type: ndcg_at_3
value: 34.622
- type: ndcg_at_5
value: 38.035999999999994
- type: precision_at_1
value: 28.528
- type: precision_at_10
value: 6.902
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 15.798000000000002
- type: precision_at_5
value: 11.655999999999999
- type: recall_at_1
value: 24.681
- type: recall_at_10
value: 55.81
- type: recall_at_100
value: 79.785
- type: recall_at_1000
value: 92.959
- type: recall_at_3
value: 39.074
- type: recall_at_5
value: 47.568
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 18.627
- type: map_at_10
value: 27.872000000000003
- type: map_at_100
value: 29.237999999999996
- type: map_at_1000
value: 29.363
- type: map_at_3
value: 24.751
- type: map_at_5
value: 26.521
- type: mrr_at_1
value: 23.021
- type: mrr_at_10
value: 31.924000000000003
- type: mrr_at_100
value: 32.922000000000004
- type: mrr_at_1000
value: 32.988
- type: mrr_at_3
value: 29.192
- type: mrr_at_5
value: 30.798
- type: ndcg_at_1
value: 23.021
- type: ndcg_at_10
value: 33.535
- type: ndcg_at_100
value: 39.732
- type: ndcg_at_1000
value: 42.201
- type: ndcg_at_3
value: 28.153
- type: ndcg_at_5
value: 30.746000000000002
- type: precision_at_1
value: 23.021
- type: precision_at_10
value: 6.459
- type: precision_at_100
value: 1.1320000000000001
- type: precision_at_1000
value: 0.153
- type: precision_at_3
value: 13.719000000000001
- type: precision_at_5
value: 10.193000000000001
- type: recall_at_1
value: 18.627
- type: recall_at_10
value: 46.463
- type: recall_at_100
value: 74.226
- type: recall_at_1000
value: 91.28500000000001
- type: recall_at_3
value: 31.357000000000003
- type: recall_at_5
value: 38.067
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 31.457
- type: map_at_10
value: 42.888
- type: map_at_100
value: 44.24
- type: map_at_1000
value: 44.327
- type: map_at_3
value: 39.588
- type: map_at_5
value: 41.423
- type: mrr_at_1
value: 37.126999999999995
- type: mrr_at_10
value: 47.083000000000006
- type: mrr_at_100
value: 47.997
- type: mrr_at_1000
value: 48.044
- type: mrr_at_3
value: 44.574000000000005
- type: mrr_at_5
value: 46.202
- type: ndcg_at_1
value: 37.126999999999995
- type: ndcg_at_10
value: 48.833
- type: ndcg_at_100
value: 54.327000000000005
- type: ndcg_at_1000
value: 56.011
- type: ndcg_at_3
value: 43.541999999999994
- type: ndcg_at_5
value: 46.127
- type: precision_at_1
value: 37.126999999999995
- type: precision_at_10
value: 8.376999999999999
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.146
- type: precision_at_3
value: 20.211000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 31.457
- type: recall_at_10
value: 62.369
- type: recall_at_100
value: 85.444
- type: recall_at_1000
value: 96.65599999999999
- type: recall_at_3
value: 47.961
- type: recall_at_5
value: 54.676
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.139999999999997
- type: map_at_10
value: 38.801
- type: map_at_100
value: 40.549
- type: map_at_1000
value: 40.802
- type: map_at_3
value: 35.05
- type: map_at_5
value: 36.884
- type: mrr_at_1
value: 33.004
- type: mrr_at_10
value: 43.864
- type: mrr_at_100
value: 44.667
- type: mrr_at_1000
value: 44.717
- type: mrr_at_3
value: 40.777
- type: mrr_at_5
value: 42.319
- type: ndcg_at_1
value: 33.004
- type: ndcg_at_10
value: 46.022
- type: ndcg_at_100
value: 51.542
- type: ndcg_at_1000
value: 53.742000000000004
- type: ndcg_at_3
value: 39.795
- type: ndcg_at_5
value: 42.272
- type: precision_at_1
value: 33.004
- type: precision_at_10
value: 9.012
- type: precision_at_100
value: 1.7770000000000001
- type: precision_at_1000
value: 0.26
- type: precision_at_3
value: 19.038
- type: precision_at_5
value: 13.675999999999998
- type: recall_at_1
value: 27.139999999999997
- type: recall_at_10
value: 60.961
- type: recall_at_100
value: 84.451
- type: recall_at_1000
value: 98.113
- type: recall_at_3
value: 43.001
- type: recall_at_5
value: 49.896
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 22.076999999999998
- type: map_at_10
value: 35.44
- type: map_at_100
value: 37.651
- type: map_at_1000
value: 37.824999999999996
- type: map_at_3
value: 30.764999999999997
- type: map_at_5
value: 33.26
- type: mrr_at_1
value: 50.163000000000004
- type: mrr_at_10
value: 61.207
- type: mrr_at_100
value: 61.675000000000004
- type: mrr_at_1000
value: 61.692
- type: mrr_at_3
value: 58.60999999999999
- type: mrr_at_5
value: 60.307
- type: ndcg_at_1
value: 50.163000000000004
- type: ndcg_at_10
value: 45.882
- type: ndcg_at_100
value: 53.239999999999995
- type: ndcg_at_1000
value: 55.852000000000004
- type: ndcg_at_3
value: 40.514
- type: ndcg_at_5
value: 42.038
- type: precision_at_1
value: 50.163000000000004
- type: precision_at_10
value: 13.466000000000001
- type: precision_at_100
value: 2.164
- type: precision_at_1000
value: 0.266
- type: precision_at_3
value: 29.707
- type: precision_at_5
value: 21.694
- type: recall_at_1
value: 22.076999999999998
- type: recall_at_10
value: 50.193
- type: recall_at_100
value: 74.993
- type: recall_at_1000
value: 89.131
- type: recall_at_3
value: 35.472
- type: recall_at_5
value: 41.814
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.953
- type: map_at_10
value: 24.515
- type: map_at_100
value: 36.173
- type: map_at_1000
value: 38.351
- type: map_at_3
value: 16.592000000000002
- type: map_at_5
value: 20.036
- type: mrr_at_1
value: 74.25
- type: mrr_at_10
value: 81.813
- type: mrr_at_100
value: 82.006
- type: mrr_at_1000
value: 82.011
- type: mrr_at_3
value: 80.875
- type: mrr_at_5
value: 81.362
- type: ndcg_at_1
value: 62.5
- type: ndcg_at_10
value: 52.42
- type: ndcg_at_100
value: 56.808
- type: ndcg_at_1000
value: 63.532999999999994
- type: ndcg_at_3
value: 56.654
- type: ndcg_at_5
value: 54.18300000000001
- type: precision_at_1
value: 74.25
- type: precision_at_10
value: 42.699999999999996
- type: precision_at_100
value: 13.675
- type: precision_at_1000
value: 2.664
- type: precision_at_3
value: 60.5
- type: precision_at_5
value: 52.800000000000004
- type: recall_at_1
value: 9.953
- type: recall_at_10
value: 30.253999999999998
- type: recall_at_100
value: 62.516000000000005
- type: recall_at_1000
value: 84.163
- type: recall_at_3
value: 18.13
- type: recall_at_5
value: 22.771
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 79.455
- type: f1
value: 74.16798697647569
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 87.531
- type: map_at_10
value: 93.16799999999999
- type: map_at_100
value: 93.341
- type: map_at_1000
value: 93.349
- type: map_at_3
value: 92.444
- type: map_at_5
value: 92.865
- type: mrr_at_1
value: 94.014
- type: mrr_at_10
value: 96.761
- type: mrr_at_100
value: 96.762
- type: mrr_at_1000
value: 96.762
- type: mrr_at_3
value: 96.672
- type: mrr_at_5
value: 96.736
- type: ndcg_at_1
value: 94.014
- type: ndcg_at_10
value: 95.112
- type: ndcg_at_100
value: 95.578
- type: ndcg_at_1000
value: 95.68900000000001
- type: ndcg_at_3
value: 94.392
- type: ndcg_at_5
value: 94.72500000000001
- type: precision_at_1
value: 94.014
- type: precision_at_10
value: 11.065
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 35.259
- type: precision_at_5
value: 21.599
- type: recall_at_1
value: 87.531
- type: recall_at_10
value: 97.356
- type: recall_at_100
value: 98.965
- type: recall_at_1000
value: 99.607
- type: recall_at_3
value: 95.312
- type: recall_at_5
value: 96.295
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.055
- type: map_at_10
value: 53.114
- type: map_at_100
value: 55.235
- type: map_at_1000
value: 55.345
- type: map_at_3
value: 45.854
- type: map_at_5
value: 50.025
- type: mrr_at_1
value: 60.34
- type: mrr_at_10
value: 68.804
- type: mrr_at_100
value: 69.309
- type: mrr_at_1000
value: 69.32199999999999
- type: mrr_at_3
value: 66.40899999999999
- type: mrr_at_5
value: 67.976
- type: ndcg_at_1
value: 60.34
- type: ndcg_at_10
value: 62.031000000000006
- type: ndcg_at_100
value: 68.00500000000001
- type: ndcg_at_1000
value: 69.286
- type: ndcg_at_3
value: 56.355999999999995
- type: ndcg_at_5
value: 58.687
- type: precision_at_1
value: 60.34
- type: precision_at_10
value: 17.176
- type: precision_at_100
value: 2.36
- type: precision_at_1000
value: 0.259
- type: precision_at_3
value: 37.14
- type: precision_at_5
value: 27.809
- type: recall_at_1
value: 32.055
- type: recall_at_10
value: 70.91
- type: recall_at_100
value: 91.83
- type: recall_at_1000
value: 98.871
- type: recall_at_3
value: 51.202999999999996
- type: recall_at_5
value: 60.563
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 43.68
- type: map_at_10
value: 64.389
- type: map_at_100
value: 65.24
- type: map_at_1000
value: 65.303
- type: map_at_3
value: 61.309000000000005
- type: map_at_5
value: 63.275999999999996
- type: mrr_at_1
value: 87.36
- type: mrr_at_10
value: 91.12
- type: mrr_at_100
value: 91.227
- type: mrr_at_1000
value: 91.229
- type: mrr_at_3
value: 90.57600000000001
- type: mrr_at_5
value: 90.912
- type: ndcg_at_1
value: 87.36
- type: ndcg_at_10
value: 73.076
- type: ndcg_at_100
value: 75.895
- type: ndcg_at_1000
value: 77.049
- type: ndcg_at_3
value: 68.929
- type: ndcg_at_5
value: 71.28
- type: precision_at_1
value: 87.36
- type: precision_at_10
value: 14.741000000000001
- type: precision_at_100
value: 1.694
- type: precision_at_1000
value: 0.185
- type: precision_at_3
value: 43.043
- type: precision_at_5
value: 27.681
- type: recall_at_1
value: 43.68
- type: recall_at_10
value: 73.707
- type: recall_at_100
value: 84.7
- type: recall_at_1000
value: 92.309
- type: recall_at_3
value: 64.564
- type: recall_at_5
value: 69.203
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.75399999999999
- type: ap
value: 95.29389839242187
- type: f1
value: 96.75348377433475
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 25.176
- type: map_at_10
value: 38.598
- type: map_at_100
value: 39.707
- type: map_at_1000
value: 39.744
- type: map_at_3
value: 34.566
- type: map_at_5
value: 36.863
- type: mrr_at_1
value: 25.874000000000002
- type: mrr_at_10
value: 39.214
- type: mrr_at_100
value: 40.251
- type: mrr_at_1000
value: 40.281
- type: mrr_at_3
value: 35.291
- type: mrr_at_5
value: 37.545
- type: ndcg_at_1
value: 25.874000000000002
- type: ndcg_at_10
value: 45.98
- type: ndcg_at_100
value: 51.197
- type: ndcg_at_1000
value: 52.073
- type: ndcg_at_3
value: 37.785999999999994
- type: ndcg_at_5
value: 41.870000000000005
- type: precision_at_1
value: 25.874000000000002
- type: precision_at_10
value: 7.181
- type: precision_at_100
value: 0.979
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 16.051000000000002
- type: precision_at_5
value: 11.713
- type: recall_at_1
value: 25.176
- type: recall_at_10
value: 68.67699999999999
- type: recall_at_100
value: 92.55
- type: recall_at_1000
value: 99.164
- type: recall_at_3
value: 46.372
- type: recall_at_5
value: 56.16
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 99.03784769721841
- type: f1
value: 98.97791641821495
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 91.88326493388054
- type: f1
value: 73.74809928034335
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 85.41358439811701
- type: f1
value: 83.503679460639
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 89.77135171486215
- type: f1
value: 88.89843747468366
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 46.22695362087359
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 44.132372165849425
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.35680810650402
- type: mrr
value: 34.72625715637218
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 7.165000000000001
- type: map_at_10
value: 15.424
- type: map_at_100
value: 20.28
- type: map_at_1000
value: 22.065
- type: map_at_3
value: 11.236
- type: map_at_5
value: 13.025999999999998
- type: mrr_at_1
value: 51.702999999999996
- type: mrr_at_10
value: 59.965
- type: mrr_at_100
value: 60.667
- type: mrr_at_1000
value: 60.702999999999996
- type: mrr_at_3
value: 58.772000000000006
- type: mrr_at_5
value: 59.267
- type: ndcg_at_1
value: 49.536
- type: ndcg_at_10
value: 40.6
- type: ndcg_at_100
value: 37.848
- type: ndcg_at_1000
value: 46.657
- type: ndcg_at_3
value: 46.117999999999995
- type: ndcg_at_5
value: 43.619
- type: precision_at_1
value: 51.393
- type: precision_at_10
value: 30.31
- type: precision_at_100
value: 9.972
- type: precision_at_1000
value: 2.329
- type: precision_at_3
value: 43.137
- type: precision_at_5
value: 37.585
- type: recall_at_1
value: 7.165000000000001
- type: recall_at_10
value: 19.689999999999998
- type: recall_at_100
value: 39.237
- type: recall_at_1000
value: 71.417
- type: recall_at_3
value: 12.247
- type: recall_at_5
value: 14.902999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 42.653999999999996
- type: map_at_10
value: 59.611999999999995
- type: map_at_100
value: 60.32300000000001
- type: map_at_1000
value: 60.336
- type: map_at_3
value: 55.584999999999994
- type: map_at_5
value: 58.19
- type: mrr_at_1
value: 47.683
- type: mrr_at_10
value: 62.06700000000001
- type: mrr_at_100
value: 62.537
- type: mrr_at_1000
value: 62.544999999999995
- type: mrr_at_3
value: 59.178
- type: mrr_at_5
value: 61.034
- type: ndcg_at_1
value: 47.654
- type: ndcg_at_10
value: 67.001
- type: ndcg_at_100
value: 69.73899999999999
- type: ndcg_at_1000
value: 69.986
- type: ndcg_at_3
value: 59.95700000000001
- type: ndcg_at_5
value: 64.025
- type: precision_at_1
value: 47.654
- type: precision_at_10
value: 10.367999999999999
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 26.651000000000003
- type: precision_at_5
value: 18.459
- type: recall_at_1
value: 42.653999999999996
- type: recall_at_10
value: 86.619
- type: recall_at_100
value: 98.04899999999999
- type: recall_at_1000
value: 99.812
- type: recall_at_3
value: 68.987
- type: recall_at_5
value: 78.158
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 72.538
- type: map_at_10
value: 86.702
- type: map_at_100
value: 87.31
- type: map_at_1000
value: 87.323
- type: map_at_3
value: 83.87
- type: map_at_5
value: 85.682
- type: mrr_at_1
value: 83.31
- type: mrr_at_10
value: 89.225
- type: mrr_at_100
value: 89.30399999999999
- type: mrr_at_1000
value: 89.30399999999999
- type: mrr_at_3
value: 88.44300000000001
- type: mrr_at_5
value: 89.005
- type: ndcg_at_1
value: 83.32000000000001
- type: ndcg_at_10
value: 90.095
- type: ndcg_at_100
value: 91.12
- type: ndcg_at_1000
value: 91.179
- type: ndcg_at_3
value: 87.606
- type: ndcg_at_5
value: 89.031
- type: precision_at_1
value: 83.32000000000001
- type: precision_at_10
value: 13.641
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.377
- type: precision_at_5
value: 25.162000000000003
- type: recall_at_1
value: 72.538
- type: recall_at_10
value: 96.47200000000001
- type: recall_at_100
value: 99.785
- type: recall_at_1000
value: 99.99900000000001
- type: recall_at_3
value: 89.278
- type: recall_at_5
value: 93.367
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 73.55219145406065
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 74.13437105242755
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.873
- type: map_at_10
value: 17.944
- type: map_at_100
value: 21.171
- type: map_at_1000
value: 21.528
- type: map_at_3
value: 12.415
- type: map_at_5
value: 15.187999999999999
- type: mrr_at_1
value: 33.800000000000004
- type: mrr_at_10
value: 46.455
- type: mrr_at_100
value: 47.378
- type: mrr_at_1000
value: 47.394999999999996
- type: mrr_at_3
value: 42.367
- type: mrr_at_5
value: 44.972
- type: ndcg_at_1
value: 33.800000000000004
- type: ndcg_at_10
value: 28.907
- type: ndcg_at_100
value: 39.695
- type: ndcg_at_1000
value: 44.582
- type: ndcg_at_3
value: 26.949
- type: ndcg_at_5
value: 23.988
- type: precision_at_1
value: 33.800000000000004
- type: precision_at_10
value: 15.079999999999998
- type: precision_at_100
value: 3.056
- type: precision_at_1000
value: 0.42100000000000004
- type: precision_at_3
value: 25.167
- type: precision_at_5
value: 21.26
- type: recall_at_1
value: 6.873
- type: recall_at_10
value: 30.568
- type: recall_at_100
value: 62.062
- type: recall_at_1000
value: 85.37700000000001
- type: recall_at_3
value: 15.312999999999999
- type: recall_at_5
value: 21.575
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.37009118256057
- type: cos_sim_spearman
value: 79.27986395671529
- type: euclidean_pearson
value: 79.18037715442115
- type: euclidean_spearman
value: 79.28004791561621
- type: manhattan_pearson
value: 79.34062972800541
- type: manhattan_spearman
value: 79.43106695543402
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.48474767383833
- type: cos_sim_spearman
value: 79.54505388752513
- type: euclidean_pearson
value: 83.43282704179565
- type: euclidean_spearman
value: 79.54579919925405
- type: manhattan_pearson
value: 83.77564492427952
- type: manhattan_spearman
value: 79.84558396989286
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.803698035802
- type: cos_sim_spearman
value: 88.83451367754881
- type: euclidean_pearson
value: 88.28939285711628
- type: euclidean_spearman
value: 88.83528996073112
- type: manhattan_pearson
value: 88.28017412671795
- type: manhattan_spearman
value: 88.9228828016344
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.27469288153428
- type: cos_sim_spearman
value: 83.87477064876288
- type: euclidean_pearson
value: 84.2601737035379
- type: euclidean_spearman
value: 83.87431082479074
- type: manhattan_pearson
value: 84.3621547772745
- type: manhattan_spearman
value: 84.12094375000423
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.12749863201587
- type: cos_sim_spearman
value: 88.54287568368565
- type: euclidean_pearson
value: 87.90429700607999
- type: euclidean_spearman
value: 88.5437689576261
- type: manhattan_pearson
value: 88.19276653356833
- type: manhattan_spearman
value: 88.99995393814679
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.68398747560902
- type: cos_sim_spearman
value: 86.48815303460574
- type: euclidean_pearson
value: 85.52356631237954
- type: euclidean_spearman
value: 86.486391949551
- type: manhattan_pearson
value: 85.67267981761788
- type: manhattan_spearman
value: 86.7073696332485
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.9057107443124
- type: cos_sim_spearman
value: 88.7312168757697
- type: euclidean_pearson
value: 88.72810439714794
- type: euclidean_spearman
value: 88.71976185854771
- type: manhattan_pearson
value: 88.50433745949111
- type: manhattan_spearman
value: 88.51726175544195
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 67.59391795109886
- type: cos_sim_spearman
value: 66.87613008631367
- type: euclidean_pearson
value: 69.23198488262217
- type: euclidean_spearman
value: 66.85427723013692
- type: manhattan_pearson
value: 69.50730124841084
- type: manhattan_spearman
value: 67.10404669820792
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.0820605344619
- type: cos_sim_spearman
value: 86.8518089863434
- type: euclidean_pearson
value: 86.31087134689284
- type: euclidean_spearman
value: 86.8518520517941
- type: manhattan_pearson
value: 86.47203796160612
- type: manhattan_spearman
value: 87.1080149734421
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 89.09255369305481
- type: mrr
value: 97.10323445617563
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 61.260999999999996
- type: map_at_10
value: 74.043
- type: map_at_100
value: 74.37700000000001
- type: map_at_1000
value: 74.384
- type: map_at_3
value: 71.222
- type: map_at_5
value: 72.875
- type: mrr_at_1
value: 64.333
- type: mrr_at_10
value: 74.984
- type: mrr_at_100
value: 75.247
- type: mrr_at_1000
value: 75.25500000000001
- type: mrr_at_3
value: 73.167
- type: mrr_at_5
value: 74.35000000000001
- type: ndcg_at_1
value: 64.333
- type: ndcg_at_10
value: 79.06
- type: ndcg_at_100
value: 80.416
- type: ndcg_at_1000
value: 80.55600000000001
- type: ndcg_at_3
value: 74.753
- type: ndcg_at_5
value: 76.97500000000001
- type: precision_at_1
value: 64.333
- type: precision_at_10
value: 10.567
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 29.889
- type: precision_at_5
value: 19.533
- type: recall_at_1
value: 61.260999999999996
- type: recall_at_10
value: 93.167
- type: recall_at_100
value: 99.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 81.667
- type: recall_at_5
value: 87.394
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.71980198019801
- type: cos_sim_ap
value: 92.81616007802704
- type: cos_sim_f1
value: 85.17548454688318
- type: cos_sim_precision
value: 89.43894389438944
- type: cos_sim_recall
value: 81.3
- type: dot_accuracy
value: 99.71980198019801
- type: dot_ap
value: 92.81398760591358
- type: dot_f1
value: 85.17548454688318
- type: dot_precision
value: 89.43894389438944
- type: dot_recall
value: 81.3
- type: euclidean_accuracy
value: 99.71980198019801
- type: euclidean_ap
value: 92.81560637245072
- type: euclidean_f1
value: 85.17548454688318
- type: euclidean_precision
value: 89.43894389438944
- type: euclidean_recall
value: 81.3
- type: manhattan_accuracy
value: 99.73069306930694
- type: manhattan_ap
value: 93.14005487480794
- type: manhattan_f1
value: 85.56263269639068
- type: manhattan_precision
value: 91.17647058823529
- type: manhattan_recall
value: 80.60000000000001
- type: max_accuracy
value: 99.73069306930694
- type: max_ap
value: 93.14005487480794
- type: max_f1
value: 85.56263269639068
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 79.86443362395185
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 49.40897096662564
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.66040806627947
- type: mrr
value: 56.58670475766064
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.51015090598575
- type: cos_sim_spearman
value: 31.35016454939226
- type: dot_pearson
value: 31.5150068731
- type: dot_spearman
value: 31.34790869023487
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.254
- type: map_at_10
value: 2.064
- type: map_at_100
value: 12.909
- type: map_at_1000
value: 31.761
- type: map_at_3
value: 0.738
- type: map_at_5
value: 1.155
- type: mrr_at_1
value: 96.0
- type: mrr_at_10
value: 98.0
- type: mrr_at_100
value: 98.0
- type: mrr_at_1000
value: 98.0
- type: mrr_at_3
value: 98.0
- type: mrr_at_5
value: 98.0
- type: ndcg_at_1
value: 93.0
- type: ndcg_at_10
value: 82.258
- type: ndcg_at_100
value: 64.34
- type: ndcg_at_1000
value: 57.912
- type: ndcg_at_3
value: 90.827
- type: ndcg_at_5
value: 86.79
- type: precision_at_1
value: 96.0
- type: precision_at_10
value: 84.8
- type: precision_at_100
value: 66.0
- type: precision_at_1000
value: 25.356
- type: precision_at_3
value: 94.667
- type: precision_at_5
value: 90.4
- type: recall_at_1
value: 0.254
- type: recall_at_10
value: 2.1950000000000003
- type: recall_at_100
value: 16.088
- type: recall_at_1000
value: 54.559000000000005
- type: recall_at_3
value: 0.75
- type: recall_at_5
value: 1.191
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.976
- type: map_at_10
value: 11.389000000000001
- type: map_at_100
value: 18.429000000000002
- type: map_at_1000
value: 20.113
- type: map_at_3
value: 6.483
- type: map_at_5
value: 8.770999999999999
- type: mrr_at_1
value: 40.816
- type: mrr_at_10
value: 58.118
- type: mrr_at_100
value: 58.489999999999995
- type: mrr_at_1000
value: 58.489999999999995
- type: mrr_at_3
value: 53.061
- type: mrr_at_5
value: 57.041
- type: ndcg_at_1
value: 40.816
- type: ndcg_at_10
value: 30.567
- type: ndcg_at_100
value: 42.44
- type: ndcg_at_1000
value: 53.480000000000004
- type: ndcg_at_3
value: 36.016
- type: ndcg_at_5
value: 34.257
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 25.714
- type: precision_at_100
value: 8.429
- type: precision_at_1000
value: 1.5939999999999999
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.878
- type: recall_at_1
value: 2.976
- type: recall_at_10
value: 17.854999999999997
- type: recall_at_100
value: 51.833
- type: recall_at_1000
value: 86.223
- type: recall_at_3
value: 7.887
- type: recall_at_5
value: 12.026
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 85.1174
- type: ap
value: 30.169441069345748
- type: f1
value: 69.79254701873245
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.58347481607245
- type: f1
value: 72.74877295564937
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.90586138221305
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.35769207844072
- type: cos_sim_ap
value: 77.9645072410354
- type: cos_sim_f1
value: 71.32352941176471
- type: cos_sim_precision
value: 66.5903890160183
- type: cos_sim_recall
value: 76.78100263852242
- type: dot_accuracy
value: 87.37557370209214
- type: dot_ap
value: 77.96250046429908
- type: dot_f1
value: 71.28932757557064
- type: dot_precision
value: 66.95249130938586
- type: dot_recall
value: 76.22691292875989
- type: euclidean_accuracy
value: 87.35173153722357
- type: euclidean_ap
value: 77.96520460741593
- type: euclidean_f1
value: 71.32470733210104
- type: euclidean_precision
value: 66.91329479768785
- type: euclidean_recall
value: 76.35883905013192
- type: manhattan_accuracy
value: 87.25636287774931
- type: manhattan_ap
value: 77.77752485611796
- type: manhattan_f1
value: 71.18148599269183
- type: manhattan_precision
value: 66.10859728506787
- type: manhattan_recall
value: 77.0976253298153
- type: max_accuracy
value: 87.37557370209214
- type: max_ap
value: 77.96520460741593
- type: max_f1
value: 71.32470733210104
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.38176737687739
- type: cos_sim_ap
value: 86.58811861657401
- type: cos_sim_f1
value: 79.09430644097604
- type: cos_sim_precision
value: 75.45085977911366
- type: cos_sim_recall
value: 83.10748383122882
- type: dot_accuracy
value: 89.38370784336554
- type: dot_ap
value: 86.58840606004333
- type: dot_f1
value: 79.10179860068133
- type: dot_precision
value: 75.44546153308643
- type: dot_recall
value: 83.13058207576223
- type: euclidean_accuracy
value: 89.38564830985369
- type: euclidean_ap
value: 86.58820721061164
- type: euclidean_f1
value: 79.09070942235888
- type: euclidean_precision
value: 75.38729937194697
- type: euclidean_recall
value: 83.17677856482906
- type: manhattan_accuracy
value: 89.40699344122326
- type: manhattan_ap
value: 86.60631843011362
- type: manhattan_f1
value: 79.14949970570925
- type: manhattan_precision
value: 75.78191039729502
- type: manhattan_recall
value: 82.83030489682784
- type: max_accuracy
value: 89.40699344122326
- type: max_ap
value: 86.60631843011362
- type: max_f1
value: 79.14949970570925
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 65.58442135663871
- type: cos_sim_spearman
value: 72.2538631361313
- type: euclidean_pearson
value: 70.97255486607429
- type: euclidean_spearman
value: 72.25374250228647
- type: manhattan_pearson
value: 70.83250199989911
- type: manhattan_spearman
value: 72.14819496536272
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 59.99478404929932
- type: cos_sim_spearman
value: 62.61836216999812
- type: euclidean_pearson
value: 66.86429811933593
- type: euclidean_spearman
value: 62.6183520374191
- type: manhattan_pearson
value: 66.8063778911633
- type: manhattan_spearman
value: 62.569607573241115
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.98400000000001
- type: f1
value: 51.21447361350723
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 79.11941660686553
- type: cos_sim_spearman
value: 81.25029594540435
- type: euclidean_pearson
value: 82.06973504238826
- type: euclidean_spearman
value: 81.2501989488524
- type: manhattan_pearson
value: 82.10094630392753
- type: manhattan_spearman
value: 81.27987244392389
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 47.07270168705156
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.98511703185043
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.19895157194931
- type: mrr
value: 90.21424603174603
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 88.03317320980119
- type: mrr
value: 89.9461507936508
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 29.037000000000003
- type: map_at_10
value: 42.001
- type: map_at_100
value: 43.773
- type: map_at_1000
value: 43.878
- type: map_at_3
value: 37.637
- type: map_at_5
value: 40.034
- type: mrr_at_1
value: 43.136
- type: mrr_at_10
value: 51.158
- type: mrr_at_100
value: 52.083
- type: mrr_at_1000
value: 52.12
- type: mrr_at_3
value: 48.733
- type: mrr_at_5
value: 50.025
- type: ndcg_at_1
value: 43.136
- type: ndcg_at_10
value: 48.685
- type: ndcg_at_100
value: 55.513
- type: ndcg_at_1000
value: 57.242000000000004
- type: ndcg_at_3
value: 43.329
- type: ndcg_at_5
value: 45.438
- type: precision_at_1
value: 43.136
- type: precision_at_10
value: 10.56
- type: precision_at_100
value: 1.6129999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 24.064
- type: precision_at_5
value: 17.269000000000002
- type: recall_at_1
value: 29.037000000000003
- type: recall_at_10
value: 59.245000000000005
- type: recall_at_100
value: 87.355
- type: recall_at_1000
value: 98.74000000000001
- type: recall_at_3
value: 42.99
- type: recall_at_5
value: 49.681999999999995
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 82.68190018039687
- type: cos_sim_ap
value: 90.18017125327886
- type: cos_sim_f1
value: 83.64080906868193
- type: cos_sim_precision
value: 79.7076890489303
- type: cos_sim_recall
value: 87.98223053542202
- type: dot_accuracy
value: 82.68190018039687
- type: dot_ap
value: 90.18782350103646
- type: dot_f1
value: 83.64242087729039
- type: dot_precision
value: 79.65313028764805
- type: dot_recall
value: 88.05237315875614
- type: euclidean_accuracy
value: 82.68190018039687
- type: euclidean_ap
value: 90.1801957900632
- type: euclidean_f1
value: 83.63636363636364
- type: euclidean_precision
value: 79.52772506852203
- type: euclidean_recall
value: 88.19265840542437
- type: manhattan_accuracy
value: 82.14070956103427
- type: manhattan_ap
value: 89.96178420101427
- type: manhattan_f1
value: 83.21087838578791
- type: manhattan_precision
value: 78.35605121850475
- type: manhattan_recall
value: 88.70703764320785
- type: max_accuracy
value: 82.68190018039687
- type: max_ap
value: 90.18782350103646
- type: max_f1
value: 83.64242087729039
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 72.234
- type: map_at_10
value: 80.10000000000001
- type: map_at_100
value: 80.36
- type: map_at_1000
value: 80.363
- type: map_at_3
value: 78.315
- type: map_at_5
value: 79.607
- type: mrr_at_1
value: 72.392
- type: mrr_at_10
value: 80.117
- type: mrr_at_100
value: 80.36999999999999
- type: mrr_at_1000
value: 80.373
- type: mrr_at_3
value: 78.469
- type: mrr_at_5
value: 79.633
- type: ndcg_at_1
value: 72.392
- type: ndcg_at_10
value: 83.651
- type: ndcg_at_100
value: 84.749
- type: ndcg_at_1000
value: 84.83000000000001
- type: ndcg_at_3
value: 80.253
- type: ndcg_at_5
value: 82.485
- type: precision_at_1
value: 72.392
- type: precision_at_10
value: 9.557
- type: precision_at_100
value: 1.004
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 28.732000000000003
- type: precision_at_5
value: 18.377
- type: recall_at_1
value: 72.234
- type: recall_at_10
value: 94.573
- type: recall_at_100
value: 99.368
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 85.669
- type: recall_at_5
value: 91.01700000000001
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 26.173999999999996
- type: map_at_10
value: 80.04
- type: map_at_100
value: 82.94500000000001
- type: map_at_1000
value: 82.98100000000001
- type: map_at_3
value: 55.562999999999995
- type: map_at_5
value: 69.89800000000001
- type: mrr_at_1
value: 89.5
- type: mrr_at_10
value: 92.996
- type: mrr_at_100
value: 93.06400000000001
- type: mrr_at_1000
value: 93.065
- type: mrr_at_3
value: 92.658
- type: mrr_at_5
value: 92.84599999999999
- type: ndcg_at_1
value: 89.5
- type: ndcg_at_10
value: 87.443
- type: ndcg_at_100
value: 90.253
- type: ndcg_at_1000
value: 90.549
- type: ndcg_at_3
value: 85.874
- type: ndcg_at_5
value: 84.842
- type: precision_at_1
value: 89.5
- type: precision_at_10
value: 41.805
- type: precision_at_100
value: 4.827
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 76.85
- type: precision_at_5
value: 64.8
- type: recall_at_1
value: 26.173999999999996
- type: recall_at_10
value: 89.101
- type: recall_at_100
value: 98.08099999999999
- type: recall_at_1000
value: 99.529
- type: recall_at_3
value: 57.902
- type: recall_at_5
value: 74.602
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 56.10000000000001
- type: map_at_10
value: 66.15299999999999
- type: map_at_100
value: 66.625
- type: map_at_1000
value: 66.636
- type: map_at_3
value: 63.632999999999996
- type: map_at_5
value: 65.293
- type: mrr_at_1
value: 56.10000000000001
- type: mrr_at_10
value: 66.15299999999999
- type: mrr_at_100
value: 66.625
- type: mrr_at_1000
value: 66.636
- type: mrr_at_3
value: 63.632999999999996
- type: mrr_at_5
value: 65.293
- type: ndcg_at_1
value: 56.10000000000001
- type: ndcg_at_10
value: 71.146
- type: ndcg_at_100
value: 73.27799999999999
- type: ndcg_at_1000
value: 73.529
- type: ndcg_at_3
value: 66.09
- type: ndcg_at_5
value: 69.08999999999999
- type: precision_at_1
value: 56.10000000000001
- type: precision_at_10
value: 8.68
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 24.4
- type: precision_at_5
value: 16.1
- type: recall_at_1
value: 56.10000000000001
- type: recall_at_10
value: 86.8
- type: recall_at_100
value: 96.39999999999999
- type: recall_at_1000
value: 98.3
- type: recall_at_3
value: 73.2
- type: recall_at_5
value: 80.5
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.52096960369373
- type: f1
value: 40.930845295808695
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 86.51031894934334
- type: ap
value: 55.9516014323483
- type: f1
value: 81.54813679326381
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 69.67437838574276
- type: cos_sim_spearman
value: 73.81314174653045
- type: euclidean_pearson
value: 72.63430276680275
- type: euclidean_spearman
value: 73.81358736777001
- type: manhattan_pearson
value: 72.58743833842829
- type: manhattan_spearman
value: 73.7590419009179
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 31.648613483640254
- type: mrr
value: 30.37420634920635
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 73.28099999999999
- type: map_at_10
value: 81.977
- type: map_at_100
value: 82.222
- type: map_at_1000
value: 82.22699999999999
- type: map_at_3
value: 80.441
- type: map_at_5
value: 81.46600000000001
- type: mrr_at_1
value: 75.673
- type: mrr_at_10
value: 82.41000000000001
- type: mrr_at_100
value: 82.616
- type: mrr_at_1000
value: 82.621
- type: mrr_at_3
value: 81.094
- type: mrr_at_5
value: 81.962
- type: ndcg_at_1
value: 75.673
- type: ndcg_at_10
value: 85.15599999999999
- type: ndcg_at_100
value: 86.151
- type: ndcg_at_1000
value: 86.26899999999999
- type: ndcg_at_3
value: 82.304
- type: ndcg_at_5
value: 84.009
- type: precision_at_1
value: 75.673
- type: precision_at_10
value: 10.042
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 30.673000000000002
- type: precision_at_5
value: 19.326999999999998
- type: recall_at_1
value: 73.28099999999999
- type: recall_at_10
value: 94.446
- type: recall_at_100
value: 98.737
- type: recall_at_1000
value: 99.649
- type: recall_at_3
value: 86.984
- type: recall_at_5
value: 91.024
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.08607935440484
- type: f1
value: 78.24879986066307
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.05917955615332
- type: f1
value: 85.05279279434997
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 56.2
- type: map_at_10
value: 62.57899999999999
- type: map_at_100
value: 63.154999999999994
- type: map_at_1000
value: 63.193
- type: map_at_3
value: 61.217
- type: map_at_5
value: 62.012
- type: mrr_at_1
value: 56.3
- type: mrr_at_10
value: 62.629000000000005
- type: mrr_at_100
value: 63.205999999999996
- type: mrr_at_1000
value: 63.244
- type: mrr_at_3
value: 61.267
- type: mrr_at_5
value: 62.062
- type: ndcg_at_1
value: 56.2
- type: ndcg_at_10
value: 65.592
- type: ndcg_at_100
value: 68.657
- type: ndcg_at_1000
value: 69.671
- type: ndcg_at_3
value: 62.808
- type: ndcg_at_5
value: 64.24499999999999
- type: precision_at_1
value: 56.2
- type: precision_at_10
value: 7.5
- type: precision_at_100
value: 0.899
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 22.467000000000002
- type: precision_at_5
value: 14.180000000000001
- type: recall_at_1
value: 56.2
- type: recall_at_10
value: 75.0
- type: recall_at_100
value: 89.9
- type: recall_at_1000
value: 97.89999999999999
- type: recall_at_3
value: 67.4
- type: recall_at_5
value: 70.89999999999999
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 76.87666666666667
- type: f1
value: 76.7317686219665
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 79.64266377910124
- type: cos_sim_ap
value: 84.78274442344829
- type: cos_sim_f1
value: 81.16947472745292
- type: cos_sim_precision
value: 76.47058823529412
- type: cos_sim_recall
value: 86.48363252375924
- type: dot_accuracy
value: 79.64266377910124
- type: dot_ap
value: 84.7851404063692
- type: dot_f1
value: 81.16947472745292
- type: dot_precision
value: 76.47058823529412
- type: dot_recall
value: 86.48363252375924
- type: euclidean_accuracy
value: 79.64266377910124
- type: euclidean_ap
value: 84.78068373762378
- type: euclidean_f1
value: 81.14794656110837
- type: euclidean_precision
value: 76.35009310986965
- type: euclidean_recall
value: 86.58922914466737
- type: manhattan_accuracy
value: 79.48023822414727
- type: manhattan_ap
value: 84.72928897427576
- type: manhattan_f1
value: 81.32084770823064
- type: manhattan_precision
value: 76.24768946395564
- type: manhattan_recall
value: 87.11721224920802
- type: max_accuracy
value: 79.64266377910124
- type: max_ap
value: 84.7851404063692
- type: max_f1
value: 81.32084770823064
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 94.3
- type: ap
value: 92.8664032274438
- type: f1
value: 94.29311102997727
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 48.51392279882909
- type: cos_sim_spearman
value: 54.06338895994974
- type: euclidean_pearson
value: 52.58480559573412
- type: euclidean_spearman
value: 54.06417276612201
- type: manhattan_pearson
value: 52.69525121721343
- type: manhattan_spearman
value: 54.048147455389675
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 29.728387290757325
- type: cos_sim_spearman
value: 31.366121633635284
- type: euclidean_pearson
value: 29.14588368552961
- type: euclidean_spearman
value: 31.36764411112844
- type: manhattan_pearson
value: 29.63517350523121
- type: manhattan_spearman
value: 31.94157020583762
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 63.64868296271406
- type: cos_sim_spearman
value: 66.12800618164744
- type: euclidean_pearson
value: 63.21405767340238
- type: euclidean_spearman
value: 66.12786567790748
- type: manhattan_pearson
value: 64.04300276525848
- type: manhattan_spearman
value: 66.5066857145652
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 81.2302623912794
- type: cos_sim_spearman
value: 81.16833673266562
- type: euclidean_pearson
value: 79.47647843876024
- type: euclidean_spearman
value: 81.16944349524972
- type: manhattan_pearson
value: 79.84947238492208
- type: manhattan_spearman
value: 81.64626599410026
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 67.80129586475687
- type: mrr
value: 77.77402311635554
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.666999999999998
- type: map_at_10
value: 81.063
- type: map_at_100
value: 84.504
- type: map_at_1000
value: 84.552
- type: map_at_3
value: 56.897
- type: map_at_5
value: 70.073
- type: mrr_at_1
value: 92.087
- type: mrr_at_10
value: 94.132
- type: mrr_at_100
value: 94.19800000000001
- type: mrr_at_1000
value: 94.19999999999999
- type: mrr_at_3
value: 93.78999999999999
- type: mrr_at_5
value: 94.002
- type: ndcg_at_1
value: 92.087
- type: ndcg_at_10
value: 87.734
- type: ndcg_at_100
value: 90.736
- type: ndcg_at_1000
value: 91.184
- type: ndcg_at_3
value: 88.78
- type: ndcg_at_5
value: 87.676
- type: precision_at_1
value: 92.087
- type: precision_at_10
value: 43.46
- type: precision_at_100
value: 5.07
- type: precision_at_1000
value: 0.518
- type: precision_at_3
value: 77.49000000000001
- type: precision_at_5
value: 65.194
- type: recall_at_1
value: 28.666999999999998
- type: recall_at_10
value: 86.632
- type: recall_at_100
value: 96.646
- type: recall_at_1000
value: 98.917
- type: recall_at_3
value: 58.333999999999996
- type: recall_at_5
value: 72.974
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 52.971999999999994
- type: f1
value: 50.2898280984929
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 86.0797948663824
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 85.10759092255017
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 65.60000000000001
- type: map_at_10
value: 74.773
- type: map_at_100
value: 75.128
- type: map_at_1000
value: 75.136
- type: map_at_3
value: 73.05
- type: map_at_5
value: 74.13499999999999
- type: mrr_at_1
value: 65.60000000000001
- type: mrr_at_10
value: 74.773
- type: mrr_at_100
value: 75.128
- type: mrr_at_1000
value: 75.136
- type: mrr_at_3
value: 73.05
- type: mrr_at_5
value: 74.13499999999999
- type: ndcg_at_1
value: 65.60000000000001
- type: ndcg_at_10
value: 78.84299999999999
- type: ndcg_at_100
value: 80.40899999999999
- type: ndcg_at_1000
value: 80.57
- type: ndcg_at_3
value: 75.40599999999999
- type: ndcg_at_5
value: 77.351
- type: precision_at_1
value: 65.60000000000001
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 27.400000000000002
- type: precision_at_5
value: 17.380000000000003
- type: recall_at_1
value: 65.60000000000001
- type: recall_at_10
value: 91.4
- type: recall_at_100
value: 98.4
- type: recall_at_1000
value: 99.6
- type: recall_at_3
value: 82.19999999999999
- type: recall_at_5
value: 86.9
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.47
- type: ap
value: 75.59561751845389
- type: f1
value: 87.95207751382563
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 76.05592323841036
- type: v_measure
value: 64.51718058866508
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
metrics:
- type: map
value: 73.08278490943373
- type: mrr
value: 74.66561454570449
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: map_at_1
value: 38.912
- type: map_at_10
value: 52.437999999999995
- type: map_at_100
value: 53.38
- type: map_at_1000
value: 53.427
- type: map_at_3
value: 48.879
- type: map_at_5
value: 50.934000000000005
- type: mrr_at_1
value: 44.085
- type: mrr_at_10
value: 55.337
- type: mrr_at_100
value: 56.016999999999996
- type: mrr_at_1000
value: 56.043
- type: mrr_at_3
value: 52.55499999999999
- type: mrr_at_5
value: 54.20399999999999
- type: ndcg_at_1
value: 44.085
- type: ndcg_at_10
value: 58.876
- type: ndcg_at_100
value: 62.714000000000006
- type: ndcg_at_1000
value: 63.721000000000004
- type: ndcg_at_3
value: 52.444
- type: ndcg_at_5
value: 55.692
- type: precision_at_1
value: 44.085
- type: precision_at_10
value: 9.21
- type: precision_at_100
value: 1.164
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 23.043
- type: precision_at_5
value: 15.898000000000001
- type: recall_at_1
value: 38.912
- type: recall_at_10
value: 75.577
- type: recall_at_100
value: 92.038
- type: recall_at_1000
value: 99.325
- type: recall_at_3
value: 58.592
- type: recall_at_5
value: 66.235
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.532000000000004
- type: f1
value: 52.5783943471605
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 8.108
- type: map_at_10
value: 14.710999999999999
- type: map_at_100
value: 15.891
- type: map_at_1000
value: 15.983
- type: map_at_3
value: 12.237
- type: map_at_5
value: 13.679
- type: mrr_at_1
value: 8.108
- type: mrr_at_10
value: 14.710999999999999
- type: mrr_at_100
value: 15.891
- type: mrr_at_1000
value: 15.983
- type: mrr_at_3
value: 12.237
- type: mrr_at_5
value: 13.679
- type: ndcg_at_1
value: 8.108
- type: ndcg_at_10
value: 18.796
- type: ndcg_at_100
value: 25.098
- type: ndcg_at_1000
value: 27.951999999999998
- type: ndcg_at_3
value: 13.712
- type: ndcg_at_5
value: 16.309
- type: precision_at_1
value: 8.108
- type: precision_at_10
value: 3.198
- type: precision_at_100
value: 0.626
- type: precision_at_1000
value: 0.086
- type: precision_at_3
value: 6.006
- type: precision_at_5
value: 4.865
- type: recall_at_1
value: 8.108
- type: recall_at_10
value: 31.982
- type: recall_at_100
value: 62.613
- type: recall_at_1000
value: 86.036
- type: recall_at_3
value: 18.018
- type: recall_at_5
value: 24.324
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 30.833269778867116
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: mlsum
config: default
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 50.0281928004713
- type: v_measure
value: 43.699961510636534
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.68963357344191
- type: f1
value: 96.45175170820961
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 87.46946445349202
- type: f1
value: 65.79860440988624
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 82.60663507109005
- type: f1
value: 77.20462646604777
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 60.19311264967803
- type: v_measure
value: 63.6235764409785
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 81.65097511768661
- type: f1
value: 78.77796091490924
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 86.64425016812373
- type: f1
value: 85.4912728670017
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 35.913000000000004
- type: map_at_10
value: 48.147
- type: map_at_100
value: 48.91
- type: map_at_1000
value: 48.949
- type: map_at_3
value: 45.269999999999996
- type: map_at_5
value: 47.115
- type: mrr_at_1
value: 35.913000000000004
- type: mrr_at_10
value: 48.147
- type: mrr_at_100
value: 48.91
- type: mrr_at_1000
value: 48.949
- type: mrr_at_3
value: 45.269999999999996
- type: mrr_at_5
value: 47.115
- type: ndcg_at_1
value: 35.913000000000004
- type: ndcg_at_10
value: 54.03
- type: ndcg_at_100
value: 57.839
- type: ndcg_at_1000
value: 58.925000000000004
- type: ndcg_at_3
value: 48.217999999999996
- type: ndcg_at_5
value: 51.56699999999999
- type: precision_at_1
value: 35.913000000000004
- type: precision_at_10
value: 7.244000000000001
- type: precision_at_100
value: 0.9039999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 18.905
- type: precision_at_5
value: 12.981000000000002
- type: recall_at_1
value: 35.913000000000004
- type: recall_at_10
value: 72.441
- type: recall_at_100
value: 90.41799999999999
- type: recall_at_1000
value: 99.099
- type: recall_at_3
value: 56.716
- type: recall_at_5
value: 64.90599999999999
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.90069513406156
- type: cos_sim_ap
value: 100.0
- type: cos_sim_f1
value: 99.95032290114257
- type: cos_sim_precision
value: 100.0
- type: cos_sim_recall
value: 99.90069513406156
- type: dot_accuracy
value: 99.90069513406156
- type: dot_ap
value: 100.0
- type: dot_f1
value: 99.95032290114257
- type: dot_precision
value: 100.0
- type: dot_recall
value: 99.90069513406156
- type: euclidean_accuracy
value: 99.90069513406156
- type: euclidean_ap
value: 100.0
- type: euclidean_f1
value: 99.95032290114257
- type: euclidean_precision
value: 100.0
- type: euclidean_recall
value: 99.90069513406156
- type: manhattan_accuracy
value: 99.90069513406156
- type: manhattan_ap
value: 100.0
- type: manhattan_f1
value: 99.95032290114257
- type: manhattan_precision
value: 100.0
- type: manhattan_recall
value: 99.90069513406156
- type: max_accuracy
value: 99.90069513406156
- type: max_ap
value: 100.0
- type: max_f1
value: 99.95032290114257
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 75.25
- type: cos_sim_ap
value: 80.86376001270014
- type: cos_sim_f1
value: 73.65945437441204
- type: cos_sim_precision
value: 64.02289452166802
- type: cos_sim_recall
value: 86.71096345514951
- type: dot_accuracy
value: 75.25
- type: dot_ap
value: 80.93686107633002
- type: dot_f1
value: 73.65945437441204
- type: dot_precision
value: 64.02289452166802
- type: dot_recall
value: 86.71096345514951
- type: euclidean_accuracy
value: 75.25
- type: euclidean_ap
value: 80.86379136218862
- type: euclidean_f1
value: 73.65945437441204
- type: euclidean_precision
value: 64.02289452166802
- type: euclidean_recall
value: 86.71096345514951
- type: manhattan_accuracy
value: 75.3
- type: manhattan_ap
value: 80.87826606097734
- type: manhattan_f1
value: 73.68421052631581
- type: manhattan_precision
value: 64.0
- type: manhattan_recall
value: 86.82170542635659
- type: max_accuracy
value: 75.3
- type: max_ap
value: 80.93686107633002
- type: max_f1
value: 73.68421052631581
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 81.42349425981143
- type: cos_sim_spearman
value: 78.90454327031226
- type: euclidean_pearson
value: 78.39086497435166
- type: euclidean_spearman
value: 78.9046133980509
- type: manhattan_pearson
value: 78.63743094286502
- type: manhattan_spearman
value: 79.12136348449269
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 81.452697919749
- type: cos_sim_spearman
value: 82.58116836039301
- type: euclidean_pearson
value: 81.04038478932786
- type: euclidean_spearman
value: 82.58116836039301
- type: manhattan_pearson
value: 81.37075396187771
- type: manhattan_spearman
value: 82.73678231355368
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: stsb_multi_mt
config: fr
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 85.7419764013806
- type: cos_sim_spearman
value: 85.46085808849622
- type: euclidean_pearson
value: 83.70449639870063
- type: euclidean_spearman
value: 85.46159013076233
- type: manhattan_pearson
value: 83.95259510313929
- type: manhattan_spearman
value: 85.8029724659458
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 32.61063271753325
- type: cos_sim_spearman
value: 31.454589417353603
- type: dot_pearson
value: 32.6106288643431
- type: dot_spearman
value: 31.454589417353603
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: b205c5084a0934ce8af14338bf03feb19499c84d
metrics:
- type: map
value: 84.31666666666666
- type: mrr
value: 84.31666666666666
- task:
type: Retrieval
dataset:
name: MTEB SyntecRetrieval
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
metrics:
- type: map_at_1
value: 63.0
- type: map_at_10
value: 73.471
- type: map_at_100
value: 73.87
- type: map_at_1000
value: 73.87
- type: map_at_3
value: 70.5
- type: map_at_5
value: 73.05
- type: mrr_at_1
value: 63.0
- type: mrr_at_10
value: 73.471
- type: mrr_at_100
value: 73.87
- type: mrr_at_1000
value: 73.87
- type: mrr_at_3
value: 70.5
- type: mrr_at_5
value: 73.05
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 78.255
- type: ndcg_at_100
value: 79.88
- type: ndcg_at_1000
value: 79.88
- type: ndcg_at_3
value: 72.702
- type: ndcg_at_5
value: 77.264
- type: precision_at_1
value: 63.0
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 18.0
- type: recall_at_1
value: 63.0
- type: recall_at_10
value: 93.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 79.0
- type: recall_at_5
value: 90.0
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
- type: map_at_1
value: 40.338
- type: map_at_10
value: 61.927
- type: map_at_100
value: 63.361999999999995
- type: map_at_1000
value: 63.405
- type: map_at_3
value: 55.479
- type: map_at_5
value: 59.732
- type: mrr_at_1
value: 63.551
- type: mrr_at_10
value: 71.006
- type: mrr_at_100
value: 71.501
- type: mrr_at_1000
value: 71.509
- type: mrr_at_3
value: 69.07
- type: mrr_at_5
value: 70.165
- type: ndcg_at_1
value: 63.551
- type: ndcg_at_10
value: 68.297
- type: ndcg_at_100
value: 73.13199999999999
- type: ndcg_at_1000
value: 73.751
- type: ndcg_at_3
value: 62.999
- type: ndcg_at_5
value: 64.89
- type: precision_at_1
value: 63.551
- type: precision_at_10
value: 15.661
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.207
- type: precision_at_3
value: 38.273
- type: precision_at_5
value: 27.61
- type: recall_at_1
value: 40.338
- type: recall_at_10
value: 77.267
- type: recall_at_100
value: 95.892
- type: recall_at_1000
value: 99.75500000000001
- type: recall_at_3
value: 60.36
- type: recall_at_5
value: 68.825
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 51.36126303874126
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 67.13717693836979
- type: f1
value: 57.27609848003782
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
metrics:
- type: map_at_1
value: 35.276999999999994
- type: map_at_10
value: 51.086
- type: map_at_100
value: 51.788000000000004
- type: map_at_1000
value: 51.791
- type: map_at_3
value: 46.147
- type: map_at_5
value: 49.078
- type: mrr_at_1
value: 35.917
- type: mrr_at_10
value: 51.315999999999995
- type: mrr_at_100
value: 52.018
- type: mrr_at_1000
value: 52.022
- type: mrr_at_3
value: 46.349000000000004
- type: mrr_at_5
value: 49.297000000000004
- type: ndcg_at_1
value: 35.276999999999994
- type: ndcg_at_10
value: 59.870999999999995
- type: ndcg_at_100
value: 62.590999999999994
- type: ndcg_at_1000
value: 62.661
- type: ndcg_at_3
value: 49.745
- type: ndcg_at_5
value: 55.067
- type: precision_at_1
value: 35.276999999999994
- type: precision_at_10
value: 8.791
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.057
- type: precision_at_5
value: 14.637
- type: recall_at_1
value: 35.276999999999994
- type: recall_at_10
value: 87.909
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 60.171
- type: recall_at_5
value: 73.18599999999999
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 78.03000000000002
- type: ap
value: 29.12548553897622
- type: f1
value: 66.54857118886073
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 89.0
- type: cos_sim_ap
value: 76.75437826834582
- type: cos_sim_f1
value: 66.4850136239782
- type: cos_sim_precision
value: 68.92655367231639
- type: cos_sim_recall
value: 64.21052631578948
- type: dot_accuracy
value: 89.0
- type: dot_ap
value: 76.75437826834582
- type: dot_f1
value: 66.4850136239782
- type: dot_precision
value: 68.92655367231639
- type: dot_recall
value: 64.21052631578948
- type: euclidean_accuracy
value: 89.0
- type: euclidean_ap
value: 76.75437826834582
- type: euclidean_f1
value: 66.4850136239782
- type: euclidean_precision
value: 68.92655367231639
- type: euclidean_recall
value: 64.21052631578948
- type: manhattan_accuracy
value: 89.0
- type: manhattan_ap
value: 76.66074220647083
- type: manhattan_f1
value: 66.47058823529412
- type: manhattan_precision
value: 75.33333333333333
- type: manhattan_recall
value: 59.473684210526315
- type: max_accuracy
value: 89.0
- type: max_ap
value: 76.75437826834582
- type: max_f1
value: 66.4850136239782
- task:
type: STS
dataset:
name: MTEB CDSC-R
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 93.12903172428328
- type: cos_sim_spearman
value: 92.66381487060741
- type: euclidean_pearson
value: 90.37278396708922
- type: euclidean_spearman
value: 92.66381487060741
- type: manhattan_pearson
value: 90.32503296540962
- type: manhattan_spearman
value: 92.6902938354313
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: clarin-knext/dbpedia-pl
config: default
split: test
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
metrics:
- type: map_at_1
value: 8.83
- type: map_at_10
value: 18.326
- type: map_at_100
value: 26.496
- type: map_at_1000
value: 28.455000000000002
- type: map_at_3
value: 12.933
- type: map_at_5
value: 15.168000000000001
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 72.76700000000001
- type: mrr_at_100
value: 73.203
- type: mrr_at_1000
value: 73.219
- type: mrr_at_3
value: 71.458
- type: mrr_at_5
value: 72.246
- type: ndcg_at_1
value: 55.375
- type: ndcg_at_10
value: 41.3
- type: ndcg_at_100
value: 45.891
- type: ndcg_at_1000
value: 52.905
- type: ndcg_at_3
value: 46.472
- type: ndcg_at_5
value: 43.734
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 33.074999999999996
- type: precision_at_100
value: 11.094999999999999
- type: precision_at_1000
value: 2.374
- type: precision_at_3
value: 48.583
- type: precision_at_5
value: 42.0
- type: recall_at_1
value: 8.83
- type: recall_at_10
value: 22.587
- type: recall_at_100
value: 50.61600000000001
- type: recall_at_1000
value: 73.559
- type: recall_at_3
value: 13.688
- type: recall_at_5
value: 16.855
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: clarin-knext/fiqa-pl
config: default
split: test
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
metrics:
- type: map_at_1
value: 20.587
- type: map_at_10
value: 33.095
- type: map_at_100
value: 35.24
- type: map_at_1000
value: 35.429
- type: map_at_3
value: 28.626
- type: map_at_5
value: 31.136999999999997
- type: mrr_at_1
value: 40.586
- type: mrr_at_10
value: 49.033
- type: mrr_at_100
value: 49.952999999999996
- type: mrr_at_1000
value: 49.992
- type: mrr_at_3
value: 46.553
- type: mrr_at_5
value: 48.035
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 41.046
- type: ndcg_at_100
value: 48.586
- type: ndcg_at_1000
value: 51.634
- type: ndcg_at_3
value: 36.773
- type: ndcg_at_5
value: 38.389
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.466
- type: precision_at_100
value: 1.909
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 24.434
- type: precision_at_5
value: 18.426000000000002
- type: recall_at_1
value: 20.587
- type: recall_at_10
value: 47.986000000000004
- type: recall_at_100
value: 75.761
- type: recall_at_1000
value: 94.065
- type: recall_at_3
value: 33.339
- type: recall_at_5
value: 39.765
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: clarin-knext/hotpotqa-pl
config: default
split: test
revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
metrics:
- type: map_at_1
value: 40.878
- type: map_at_10
value: 58.775999999999996
- type: map_at_100
value: 59.632
- type: map_at_1000
value: 59.707
- type: map_at_3
value: 56.074
- type: map_at_5
value: 57.629
- type: mrr_at_1
value: 81.756
- type: mrr_at_10
value: 86.117
- type: mrr_at_100
value: 86.299
- type: mrr_at_1000
value: 86.30600000000001
- type: mrr_at_3
value: 85.345
- type: mrr_at_5
value: 85.832
- type: ndcg_at_1
value: 81.756
- type: ndcg_at_10
value: 67.608
- type: ndcg_at_100
value: 70.575
- type: ndcg_at_1000
value: 71.99600000000001
- type: ndcg_at_3
value: 63.723
- type: ndcg_at_5
value: 65.70700000000001
- type: precision_at_1
value: 81.756
- type: precision_at_10
value: 13.619
- type: precision_at_100
value: 1.5939999999999999
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 39.604
- type: precision_at_5
value: 25.332
- type: recall_at_1
value: 40.878
- type: recall_at_10
value: 68.096
- type: recall_at_100
value: 79.696
- type: recall_at_1000
value: 89.082
- type: recall_at_3
value: 59.406000000000006
- type: recall_at_5
value: 63.329
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: clarin-knext/msmarco-pl
config: default
split: test
revision: 8634c07806d5cce3a6138e260e59b81760a0a640
metrics:
- type: map_at_1
value: 2.1839999999999997
- type: map_at_10
value: 11.346
- type: map_at_100
value: 30.325000000000003
- type: map_at_1000
value: 37.806
- type: map_at_3
value: 4.842
- type: map_at_5
value: 6.891
- type: mrr_at_1
value: 86.047
- type: mrr_at_10
value: 89.14699999999999
- type: mrr_at_100
value: 89.46600000000001
- type: mrr_at_1000
value: 89.46600000000001
- type: mrr_at_3
value: 89.14699999999999
- type: mrr_at_5
value: 89.14699999999999
- type: ndcg_at_1
value: 67.829
- type: ndcg_at_10
value: 62.222
- type: ndcg_at_100
value: 55.337
- type: ndcg_at_1000
value: 64.076
- type: ndcg_at_3
value: 68.12700000000001
- type: ndcg_at_5
value: 64.987
- type: precision_at_1
value: 86.047
- type: precision_at_10
value: 69.535
- type: precision_at_100
value: 32.93
- type: precision_at_1000
value: 6.6049999999999995
- type: precision_at_3
value: 79.845
- type: precision_at_5
value: 75.349
- type: recall_at_1
value: 2.1839999999999997
- type: recall_at_10
value: 12.866
- type: recall_at_100
value: 43.505
- type: recall_at_1000
value: 72.366
- type: recall_at_3
value: 4.947
- type: recall_at_5
value: 7.192
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.75319435104238
- type: f1
value: 77.58961444860606
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 85.54472091459313
- type: f1
value: 84.29498563572106
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: clarin-knext/nfcorpus-pl
config: default
split: test
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
metrics:
- type: map_at_1
value: 4.367
- type: map_at_10
value: 10.38
- type: map_at_100
value: 13.516
- type: map_at_1000
value: 14.982000000000001
- type: map_at_3
value: 7.367
- type: map_at_5
value: 8.59
- type: mrr_at_1
value: 41.486000000000004
- type: mrr_at_10
value: 48.886
- type: mrr_at_100
value: 49.657000000000004
- type: mrr_at_1000
value: 49.713
- type: mrr_at_3
value: 46.904
- type: mrr_at_5
value: 48.065000000000005
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 30.885
- type: ndcg_at_100
value: 28.393
- type: ndcg_at_1000
value: 37.428
- type: ndcg_at_3
value: 35.394999999999996
- type: ndcg_at_5
value: 33.391999999999996
- type: precision_at_1
value: 41.486000000000004
- type: precision_at_10
value: 23.437
- type: precision_at_100
value: 7.638
- type: precision_at_1000
value: 2.0389999999999997
- type: precision_at_3
value: 32.817
- type: precision_at_5
value: 28.915999999999997
- type: recall_at_1
value: 4.367
- type: recall_at_10
value: 14.655000000000001
- type: recall_at_100
value: 29.665999999999997
- type: recall_at_1000
value: 62.073
- type: recall_at_3
value: 8.51
- type: recall_at_5
value: 10.689
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
metrics:
- type: map_at_1
value: 28.616000000000003
- type: map_at_10
value: 41.626000000000005
- type: map_at_100
value: 42.689
- type: map_at_1000
value: 42.733
- type: map_at_3
value: 37.729
- type: map_at_5
value: 39.879999999999995
- type: mrr_at_1
value: 32.068000000000005
- type: mrr_at_10
value: 44.029
- type: mrr_at_100
value: 44.87
- type: mrr_at_1000
value: 44.901
- type: mrr_at_3
value: 40.687
- type: mrr_at_5
value: 42.625
- type: ndcg_at_1
value: 32.068000000000005
- type: ndcg_at_10
value: 48.449999999999996
- type: ndcg_at_100
value: 53.13
- type: ndcg_at_1000
value: 54.186
- type: ndcg_at_3
value: 40.983999999999995
- type: ndcg_at_5
value: 44.628
- type: precision_at_1
value: 32.068000000000005
- type: precision_at_10
value: 7.9750000000000005
- type: precision_at_100
value: 1.061
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 18.404999999999998
- type: precision_at_5
value: 13.111
- type: recall_at_1
value: 28.616000000000003
- type: recall_at_10
value: 66.956
- type: recall_at_100
value: 87.657
- type: recall_at_1000
value: 95.548
- type: recall_at_3
value: 47.453
- type: recall_at_5
value: 55.87800000000001
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.04141326382856
- type: ap
value: 77.47589122111044
- type: f1
value: 66.6332277374775
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 86.4
- type: cos_sim_ap
value: 94.1044939667201
- type: cos_sim_f1
value: 88.78048780487805
- type: cos_sim_precision
value: 87.22044728434504
- type: cos_sim_recall
value: 90.39735099337747
- type: dot_accuracy
value: 86.4
- type: dot_ap
value: 94.1044939667201
- type: dot_f1
value: 88.78048780487805
- type: dot_precision
value: 87.22044728434504
- type: dot_recall
value: 90.39735099337747
- type: euclidean_accuracy
value: 86.4
- type: euclidean_ap
value: 94.1044939667201
- type: euclidean_f1
value: 88.78048780487805
- type: euclidean_precision
value: 87.22044728434504
- type: euclidean_recall
value: 90.39735099337747
- type: manhattan_accuracy
value: 86.4
- type: manhattan_ap
value: 94.11438365697387
- type: manhattan_f1
value: 88.77968877968877
- type: manhattan_precision
value: 87.84440842787681
- type: manhattan_recall
value: 89.73509933774835
- type: max_accuracy
value: 86.4
- type: max_ap
value: 94.11438365697387
- type: max_f1
value: 88.78048780487805
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 97.86641929499072
- type: cos_sim_ap
value: 99.36904211868182
- type: cos_sim_f1
value: 96.56203288490283
- type: cos_sim_precision
value: 94.72140762463343
- type: cos_sim_recall
value: 98.47560975609755
- type: dot_accuracy
value: 97.86641929499072
- type: dot_ap
value: 99.36904211868183
- type: dot_f1
value: 96.56203288490283
- type: dot_precision
value: 94.72140762463343
- type: dot_recall
value: 98.47560975609755
- type: euclidean_accuracy
value: 97.86641929499072
- type: euclidean_ap
value: 99.36904211868183
- type: euclidean_f1
value: 96.56203288490283
- type: euclidean_precision
value: 94.72140762463343
- type: euclidean_recall
value: 98.47560975609755
- type: manhattan_accuracy
value: 98.14471243042672
- type: manhattan_ap
value: 99.43359540492416
- type: manhattan_f1
value: 96.98795180722892
- type: manhattan_precision
value: 95.83333333333334
- type: manhattan_recall
value: 98.17073170731707
- type: max_accuracy
value: 98.14471243042672
- type: max_ap
value: 99.43359540492416
- type: max_f1
value: 96.98795180722892
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 89.39058171745152
- type: f1
value: 86.8552093529568
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 74.97975708502024
- type: f1
value: 58.73081628832407
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
metrics:
- type: map_at_1
value: 64.917
- type: map_at_10
value: 78.74600000000001
- type: map_at_100
value: 79.501
- type: map_at_1000
value: 79.524
- type: map_at_3
value: 75.549
- type: map_at_5
value: 77.495
- type: mrr_at_1
value: 74.9
- type: mrr_at_10
value: 82.112
- type: mrr_at_100
value: 82.314
- type: mrr_at_1000
value: 82.317
- type: mrr_at_3
value: 80.745
- type: mrr_at_5
value: 81.607
- type: ndcg_at_1
value: 74.83999999999999
- type: ndcg_at_10
value: 83.214
- type: ndcg_at_100
value: 84.997
- type: ndcg_at_1000
value: 85.207
- type: ndcg_at_3
value: 79.547
- type: ndcg_at_5
value: 81.46600000000001
- type: precision_at_1
value: 74.83999999999999
- type: precision_at_10
value: 12.822
- type: precision_at_100
value: 1.506
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 34.903
- type: precision_at_5
value: 23.16
- type: recall_at_1
value: 64.917
- type: recall_at_10
value: 92.27199999999999
- type: recall_at_100
value: 98.715
- type: recall_at_1000
value: 99.854
- type: recall_at_3
value: 82.04599999999999
- type: recall_at_5
value: 87.2
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
metrics:
- type: map_at_1
value: 3.51
- type: map_at_10
value: 9.046999999999999
- type: map_at_100
value: 10.823
- type: map_at_1000
value: 11.144
- type: map_at_3
value: 6.257
- type: map_at_5
value: 7.648000000000001
- type: mrr_at_1
value: 17.299999999999997
- type: mrr_at_10
value: 27.419
- type: mrr_at_100
value: 28.618
- type: mrr_at_1000
value: 28.685
- type: mrr_at_3
value: 23.817
- type: mrr_at_5
value: 25.927
- type: ndcg_at_1
value: 17.299999999999997
- type: ndcg_at_10
value: 16.084
- type: ndcg_at_100
value: 23.729
- type: ndcg_at_1000
value: 29.476999999999997
- type: ndcg_at_3
value: 14.327000000000002
- type: ndcg_at_5
value: 13.017999999999999
- type: precision_at_1
value: 17.299999999999997
- type: precision_at_10
value: 8.63
- type: precision_at_100
value: 1.981
- type: precision_at_1000
value: 0.336
- type: precision_at_3
value: 13.4
- type: precision_at_5
value: 11.700000000000001
- type: recall_at_1
value: 3.51
- type: recall_at_10
value: 17.518
- type: recall_at_100
value: 40.275
- type: recall_at_1000
value: 68.203
- type: recall_at_3
value: 8.155
- type: recall_at_5
value: 11.875
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 86.30248675091724
- type: cos_sim_ap
value: 83.6756734006714
- type: cos_sim_f1
value: 74.97367497367497
- type: cos_sim_precision
value: 73.91003460207612
- type: cos_sim_recall
value: 76.06837606837607
- type: dot_accuracy
value: 86.30248675091724
- type: dot_ap
value: 83.6756734006714
- type: dot_f1
value: 74.97367497367497
- type: dot_precision
value: 73.91003460207612
- type: dot_recall
value: 76.06837606837607
- type: euclidean_accuracy
value: 86.30248675091724
- type: euclidean_ap
value: 83.67566984333091
- type: euclidean_f1
value: 74.97367497367497
- type: euclidean_precision
value: 73.91003460207612
- type: euclidean_recall
value: 76.06837606837607
- type: manhattan_accuracy
value: 86.28210354667753
- type: manhattan_ap
value: 83.64216119130171
- type: manhattan_f1
value: 74.92152075340078
- type: manhattan_precision
value: 73.4107997265892
- type: manhattan_recall
value: 76.49572649572649
- type: max_accuracy
value: 86.30248675091724
- type: max_ap
value: 83.6756734006714
- type: max_f1
value: 74.97367497367497
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 82.23295940859121
- type: cos_sim_spearman
value: 78.89329160768719
- type: euclidean_pearson
value: 79.56019107076818
- type: euclidean_spearman
value: 78.89330209904084
- type: manhattan_pearson
value: 79.76098513973719
- type: manhattan_spearman
value: 79.05490162570123
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 37.732606308062486
- type: cos_sim_spearman
value: 41.01645667030284
- type: euclidean_pearson
value: 26.61722556367085
- type: euclidean_spearman
value: 41.01645667030284
- type: manhattan_pearson
value: 26.60917378970807
- type: manhattan_spearman
value: 41.51335727617614
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
- type: map_at_1
value: 54.31700000000001
- type: map_at_10
value: 65.564
- type: map_at_100
value: 66.062
- type: map_at_1000
value: 66.08699999999999
- type: map_at_3
value: 62.592999999999996
- type: map_at_5
value: 63.888
- type: mrr_at_1
value: 56.99999999999999
- type: mrr_at_10
value: 66.412
- type: mrr_at_100
value: 66.85900000000001
- type: mrr_at_1000
value: 66.88
- type: mrr_at_3
value: 64.22200000000001
- type: mrr_at_5
value: 65.206
- type: ndcg_at_1
value: 56.99999999999999
- type: ndcg_at_10
value: 70.577
- type: ndcg_at_100
value: 72.879
- type: ndcg_at_1000
value: 73.45
- type: ndcg_at_3
value: 65.5
- type: ndcg_at_5
value: 67.278
- type: precision_at_1
value: 56.99999999999999
- type: precision_at_10
value: 9.667
- type: precision_at_100
value: 1.083
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.0
- type: precision_at_5
value: 16.933
- type: recall_at_1
value: 54.31700000000001
- type: recall_at_10
value: 85.056
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 71.0
- type: recall_at_5
value: 75.672
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: clarin-knext/trec-covid-pl
config: default
split: test
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 2.051
- type: map_at_100
value: 12.009
- type: map_at_1000
value: 27.448
- type: map_at_3
value: 0.721
- type: map_at_5
value: 1.13
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.0
- type: mrr_at_100
value: 93.0
- type: mrr_at_1000
value: 93.0
- type: mrr_at_3
value: 93.0
- type: mrr_at_5
value: 93.0
- type: ndcg_at_1
value: 85.0
- type: ndcg_at_10
value: 80.303
- type: ndcg_at_100
value: 61.23499999999999
- type: ndcg_at_1000
value: 52.978
- type: ndcg_at_3
value: 84.419
- type: ndcg_at_5
value: 82.976
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 83.39999999999999
- type: precision_at_100
value: 61.96
- type: precision_at_1000
value: 22.648
- type: precision_at_3
value: 89.333
- type: precision_at_5
value: 87.2
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.193
- type: recall_at_100
value: 14.938
- type: recall_at_1000
value: 48.563
- type: recall_at_3
value: 0.738
- type: recall_at_5
value: 1.173
---
## gte-Qwen2-7B-instruct
**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).
Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
## Model Information
- Model Size: 7B
- Embedding Dimension: 3584
- Max Input Tokens: 32k
## Requirements
```
transformers>=4.39.2
flash_attn>=2.5.6
```
## Usage
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
```
Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.
### Transformers
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Infinity_emb
Usage via [infinity](https://github.com/michaelfeil/infinity), a MIT Licensed inference server.
```
# requires ~16-32GB VRAM NVIDIA Compute Capability >= 8.0
docker run \
-v $PWD/data:/app/.cache --gpus "0" -p "7997":"7997" \
michaelf34/infinity:0.0.68-trt-onnx \
v2 --model-id Alibaba-NLP/gte-Qwen2-7B-instruct --revision "refs/pr/38" --dtype bfloat16 --batch-size 8 --device cuda --engine torch --port 7997 --no-bettertransformer
```
## Evaluation
### MTEB & C-MTEB
You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|:----:|:---------:|:----------:|:----------:|:----------:|
| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | - | - |
| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | - | - |
| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | - | - |
| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | - | - |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | - | - |
| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | - | - |
| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | - | - |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | - | - |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | - | - |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | - | - |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | - | - |
| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | - | - |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | - | - |
| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | **68.25** | **67.86** |
| gte-Qwen2-1.5B-instruc(https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | 67.16 | 67.65 | 66.60 | 64.04 |
### GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:|
| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB |
| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB |
| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB |
| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB |
| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB |
| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB |
| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB |
| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB |
| [GTE-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) | Multilingual | 32000 | 1536 | 6.62GB |
## Cloud API Services
In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud.
- [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
- [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Community support
### Fine-tuning
GTE models can be fine-tuned with a third party framework SWIFT.
```shell
pip install ms-swift -U
```
```shell
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
NPROC_PER_NODE=$nproc_per_node \
USE_HF=1 \
swift sft \
--model Alibaba-NLP/gte-Qwen2-7B-instruct \
--train_type lora \
--dataset 'sentence-transformers/stsb' \
--torch_dtype bfloat16 \
--num_train_epochs 10 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--use_chat_template false \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type cosine_similarity \
--dataloader_drop_last true
```
## Citation
If you find our paper or models helpful, please consider cite:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
TaylorAI/bge-micro-v2 | TaylorAI | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-11T05:55:09 | 2024-06-06T22:44:08 | 105,079 | 46 | ---
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge_micro
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 67.76119402985074
- type: ap
value: 29.637849284211114
- type: f1
value: 61.31181187111905
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 79.7547
- type: ap
value: 74.21401629809145
- type: f1
value: 79.65319615433783
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.452000000000005
- type: f1
value: 37.0245198854966
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.152
- type: map_at_10
value: 46.702
- type: map_at_100
value: 47.563
- type: map_at_1000
value: 47.567
- type: map_at_3
value: 42.058
- type: map_at_5
value: 44.608
- type: mrr_at_1
value: 32.006
- type: mrr_at_10
value: 47.064
- type: mrr_at_100
value: 47.910000000000004
- type: mrr_at_1000
value: 47.915
- type: mrr_at_3
value: 42.283
- type: mrr_at_5
value: 44.968
- type: ndcg_at_1
value: 31.152
- type: ndcg_at_10
value: 55.308
- type: ndcg_at_100
value: 58.965
- type: ndcg_at_1000
value: 59.067
- type: ndcg_at_3
value: 45.698
- type: ndcg_at_5
value: 50.296
- type: precision_at_1
value: 31.152
- type: precision_at_10
value: 8.279
- type: precision_at_100
value: 0.987
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.753
- type: precision_at_5
value: 13.485
- type: recall_at_1
value: 31.152
- type: recall_at_10
value: 82.788
- type: recall_at_100
value: 98.72
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 56.259
- type: recall_at_5
value: 67.425
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.52692241938116
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 33.245710292773595
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 58.08493637155168
- type: mrr
value: 71.94378490084861
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.1602804378326
- type: cos_sim_spearman
value: 82.92478106365587
- type: euclidean_pearson
value: 82.27930167277077
- type: euclidean_spearman
value: 82.18560759458093
- type: manhattan_pearson
value: 82.34277425888187
- type: manhattan_spearman
value: 81.72776583704467
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.17207792207792
- type: f1
value: 81.09893836310513
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 36.109308463095516
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.06048212317168
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.233999999999998
- type: map_at_10
value: 38.092999999999996
- type: map_at_100
value: 39.473
- type: map_at_1000
value: 39.614
- type: map_at_3
value: 34.839
- type: map_at_5
value: 36.523
- type: mrr_at_1
value: 35.193000000000005
- type: mrr_at_10
value: 44.089
- type: mrr_at_100
value: 44.927
- type: mrr_at_1000
value: 44.988
- type: mrr_at_3
value: 41.559000000000005
- type: mrr_at_5
value: 43.162
- type: ndcg_at_1
value: 35.193000000000005
- type: ndcg_at_10
value: 44.04
- type: ndcg_at_100
value: 49.262
- type: ndcg_at_1000
value: 51.847
- type: ndcg_at_3
value: 39.248
- type: ndcg_at_5
value: 41.298
- type: precision_at_1
value: 35.193000000000005
- type: precision_at_10
value: 8.555
- type: precision_at_100
value: 1.3820000000000001
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 19.123
- type: precision_at_5
value: 13.648
- type: recall_at_1
value: 28.233999999999998
- type: recall_at_10
value: 55.094
- type: recall_at_100
value: 76.85300000000001
- type: recall_at_1000
value: 94.163
- type: recall_at_3
value: 40.782000000000004
- type: recall_at_5
value: 46.796
- type: map_at_1
value: 21.538
- type: map_at_10
value: 28.449
- type: map_at_100
value: 29.471000000000004
- type: map_at_1000
value: 29.599999999999998
- type: map_at_3
value: 26.371
- type: map_at_5
value: 27.58
- type: mrr_at_1
value: 26.815
- type: mrr_at_10
value: 33.331
- type: mrr_at_100
value: 34.114
- type: mrr_at_1000
value: 34.182
- type: mrr_at_3
value: 31.561
- type: mrr_at_5
value: 32.608
- type: ndcg_at_1
value: 26.815
- type: ndcg_at_10
value: 32.67
- type: ndcg_at_100
value: 37.039
- type: ndcg_at_1000
value: 39.769
- type: ndcg_at_3
value: 29.523
- type: ndcg_at_5
value: 31.048
- type: precision_at_1
value: 26.815
- type: precision_at_10
value: 5.955
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 14.033999999999999
- type: precision_at_5
value: 9.911
- type: recall_at_1
value: 21.538
- type: recall_at_10
value: 40.186
- type: recall_at_100
value: 58.948
- type: recall_at_1000
value: 77.158
- type: recall_at_3
value: 30.951
- type: recall_at_5
value: 35.276
- type: map_at_1
value: 35.211999999999996
- type: map_at_10
value: 46.562
- type: map_at_100
value: 47.579
- type: map_at_1000
value: 47.646
- type: map_at_3
value: 43.485
- type: map_at_5
value: 45.206
- type: mrr_at_1
value: 40.627
- type: mrr_at_10
value: 49.928
- type: mrr_at_100
value: 50.647
- type: mrr_at_1000
value: 50.685
- type: mrr_at_3
value: 47.513
- type: mrr_at_5
value: 48.958
- type: ndcg_at_1
value: 40.627
- type: ndcg_at_10
value: 52.217
- type: ndcg_at_100
value: 56.423
- type: ndcg_at_1000
value: 57.821999999999996
- type: ndcg_at_3
value: 46.949000000000005
- type: ndcg_at_5
value: 49.534
- type: precision_at_1
value: 40.627
- type: precision_at_10
value: 8.476
- type: precision_at_100
value: 1.15
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 21.003
- type: precision_at_5
value: 14.469999999999999
- type: recall_at_1
value: 35.211999999999996
- type: recall_at_10
value: 65.692
- type: recall_at_100
value: 84.011
- type: recall_at_1000
value: 94.03099999999999
- type: recall_at_3
value: 51.404
- type: recall_at_5
value: 57.882
- type: map_at_1
value: 22.09
- type: map_at_10
value: 29.516
- type: map_at_100
value: 30.462
- type: map_at_1000
value: 30.56
- type: map_at_3
value: 26.945000000000004
- type: map_at_5
value: 28.421999999999997
- type: mrr_at_1
value: 23.616
- type: mrr_at_10
value: 31.221
- type: mrr_at_100
value: 32.057
- type: mrr_at_1000
value: 32.137
- type: mrr_at_3
value: 28.738000000000003
- type: mrr_at_5
value: 30.156
- type: ndcg_at_1
value: 23.616
- type: ndcg_at_10
value: 33.97
- type: ndcg_at_100
value: 38.806000000000004
- type: ndcg_at_1000
value: 41.393
- type: ndcg_at_3
value: 28.908
- type: ndcg_at_5
value: 31.433
- type: precision_at_1
value: 23.616
- type: precision_at_10
value: 5.299
- type: precision_at_100
value: 0.812
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 12.015
- type: precision_at_5
value: 8.701
- type: recall_at_1
value: 22.09
- type: recall_at_10
value: 46.089999999999996
- type: recall_at_100
value: 68.729
- type: recall_at_1000
value: 88.435
- type: recall_at_3
value: 32.584999999999994
- type: recall_at_5
value: 38.550000000000004
- type: map_at_1
value: 15.469
- type: map_at_10
value: 22.436
- type: map_at_100
value: 23.465
- type: map_at_1000
value: 23.608999999999998
- type: map_at_3
value: 19.716
- type: map_at_5
value: 21.182000000000002
- type: mrr_at_1
value: 18.905
- type: mrr_at_10
value: 26.55
- type: mrr_at_100
value: 27.46
- type: mrr_at_1000
value: 27.553
- type: mrr_at_3
value: 23.921999999999997
- type: mrr_at_5
value: 25.302999999999997
- type: ndcg_at_1
value: 18.905
- type: ndcg_at_10
value: 27.437
- type: ndcg_at_100
value: 32.555
- type: ndcg_at_1000
value: 35.885
- type: ndcg_at_3
value: 22.439
- type: ndcg_at_5
value: 24.666
- type: precision_at_1
value: 18.905
- type: precision_at_10
value: 5.2490000000000006
- type: precision_at_100
value: 0.889
- type: precision_at_1000
value: 0.131
- type: precision_at_3
value: 10.862
- type: precision_at_5
value: 8.085
- type: recall_at_1
value: 15.469
- type: recall_at_10
value: 38.706
- type: recall_at_100
value: 61.242
- type: recall_at_1000
value: 84.84
- type: recall_at_3
value: 24.973
- type: recall_at_5
value: 30.603
- type: map_at_1
value: 24.918000000000003
- type: map_at_10
value: 34.296
- type: map_at_100
value: 35.632000000000005
- type: map_at_1000
value: 35.748999999999995
- type: map_at_3
value: 31.304
- type: map_at_5
value: 33.166000000000004
- type: mrr_at_1
value: 30.703000000000003
- type: mrr_at_10
value: 39.655
- type: mrr_at_100
value: 40.569
- type: mrr_at_1000
value: 40.621
- type: mrr_at_3
value: 37.023
- type: mrr_at_5
value: 38.664
- type: ndcg_at_1
value: 30.703000000000003
- type: ndcg_at_10
value: 39.897
- type: ndcg_at_100
value: 45.777
- type: ndcg_at_1000
value: 48.082
- type: ndcg_at_3
value: 35.122
- type: ndcg_at_5
value: 37.691
- type: precision_at_1
value: 30.703000000000003
- type: precision_at_10
value: 7.305000000000001
- type: precision_at_100
value: 1.208
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 16.811
- type: precision_at_5
value: 12.203999999999999
- type: recall_at_1
value: 24.918000000000003
- type: recall_at_10
value: 51.31
- type: recall_at_100
value: 76.534
- type: recall_at_1000
value: 91.911
- type: recall_at_3
value: 37.855
- type: recall_at_5
value: 44.493
- type: map_at_1
value: 22.416
- type: map_at_10
value: 30.474
- type: map_at_100
value: 31.759999999999998
- type: map_at_1000
value: 31.891000000000002
- type: map_at_3
value: 27.728
- type: map_at_5
value: 29.247
- type: mrr_at_1
value: 28.881
- type: mrr_at_10
value: 36.418
- type: mrr_at_100
value: 37.347
- type: mrr_at_1000
value: 37.415
- type: mrr_at_3
value: 33.942
- type: mrr_at_5
value: 35.386
- type: ndcg_at_1
value: 28.881
- type: ndcg_at_10
value: 35.812
- type: ndcg_at_100
value: 41.574
- type: ndcg_at_1000
value: 44.289
- type: ndcg_at_3
value: 31.239
- type: ndcg_at_5
value: 33.302
- type: precision_at_1
value: 28.881
- type: precision_at_10
value: 6.598
- type: precision_at_100
value: 1.1079999999999999
- type: precision_at_1000
value: 0.151
- type: precision_at_3
value: 14.954
- type: precision_at_5
value: 10.776
- type: recall_at_1
value: 22.416
- type: recall_at_10
value: 46.243
- type: recall_at_100
value: 71.352
- type: recall_at_1000
value: 90.034
- type: recall_at_3
value: 32.873000000000005
- type: recall_at_5
value: 38.632
- type: map_at_1
value: 22.528166666666667
- type: map_at_10
value: 30.317833333333333
- type: map_at_100
value: 31.44108333333333
- type: map_at_1000
value: 31.566666666666666
- type: map_at_3
value: 27.84425
- type: map_at_5
value: 29.233333333333334
- type: mrr_at_1
value: 26.75733333333333
- type: mrr_at_10
value: 34.24425
- type: mrr_at_100
value: 35.11375
- type: mrr_at_1000
value: 35.184333333333335
- type: mrr_at_3
value: 32.01225
- type: mrr_at_5
value: 33.31225
- type: ndcg_at_1
value: 26.75733333333333
- type: ndcg_at_10
value: 35.072583333333334
- type: ndcg_at_100
value: 40.13358333333334
- type: ndcg_at_1000
value: 42.81825
- type: ndcg_at_3
value: 30.79275000000001
- type: ndcg_at_5
value: 32.822
- type: precision_at_1
value: 26.75733333333333
- type: precision_at_10
value: 6.128083333333334
- type: precision_at_100
value: 1.019
- type: precision_at_1000
value: 0.14391666666666664
- type: precision_at_3
value: 14.129916666666665
- type: precision_at_5
value: 10.087416666666668
- type: recall_at_1
value: 22.528166666666667
- type: recall_at_10
value: 45.38341666666667
- type: recall_at_100
value: 67.81791666666668
- type: recall_at_1000
value: 86.71716666666666
- type: recall_at_3
value: 33.38741666666667
- type: recall_at_5
value: 38.62041666666667
- type: map_at_1
value: 21.975
- type: map_at_10
value: 28.144999999999996
- type: map_at_100
value: 28.994999999999997
- type: map_at_1000
value: 29.086000000000002
- type: map_at_3
value: 25.968999999999998
- type: map_at_5
value: 27.321
- type: mrr_at_1
value: 25
- type: mrr_at_10
value: 30.822
- type: mrr_at_100
value: 31.647
- type: mrr_at_1000
value: 31.712
- type: mrr_at_3
value: 28.860000000000003
- type: mrr_at_5
value: 30.041
- type: ndcg_at_1
value: 25
- type: ndcg_at_10
value: 31.929999999999996
- type: ndcg_at_100
value: 36.258
- type: ndcg_at_1000
value: 38.682
- type: ndcg_at_3
value: 27.972
- type: ndcg_at_5
value: 30.089
- type: precision_at_1
value: 25
- type: precision_at_10
value: 4.923
- type: precision_at_100
value: 0.767
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 11.860999999999999
- type: precision_at_5
value: 8.466
- type: recall_at_1
value: 21.975
- type: recall_at_10
value: 41.102
- type: recall_at_100
value: 60.866
- type: recall_at_1000
value: 78.781
- type: recall_at_3
value: 30.268
- type: recall_at_5
value: 35.552
- type: map_at_1
value: 15.845999999999998
- type: map_at_10
value: 21.861
- type: map_at_100
value: 22.798
- type: map_at_1000
value: 22.925
- type: map_at_3
value: 19.922
- type: map_at_5
value: 21.054000000000002
- type: mrr_at_1
value: 19.098000000000003
- type: mrr_at_10
value: 25.397
- type: mrr_at_100
value: 26.246000000000002
- type: mrr_at_1000
value: 26.33
- type: mrr_at_3
value: 23.469
- type: mrr_at_5
value: 24.646
- type: ndcg_at_1
value: 19.098000000000003
- type: ndcg_at_10
value: 25.807999999999996
- type: ndcg_at_100
value: 30.445
- type: ndcg_at_1000
value: 33.666000000000004
- type: ndcg_at_3
value: 22.292
- type: ndcg_at_5
value: 24.075
- type: precision_at_1
value: 19.098000000000003
- type: precision_at_10
value: 4.58
- type: precision_at_100
value: 0.8099999999999999
- type: precision_at_1000
value: 0.126
- type: precision_at_3
value: 10.346
- type: precision_at_5
value: 7.542999999999999
- type: recall_at_1
value: 15.845999999999998
- type: recall_at_10
value: 34.172999999999995
- type: recall_at_100
value: 55.24099999999999
- type: recall_at_1000
value: 78.644
- type: recall_at_3
value: 24.401
- type: recall_at_5
value: 28.938000000000002
- type: map_at_1
value: 22.974
- type: map_at_10
value: 30.108
- type: map_at_100
value: 31.208000000000002
- type: map_at_1000
value: 31.330999999999996
- type: map_at_3
value: 27.889999999999997
- type: map_at_5
value: 29.023
- type: mrr_at_1
value: 26.493
- type: mrr_at_10
value: 33.726
- type: mrr_at_100
value: 34.622
- type: mrr_at_1000
value: 34.703
- type: mrr_at_3
value: 31.575999999999997
- type: mrr_at_5
value: 32.690999999999995
- type: ndcg_at_1
value: 26.493
- type: ndcg_at_10
value: 34.664
- type: ndcg_at_100
value: 39.725
- type: ndcg_at_1000
value: 42.648
- type: ndcg_at_3
value: 30.447999999999997
- type: ndcg_at_5
value: 32.145
- type: precision_at_1
value: 26.493
- type: precision_at_10
value: 5.7090000000000005
- type: precision_at_100
value: 0.9199999999999999
- type: precision_at_1000
value: 0.129
- type: precision_at_3
value: 13.464
- type: precision_at_5
value: 9.384
- type: recall_at_1
value: 22.974
- type: recall_at_10
value: 45.097
- type: recall_at_100
value: 66.908
- type: recall_at_1000
value: 87.495
- type: recall_at_3
value: 33.338
- type: recall_at_5
value: 37.499
- type: map_at_1
value: 22.408
- type: map_at_10
value: 29.580000000000002
- type: map_at_100
value: 31.145
- type: map_at_1000
value: 31.369000000000003
- type: map_at_3
value: 27.634999999999998
- type: map_at_5
value: 28.766000000000002
- type: mrr_at_1
value: 27.272999999999996
- type: mrr_at_10
value: 33.93
- type: mrr_at_100
value: 34.963
- type: mrr_at_1000
value: 35.031
- type: mrr_at_3
value: 32.016
- type: mrr_at_5
value: 33.221000000000004
- type: ndcg_at_1
value: 27.272999999999996
- type: ndcg_at_10
value: 33.993
- type: ndcg_at_100
value: 40.333999999999996
- type: ndcg_at_1000
value: 43.361
- type: ndcg_at_3
value: 30.918
- type: ndcg_at_5
value: 32.552
- type: precision_at_1
value: 27.272999999999996
- type: precision_at_10
value: 6.285
- type: precision_at_100
value: 1.389
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 14.427000000000001
- type: precision_at_5
value: 10.356
- type: recall_at_1
value: 22.408
- type: recall_at_10
value: 41.318
- type: recall_at_100
value: 70.539
- type: recall_at_1000
value: 90.197
- type: recall_at_3
value: 32.513
- type: recall_at_5
value: 37
- type: map_at_1
value: 17.258000000000003
- type: map_at_10
value: 24.294
- type: map_at_100
value: 25.305
- type: map_at_1000
value: 25.419999999999998
- type: map_at_3
value: 22.326999999999998
- type: map_at_5
value: 23.31
- type: mrr_at_1
value: 18.484
- type: mrr_at_10
value: 25.863999999999997
- type: mrr_at_100
value: 26.766000000000002
- type: mrr_at_1000
value: 26.855
- type: mrr_at_3
value: 23.968
- type: mrr_at_5
value: 24.911
- type: ndcg_at_1
value: 18.484
- type: ndcg_at_10
value: 28.433000000000003
- type: ndcg_at_100
value: 33.405
- type: ndcg_at_1000
value: 36.375
- type: ndcg_at_3
value: 24.455
- type: ndcg_at_5
value: 26.031
- type: precision_at_1
value: 18.484
- type: precision_at_10
value: 4.603
- type: precision_at_100
value: 0.773
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 10.659
- type: precision_at_5
value: 7.505000000000001
- type: recall_at_1
value: 17.258000000000003
- type: recall_at_10
value: 39.589999999999996
- type: recall_at_100
value: 62.592000000000006
- type: recall_at_1000
value: 84.917
- type: recall_at_3
value: 28.706
- type: recall_at_5
value: 32.224000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.578999999999999
- type: map_at_10
value: 17.642
- type: map_at_100
value: 19.451
- type: map_at_1000
value: 19.647000000000002
- type: map_at_3
value: 14.618
- type: map_at_5
value: 16.145
- type: mrr_at_1
value: 23.322000000000003
- type: mrr_at_10
value: 34.204
- type: mrr_at_100
value: 35.185
- type: mrr_at_1000
value: 35.235
- type: mrr_at_3
value: 30.847
- type: mrr_at_5
value: 32.824
- type: ndcg_at_1
value: 23.322000000000003
- type: ndcg_at_10
value: 25.352999999999998
- type: ndcg_at_100
value: 32.574
- type: ndcg_at_1000
value: 36.073
- type: ndcg_at_3
value: 20.318
- type: ndcg_at_5
value: 22.111
- type: precision_at_1
value: 23.322000000000003
- type: precision_at_10
value: 8.02
- type: precision_at_100
value: 1.5730000000000002
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 15.049000000000001
- type: precision_at_5
value: 11.87
- type: recall_at_1
value: 10.578999999999999
- type: recall_at_10
value: 30.964999999999996
- type: recall_at_100
value: 55.986000000000004
- type: recall_at_1000
value: 75.565
- type: recall_at_3
value: 18.686
- type: recall_at_5
value: 23.629
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.327
- type: map_at_10
value: 14.904
- type: map_at_100
value: 20.29
- type: map_at_1000
value: 21.42
- type: map_at_3
value: 10.911
- type: map_at_5
value: 12.791
- type: mrr_at_1
value: 57.25
- type: mrr_at_10
value: 66.62700000000001
- type: mrr_at_100
value: 67.035
- type: mrr_at_1000
value: 67.052
- type: mrr_at_3
value: 64.833
- type: mrr_at_5
value: 65.908
- type: ndcg_at_1
value: 43.75
- type: ndcg_at_10
value: 32.246
- type: ndcg_at_100
value: 35.774
- type: ndcg_at_1000
value: 42.872
- type: ndcg_at_3
value: 36.64
- type: ndcg_at_5
value: 34.487
- type: precision_at_1
value: 57.25
- type: precision_at_10
value: 25.924999999999997
- type: precision_at_100
value: 7.670000000000001
- type: precision_at_1000
value: 1.599
- type: precision_at_3
value: 41.167
- type: precision_at_5
value: 34.65
- type: recall_at_1
value: 7.327
- type: recall_at_10
value: 19.625
- type: recall_at_100
value: 41.601
- type: recall_at_1000
value: 65.117
- type: recall_at_3
value: 12.308
- type: recall_at_5
value: 15.437999999999999
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.53
- type: f1
value: 39.39884255816736
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.913000000000004
- type: map_at_10
value: 69.592
- type: map_at_100
value: 69.95599999999999
- type: map_at_1000
value: 69.973
- type: map_at_3
value: 67.716
- type: map_at_5
value: 68.899
- type: mrr_at_1
value: 63.561
- type: mrr_at_10
value: 74.2
- type: mrr_at_100
value: 74.468
- type: mrr_at_1000
value: 74.47500000000001
- type: mrr_at_3
value: 72.442
- type: mrr_at_5
value: 73.58
- type: ndcg_at_1
value: 63.561
- type: ndcg_at_10
value: 74.988
- type: ndcg_at_100
value: 76.52799999999999
- type: ndcg_at_1000
value: 76.88000000000001
- type: ndcg_at_3
value: 71.455
- type: ndcg_at_5
value: 73.42699999999999
- type: precision_at_1
value: 63.561
- type: precision_at_10
value: 9.547
- type: precision_at_100
value: 1.044
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 28.143
- type: precision_at_5
value: 18.008
- type: recall_at_1
value: 58.913000000000004
- type: recall_at_10
value: 87.18
- type: recall_at_100
value: 93.852
- type: recall_at_1000
value: 96.256
- type: recall_at_3
value: 77.55199999999999
- type: recall_at_5
value: 82.42399999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.761000000000001
- type: map_at_10
value: 19.564999999999998
- type: map_at_100
value: 21.099
- type: map_at_1000
value: 21.288999999999998
- type: map_at_3
value: 16.683999999999997
- type: map_at_5
value: 18.307000000000002
- type: mrr_at_1
value: 23.302
- type: mrr_at_10
value: 30.979
- type: mrr_at_100
value: 32.121
- type: mrr_at_1000
value: 32.186
- type: mrr_at_3
value: 28.549000000000003
- type: mrr_at_5
value: 30.038999999999998
- type: ndcg_at_1
value: 23.302
- type: ndcg_at_10
value: 25.592
- type: ndcg_at_100
value: 32.416
- type: ndcg_at_1000
value: 36.277
- type: ndcg_at_3
value: 22.151
- type: ndcg_at_5
value: 23.483999999999998
- type: precision_at_1
value: 23.302
- type: precision_at_10
value: 7.377000000000001
- type: precision_at_100
value: 1.415
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 14.712
- type: precision_at_5
value: 11.358
- type: recall_at_1
value: 11.761000000000001
- type: recall_at_10
value: 31.696
- type: recall_at_100
value: 58.01500000000001
- type: recall_at_1000
value: 81.572
- type: recall_at_3
value: 20.742
- type: recall_at_5
value: 25.707
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.275
- type: map_at_10
value: 44.712
- type: map_at_100
value: 45.621
- type: map_at_1000
value: 45.698
- type: map_at_3
value: 42.016999999999996
- type: map_at_5
value: 43.659
- type: mrr_at_1
value: 64.551
- type: mrr_at_10
value: 71.58099999999999
- type: mrr_at_100
value: 71.952
- type: mrr_at_1000
value: 71.96900000000001
- type: mrr_at_3
value: 70.236
- type: mrr_at_5
value: 71.051
- type: ndcg_at_1
value: 64.551
- type: ndcg_at_10
value: 53.913999999999994
- type: ndcg_at_100
value: 57.421
- type: ndcg_at_1000
value: 59.06
- type: ndcg_at_3
value: 49.716
- type: ndcg_at_5
value: 51.971999999999994
- type: precision_at_1
value: 64.551
- type: precision_at_10
value: 11.110000000000001
- type: precision_at_100
value: 1.388
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 30.822
- type: precision_at_5
value: 20.273
- type: recall_at_1
value: 32.275
- type: recall_at_10
value: 55.55
- type: recall_at_100
value: 69.38600000000001
- type: recall_at_1000
value: 80.35799999999999
- type: recall_at_3
value: 46.232
- type: recall_at_5
value: 50.682
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 76.4604
- type: ap
value: 70.40498168422701
- type: f1
value: 76.38572688476046
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 15.065999999999999
- type: map_at_10
value: 25.058000000000003
- type: map_at_100
value: 26.268
- type: map_at_1000
value: 26.344
- type: map_at_3
value: 21.626
- type: map_at_5
value: 23.513
- type: mrr_at_1
value: 15.501000000000001
- type: mrr_at_10
value: 25.548
- type: mrr_at_100
value: 26.723000000000003
- type: mrr_at_1000
value: 26.793
- type: mrr_at_3
value: 22.142
- type: mrr_at_5
value: 24.024
- type: ndcg_at_1
value: 15.501000000000001
- type: ndcg_at_10
value: 31.008000000000003
- type: ndcg_at_100
value: 37.08
- type: ndcg_at_1000
value: 39.102
- type: ndcg_at_3
value: 23.921999999999997
- type: ndcg_at_5
value: 27.307
- type: precision_at_1
value: 15.501000000000001
- type: precision_at_10
value: 5.155
- type: precision_at_100
value: 0.822
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 10.363
- type: precision_at_5
value: 7.917000000000001
- type: recall_at_1
value: 15.065999999999999
- type: recall_at_10
value: 49.507
- type: recall_at_100
value: 78.118
- type: recall_at_1000
value: 93.881
- type: recall_at_3
value: 30.075000000000003
- type: recall_at_5
value: 38.222
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.6703146374829
- type: f1
value: 90.1258004293966
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 68.29229366165072
- type: f1
value: 50.016194478997875
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.57767316745124
- type: f1
value: 67.16194062146954
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.92064559515804
- type: f1
value: 73.6680729569968
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.56335607367883
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.131807833734268
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.07390328719844
- type: mrr
value: 32.117370992867905
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.274
- type: map_at_10
value: 11.489
- type: map_at_100
value: 14.518
- type: map_at_1000
value: 15.914
- type: map_at_3
value: 8.399
- type: map_at_5
value: 9.889000000000001
- type: mrr_at_1
value: 42.724000000000004
- type: mrr_at_10
value: 51.486
- type: mrr_at_100
value: 51.941
- type: mrr_at_1000
value: 51.99
- type: mrr_at_3
value: 49.278
- type: mrr_at_5
value: 50.485
- type: ndcg_at_1
value: 39.938
- type: ndcg_at_10
value: 31.862000000000002
- type: ndcg_at_100
value: 29.235
- type: ndcg_at_1000
value: 37.802
- type: ndcg_at_3
value: 35.754999999999995
- type: ndcg_at_5
value: 34.447
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 23.901
- type: precision_at_100
value: 7.715
- type: precision_at_1000
value: 2.045
- type: precision_at_3
value: 33.437
- type: precision_at_5
value: 29.782999999999998
- type: recall_at_1
value: 5.274
- type: recall_at_10
value: 15.351
- type: recall_at_100
value: 29.791
- type: recall_at_1000
value: 60.722
- type: recall_at_3
value: 9.411
- type: recall_at_5
value: 12.171999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.099
- type: map_at_10
value: 27.913
- type: map_at_100
value: 29.281000000000002
- type: map_at_1000
value: 29.343999999999998
- type: map_at_3
value: 23.791
- type: map_at_5
value: 26.049
- type: mrr_at_1
value: 18.337
- type: mrr_at_10
value: 29.953999999999997
- type: mrr_at_100
value: 31.080999999999996
- type: mrr_at_1000
value: 31.130000000000003
- type: mrr_at_3
value: 26.168000000000003
- type: mrr_at_5
value: 28.277
- type: ndcg_at_1
value: 18.308
- type: ndcg_at_10
value: 34.938
- type: ndcg_at_100
value: 41.125
- type: ndcg_at_1000
value: 42.708
- type: ndcg_at_3
value: 26.805
- type: ndcg_at_5
value: 30.686999999999998
- type: precision_at_1
value: 18.308
- type: precision_at_10
value: 6.476999999999999
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.784999999999998
- type: precision_at_5
value: 9.878
- type: recall_at_1
value: 16.099
- type: recall_at_10
value: 54.63
- type: recall_at_100
value: 82.24900000000001
- type: recall_at_1000
value: 94.242
- type: recall_at_3
value: 33.174
- type: recall_at_5
value: 42.164
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.947
- type: map_at_10
value: 81.499
- type: map_at_100
value: 82.17
- type: map_at_1000
value: 82.194
- type: map_at_3
value: 78.567
- type: map_at_5
value: 80.34400000000001
- type: mrr_at_1
value: 78.18
- type: mrr_at_10
value: 85.05
- type: mrr_at_100
value: 85.179
- type: mrr_at_1000
value: 85.181
- type: mrr_at_3
value: 83.91
- type: mrr_at_5
value: 84.638
- type: ndcg_at_1
value: 78.2
- type: ndcg_at_10
value: 85.715
- type: ndcg_at_100
value: 87.2
- type: ndcg_at_1000
value: 87.39
- type: ndcg_at_3
value: 82.572
- type: ndcg_at_5
value: 84.176
- type: precision_at_1
value: 78.2
- type: precision_at_10
value: 12.973
- type: precision_at_100
value: 1.5010000000000001
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.949999999999996
- type: precision_at_5
value: 23.62
- type: recall_at_1
value: 67.947
- type: recall_at_10
value: 93.804
- type: recall_at_100
value: 98.971
- type: recall_at_1000
value: 99.91600000000001
- type: recall_at_3
value: 84.75399999999999
- type: recall_at_5
value: 89.32
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 45.457201684255104
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 55.162226937477875
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.173
- type: map_at_10
value: 10.463000000000001
- type: map_at_100
value: 12.278
- type: map_at_1000
value: 12.572
- type: map_at_3
value: 7.528
- type: map_at_5
value: 8.863
- type: mrr_at_1
value: 20.599999999999998
- type: mrr_at_10
value: 30.422
- type: mrr_at_100
value: 31.6
- type: mrr_at_1000
value: 31.663000000000004
- type: mrr_at_3
value: 27.400000000000002
- type: mrr_at_5
value: 29.065
- type: ndcg_at_1
value: 20.599999999999998
- type: ndcg_at_10
value: 17.687
- type: ndcg_at_100
value: 25.172
- type: ndcg_at_1000
value: 30.617
- type: ndcg_at_3
value: 16.81
- type: ndcg_at_5
value: 14.499
- type: precision_at_1
value: 20.599999999999998
- type: precision_at_10
value: 9.17
- type: precision_at_100
value: 2.004
- type: precision_at_1000
value: 0.332
- type: precision_at_3
value: 15.6
- type: precision_at_5
value: 12.58
- type: recall_at_1
value: 4.173
- type: recall_at_10
value: 18.575
- type: recall_at_100
value: 40.692
- type: recall_at_1000
value: 67.467
- type: recall_at_3
value: 9.488000000000001
- type: recall_at_5
value: 12.738
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 81.12603499315416
- type: cos_sim_spearman
value: 73.62060290948378
- type: euclidean_pearson
value: 78.14083565781135
- type: euclidean_spearman
value: 73.16840437541543
- type: manhattan_pearson
value: 77.92017261109734
- type: manhattan_spearman
value: 72.8805059949965
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 79.75955377133172
- type: cos_sim_spearman
value: 71.8872633964069
- type: euclidean_pearson
value: 76.31922068538256
- type: euclidean_spearman
value: 70.86449661855376
- type: manhattan_pearson
value: 76.47852229730407
- type: manhattan_spearman
value: 70.99367421984789
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 78.80762722908158
- type: cos_sim_spearman
value: 79.84588978756372
- type: euclidean_pearson
value: 79.8216849781164
- type: euclidean_spearman
value: 80.22647061695481
- type: manhattan_pearson
value: 79.56604194112572
- type: manhattan_spearman
value: 79.96495189862462
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.1012718092742
- type: cos_sim_spearman
value: 76.86011381793661
- type: euclidean_pearson
value: 79.94426039862019
- type: euclidean_spearman
value: 77.36751135465131
- type: manhattan_pearson
value: 79.87959373304288
- type: manhattan_spearman
value: 77.37717129004746
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.90618420346104
- type: cos_sim_spearman
value: 84.77290791243722
- type: euclidean_pearson
value: 84.64732258073293
- type: euclidean_spearman
value: 85.21053649543357
- type: manhattan_pearson
value: 84.61616883522647
- type: manhattan_spearman
value: 85.19803126766931
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.52192114059063
- type: cos_sim_spearman
value: 81.9103244827937
- type: euclidean_pearson
value: 80.99375176138985
- type: euclidean_spearman
value: 81.540250641079
- type: manhattan_pearson
value: 80.84979573396426
- type: manhattan_spearman
value: 81.3742591621492
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.82166001234197
- type: cos_sim_spearman
value: 86.81857495659123
- type: euclidean_pearson
value: 85.72798403202849
- type: euclidean_spearman
value: 85.70482438950965
- type: manhattan_pearson
value: 85.51579093130357
- type: manhattan_spearman
value: 85.41233705379751
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.48071151079803
- type: cos_sim_spearman
value: 65.37838108084044
- type: euclidean_pearson
value: 64.67378947096257
- type: euclidean_spearman
value: 65.39187147219869
- type: manhattan_pearson
value: 65.35487466133208
- type: manhattan_spearman
value: 65.51328499442272
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.64702367823314
- type: cos_sim_spearman
value: 82.49732953181818
- type: euclidean_pearson
value: 83.05996062475664
- type: euclidean_spearman
value: 82.28159546751176
- type: manhattan_pearson
value: 82.98305503664952
- type: manhattan_spearman
value: 82.18405771943928
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.5744649318696
- type: mrr
value: 93.35386291268645
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.093999999999994
- type: map_at_10
value: 61.646
- type: map_at_100
value: 62.197
- type: map_at_1000
value: 62.22800000000001
- type: map_at_3
value: 58.411
- type: map_at_5
value: 60.585
- type: mrr_at_1
value: 55.00000000000001
- type: mrr_at_10
value: 62.690999999999995
- type: mrr_at_100
value: 63.139
- type: mrr_at_1000
value: 63.166999999999994
- type: mrr_at_3
value: 60.111000000000004
- type: mrr_at_5
value: 61.778
- type: ndcg_at_1
value: 55.00000000000001
- type: ndcg_at_10
value: 66.271
- type: ndcg_at_100
value: 68.879
- type: ndcg_at_1000
value: 69.722
- type: ndcg_at_3
value: 60.672000000000004
- type: ndcg_at_5
value: 63.929
- type: precision_at_1
value: 55.00000000000001
- type: precision_at_10
value: 9
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.555999999999997
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 52.093999999999994
- type: recall_at_10
value: 79.567
- type: recall_at_100
value: 91.60000000000001
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.633
- type: recall_at_5
value: 72.68299999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83267326732673
- type: cos_sim_ap
value: 95.77995366495178
- type: cos_sim_f1
value: 91.51180311401306
- type: cos_sim_precision
value: 91.92734611503532
- type: cos_sim_recall
value: 91.10000000000001
- type: dot_accuracy
value: 99.63366336633663
- type: dot_ap
value: 88.53996286967461
- type: dot_f1
value: 81.06537530266343
- type: dot_precision
value: 78.59154929577464
- type: dot_recall
value: 83.7
- type: euclidean_accuracy
value: 99.82376237623762
- type: euclidean_ap
value: 95.53192209281187
- type: euclidean_f1
value: 91.19683481701286
- type: euclidean_precision
value: 90.21526418786692
- type: euclidean_recall
value: 92.2
- type: manhattan_accuracy
value: 99.82376237623762
- type: manhattan_ap
value: 95.55642082191741
- type: manhattan_f1
value: 91.16186693147964
- type: manhattan_precision
value: 90.53254437869822
- type: manhattan_recall
value: 91.8
- type: max_accuracy
value: 99.83267326732673
- type: max_ap
value: 95.77995366495178
- type: max_f1
value: 91.51180311401306
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 54.508462134213474
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.06549765184959
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.43129549466616
- type: mrr
value: 50.20613169510227
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.069516173193044
- type: cos_sim_spearman
value: 29.872498354017353
- type: dot_pearson
value: 28.80761257516063
- type: dot_spearman
value: 28.397422678527708
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.169
- type: map_at_10
value: 1.208
- type: map_at_100
value: 5.925
- type: map_at_1000
value: 14.427000000000001
- type: map_at_3
value: 0.457
- type: map_at_5
value: 0.716
- type: mrr_at_1
value: 64
- type: mrr_at_10
value: 74.075
- type: mrr_at_100
value: 74.303
- type: mrr_at_1000
value: 74.303
- type: mrr_at_3
value: 71
- type: mrr_at_5
value: 72.89999999999999
- type: ndcg_at_1
value: 57.99999999999999
- type: ndcg_at_10
value: 50.376
- type: ndcg_at_100
value: 38.582
- type: ndcg_at_1000
value: 35.663
- type: ndcg_at_3
value: 55.592
- type: ndcg_at_5
value: 53.647999999999996
- type: precision_at_1
value: 64
- type: precision_at_10
value: 53.2
- type: precision_at_100
value: 39.6
- type: precision_at_1000
value: 16.218
- type: precision_at_3
value: 59.333000000000006
- type: precision_at_5
value: 57.599999999999994
- type: recall_at_1
value: 0.169
- type: recall_at_10
value: 1.423
- type: recall_at_100
value: 9.049999999999999
- type: recall_at_1000
value: 34.056999999999995
- type: recall_at_3
value: 0.48700000000000004
- type: recall_at_5
value: 0.792
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.319
- type: map_at_10
value: 7.112
- type: map_at_100
value: 12.588
- type: map_at_1000
value: 14.056
- type: map_at_3
value: 2.8049999999999997
- type: map_at_5
value: 4.68
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 33.94
- type: mrr_at_100
value: 35.193000000000005
- type: mrr_at_1000
value: 35.193000000000005
- type: mrr_at_3
value: 29.932
- type: mrr_at_5
value: 32.279
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 18.096
- type: ndcg_at_100
value: 30.512
- type: ndcg_at_1000
value: 42.148
- type: ndcg_at_3
value: 17.034
- type: ndcg_at_5
value: 18.509
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 18.776
- type: precision_at_100
value: 7.02
- type: precision_at_1000
value: 1.467
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 22.041
- type: recall_at_1
value: 1.319
- type: recall_at_10
value: 13.748
- type: recall_at_100
value: 43.972
- type: recall_at_1000
value: 79.557
- type: recall_at_3
value: 4.042
- type: recall_at_5
value: 7.742
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.2282
- type: ap
value: 13.995763859570426
- type: f1
value: 54.08126256731344
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 57.64006791171477
- type: f1
value: 57.95841320748957
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 40.19267841788564
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.96614412588663
- type: cos_sim_ap
value: 67.75985678572738
- type: cos_sim_f1
value: 64.04661542276222
- type: cos_sim_precision
value: 60.406922357343305
- type: cos_sim_recall
value: 68.15303430079156
- type: dot_accuracy
value: 79.5732252488526
- type: dot_ap
value: 51.30562107572645
- type: dot_f1
value: 53.120759837177744
- type: dot_precision
value: 46.478037198258804
- type: dot_recall
value: 61.97889182058047
- type: euclidean_accuracy
value: 84.00786791440663
- type: euclidean_ap
value: 67.58930214486998
- type: euclidean_f1
value: 64.424821579775
- type: euclidean_precision
value: 59.4817958454322
- type: euclidean_recall
value: 70.26385224274406
- type: manhattan_accuracy
value: 83.87673600762949
- type: manhattan_ap
value: 67.4250981523309
- type: manhattan_f1
value: 64.10286658015808
- type: manhattan_precision
value: 57.96885001066781
- type: manhattan_recall
value: 71.68865435356201
- type: max_accuracy
value: 84.00786791440663
- type: max_ap
value: 67.75985678572738
- type: max_f1
value: 64.424821579775
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.41347459929368
- type: cos_sim_ap
value: 84.89261930113058
- type: cos_sim_f1
value: 77.13677607258877
- type: cos_sim_precision
value: 74.88581164358733
- type: cos_sim_recall
value: 79.52725592854944
- type: dot_accuracy
value: 86.32359219156285
- type: dot_ap
value: 79.29794992131094
- type: dot_f1
value: 72.84356337679777
- type: dot_precision
value: 67.31761478675462
- type: dot_recall
value: 79.35786880197105
- type: euclidean_accuracy
value: 88.33585593976791
- type: euclidean_ap
value: 84.73257641312746
- type: euclidean_f1
value: 76.83529582788195
- type: euclidean_precision
value: 72.76294052863436
- type: euclidean_recall
value: 81.3905143209116
- type: manhattan_accuracy
value: 88.3086894089339
- type: manhattan_ap
value: 84.66304891729399
- type: manhattan_f1
value: 76.8181650632165
- type: manhattan_precision
value: 73.6864436744219
- type: manhattan_recall
value: 80.22790267939637
- type: max_accuracy
value: 88.41347459929368
- type: max_ap
value: 84.89261930113058
- type: max_f1
value: 77.13677607258877
---
# bge-micro-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
pszemraj/long-t5-tglobal-base-sci-simplify | pszemraj | summarization | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"lay summaries",
"paper summaries",
"biology",
"medical",
"summarization",
"en",
"dataset:pszemraj/scientific_lay_summarisation-plos-norm",
"base_model:google/long-t5-tglobal-base",
"base_model:quantized:google/long-t5-tglobal-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-04-06T23:40:30 | 2023-09-23T20:42:40 | 98,677 | 10 | ---
base_model: google/long-t5-tglobal-base
datasets:
- pszemraj/scientific_lay_summarisation-plos-norm
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: summarization
tags:
- lay summaries
- paper summaries
- biology
- medical
widget:
- text: large earthquakes along a given fault segment do not occur at random intervals
because it takes time to accumulate the strain energy for the rupture. The rates
at which tectonic plates move and accumulate strain at their boundaries are approximately
uniform. Therefore, in first approximation, one may expect that large ruptures
of the same fault segment will occur at approximately constant time intervals.
If subsequent main shocks have different amounts of slip across the fault, then
the recurrence time may vary, and the basic idea of periodic mainshocks must be
modified. For great plate boundary ruptures the length and slip often vary by
a factor of 2. Along the southern segment of the San Andreas fault the recurrence
interval is 145 years with variations of several decades. The smaller the standard
deviation of the average recurrence interval, the more specific could be the long
term prediction of a future mainshock.
example_title: earthquakes
- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
are fed into a neural network that predicts values in the reconstructed domain.
Then, this domain is mapped to the sensor domain where sensor measurements are
available as supervision. Class and Section Problems Addressed Generalization
(Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
Representations (Section 3) Computation & memory efficiency, representation capacity,
editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
in the neural field toolbox each addresses problems that arise in learning, inference,
and control. (Section 3). We can supervise reconstruction via differentiable forward
maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
Section 4) With appropriate network architecture choices, we can overcome neural
network spectral biases (blurriness) and efficiently compute derivatives and integrals
(Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
and to achieve editable representations (Section 6). Collectively, these classes
constitute a ''toolbox'' of techniques to help solve problems with neural fields
There are three components in a conditional neural field: (1) An encoder or inference
function € that outputs the conditioning latent variable 2 given an observation
0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
the inverse conditional probability to find the most probable 0 given Z: arg-
max P(Olz). We discuss different encoding schemes with different optimality guarantees
(Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
prior over the sur- face in its reconstruction domain to generalize to the partial
observations. A neural network expresses a prior via the function space of its
architecture and parameters 0, and generalization is influenced by the inductive
bias of this function space (Section 5).'
example_title: scientific paper
- text: 'Is a else or outside the cob and tree written being of early client rope
and you have is for good reasons. On to the ocean in Orange for time. By''s the
aggregate we can bed it yet. Why this please pick up on a sort is do and also
M Getoi''s nerocos and do rain become you to let so is his brother is made in
use and Mjulia''s''s the lay major is aging Masastup coin present sea only of
Oosii rooms set to you We do er do we easy this private oliiishs lonthen might
be okay. Good afternoon everybody. Welcome to this lecture of Computational Statistics.
As you can see, I''m not socially my name is Michael Zelinger. I''m one of the
task for this class and you might have already seen me in the first lecture where
I made a quick appearance. I''m also going to give the tortillas in the last third
of this course. So to give you a little bit about me, I''m a old student here
with better Bulman and my research centres on casual inference applied to biomedical
disasters, so that could be genomics or that could be hospital data. If any of
you is interested in writing a bachelor thesis, a semester paper may be mastathesis
about this topic feel for reach out to me. you have my name on models and my email
address you can find in the directory I''d Be very happy to talk about it. you
do not need to be sure about it, we can just have a chat. So with that said, let''s
get on with the lecture. There''s an exciting topic today I''m going to start
by sharing some slides with you and later on during the lecture we''ll move to
the paper. So bear with me for a few seconds. Well, the projector is starting
up. Okay, so let''s get started. Today''s topic is a very important one. It''s
about a technique which really forms one of the fundamentals of data science,
machine learning, and any sort of modern statistics. It''s called cross validation.
I know you really want to understand this topic I Want you to understand this
and frankly, nobody''s gonna leave Professor Mineshousen''s class without understanding
cross validation. So to set the stage for this, I Want to introduce you to the
validation problem in computational statistics. So the problem is the following:
You trained a model on available data. You fitted your model, but you know the
training data you got could always have been different and some data from the
environment. Maybe it''s a random process. You do not really know what it is,
but you know that somebody else who gets a different batch of data from the same
environment they would get slightly different training data and you do not care
that your method performs as well. On this training data. you want to to perform
well on other data that you have not seen other data from the same environment.
So in other words, the validation problem is you want to quantify the performance
of your model on data that you have not seen. So how is this even possible? How
could you possibly measure the performance on data that you do not know The solution
to? This is the following realization is that given that you have a bunch of data,
you were in charge. You get to control how much that your model sees. It works
in the following way: You can hide data firms model. Let''s say you have a training
data set which is a bunch of doubtless so X eyes are the features those are typically
hide and national vector. It''s got more than one dimension for sure. And the
why why eyes. Those are the labels for supervised learning. As you''ve seen before,
it''s the same set up as we have in regression. And so you have this training
data and now you choose that you only use some of those data to fit your model.
You''re not going to use everything, you only use some of it the other part you
hide from your model. And then you can use this hidden data to do validation from
the point of you of your model. This hidden data is complete by unseen. In other
words, we solve our problem of validation.'
example_title: transcribed audio - lecture
- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
& memory complexity (where nn is sequence length). Hence, it''s computationally
very expensive to apply transformer-based models on long sequences n > 512n>512.
Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
try to remedy this problem by approximating the full attention matrix. You can
checkout 🤗''s recent blog post in case you are unfamiliar with these models.
BigBird (introduced in paper) is one of such recent models to address this issue.
BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
attention) and can handle sequences up to a length of 4096 at a much lower computational
cost compared to BERT. It has achieved SOTA on various tasks involving very long
sequences such as long documents summarization, question-answering with long contexts.
BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
post is to give the reader an in-depth understanding of big bird implementation
& ease one''s life in using BigBird with 🤗Transformers. But, before going into
more depth, it is important to remember that the BigBird''s attention is an approximation
of BERT''s full attention and therefore does not strive to be better than BERT''s
full attention, but rather to be more efficient. It simply allows to apply transformer-based
models to much longer sequences since BERT''s quadratic memory requirement quickly
becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
would be preferred over block sparse attention (which we are going to discuss
in this post).
If you wonder why we need more compute when working with longer sequences, this
blog post is just right for you!
Some of the main questions one might have when working with standard BERT-like
attention include:
Do all tokens really have to attend to all other tokens? Why not compute attention
only over important tokens? How to decide what tokens are important? How to attend
to just a few tokens in a very efficient way? In this blog post, we will try to
answer those questions.
What tokens should be attended to? We will give a practical example of how attention
works by considering the sentence ''BigBird is now available in HuggingFace for
extractive question answering''. In BERT-like attention, every word would simply
attend to all other tokens.
Let''s think about a sensible choice of key tokens that a queried token actually
only should attend to by writing some pseudo-code. Will will assume that the token
available is queried and build a sensible list of key tokens to attend to.
>>> # let''s consider following sentence as an example >>> example = [''BigBird'',
''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
''question'', ''answering'']
>>> # further let''s assume, we''re trying to understand the representation of
''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
empty `set` and fill up the tokens of our interest as we proceed in this section.
>>> key_tokens = [] # => currently ''available'' token doesn''t have anything
to attend Nearby tokens should be important because, in a sentence (sequence of
words), the current word is highly dependent on neighboring past & future tokens.
This intuition is the idea behind the concept of sliding attention.'
example_title: bigbird blog intro
- text: 'To be fair, you have to have a very high IQ to understand Rick and Morty.
The humour is extremely subtle, and without a solid grasp of theoretical physics
most of the jokes will go over a typical viewer''s head. There''s also Rick''s
nihilistic outlook, which is deftly woven into his characterisation- his personal
philosophy draws heavily from Narodnaya Volya literature, for instance. The fans
understand this stuff; they have the intellectual capacity to truly appreciate
the depths of these jokes, to realise that they''re not just funny- they say something
deep about LIFE. As a consequence people who dislike Rick & Morty truly ARE idiots-
of course they wouldn''t appreciate, for instance, the humour in Rick''s existential
catchphrase ''Wubba Lubba Dub Dub,'' which itself is a cryptic reference to Turgenev''s
Russian epic Fathers and Sons. I''m smirking right now just imagining one of those
addlepated simpletons scratching their heads in confusion as Dan Harmon''s genius
wit unfolds itself on their television screens. What fools.. how I pity them.
😂
And yes, by the way, i DO have a Rick & Morty tattoo. And no, you cannot see it.
It''s for the ladies'' eyes only- and even then they have to demonstrate that
they''re within 5 IQ points of my own (preferably lower) beforehand. Nothin personnel
kid 😎'
example_title: Richard & Mortimer
- text: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey
building, and the tallest structure in Paris. Its base is square, measuring 125
metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed
the Washington Monument to become the tallest man-made structure in the world,
a title it held for 41 years until the Chrysler Building in New York City was
finished in 1930. It was the first structure to reach a height of 300 metres.
Due to the addition of a broadcasting aerial at the top of the tower in 1957,
it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters,
the Eiffel Tower is the second tallest free-standing structure in France after
the Millau Viaduct.
example_title: eiffel
parameters:
max_length: 64
min_length: 8
no_repeat_ngram_size: 3
early_stopping: true
repetition_penalty: 3.5
encoder_no_repeat_ngram_size: 4
length_penalty: 0.4
num_beams: 4
---
# long-t5-tglobal-base-sci-simplify
<a href="https://colab.research.google.com/gist/pszemraj/f0dc02c4d4a5c7ad1d5bf3953251145d/long-t5-tglobal-base-sci-simplify-plos-example-with-textsum.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Exploring how well long-document models trained on "lay summaries" of scientific papers generalize.
> A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts.
## Model description
This model is a fine-tuned version of [google/long-t5-tglobal-base](https://huggingface.co/google/long-t5-tglobal-base) on the `pszemraj/scientific_lay_summarisation-plos-norm` dataset for two epochs.
- The variant trained on the ELIFE subset can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-base-sci-simplify-elife)
## Usage
It's recommended to use this model with [beam search decoding](https://huggingface.co/docs/transformers/generation_strategies#beamsearch-decoding). If you are interested, you can also use the `textsum` util repo to have most of this abstracted for you:
Install with `pip`:
```bash
pip install -U textsum
```
Use in python:
```python
from textsum.summarize import Summarizer
summarizer = Summarizer('pszemraj/long-t5-tglobal-base-sci-simplify')
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)
```
## Intended uses & limitations
- Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.
## Training procedure
### Eval results
It achieves the following results on the evaluation set:
- Loss: 1.6778
- Rouge1: 49.1475
- Rouge2: 18.9281
- Rougel: 26.9893
- Rougelsum: 45.0973
- Gen Len: 399.4125
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.966 | 0.52 | 200 | 1.7171 | 48.6521 | 18.427 | 26.7726 | 44.3947 | 376.335 |
| 1.877 | 1.03 | 400 | 1.6909 | 49.3263 | 18.7945 | 27.0741 | 45.1737 | 382.205 |
| 1.9007 | 1.55 | 600 | 1.6778 | 49.1475 | 18.9281 | 26.9893 | 45.0973 | 399.4125 |
| [
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | [
"BEAR"
] |
BAAI/bge-base-en | BAAI | feature-extraction | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"en",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-08-05T08:03:50 | 2024-04-17T13:00:18 | 94,739 | 57 | ---
language:
- en
license: mit
tags:
- mteb
model-index:
- name: bge-base-en
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.73134328358209
- type: ap
value: 38.97277232632892
- type: f1
value: 69.81740361139785
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.56522500000001
- type: ap
value: 88.88821771869553
- type: f1
value: 92.54817512659696
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.91
- type: f1
value: 46.28536394320311
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.834
- type: map_at_10
value: 53.564
- type: map_at_100
value: 54.230000000000004
- type: map_at_1000
value: 54.235
- type: map_at_3
value: 49.49
- type: map_at_5
value: 51.784
- type: mrr_at_1
value: 39.26
- type: mrr_at_10
value: 53.744
- type: mrr_at_100
value: 54.410000000000004
- type: mrr_at_1000
value: 54.415
- type: mrr_at_3
value: 49.656
- type: mrr_at_5
value: 52.018
- type: ndcg_at_1
value: 38.834
- type: ndcg_at_10
value: 61.487
- type: ndcg_at_100
value: 64.303
- type: ndcg_at_1000
value: 64.408
- type: ndcg_at_3
value: 53.116
- type: ndcg_at_5
value: 57.248
- type: precision_at_1
value: 38.834
- type: precision_at_10
value: 8.663
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.218999999999998
- type: precision_at_5
value: 14.737
- type: recall_at_1
value: 38.834
- type: recall_at_10
value: 86.629
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 63.656
- type: recall_at_5
value: 73.68400000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.88475477433035
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.85053138403176
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.23221013208242
- type: mrr
value: 74.64857318735436
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.4403443247284
- type: cos_sim_spearman
value: 85.5326718115169
- type: euclidean_pearson
value: 86.0114007449595
- type: euclidean_spearman
value: 86.05979225604875
- type: manhattan_pearson
value: 86.05423806568598
- type: manhattan_spearman
value: 86.02485170086835
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.44480519480518
- type: f1
value: 86.41301900941988
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.17547250880036
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.74514172687293
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.096000000000004
- type: map_at_10
value: 43.345
- type: map_at_100
value: 44.73
- type: map_at_1000
value: 44.85
- type: map_at_3
value: 39.956
- type: map_at_5
value: 41.727
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 48.742000000000004
- type: mrr_at_100
value: 49.474000000000004
- type: mrr_at_1000
value: 49.513
- type: mrr_at_3
value: 46.161
- type: mrr_at_5
value: 47.721000000000004
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 49.464999999999996
- type: ndcg_at_100
value: 54.632000000000005
- type: ndcg_at_1000
value: 56.52
- type: ndcg_at_3
value: 44.687
- type: ndcg_at_5
value: 46.814
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 9.471
- type: precision_at_100
value: 1.4909999999999999
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 21.268
- type: precision_at_5
value: 15.079
- type: recall_at_1
value: 32.096000000000004
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 83.075
- type: recall_at_1000
value: 95.178
- type: recall_at_3
value: 47.009
- type: recall_at_5
value: 53.348
- type: map_at_1
value: 32.588
- type: map_at_10
value: 42.251
- type: map_at_100
value: 43.478
- type: map_at_1000
value: 43.617
- type: map_at_3
value: 39.381
- type: map_at_5
value: 41.141
- type: mrr_at_1
value: 41.21
- type: mrr_at_10
value: 48.765
- type: mrr_at_100
value: 49.403000000000006
- type: mrr_at_1000
value: 49.451
- type: mrr_at_3
value: 46.73
- type: mrr_at_5
value: 47.965999999999994
- type: ndcg_at_1
value: 41.21
- type: ndcg_at_10
value: 47.704
- type: ndcg_at_100
value: 51.916
- type: ndcg_at_1000
value: 54.013999999999996
- type: ndcg_at_3
value: 44.007000000000005
- type: ndcg_at_5
value: 45.936
- type: precision_at_1
value: 41.21
- type: precision_at_10
value: 8.885
- type: precision_at_100
value: 1.409
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 21.274
- type: precision_at_5
value: 15.045
- type: recall_at_1
value: 32.588
- type: recall_at_10
value: 56.333
- type: recall_at_100
value: 74.251
- type: recall_at_1000
value: 87.518
- type: recall_at_3
value: 44.962
- type: recall_at_5
value: 50.609
- type: map_at_1
value: 40.308
- type: map_at_10
value: 53.12
- type: map_at_100
value: 54.123
- type: map_at_1000
value: 54.173
- type: map_at_3
value: 50.017999999999994
- type: map_at_5
value: 51.902
- type: mrr_at_1
value: 46.394999999999996
- type: mrr_at_10
value: 56.531
- type: mrr_at_100
value: 57.19800000000001
- type: mrr_at_1000
value: 57.225
- type: mrr_at_3
value: 54.368
- type: mrr_at_5
value: 55.713
- type: ndcg_at_1
value: 46.394999999999996
- type: ndcg_at_10
value: 58.811
- type: ndcg_at_100
value: 62.834
- type: ndcg_at_1000
value: 63.849999999999994
- type: ndcg_at_3
value: 53.88699999999999
- type: ndcg_at_5
value: 56.477999999999994
- type: precision_at_1
value: 46.394999999999996
- type: precision_at_10
value: 9.398
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.539
- type: recall_at_1
value: 40.308
- type: recall_at_10
value: 72.146
- type: recall_at_100
value: 89.60900000000001
- type: recall_at_1000
value: 96.733
- type: recall_at_3
value: 58.91499999999999
- type: recall_at_5
value: 65.34299999999999
- type: map_at_1
value: 27.383000000000003
- type: map_at_10
value: 35.802
- type: map_at_100
value: 36.756
- type: map_at_1000
value: 36.826
- type: map_at_3
value: 32.923
- type: map_at_5
value: 34.577999999999996
- type: mrr_at_1
value: 29.604999999999997
- type: mrr_at_10
value: 37.918
- type: mrr_at_100
value: 38.732
- type: mrr_at_1000
value: 38.786
- type: mrr_at_3
value: 35.198
- type: mrr_at_5
value: 36.808
- type: ndcg_at_1
value: 29.604999999999997
- type: ndcg_at_10
value: 40.836
- type: ndcg_at_100
value: 45.622
- type: ndcg_at_1000
value: 47.427
- type: ndcg_at_3
value: 35.208
- type: ndcg_at_5
value: 38.066
- type: precision_at_1
value: 29.604999999999997
- type: precision_at_10
value: 6.226
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 14.463000000000001
- type: precision_at_5
value: 10.35
- type: recall_at_1
value: 27.383000000000003
- type: recall_at_10
value: 54.434000000000005
- type: recall_at_100
value: 76.632
- type: recall_at_1000
value: 90.25
- type: recall_at_3
value: 39.275
- type: recall_at_5
value: 46.225
- type: map_at_1
value: 17.885
- type: map_at_10
value: 25.724000000000004
- type: map_at_100
value: 26.992
- type: map_at_1000
value: 27.107999999999997
- type: map_at_3
value: 23.04
- type: map_at_5
value: 24.529
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 30.548
- type: mrr_at_100
value: 31.593
- type: mrr_at_1000
value: 31.657999999999998
- type: mrr_at_3
value: 27.756999999999998
- type: mrr_at_5
value: 29.398999999999997
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 30.902
- type: ndcg_at_100
value: 36.918
- type: ndcg_at_1000
value: 39.735
- type: ndcg_at_3
value: 25.915
- type: ndcg_at_5
value: 28.255999999999997
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 5.634
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.055
- type: recall_at_1
value: 17.885
- type: recall_at_10
value: 42.237
- type: recall_at_100
value: 68.489
- type: recall_at_1000
value: 88.721
- type: recall_at_3
value: 28.283
- type: recall_at_5
value: 34.300000000000004
- type: map_at_1
value: 29.737000000000002
- type: map_at_10
value: 39.757
- type: map_at_100
value: 40.992
- type: map_at_1000
value: 41.102
- type: map_at_3
value: 36.612
- type: map_at_5
value: 38.413000000000004
- type: mrr_at_1
value: 35.804
- type: mrr_at_10
value: 45.178000000000004
- type: mrr_at_100
value: 45.975
- type: mrr_at_1000
value: 46.021
- type: mrr_at_3
value: 42.541000000000004
- type: mrr_at_5
value: 44.167
- type: ndcg_at_1
value: 35.804
- type: ndcg_at_10
value: 45.608
- type: ndcg_at_100
value: 50.746
- type: ndcg_at_1000
value: 52.839999999999996
- type: ndcg_at_3
value: 40.52
- type: ndcg_at_5
value: 43.051
- type: precision_at_1
value: 35.804
- type: precision_at_10
value: 8.104
- type: precision_at_100
value: 1.256
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 19.121
- type: precision_at_5
value: 13.532
- type: recall_at_1
value: 29.737000000000002
- type: recall_at_10
value: 57.66
- type: recall_at_100
value: 79.121
- type: recall_at_1000
value: 93.023
- type: recall_at_3
value: 43.13
- type: recall_at_5
value: 49.836000000000006
- type: map_at_1
value: 26.299
- type: map_at_10
value: 35.617
- type: map_at_100
value: 36.972
- type: map_at_1000
value: 37.096000000000004
- type: map_at_3
value: 32.653999999999996
- type: map_at_5
value: 34.363
- type: mrr_at_1
value: 32.877
- type: mrr_at_10
value: 41.423
- type: mrr_at_100
value: 42.333999999999996
- type: mrr_at_1000
value: 42.398
- type: mrr_at_3
value: 39.193
- type: mrr_at_5
value: 40.426
- type: ndcg_at_1
value: 32.877
- type: ndcg_at_10
value: 41.271
- type: ndcg_at_100
value: 46.843
- type: ndcg_at_1000
value: 49.366
- type: ndcg_at_3
value: 36.735
- type: ndcg_at_5
value: 38.775999999999996
- type: precision_at_1
value: 32.877
- type: precision_at_10
value: 7.580000000000001
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.541999999999998
- type: precision_at_5
value: 12.443
- type: recall_at_1
value: 26.299
- type: recall_at_10
value: 52.256
- type: recall_at_100
value: 75.919
- type: recall_at_1000
value: 93.185
- type: recall_at_3
value: 39.271
- type: recall_at_5
value: 44.901
- type: map_at_1
value: 27.05741666666667
- type: map_at_10
value: 36.086416666666665
- type: map_at_100
value: 37.26916666666667
- type: map_at_1000
value: 37.38191666666666
- type: map_at_3
value: 33.34225
- type: map_at_5
value: 34.86425
- type: mrr_at_1
value: 32.06008333333333
- type: mrr_at_10
value: 40.36658333333333
- type: mrr_at_100
value: 41.206500000000005
- type: mrr_at_1000
value: 41.261083333333325
- type: mrr_at_3
value: 38.01208333333334
- type: mrr_at_5
value: 39.36858333333333
- type: ndcg_at_1
value: 32.06008333333333
- type: ndcg_at_10
value: 41.3535
- type: ndcg_at_100
value: 46.42066666666666
- type: ndcg_at_1000
value: 48.655166666666666
- type: ndcg_at_3
value: 36.78041666666667
- type: ndcg_at_5
value: 38.91783333333334
- type: precision_at_1
value: 32.06008333333333
- type: precision_at_10
value: 7.169833333333332
- type: precision_at_100
value: 1.1395
- type: precision_at_1000
value: 0.15158333333333332
- type: precision_at_3
value: 16.852
- type: precision_at_5
value: 11.8645
- type: recall_at_1
value: 27.05741666666667
- type: recall_at_10
value: 52.64491666666666
- type: recall_at_100
value: 74.99791666666667
- type: recall_at_1000
value: 90.50524999999999
- type: recall_at_3
value: 39.684000000000005
- type: recall_at_5
value: 45.37225
- type: map_at_1
value: 25.607999999999997
- type: map_at_10
value: 32.28
- type: map_at_100
value: 33.261
- type: map_at_1000
value: 33.346
- type: map_at_3
value: 30.514999999999997
- type: map_at_5
value: 31.415
- type: mrr_at_1
value: 28.988000000000003
- type: mrr_at_10
value: 35.384
- type: mrr_at_100
value: 36.24
- type: mrr_at_1000
value: 36.299
- type: mrr_at_3
value: 33.717000000000006
- type: mrr_at_5
value: 34.507
- type: ndcg_at_1
value: 28.988000000000003
- type: ndcg_at_10
value: 36.248000000000005
- type: ndcg_at_100
value: 41.034
- type: ndcg_at_1000
value: 43.35
- type: ndcg_at_3
value: 32.987
- type: ndcg_at_5
value: 34.333999999999996
- type: precision_at_1
value: 28.988000000000003
- type: precision_at_10
value: 5.506
- type: precision_at_100
value: 0.853
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.11
- type: precision_at_5
value: 9.417
- type: recall_at_1
value: 25.607999999999997
- type: recall_at_10
value: 45.344
- type: recall_at_100
value: 67.132
- type: recall_at_1000
value: 84.676
- type: recall_at_3
value: 36.02
- type: recall_at_5
value: 39.613
- type: map_at_1
value: 18.44
- type: map_at_10
value: 25.651000000000003
- type: map_at_100
value: 26.735
- type: map_at_1000
value: 26.86
- type: map_at_3
value: 23.409
- type: map_at_5
value: 24.604
- type: mrr_at_1
value: 22.195
- type: mrr_at_10
value: 29.482000000000003
- type: mrr_at_100
value: 30.395
- type: mrr_at_1000
value: 30.471999999999998
- type: mrr_at_3
value: 27.409
- type: mrr_at_5
value: 28.553
- type: ndcg_at_1
value: 22.195
- type: ndcg_at_10
value: 30.242
- type: ndcg_at_100
value: 35.397
- type: ndcg_at_1000
value: 38.287
- type: ndcg_at_3
value: 26.201
- type: ndcg_at_5
value: 28.008
- type: precision_at_1
value: 22.195
- type: precision_at_10
value: 5.372
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 12.228
- type: precision_at_5
value: 8.727
- type: recall_at_1
value: 18.44
- type: recall_at_10
value: 40.325
- type: recall_at_100
value: 63.504000000000005
- type: recall_at_1000
value: 83.909
- type: recall_at_3
value: 28.925
- type: recall_at_5
value: 33.641
- type: map_at_1
value: 26.535999999999998
- type: map_at_10
value: 35.358000000000004
- type: map_at_100
value: 36.498999999999995
- type: map_at_1000
value: 36.597
- type: map_at_3
value: 32.598
- type: map_at_5
value: 34.185
- type: mrr_at_1
value: 31.25
- type: mrr_at_10
value: 39.593
- type: mrr_at_100
value: 40.443
- type: mrr_at_1000
value: 40.498
- type: mrr_at_3
value: 37.018
- type: mrr_at_5
value: 38.492
- type: ndcg_at_1
value: 31.25
- type: ndcg_at_10
value: 40.71
- type: ndcg_at_100
value: 46.079
- type: ndcg_at_1000
value: 48.287
- type: ndcg_at_3
value: 35.667
- type: ndcg_at_5
value: 38.080000000000005
- type: precision_at_1
value: 31.25
- type: precision_at_10
value: 6.847
- type: precision_at_100
value: 1.079
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 16.262
- type: precision_at_5
value: 11.455
- type: recall_at_1
value: 26.535999999999998
- type: recall_at_10
value: 52.92099999999999
- type: recall_at_100
value: 76.669
- type: recall_at_1000
value: 92.096
- type: recall_at_3
value: 38.956
- type: recall_at_5
value: 45.239000000000004
- type: map_at_1
value: 24.691
- type: map_at_10
value: 33.417
- type: map_at_100
value: 35.036
- type: map_at_1000
value: 35.251
- type: map_at_3
value: 30.646
- type: map_at_5
value: 32.177
- type: mrr_at_1
value: 30.04
- type: mrr_at_10
value: 37.905
- type: mrr_at_100
value: 38.929
- type: mrr_at_1000
value: 38.983000000000004
- type: mrr_at_3
value: 35.276999999999994
- type: mrr_at_5
value: 36.897000000000006
- type: ndcg_at_1
value: 30.04
- type: ndcg_at_10
value: 39.037
- type: ndcg_at_100
value: 44.944
- type: ndcg_at_1000
value: 47.644
- type: ndcg_at_3
value: 34.833999999999996
- type: ndcg_at_5
value: 36.83
- type: precision_at_1
value: 30.04
- type: precision_at_10
value: 7.4510000000000005
- type: precision_at_100
value: 1.492
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 11.897
- type: recall_at_1
value: 24.691
- type: recall_at_10
value: 49.303999999999995
- type: recall_at_100
value: 76.20400000000001
- type: recall_at_1000
value: 93.30000000000001
- type: recall_at_3
value: 36.594
- type: recall_at_5
value: 42.41
- type: map_at_1
value: 23.118
- type: map_at_10
value: 30.714999999999996
- type: map_at_100
value: 31.656000000000002
- type: map_at_1000
value: 31.757
- type: map_at_3
value: 28.355000000000004
- type: map_at_5
value: 29.337000000000003
- type: mrr_at_1
value: 25.323
- type: mrr_at_10
value: 32.93
- type: mrr_at_100
value: 33.762
- type: mrr_at_1000
value: 33.829
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 31.774
- type: ndcg_at_1
value: 25.323
- type: ndcg_at_10
value: 35.408
- type: ndcg_at_100
value: 40.083
- type: ndcg_at_1000
value: 42.542
- type: ndcg_at_3
value: 30.717
- type: ndcg_at_5
value: 32.385000000000005
- type: precision_at_1
value: 25.323
- type: precision_at_10
value: 5.564
- type: precision_at_100
value: 0.843
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 13.001
- type: precision_at_5
value: 8.834999999999999
- type: recall_at_1
value: 23.118
- type: recall_at_10
value: 47.788000000000004
- type: recall_at_100
value: 69.37
- type: recall_at_1000
value: 87.47399999999999
- type: recall_at_3
value: 34.868
- type: recall_at_5
value: 39.001999999999995
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.288
- type: map_at_10
value: 23.256
- type: map_at_100
value: 25.115
- type: map_at_1000
value: 25.319000000000003
- type: map_at_3
value: 20.005
- type: map_at_5
value: 21.529999999999998
- type: mrr_at_1
value: 31.401
- type: mrr_at_10
value: 42.251
- type: mrr_at_100
value: 43.236999999999995
- type: mrr_at_1000
value: 43.272
- type: mrr_at_3
value: 39.164
- type: mrr_at_5
value: 40.881
- type: ndcg_at_1
value: 31.401
- type: ndcg_at_10
value: 31.615
- type: ndcg_at_100
value: 38.982
- type: ndcg_at_1000
value: 42.496
- type: ndcg_at_3
value: 26.608999999999998
- type: ndcg_at_5
value: 28.048000000000002
- type: precision_at_1
value: 31.401
- type: precision_at_10
value: 9.536999999999999
- type: precision_at_100
value: 1.763
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 19.153000000000002
- type: precision_at_5
value: 14.228
- type: recall_at_1
value: 14.288
- type: recall_at_10
value: 36.717
- type: recall_at_100
value: 61.9
- type: recall_at_1000
value: 81.676
- type: recall_at_3
value: 24.203
- type: recall_at_5
value: 28.793999999999997
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.019
- type: map_at_10
value: 19.963
- type: map_at_100
value: 28.834
- type: map_at_1000
value: 30.537999999999997
- type: map_at_3
value: 14.45
- type: map_at_5
value: 16.817999999999998
- type: mrr_at_1
value: 65.75
- type: mrr_at_10
value: 74.646
- type: mrr_at_100
value: 74.946
- type: mrr_at_1000
value: 74.95100000000001
- type: mrr_at_3
value: 72.625
- type: mrr_at_5
value: 74.012
- type: ndcg_at_1
value: 54
- type: ndcg_at_10
value: 42.014
- type: ndcg_at_100
value: 47.527
- type: ndcg_at_1000
value: 54.911
- type: ndcg_at_3
value: 46.586
- type: ndcg_at_5
value: 43.836999999999996
- type: precision_at_1
value: 65.75
- type: precision_at_10
value: 33.475
- type: precision_at_100
value: 11.16
- type: precision_at_1000
value: 2.145
- type: precision_at_3
value: 50.083
- type: precision_at_5
value: 42.55
- type: recall_at_1
value: 9.019
- type: recall_at_10
value: 25.558999999999997
- type: recall_at_100
value: 53.937999999999995
- type: recall_at_1000
value: 77.67399999999999
- type: recall_at_3
value: 15.456
- type: recall_at_5
value: 19.259
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.635
- type: f1
value: 47.692783881403926
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 76.893
- type: map_at_10
value: 84.897
- type: map_at_100
value: 85.122
- type: map_at_1000
value: 85.135
- type: map_at_3
value: 83.88
- type: map_at_5
value: 84.565
- type: mrr_at_1
value: 83.003
- type: mrr_at_10
value: 89.506
- type: mrr_at_100
value: 89.574
- type: mrr_at_1000
value: 89.575
- type: mrr_at_3
value: 88.991
- type: mrr_at_5
value: 89.349
- type: ndcg_at_1
value: 83.003
- type: ndcg_at_10
value: 88.351
- type: ndcg_at_100
value: 89.128
- type: ndcg_at_1000
value: 89.34100000000001
- type: ndcg_at_3
value: 86.92
- type: ndcg_at_5
value: 87.78200000000001
- type: precision_at_1
value: 83.003
- type: precision_at_10
value: 10.517999999999999
- type: precision_at_100
value: 1.115
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.062999999999995
- type: precision_at_5
value: 20.498
- type: recall_at_1
value: 76.893
- type: recall_at_10
value: 94.374
- type: recall_at_100
value: 97.409
- type: recall_at_1000
value: 98.687
- type: recall_at_3
value: 90.513
- type: recall_at_5
value: 92.709
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.829
- type: map_at_10
value: 32.86
- type: map_at_100
value: 34.838
- type: map_at_1000
value: 35.006
- type: map_at_3
value: 28.597
- type: map_at_5
value: 31.056
- type: mrr_at_1
value: 41.358
- type: mrr_at_10
value: 49.542
- type: mrr_at_100
value: 50.29900000000001
- type: mrr_at_1000
value: 50.334999999999994
- type: mrr_at_3
value: 46.579
- type: mrr_at_5
value: 48.408
- type: ndcg_at_1
value: 41.358
- type: ndcg_at_10
value: 40.758
- type: ndcg_at_100
value: 47.799
- type: ndcg_at_1000
value: 50.589
- type: ndcg_at_3
value: 36.695
- type: ndcg_at_5
value: 38.193
- type: precision_at_1
value: 41.358
- type: precision_at_10
value: 11.142000000000001
- type: precision_at_100
value: 1.8350000000000002
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 24.023
- type: precision_at_5
value: 17.963
- type: recall_at_1
value: 20.829
- type: recall_at_10
value: 47.467999999999996
- type: recall_at_100
value: 73.593
- type: recall_at_1000
value: 90.122
- type: recall_at_3
value: 32.74
- type: recall_at_5
value: 39.608
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.324
- type: map_at_10
value: 64.183
- type: map_at_100
value: 65.037
- type: map_at_1000
value: 65.094
- type: map_at_3
value: 60.663
- type: map_at_5
value: 62.951
- type: mrr_at_1
value: 80.648
- type: mrr_at_10
value: 86.005
- type: mrr_at_100
value: 86.157
- type: mrr_at_1000
value: 86.162
- type: mrr_at_3
value: 85.116
- type: mrr_at_5
value: 85.703
- type: ndcg_at_1
value: 80.648
- type: ndcg_at_10
value: 72.351
- type: ndcg_at_100
value: 75.279
- type: ndcg_at_1000
value: 76.357
- type: ndcg_at_3
value: 67.484
- type: ndcg_at_5
value: 70.31500000000001
- type: precision_at_1
value: 80.648
- type: precision_at_10
value: 15.103
- type: precision_at_100
value: 1.7399999999999998
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 43.232
- type: precision_at_5
value: 28.165000000000003
- type: recall_at_1
value: 40.324
- type: recall_at_10
value: 75.517
- type: recall_at_100
value: 86.982
- type: recall_at_1000
value: 94.072
- type: recall_at_3
value: 64.848
- type: recall_at_5
value: 70.41199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.4
- type: ap
value: 87.4422032289312
- type: f1
value: 91.39249564302281
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.03
- type: map_at_10
value: 34.402
- type: map_at_100
value: 35.599
- type: map_at_1000
value: 35.648
- type: map_at_3
value: 30.603
- type: map_at_5
value: 32.889
- type: mrr_at_1
value: 22.679
- type: mrr_at_10
value: 35.021
- type: mrr_at_100
value: 36.162
- type: mrr_at_1000
value: 36.205
- type: mrr_at_3
value: 31.319999999999997
- type: mrr_at_5
value: 33.562
- type: ndcg_at_1
value: 22.692999999999998
- type: ndcg_at_10
value: 41.258
- type: ndcg_at_100
value: 46.967
- type: ndcg_at_1000
value: 48.175000000000004
- type: ndcg_at_3
value: 33.611000000000004
- type: ndcg_at_5
value: 37.675
- type: precision_at_1
value: 22.692999999999998
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.936
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.413
- type: precision_at_5
value: 10.702
- type: recall_at_1
value: 22.03
- type: recall_at_10
value: 62.248000000000005
- type: recall_at_100
value: 88.524
- type: recall_at_1000
value: 97.714
- type: recall_at_3
value: 41.617
- type: recall_at_5
value: 51.359
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.36844505243957
- type: f1
value: 94.12408743818202
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.43410852713177
- type: f1
value: 58.501855709435624
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.04909213180902
- type: f1
value: 74.1800860395823
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.76126429051781
- type: f1
value: 79.85705217473232
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.70119520292863
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.33544316467486
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.75499243990726
- type: mrr
value: 31.70602251821063
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.451999999999999
- type: map_at_10
value: 13.918
- type: map_at_100
value: 17.316000000000003
- type: map_at_1000
value: 18.747
- type: map_at_3
value: 10.471
- type: map_at_5
value: 12.104
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.717000000000006
- type: mrr_at_100
value: 56.249
- type: mrr_at_1000
value: 56.288000000000004
- type: mrr_at_3
value: 53.818
- type: mrr_at_5
value: 55.103
- type: ndcg_at_1
value: 45.201
- type: ndcg_at_10
value: 35.539
- type: ndcg_at_100
value: 32.586
- type: ndcg_at_1000
value: 41.486000000000004
- type: ndcg_at_3
value: 41.174
- type: ndcg_at_5
value: 38.939
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 25.944
- type: precision_at_100
value: 8.084
- type: precision_at_1000
value: 2.076
- type: precision_at_3
value: 38.7
- type: precision_at_5
value: 33.56
- type: recall_at_1
value: 6.451999999999999
- type: recall_at_10
value: 17.302
- type: recall_at_100
value: 32.14
- type: recall_at_1000
value: 64.12
- type: recall_at_3
value: 11.219
- type: recall_at_5
value: 13.993
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.037
- type: map_at_10
value: 46.565
- type: map_at_100
value: 47.606
- type: map_at_1000
value: 47.636
- type: map_at_3
value: 42.459
- type: map_at_5
value: 44.762
- type: mrr_at_1
value: 36.181999999999995
- type: mrr_at_10
value: 49.291000000000004
- type: mrr_at_100
value: 50.059
- type: mrr_at_1000
value: 50.078
- type: mrr_at_3
value: 45.829
- type: mrr_at_5
value: 47.797
- type: ndcg_at_1
value: 36.153
- type: ndcg_at_10
value: 53.983000000000004
- type: ndcg_at_100
value: 58.347
- type: ndcg_at_1000
value: 59.058
- type: ndcg_at_3
value: 46.198
- type: ndcg_at_5
value: 50.022
- type: precision_at_1
value: 36.153
- type: precision_at_10
value: 8.763
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 20.751
- type: precision_at_5
value: 14.646999999999998
- type: recall_at_1
value: 32.037
- type: recall_at_10
value: 74.008
- type: recall_at_100
value: 92.893
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 53.705999999999996
- type: recall_at_5
value: 62.495
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.152
- type: map_at_10
value: 85.104
- type: map_at_100
value: 85.745
- type: map_at_1000
value: 85.761
- type: map_at_3
value: 82.175
- type: map_at_5
value: 84.066
- type: mrr_at_1
value: 82.03
- type: mrr_at_10
value: 88.115
- type: mrr_at_100
value: 88.21
- type: mrr_at_1000
value: 88.211
- type: mrr_at_3
value: 87.19200000000001
- type: mrr_at_5
value: 87.85
- type: ndcg_at_1
value: 82.03
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.96300000000001
- type: ndcg_at_1000
value: 90.056
- type: ndcg_at_3
value: 86.051
- type: ndcg_at_5
value: 87.63499999999999
- type: precision_at_1
value: 82.03
- type: precision_at_10
value: 13.450000000000001
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.627
- type: precision_at_5
value: 24.784
- type: recall_at_1
value: 71.152
- type: recall_at_10
value: 95.649
- type: recall_at_100
value: 99.58200000000001
- type: recall_at_1000
value: 99.981
- type: recall_at_3
value: 87.767
- type: recall_at_5
value: 92.233
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.48713646277477
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.394940772438545
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.043
- type: map_at_10
value: 12.949
- type: map_at_100
value: 15.146
- type: map_at_1000
value: 15.495000000000001
- type: map_at_3
value: 9.333
- type: map_at_5
value: 11.312999999999999
- type: mrr_at_1
value: 24.9
- type: mrr_at_10
value: 35.958
- type: mrr_at_100
value: 37.152
- type: mrr_at_1000
value: 37.201
- type: mrr_at_3
value: 32.667
- type: mrr_at_5
value: 34.567
- type: ndcg_at_1
value: 24.9
- type: ndcg_at_10
value: 21.298000000000002
- type: ndcg_at_100
value: 29.849999999999998
- type: ndcg_at_1000
value: 35.506
- type: ndcg_at_3
value: 20.548
- type: ndcg_at_5
value: 18.064
- type: precision_at_1
value: 24.9
- type: precision_at_10
value: 10.9
- type: precision_at_100
value: 2.331
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 19.267
- type: precision_at_5
value: 15.939999999999998
- type: recall_at_1
value: 5.043
- type: recall_at_10
value: 22.092
- type: recall_at_100
value: 47.323
- type: recall_at_1000
value: 74.553
- type: recall_at_3
value: 11.728
- type: recall_at_5
value: 16.188
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.7007085938325
- type: cos_sim_spearman
value: 80.0171084446234
- type: euclidean_pearson
value: 81.28133218355893
- type: euclidean_spearman
value: 79.99291731740131
- type: manhattan_pearson
value: 81.22926922327846
- type: manhattan_spearman
value: 79.94444878127038
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.7411883252923
- type: cos_sim_spearman
value: 77.93462937801245
- type: euclidean_pearson
value: 83.00858563882404
- type: euclidean_spearman
value: 77.82717362433257
- type: manhattan_pearson
value: 82.92887645790769
- type: manhattan_spearman
value: 77.78807488222115
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.04222459361023
- type: cos_sim_spearman
value: 83.85931509330395
- type: euclidean_pearson
value: 83.26916063876055
- type: euclidean_spearman
value: 83.98621985648353
- type: manhattan_pearson
value: 83.14935679184327
- type: manhattan_spearman
value: 83.87938828586304
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.41136639535318
- type: cos_sim_spearman
value: 81.51200091040481
- type: euclidean_pearson
value: 81.45382456114775
- type: euclidean_spearman
value: 81.46201181707931
- type: manhattan_pearson
value: 81.37243088439584
- type: manhattan_spearman
value: 81.39828421893426
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.71942451732227
- type: cos_sim_spearman
value: 87.33044482064973
- type: euclidean_pearson
value: 86.58580899365178
- type: euclidean_spearman
value: 87.09206723832895
- type: manhattan_pearson
value: 86.47460784157013
- type: manhattan_spearman
value: 86.98367656583076
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.55868078863449
- type: cos_sim_spearman
value: 85.38299230074065
- type: euclidean_pearson
value: 84.64715256244595
- type: euclidean_spearman
value: 85.49112229604047
- type: manhattan_pearson
value: 84.60814346792462
- type: manhattan_spearman
value: 85.44886026766822
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.99292526370614
- type: cos_sim_spearman
value: 85.58139465695983
- type: euclidean_pearson
value: 86.51325066734084
- type: euclidean_spearman
value: 85.56736418284562
- type: manhattan_pearson
value: 86.48190836601357
- type: manhattan_spearman
value: 85.51616256224258
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.54124715078807
- type: cos_sim_spearman
value: 65.32134275948374
- type: euclidean_pearson
value: 67.09791698300816
- type: euclidean_spearman
value: 65.79468982468465
- type: manhattan_pearson
value: 67.13304723693966
- type: manhattan_spearman
value: 65.68439995849283
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.4231099581624
- type: cos_sim_spearman
value: 85.95475815226862
- type: euclidean_pearson
value: 85.00339401999706
- type: euclidean_spearman
value: 85.74133081802971
- type: manhattan_pearson
value: 85.00407987181666
- type: manhattan_spearman
value: 85.77509596397363
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.25666719585716
- type: mrr
value: 96.32769917083642
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.828
- type: map_at_10
value: 68.369
- type: map_at_100
value: 68.83399999999999
- type: map_at_1000
value: 68.856
- type: map_at_3
value: 65.38000000000001
- type: map_at_5
value: 67.06299999999999
- type: mrr_at_1
value: 61
- type: mrr_at_10
value: 69.45400000000001
- type: mrr_at_100
value: 69.785
- type: mrr_at_1000
value: 69.807
- type: mrr_at_3
value: 67
- type: mrr_at_5
value: 68.43299999999999
- type: ndcg_at_1
value: 61
- type: ndcg_at_10
value: 73.258
- type: ndcg_at_100
value: 75.173
- type: ndcg_at_1000
value: 75.696
- type: ndcg_at_3
value: 68.162
- type: ndcg_at_5
value: 70.53399999999999
- type: precision_at_1
value: 61
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 57.828
- type: recall_at_10
value: 87.122
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 73.139
- type: recall_at_5
value: 79.361
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85247524752475
- type: cos_sim_ap
value: 96.25640197639723
- type: cos_sim_f1
value: 92.37851662404091
- type: cos_sim_precision
value: 94.55497382198953
- type: cos_sim_recall
value: 90.3
- type: dot_accuracy
value: 99.76138613861386
- type: dot_ap
value: 93.40295864389073
- type: dot_f1
value: 87.64267990074441
- type: dot_precision
value: 86.99507389162562
- type: dot_recall
value: 88.3
- type: euclidean_accuracy
value: 99.85049504950496
- type: euclidean_ap
value: 96.24254350525462
- type: euclidean_f1
value: 92.32323232323232
- type: euclidean_precision
value: 93.26530612244898
- type: euclidean_recall
value: 91.4
- type: manhattan_accuracy
value: 99.85346534653465
- type: manhattan_ap
value: 96.2635334753325
- type: manhattan_f1
value: 92.37899073120495
- type: manhattan_precision
value: 95.22292993630573
- type: manhattan_recall
value: 89.7
- type: max_accuracy
value: 99.85346534653465
- type: max_ap
value: 96.2635334753325
- type: max_f1
value: 92.37899073120495
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.83905786483794
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.031896152126436
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.551326709447146
- type: mrr
value: 55.43758222986165
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.305688567308874
- type: cos_sim_spearman
value: 29.27135743434515
- type: dot_pearson
value: 30.336741878796563
- type: dot_spearman
value: 30.513365725895937
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 1.92
- type: map_at_100
value: 10.519
- type: map_at_1000
value: 23.874000000000002
- type: map_at_3
value: 0.629
- type: map_at_5
value: 1.0290000000000001
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.5
- type: mrr_at_100
value: 93.5
- type: mrr_at_1000
value: 93.5
- type: mrr_at_3
value: 93
- type: mrr_at_5
value: 93.5
- type: ndcg_at_1
value: 84
- type: ndcg_at_10
value: 76.447
- type: ndcg_at_100
value: 56.516
- type: ndcg_at_1000
value: 48.583999999999996
- type: ndcg_at_3
value: 78.877
- type: ndcg_at_5
value: 79.174
- type: precision_at_1
value: 88
- type: precision_at_10
value: 80.60000000000001
- type: precision_at_100
value: 57.64
- type: precision_at_1000
value: 21.227999999999998
- type: precision_at_3
value: 82
- type: precision_at_5
value: 83.6
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.128
- type: recall_at_100
value: 13.767
- type: recall_at_1000
value: 44.958
- type: recall_at_3
value: 0.654
- type: recall_at_5
value: 1.111
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.5170000000000003
- type: map_at_10
value: 10.915
- type: map_at_100
value: 17.535
- type: map_at_1000
value: 19.042
- type: map_at_3
value: 5.689
- type: map_at_5
value: 7.837
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 49.547999999999995
- type: mrr_at_100
value: 50.653000000000006
- type: mrr_at_1000
value: 50.653000000000006
- type: mrr_at_3
value: 44.558
- type: mrr_at_5
value: 48.333
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 26.543
- type: ndcg_at_100
value: 38.946
- type: ndcg_at_1000
value: 49.406
- type: ndcg_at_3
value: 29.903000000000002
- type: ndcg_at_5
value: 29.231
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.265
- type: precision_at_100
value: 8.102
- type: precision_at_1000
value: 1.5
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.5170000000000003
- type: recall_at_10
value: 16.88
- type: recall_at_100
value: 49.381
- type: recall_at_1000
value: 81.23899999999999
- type: recall_at_3
value: 6.965000000000001
- type: recall_at_5
value: 10.847999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.5942
- type: ap
value: 13.92074156956546
- type: f1
value: 54.671999698839066
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.39728353140916
- type: f1
value: 59.68980496759517
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.11181870104935
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.46957143708649
- type: cos_sim_ap
value: 76.16120197845457
- type: cos_sim_f1
value: 69.69919295671315
- type: cos_sim_precision
value: 64.94986326344576
- type: cos_sim_recall
value: 75.19788918205805
- type: dot_accuracy
value: 83.0780234845324
- type: dot_ap
value: 64.21717343541934
- type: dot_f1
value: 59.48375497624245
- type: dot_precision
value: 57.94345759319489
- type: dot_recall
value: 61.108179419525065
- type: euclidean_accuracy
value: 86.6543482148179
- type: euclidean_ap
value: 76.4527555010203
- type: euclidean_f1
value: 70.10156056477584
- type: euclidean_precision
value: 66.05975723622782
- type: euclidean_recall
value: 74.67018469656992
- type: manhattan_accuracy
value: 86.66030875603504
- type: manhattan_ap
value: 76.40304567255436
- type: manhattan_f1
value: 70.05275426328058
- type: manhattan_precision
value: 65.4666360926393
- type: manhattan_recall
value: 75.32981530343008
- type: max_accuracy
value: 86.66030875603504
- type: max_ap
value: 76.4527555010203
- type: max_f1
value: 70.10156056477584
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.42123646524624
- type: cos_sim_ap
value: 85.15431437761646
- type: cos_sim_f1
value: 76.98069301530742
- type: cos_sim_precision
value: 72.9314502239063
- type: cos_sim_recall
value: 81.50600554357868
- type: dot_accuracy
value: 86.70974502270346
- type: dot_ap
value: 80.77621563599457
- type: dot_f1
value: 73.87058697285117
- type: dot_precision
value: 68.98256396552877
- type: dot_recall
value: 79.50415768401602
- type: euclidean_accuracy
value: 88.46392672798541
- type: euclidean_ap
value: 85.20370297495491
- type: euclidean_f1
value: 77.01372369624886
- type: euclidean_precision
value: 73.39052800446397
- type: euclidean_recall
value: 81.01324299353249
- type: manhattan_accuracy
value: 88.43481973066325
- type: manhattan_ap
value: 85.16318289864545
- type: manhattan_f1
value: 76.90884877182597
- type: manhattan_precision
value: 74.01737396753062
- type: manhattan_recall
value: 80.03541730828458
- type: max_accuracy
value: 88.46392672798541
- type: max_ap
value: 85.20370297495491
- type: max_f1
value: 77.01372369624886
---
**Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR",
"BIOSSES",
"SCIFACT"
] |
ibm-granite/granite-embedding-30m-english | ibm-granite | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"language",
"granite",
"embeddings",
"mteb",
"transformers",
"sentence-similarity",
"en",
"arxiv:0000.00000",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-04T20:28:47 | 2025-03-04T15:10:29 | 90,310 | 9 | ---
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- language
- granite
- embeddings
- mteb
- transformers
model-index:
- name: ibm-granite/granite-embedding-30m-english
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 62.856100000000005
- type: f1
value: 51.5046
- type: f1_weighted
value: 69.9775
- type: ap
value: 15.4995
- type: ap_weighted
value: 15.4995
- type: main_score
value: 62.856100000000005
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 60.925399999999996
- type: f1
value: 55.0092
- type: f1_weighted
value: 64.8014
- type: ap
value: 25.0517
- type: ap_weighted
value: 25.0517
- type: main_score
value: 60.925399999999996
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 62.983599999999996
- type: f1
value: 62.553599999999996
- type: f1_weighted
value: 62.553599999999996
- type: ap
value: 58.3423
- type: ap_weighted
value: 58.3423
- type: main_score
value: 62.983599999999996
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 32.178000000000004
- type: f1
value: 31.5201
- type: f1_weighted
value: 31.5201
- type: main_score
value: 32.178000000000004
- task:
type: Retrieval
dataset:
name: MTEB AppsRetrieval (default)
type: CoIR-Retrieval/apps
config: default
split: test
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
metrics:
- type: ndcg_at_1
value: 3.5060000000000002
- type: ndcg_at_3
value: 4.789000000000001
- type: ndcg_at_5
value: 5.314
- type: ndcg_at_10
value: 6.203
- type: ndcg_at_20
value: 6.801
- type: ndcg_at_100
value: 8.588
- type: ndcg_at_1000
value: 12.418999999999999
- type: map_at_1
value: 3.5060000000000002
- type: map_at_3
value: 4.471
- type: map_at_5
value: 4.7620000000000005
- type: map_at_10
value: 5.117
- type: map_at_20
value: 5.281000000000001
- type: map_at_100
value: 5.501
- type: map_at_1000
value: 5.611
- type: recall_at_1
value: 3.5060000000000002
- type: recall_at_3
value: 5.71
- type: recall_at_5
value: 6.984999999999999
- type: recall_at_10
value: 9.801
- type: recall_at_20
value: 12.165
- type: recall_at_100
value: 22.205
- type: recall_at_1000
value: 54.396
- type: precision_at_1
value: 3.5060000000000002
- type: precision_at_3
value: 1.9029999999999998
- type: precision_at_5
value: 1.397
- type: precision_at_10
value: 0.98
- type: precision_at_20
value: 0.608
- type: precision_at_100
value: 0.22200000000000003
- type: precision_at_1000
value: 0.054
- type: mrr_at_1
value: 3.5060000000000002
- type: mrr_at_3
value: 4.471
- type: mrr_at_5
value: 4.7618
- type: mrr_at_10
value: 5.1166
- type: mrr_at_20
value: 5.2806
- type: mrr_at_100
value: 5.5014
- type: mrr_at_1000
value: 5.6113
- type: nauc_ndcg_at_1_max
value: 32.8089
- type: nauc_ndcg_at_1_std
value: 13.0518
- type: nauc_ndcg_at_1_diff1
value: 44.3602
- type: nauc_ndcg_at_3_max
value: 28.5037
- type: nauc_ndcg_at_3_std
value: 12.1308
- type: nauc_ndcg_at_3_diff1
value: 33.0191
- type: nauc_ndcg_at_5_max
value: 25.970100000000002
- type: nauc_ndcg_at_5_std
value: 12.089500000000001
- type: nauc_ndcg_at_5_diff1
value: 30.098200000000002
- type: nauc_ndcg_at_10_max
value: 23.9177
- type: nauc_ndcg_at_10_std
value: 12.1279
- type: nauc_ndcg_at_10_diff1
value: 26.3951
- type: nauc_ndcg_at_20_max
value: 22.2086
- type: nauc_ndcg_at_20_std
value: 11.355
- type: nauc_ndcg_at_20_diff1
value: 24.9668
- type: nauc_ndcg_at_100_max
value: 20.1961
- type: nauc_ndcg_at_100_std
value: 11.368300000000001
- type: nauc_ndcg_at_100_diff1
value: 21.654200000000003
- type: nauc_ndcg_at_1000_max
value: 19.7802
- type: nauc_ndcg_at_1000_std
value: 11.9399
- type: nauc_ndcg_at_1000_diff1
value: 19.8429
- type: nauc_map_at_1_max
value: 32.8089
- type: nauc_map_at_1_std
value: 13.0518
- type: nauc_map_at_1_diff1
value: 44.3602
- type: nauc_map_at_3_max
value: 29.285600000000002
- type: nauc_map_at_3_std
value: 12.4277
- type: nauc_map_at_3_diff1
value: 35.2678
- type: nauc_map_at_5_max
value: 27.6754
- type: nauc_map_at_5_std
value: 12.4042
- type: nauc_map_at_5_diff1
value: 33.330799999999996
- type: nauc_map_at_10_max
value: 26.571299999999997
- type: nauc_map_at_10_std
value: 12.439400000000001
- type: nauc_map_at_10_diff1
value: 31.275399999999998
- type: nauc_map_at_20_max
value: 25.8795
- type: nauc_map_at_20_std
value: 12.1596
- type: nauc_map_at_20_diff1
value: 30.6354
- type: nauc_map_at_100_max
value: 25.3369
- type: nauc_map_at_100_std
value: 12.0245
- type: nauc_map_at_100_diff1
value: 29.8703
- type: nauc_map_at_1000_max
value: 25.239800000000002
- type: nauc_map_at_1000_std
value: 12.0242
- type: nauc_map_at_1000_diff1
value: 29.7235
- type: nauc_recall_at_1_max
value: 32.8089
- type: nauc_recall_at_1_std
value: 13.0518
- type: nauc_recall_at_1_diff1
value: 44.3602
- type: nauc_recall_at_3_max
value: 26.747700000000002
- type: nauc_recall_at_3_std
value: 11.4203
- type: nauc_recall_at_3_diff1
value: 27.9047
- type: nauc_recall_at_5_max
value: 22.3707
- type: nauc_recall_at_5_std
value: 11.4164
- type: nauc_recall_at_5_diff1
value: 23.4182
- type: nauc_recall_at_10_max
value: 19.2758
- type: nauc_recall_at_10_std
value: 11.578800000000001
- type: nauc_recall_at_10_diff1
value: 18.030099999999997
- type: nauc_recall_at_20_max
value: 16.1643
- type: nauc_recall_at_20_std
value: 9.9037
- type: nauc_recall_at_20_diff1
value: 16.0833
- type: nauc_recall_at_100_max
value: 13.644700000000002
- type: nauc_recall_at_100_std
value: 10.986799999999999
- type: nauc_recall_at_100_diff1
value: 11.0515
- type: nauc_recall_at_1000_max
value: 13.9712
- type: nauc_recall_at_1000_std
value: 13.4048
- type: nauc_recall_at_1000_diff1
value: 6.569500000000001
- type: nauc_precision_at_1_max
value: 32.8089
- type: nauc_precision_at_1_std
value: 13.0518
- type: nauc_precision_at_1_diff1
value: 44.3602
- type: nauc_precision_at_3_max
value: 26.747700000000002
- type: nauc_precision_at_3_std
value: 11.4203
- type: nauc_precision_at_3_diff1
value: 27.9047
- type: nauc_precision_at_5_max
value: 22.3707
- type: nauc_precision_at_5_std
value: 11.4164
- type: nauc_precision_at_5_diff1
value: 23.4182
- type: nauc_precision_at_10_max
value: 19.2758
- type: nauc_precision_at_10_std
value: 11.578800000000001
- type: nauc_precision_at_10_diff1
value: 18.030099999999997
- type: nauc_precision_at_20_max
value: 16.1643
- type: nauc_precision_at_20_std
value: 9.9037
- type: nauc_precision_at_20_diff1
value: 16.0833
- type: nauc_precision_at_100_max
value: 13.644700000000002
- type: nauc_precision_at_100_std
value: 10.986799999999999
- type: nauc_precision_at_100_diff1
value: 11.0515
- type: nauc_precision_at_1000_max
value: 13.9712
- type: nauc_precision_at_1000_std
value: 13.4048
- type: nauc_precision_at_1000_diff1
value: 6.569500000000001
- type: nauc_mrr_at_1_max
value: 32.8089
- type: nauc_mrr_at_1_std
value: 13.0518
- type: nauc_mrr_at_1_diff1
value: 44.3602
- type: nauc_mrr_at_3_max
value: 29.285600000000002
- type: nauc_mrr_at_3_std
value: 12.4277
- type: nauc_mrr_at_3_diff1
value: 35.2678
- type: nauc_mrr_at_5_max
value: 27.6754
- type: nauc_mrr_at_5_std
value: 12.4042
- type: nauc_mrr_at_5_diff1
value: 33.330799999999996
- type: nauc_mrr_at_10_max
value: 26.571299999999997
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revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
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type: CoIR-Retrieval/CodeSearchNet
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revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
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type: CoIR-Retrieval/CodeSearchNet
config: ruby
split: test
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
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revision: ab518f4d6fcca38d87c25209f94beba119d02014
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type: Classification
dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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type: Retrieval
dataset:
name: MTEB MSMARCO (default)
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
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- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 39.2152
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- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 64.768
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- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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value: 71.3416
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value: 69.9576
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value: 71.3416
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P (default)
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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value: 32.5684
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S (default)
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
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value: 31.551299999999998
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
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value: 30.883
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
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value: 76.4182
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value: -33.873
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value: 75.58890000000001
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value: -33.5451
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value: 75.7153
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value: 38.7213
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value: 75.6934
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value: 38.6943
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value: -33.4013
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value: 75.6932
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value: -33.4051
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value: 86.696
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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value: 50.019999999999996
- type: v_measure_std
value: 4.5914
- type: main_score
value: 50.019999999999996
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 53.9756
- type: v_measure_std
value: 11.6573
- type: main_score
value: 53.9756
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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value: 24.6
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value: 20.896
- type: ndcg_at_5
value: 18.497
- type: ndcg_at_10
value: 22.542
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value: 25.812
- type: ndcg_at_100
value: 32.326
- type: ndcg_at_1000
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value: 4.988
- type: map_at_3
value: 9.439
- type: map_at_5
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- type: map_at_20
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- type: map_at_100
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- type: map_at_1000
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- type: recall_at_1
value: 4.988
- type: recall_at_3
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- type: recall_at_5
value: 16.777
- type: recall_at_10
value: 24.212
- type: recall_at_20
value: 31.885
- type: recall_at_100
value: 53.105000000000004
- type: recall_at_1000
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- type: precision_at_1
value: 24.6
- type: precision_at_3
value: 19.8
- type: precision_at_5
value: 16.54
- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
value: 24.6
- type: mrr_at_3
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- type: mrr_at_5
value: 35.1717
- type: mrr_at_10
value: 36.7925
- type: mrr_at_20
value: 37.5284
- type: mrr_at_100
value: 37.9725
- type: mrr_at_1000
value: 38.0112
- type: nauc_ndcg_at_1_max
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- type: nauc_ndcg_at_1_std
value: 9.1225
- type: nauc_ndcg_at_1_diff1
value: 22.665399999999998
- type: nauc_ndcg_at_3_max
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value: 17.589299999999998
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value: 25.3398
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- type: nauc_ndcg_at_5_diff1
value: 16.8155
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- type: nauc_ndcg_at_10_std
value: 22.7388
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- type: nauc_ndcg_at_20_max
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value: 25.389
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value: 28.0069
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value: 31.439899999999998
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value: 10.4784
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value: 28.853299999999997
- type: nauc_precision_at_100_std
value: 39.3115
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- type: nauc_precision_at_1000_max
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- type: nauc_precision_at_1000_std
value: 38.554300000000005
- type: nauc_precision_at_1000_diff1
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- type: nauc_mrr_at_1_max
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value: 9.1225
- type: nauc_mrr_at_1_diff1
value: 22.665399999999998
- type: nauc_mrr_at_3_max
value: 21.2588
- type: nauc_mrr_at_3_std
value: 12.7528
- type: nauc_mrr_at_3_diff1
value: 19.808999999999997
- type: nauc_mrr_at_5_max
value: 22.572200000000002
- type: nauc_mrr_at_5_std
value: 14.210500000000001
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value: 20.502000000000002
- type: nauc_mrr_at_10_max
value: 23.372799999999998
- type: nauc_mrr_at_10_std
value: 15.1215
- type: nauc_mrr_at_10_diff1
value: 20.8449
- type: nauc_mrr_at_20_max
value: 23.017599999999998
- type: nauc_mrr_at_20_std
value: 15.0391
- type: nauc_mrr_at_20_diff1
value: 20.8233
- type: nauc_mrr_at_100_max
value: 22.8993
- type: nauc_mrr_at_100_std
value: 14.8474
- type: nauc_mrr_at_100_diff1
value: 20.8759
- type: nauc_mrr_at_1000_max
value: 22.8744
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value: 14.8178
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value: 20.8635
- type: main_score
value: 22.542
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 77.4874
- type: spearman
value: 68.79809999999999
- type: cosine_pearson
value: 77.4874
- type: cosine_spearman
value: 68.79809999999999
- type: manhattan_pearson
value: 73.3583
- type: manhattan_spearman
value: 68.6911
- type: euclidean_pearson
value: 73.82039999999999
- type: euclidean_spearman
value: 68.79809999999999
- type: main_score
value: 68.79809999999999
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 67.8391
- type: spearman
value: 64.77380000000001
- type: cosine_pearson
value: 67.8391
- type: cosine_spearman
value: 64.77380000000001
- type: manhattan_pearson
value: 64.7258
- type: manhattan_spearman
value: 64.1558
- type: euclidean_pearson
value: 65.68469999999999
- type: euclidean_spearman
value: 64.7722
- type: main_score
value: 64.77380000000001
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 78.8177
- type: spearman
value: 79.3253
- type: cosine_pearson
value: 78.8177
- type: cosine_spearman
value: 79.3253
- type: manhattan_pearson
value: 78.6048
- type: manhattan_spearman
value: 79.1874
- type: euclidean_pearson
value: 78.71010000000001
- type: euclidean_spearman
value: 79.3253
- type: main_score
value: 79.3253
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 75.6791
- type: spearman
value: 70.1701
- type: cosine_pearson
value: 75.6791
- type: cosine_spearman
value: 70.1701
- type: manhattan_pearson
value: 73.85239999999999
- type: manhattan_spearman
value: 69.9223
- type: euclidean_pearson
value: 74.143
- type: euclidean_spearman
value: 70.1701
- type: main_score
value: 70.1701
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 80.4413
- type: spearman
value: 82.0343
- type: cosine_pearson
value: 80.4413
- type: cosine_spearman
value: 82.0343
- type: manhattan_pearson
value: 81.3627
- type: manhattan_spearman
value: 81.8838
- type: euclidean_pearson
value: 81.47569999999999
- type: euclidean_spearman
value: 82.0343
- type: main_score
value: 82.0343
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 77.172
- type: spearman
value: 78.9633
- type: cosine_pearson
value: 77.172
- type: cosine_spearman
value: 78.9633
- type: manhattan_pearson
value: 78.35849999999999
- type: manhattan_spearman
value: 78.7975
- type: euclidean_pearson
value: 78.5236
- type: euclidean_spearman
value: 78.9633
- type: main_score
value: 78.9633
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 83.5117
- type: spearman
value: 84.64970000000001
- type: cosine_pearson
value: 83.5117
- type: cosine_spearman
value: 84.64970000000001
- type: manhattan_pearson
value: 84.5137
- type: manhattan_spearman
value: 84.7848
- type: euclidean_pearson
value: 84.531
- type: euclidean_spearman
value: 84.64970000000001
- type: main_score
value: 84.64970000000001
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 29.0052
- type: spearman
value: 30.640299999999996
- type: cosine_pearson
value: 29.0052
- type: cosine_spearman
value: 30.640299999999996
- type: manhattan_pearson
value: 25.988099999999996
- type: manhattan_spearman
value: 26.935399999999998
- type: euclidean_pearson
value: 28.5366
- type: euclidean_spearman
value: 30.640299999999996
- type: main_score
value: 30.640299999999996
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 42.0755
- type: spearman
value: 39.763999999999996
- type: cosine_pearson
value: 42.0755
- type: cosine_spearman
value: 39.763999999999996
- type: manhattan_pearson
value: 40.872
- type: manhattan_spearman
value: 38.4749
- type: euclidean_pearson
value: 42.051500000000004
- type: euclidean_spearman
value: 39.7565
- type: main_score
value: 39.763999999999996
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 44.2318
- type: spearman
value: 46.5518
- type: cosine_pearson
value: 44.2318
- type: cosine_spearman
value: 46.5518
- type: manhattan_pearson
value: 43.396699999999996
- type: manhattan_spearman
value: 46.1132
- type: euclidean_pearson
value: 43.993500000000004
- type: euclidean_spearman
value: 46.5518
- type: main_score
value: 46.5518
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 36.716100000000004
- type: spearman
value: 34.6968
- type: cosine_pearson
value: 36.716100000000004
- type: cosine_spearman
value: 34.6968
- type: manhattan_pearson
value: 35.1918
- type: manhattan_spearman
value: 33.3692
- type: euclidean_pearson
value: 36.3921
- type: euclidean_spearman
value: 34.6968
- type: main_score
value: 34.6968
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 21.2825
- type: spearman
value: 17.6922
- type: cosine_pearson
value: 21.2825
- type: cosine_spearman
value: 17.6922
- type: manhattan_pearson
value: 19.491
- type: manhattan_spearman
value: 15.989700000000001
- type: euclidean_pearson
value: 21.583
- type: euclidean_spearman
value: 17.6922
- type: main_score
value: 17.6922
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 32.1584
- type: spearman
value: 27.9254
- type: cosine_pearson
value: 32.1584
- type: cosine_spearman
value: 27.9254
- type: manhattan_pearson
value: 34.2047
- type: manhattan_spearman
value: 31.1955
- type: euclidean_pearson
value: 32.4369
- type: euclidean_spearman
value: 27.9254
- type: main_score
value: 27.9254
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 21.0842
- type: spearman
value: 18.5115
- type: cosine_pearson
value: 21.0842
- type: cosine_spearman
value: 18.5115
- type: manhattan_pearson
value: 23.5904
- type: manhattan_spearman
value: 21.032400000000003
- type: euclidean_pearson
value: 21.2805
- type: euclidean_spearman
value: 18.5115
- type: main_score
value: 18.5115
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 66.9563
- type: spearman
value: 67.4747
- type: cosine_pearson
value: 66.9563
- type: cosine_spearman
value: 67.4747
- type: manhattan_pearson
value: 68.32629999999999
- type: manhattan_spearman
value: 66.8163
- type: euclidean_pearson
value: 68.731
- type: euclidean_spearman
value: 67.4747
- type: main_score
value: 67.4747
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 56.3095
- type: spearman
value: 54.1005
- type: cosine_pearson
value: 56.3095
- type: cosine_spearman
value: 54.1005
- type: manhattan_pearson
value: 59.4023
- type: manhattan_spearman
value: 52.6259
- type: euclidean_pearson
value: 58.6527
- type: euclidean_spearman
value: 54.1005
- type: main_score
value: 54.1005
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 62.0575
- type: spearman
value: 66.9527
- type: cosine_pearson
value: 62.0575
- type: cosine_spearman
value: 66.9527
- type: manhattan_pearson
value: 62.648700000000005
- type: manhattan_spearman
value: 65.6446
- type: euclidean_pearson
value: 63.546800000000005
- type: euclidean_spearman
value: 66.9527
- type: main_score
value: 66.9527
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 68.42439999999999
- type: spearman
value: 69.0444
- type: cosine_pearson
value: 68.42439999999999
- type: cosine_spearman
value: 69.0444
- type: manhattan_pearson
value: 65.1492
- type: manhattan_spearman
value: 65.2364
- type: euclidean_pearson
value: 68.4923
- type: euclidean_spearman
value: 69.0444
- type: main_score
value: 69.0444
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 34.164699999999996
- type: spearman
value: 36.1776
- type: cosine_pearson
value: 34.164699999999996
- type: cosine_spearman
value: 36.1776
- type: manhattan_pearson
value: 33.0685
- type: manhattan_spearman
value: 34.4054
- type: euclidean_pearson
value: 34.1002
- type: euclidean_spearman
value: 36.1776
- type: main_score
value: 36.1776
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 78.0802
- type: spearman
value: 78.0444
- type: cosine_pearson
value: 78.0802
- type: cosine_spearman
value: 78.0444
- type: manhattan_pearson
value: 78.0703
- type: manhattan_spearman
value: 77.681
- type: euclidean_pearson
value: 78.4998
- type: euclidean_spearman
value: 78.0444
- type: main_score
value: 78.0444
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.4489
- type: mrr
value: 96.0178
- type: nAUC_map_max
value: 49.2333
- type: nAUC_map_std
value: 63.6541
- type: nAUC_map_diff1
value: 0.40959999999999996
- type: nAUC_mrr_max
value: 83.6216
- type: nAUC_mrr_std
value: 76.7559
- type: nAUC_mrr_diff1
value: 42.9429
- type: main_score
value: 86.4489
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 59.333000000000006
- type: ndcg_at_3
value: 65.793
- type: ndcg_at_5
value: 69.429
- type: ndcg_at_10
value: 71.27
- type: ndcg_at_20
value: 72.929
- type: ndcg_at_100
value: 73.88900000000001
- type: ndcg_at_1000
value: 74.41
- type: map_at_1
value: 56.577999999999996
- type: map_at_3
value: 63.416
- type: map_at_5
value: 65.77
- type: map_at_10
value: 66.725
- type: map_at_20
value: 67.24799999999999
- type: map_at_100
value: 67.379
- type: map_at_1000
value: 67.4
- type: recall_at_1
value: 56.577999999999996
- type: recall_at_3
value: 70.072
- type: recall_at_5
value: 79.011
- type: recall_at_10
value: 84.2
- type: recall_at_20
value: 90.5
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 99.667
- type: precision_at_1
value: 59.333000000000006
- type: precision_at_3
value: 25.556
- type: precision_at_5
value: 17.666999999999998
- type: precision_at_10
value: 9.6
- type: precision_at_20
value: 5.167
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 59.3333
- type: mrr_at_3
value: 64.9444
- type: mrr_at_5
value: 66.9278
- type: mrr_at_10
value: 67.5327
- type: mrr_at_20
value: 67.9354
- type: mrr_at_100
value: 68.0616
- type: mrr_at_1000
value: 68.08239999999999
- type: nauc_ndcg_at_1_max
value: 62.536199999999994
- type: nauc_ndcg_at_1_std
value: 4.3275
- type: nauc_ndcg_at_1_diff1
value: 78.2294
- type: nauc_ndcg_at_3_max
value: 63.0626
- type: nauc_ndcg_at_3_std
value: 6.0584
- type: nauc_ndcg_at_3_diff1
value: 74.4931
- type: nauc_ndcg_at_5_max
value: 64.73989999999999
- type: nauc_ndcg_at_5_std
value: 5.6514
- type: nauc_ndcg_at_5_diff1
value: 73.5498
- type: nauc_ndcg_at_10_max
value: 65.43090000000001
- type: nauc_ndcg_at_10_std
value: 9.1274
- type: nauc_ndcg_at_10_diff1
value: 72.4814
- type: nauc_ndcg_at_20_max
value: 65.7156
- type: nauc_ndcg_at_20_std
value: 9.9385
- type: nauc_ndcg_at_20_diff1
value: 73.0996
- type: nauc_ndcg_at_100_max
value: 65.5687
- type: nauc_ndcg_at_100_std
value: 8.818299999999999
- type: nauc_ndcg_at_100_diff1
value: 73.6361
- type: nauc_ndcg_at_1000_max
value: 65.1956
- type: nauc_ndcg_at_1000_std
value: 8.4772
- type: nauc_ndcg_at_1000_diff1
value: 74.0393
- type: nauc_map_at_1_max
value: 58.2314
- type: nauc_map_at_1_std
value: -2.7946
- type: nauc_map_at_1_diff1
value: 78.24940000000001
- type: nauc_map_at_3_max
value: 61.364200000000004
- type: nauc_map_at_3_std
value: 2.7072
- type: nauc_map_at_3_diff1
value: 75.4798
- type: nauc_map_at_5_max
value: 63.1297
- type: nauc_map_at_5_std
value: 3.9505
- type: nauc_map_at_5_diff1
value: 74.9693
- type: nauc_map_at_10_max
value: 63.6643
- type: nauc_map_at_10_std
value: 5.8328999999999995
- type: nauc_map_at_10_diff1
value: 74.5464
- type: nauc_map_at_20_max
value: 63.8666
- type: nauc_map_at_20_std
value: 6.1967
- type: nauc_map_at_20_diff1
value: 74.7224
- type: nauc_map_at_100_max
value: 63.8254
- type: nauc_map_at_100_std
value: 6.0627
- type: nauc_map_at_100_diff1
value: 74.791
- type: nauc_map_at_1000_max
value: 63.811499999999995
- type: nauc_map_at_1000_std
value: 6.0484
- type: nauc_map_at_1000_diff1
value: 74.807
- type: nauc_recall_at_1_max
value: 58.2314
- type: nauc_recall_at_1_std
value: -2.7946
- type: nauc_recall_at_1_diff1
value: 78.24940000000001
- type: nauc_recall_at_3_max
value: 61.132299999999994
- type: nauc_recall_at_3_std
value: 6.1988
- type: nauc_recall_at_3_diff1
value: 70.7273
- type: nauc_recall_at_5_max
value: 66.542
- type: nauc_recall_at_5_std
value: 5.7653
- type: nauc_recall_at_5_diff1
value: 66.4586
- type: nauc_recall_at_10_max
value: 69.3605
- type: nauc_recall_at_10_std
value: 19.6237
- type: nauc_recall_at_10_diff1
value: 60.2814
- type: nauc_recall_at_20_max
value: 72.6154
- type: nauc_recall_at_20_std
value: 31.3504
- type: nauc_recall_at_20_diff1
value: 58.8899
- type: nauc_recall_at_100_max
value: 78.6002
- type: nauc_recall_at_100_std
value: 26.484999999999996
- type: nauc_recall_at_100_diff1
value: 56.4605
- type: nauc_recall_at_1000_max
value: 55.415499999999994
- type: nauc_recall_at_1000_std
value: 72.2222
- type: nauc_recall_at_1000_diff1
value: 35.8077
- type: nauc_precision_at_1_max
value: 62.536199999999994
- type: nauc_precision_at_1_std
value: 4.3275
- type: nauc_precision_at_1_diff1
value: 78.2294
- type: nauc_precision_at_3_max
value: 53.5524
- type: nauc_precision_at_3_std
value: 23.5724
- type: nauc_precision_at_3_diff1
value: 47.5389
- type: nauc_precision_at_5_max
value: 49.1594
- type: nauc_precision_at_5_std
value: 32.3563
- type: nauc_precision_at_5_diff1
value: 28.2105
- type: nauc_precision_at_10_max
value: 41.955799999999996
- type: nauc_precision_at_10_std
value: 44.039699999999996
- type: nauc_precision_at_10_diff1
value: 12.0187
- type: nauc_precision_at_20_max
value: 34.2442
- type: nauc_precision_at_20_std
value: 50.204899999999995
- type: nauc_precision_at_20_diff1
value: -0.1954
- type: nauc_precision_at_100_max
value: 26.8264
- type: nauc_precision_at_100_std
value: 51.4247
- type: nauc_precision_at_100_diff1
value: -11.9827
- type: nauc_precision_at_1000_max
value: 17.467
- type: nauc_precision_at_1000_std
value: 56.435100000000006
- type: nauc_precision_at_1000_diff1
value: -24.2103
- type: nauc_mrr_at_1_max
value: 62.536199999999994
- type: nauc_mrr_at_1_std
value: 4.3275
- type: nauc_mrr_at_1_diff1
value: 78.2294
- type: nauc_mrr_at_3_max
value: 64.5911
- type: nauc_mrr_at_3_std
value: 7.8005
- type: nauc_mrr_at_3_diff1
value: 75.82140000000001
- type: nauc_mrr_at_5_max
value: 65.1643
- type: nauc_mrr_at_5_std
value: 7.258100000000001
- type: nauc_mrr_at_5_diff1
value: 75.2062
- type: nauc_mrr_at_10_max
value: 65.3198
- type: nauc_mrr_at_10_std
value: 8.2173
- type: nauc_mrr_at_10_diff1
value: 74.9449
- type: nauc_mrr_at_20_max
value: 65.2169
- type: nauc_mrr_at_20_std
value: 8.115400000000001
- type: nauc_mrr_at_20_diff1
value: 75.1765
- type: nauc_mrr_at_100_max
value: 65.1744
- type: nauc_mrr_at_100_std
value: 7.994700000000001
- type: nauc_mrr_at_100_diff1
value: 75.2388
- type: nauc_mrr_at_1000_max
value: 65.1615
- type: nauc_mrr_at_1000_std
value: 7.9817
- type: nauc_mrr_at_1000_diff1
value: 75.2553
- type: main_score
value: 71.27
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: similarity_accuracy
value: 99.7604
- type: similarity_accuracy_threshold
value: 84.88210000000001
- type: similarity_f1
value: 87.86359999999999
- type: similarity_f1_threshold
value: 84.88210000000001
- type: similarity_precision
value: 88.1288
- type: similarity_recall
value: 87.6
- type: similarity_ap
value: 94.07140000000001
- type: cosine_accuracy
value: 99.7604
- type: cosine_accuracy_threshold
value: 84.88210000000001
- type: cosine_f1
value: 87.86359999999999
- type: cosine_f1_threshold
value: 84.88210000000001
- type: cosine_precision
value: 88.1288
- type: cosine_recall
value: 87.6
- type: cosine_ap
value: 94.07140000000001
- type: manhattan_accuracy
value: 99.7644
- type: manhattan_accuracy_threshold
value: 829.5789
- type: manhattan_f1
value: 87.92320000000001
- type: manhattan_f1_threshold
value: 840.6424
- type: manhattan_precision
value: 88.86619999999999
- type: manhattan_recall
value: 87.0
- type: manhattan_ap
value: 94.17
- type: euclidean_accuracy
value: 99.7604
- type: euclidean_accuracy_threshold
value: 54.986999999999995
- type: euclidean_f1
value: 87.86359999999999
- type: euclidean_f1_threshold
value: 54.986999999999995
- type: euclidean_precision
value: 88.1288
- type: euclidean_recall
value: 87.6
- type: euclidean_ap
value: 94.07140000000001
- type: dot_accuracy
value: 99.7604
- type: dot_accuracy_threshold
value: 84.88210000000001
- type: dot_f1
value: 87.86359999999999
- type: dot_f1_threshold
value: 84.88210000000001
- type: dot_precision
value: 88.1288
- type: dot_recall
value: 87.6
- type: dot_ap
value: 94.07140000000001
- type: max_accuracy
value: 99.7644
- type: max_f1
value: 87.92320000000001
- type: max_precision
value: 88.86619999999999
- type: max_recall
value: 87.6
- type: max_ap
value: 94.17
- type: main_score
value: 94.17
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.6589
- type: v_measure_std
value: 4.734
- type: main_score
value: 64.6589
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.9388
- type: v_measure_std
value: 1.6312
- type: main_score
value: 32.9388
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.645399999999995
- type: mrr
value: 53.5346
- type: nAUC_map_max
value: 12.8874
- type: nAUC_map_std
value: 9.2781
- type: nAUC_map_diff1
value: 39.864
- type: nAUC_mrr_max
value: 13.278
- type: nAUC_mrr_std
value: 9.501999999999999
- type: nAUC_mrr_diff1
value: 39.409499999999994
- type: main_score
value: 52.645399999999995
- task:
type: Retrieval
dataset:
name: MTEB StackOverflowQA (default)
type: CoIR-Retrieval/stackoverflow-qa
config: default
split: test
revision: db8f169f3894c14a00251061f957b2063eef2bd5
metrics:
- type: ndcg_at_1
value: 74.97500000000001
- type: ndcg_at_3
value: 81.247
- type: ndcg_at_5
value: 82.921
- type: ndcg_at_10
value: 83.92699999999999
- type: ndcg_at_20
value: 84.57000000000001
- type: ndcg_at_100
value: 85.095
- type: ndcg_at_1000
value: 85.33800000000001
- type: map_at_1
value: 74.97500000000001
- type: map_at_3
value: 79.781
- type: map_at_5
value: 80.711
- type: map_at_10
value: 81.126
- type: map_at_20
value: 81.308
- type: map_at_100
value: 81.389
- type: map_at_1000
value: 81.39699999999999
- type: recall_at_1
value: 74.97500000000001
- type: recall_at_3
value: 85.456
- type: recall_at_5
value: 89.519
- type: recall_at_10
value: 92.628
- type: recall_at_20
value: 95.135
- type: recall_at_100
value: 97.844
- type: recall_at_1000
value: 99.799
- type: precision_at_1
value: 74.97500000000001
- type: precision_at_3
value: 28.485
- type: precision_at_5
value: 17.904
- type: precision_at_10
value: 9.263
- type: precision_at_20
value: 4.757
- type: precision_at_100
value: 0.9780000000000001
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 74.9749
- type: mrr_at_3
value: 79.781
- type: mrr_at_5
value: 80.7113
- type: mrr_at_10
value: 81.12610000000001
- type: mrr_at_20
value: 81.30760000000001
- type: mrr_at_100
value: 81.38889999999999
- type: mrr_at_1000
value: 81.3974
- type: nauc_ndcg_at_1_max
value: 76.1721
- type: nauc_ndcg_at_1_std
value: -5.5159
- type: nauc_ndcg_at_1_diff1
value: 84.6697
- type: nauc_ndcg_at_3_max
value: 78.27629999999999
- type: nauc_ndcg_at_3_std
value: -1.2
- type: nauc_ndcg_at_3_diff1
value: 81.1214
- type: nauc_ndcg_at_5_max
value: 77.7687
- type: nauc_ndcg_at_5_std
value: -1.8698
- type: nauc_ndcg_at_5_diff1
value: 80.9252
- type: nauc_ndcg_at_10_max
value: 77.8029
- type: nauc_ndcg_at_10_std
value: -1.5579
- type: nauc_ndcg_at_10_diff1
value: 81.1043
- type: nauc_ndcg_at_20_max
value: 77.79310000000001
- type: nauc_ndcg_at_20_std
value: -1.7669000000000001
- type: nauc_ndcg_at_20_diff1
value: 81.4121
- type: nauc_ndcg_at_100_max
value: 77.7522
- type: nauc_ndcg_at_100_std
value: -1.4502
- type: nauc_ndcg_at_100_diff1
value: 81.684
- type: nauc_ndcg_at_1000_max
value: 77.6032
- type: nauc_ndcg_at_1000_std
value: -2.0256
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- type: nauc_precision_at_10_diff1
value: -22.6982
- type: nauc_precision_at_20_max
value: 57.3052
- type: nauc_precision_at_20_std
value: 72.00619999999999
- type: nauc_precision_at_20_diff1
value: -18.2329
- type: nauc_precision_at_100_max
value: 50.0873
- type: nauc_precision_at_100_std
value: 84.9689
- type: nauc_precision_at_100_diff1
value: -27.625300000000003
- type: nauc_precision_at_1000_max
value: 29.3103
- type: nauc_precision_at_1000_std
value: 57.898700000000005
- type: nauc_precision_at_1000_diff1
value: -28.8765
- type: nauc_mrr_at_1_max
value: 59.8382
- type: nauc_mrr_at_1_std
value: 66.9075
- type: nauc_mrr_at_1_diff1
value: -5.1873000000000005
- type: nauc_mrr_at_3_max
value: 58.4682
- type: nauc_mrr_at_3_std
value: 64.6751
- type: nauc_mrr_at_3_diff1
value: -5.9737
- type: nauc_mrr_at_5_max
value: 59.099999999999994
- type: nauc_mrr_at_5_std
value: 63.6902
- type: nauc_mrr_at_5_diff1
value: -6.482499999999999
- type: nauc_mrr_at_10_max
value: 57.9638
- type: nauc_mrr_at_10_std
value: 63.716300000000004
- type: nauc_mrr_at_10_diff1
value: -5.6598999999999995
- type: nauc_mrr_at_20_max
value: 57.9638
- type: nauc_mrr_at_20_std
value: 63.716300000000004
- type: nauc_mrr_at_20_diff1
value: -5.6598999999999995
- type: nauc_mrr_at_100_max
value: 57.9638
- type: nauc_mrr_at_100_std
value: 63.716300000000004
- type: nauc_mrr_at_100_diff1
value: -5.6598999999999995
- type: nauc_mrr_at_1000_max
value: 57.9638
- type: nauc_mrr_at_1000_std
value: 63.716300000000004
- type: nauc_mrr_at_1000_diff1
value: -5.6598999999999995
- type: main_score
value: 63.098
- task:
type: Retrieval
dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: ndcg_at_1
value: 23.469
- type: ndcg_at_3
value: 25.522
- type: ndcg_at_5
value: 24.333
- type: ndcg_at_10
value: 24.029
- type: ndcg_at_20
value: 24.573
- type: ndcg_at_100
value: 34.425
- type: ndcg_at_1000
value: 46.907
- type: map_at_1
value: 1.976
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.555999999999999
- type: map_at_10
value: 9.687999999999999
- type: map_at_20
value: 11.926
- type: map_at_100
value: 15.116999999999999
- type: map_at_1000
value: 16.769000000000002
- type: recall_at_1
value: 1.976
- type: recall_at_3
value: 6.101
- type: recall_at_5
value: 9.68
- type: recall_at_10
value: 16.633
- type: recall_at_20
value: 23.589
- type: recall_at_100
value: 45.61
- type: recall_at_1000
value: 82.48100000000001
- type: precision_at_1
value: 26.531
- type: precision_at_3
value: 27.891
- type: precision_at_5
value: 25.714
- type: precision_at_10
value: 22.448999999999998
- type: precision_at_20
value: 16.837
- type: precision_at_100
value: 7.122000000000001
- type: precision_at_1000
value: 1.5270000000000001
- type: mrr_at_1
value: 26.5306
- type: mrr_at_3
value: 39.1156
- type: mrr_at_5
value: 41.1565
- type: mrr_at_10
value: 43.863
- type: mrr_at_20
value: 44.5963
- type: mrr_at_100
value: 44.766600000000004
- type: mrr_at_1000
value: 44.766600000000004
- type: nauc_ndcg_at_1_max
value: -31.661099999999998
- type: nauc_ndcg_at_1_std
value: 2.8871
- type: nauc_ndcg_at_1_diff1
value: 3.4787
- type: nauc_ndcg_at_3_max
value: -34.6673
- type: nauc_ndcg_at_3_std
value: -3.8882
- type: nauc_ndcg_at_3_diff1
value: 0.6512
- type: nauc_ndcg_at_5_max
value: -33.815
- type: nauc_ndcg_at_5_std
value: 0.20209999999999997
- type: nauc_ndcg_at_5_diff1
value: -6.4072000000000005
- type: nauc_ndcg_at_10_max
value: -26.9953
- type: nauc_ndcg_at_10_std
value: -3.6511
- type: nauc_ndcg_at_10_diff1
value: -3.8763
- type: nauc_ndcg_at_20_max
value: -30.218600000000002
- type: nauc_ndcg_at_20_std
value: -1.4384
- type: nauc_ndcg_at_20_diff1
value: -8.5927
- type: nauc_ndcg_at_100_max
value: -32.1409
- type: nauc_ndcg_at_100_std
value: 20.1662
- type: nauc_ndcg_at_100_diff1
value: -12.0591
- type: nauc_ndcg_at_1000_max
value: -31.6892
- type: nauc_ndcg_at_1000_std
value: 32.1464
- type: nauc_ndcg_at_1000_diff1
value: -8.3651
- type: nauc_map_at_1_max
value: -41.9612
- type: nauc_map_at_1_std
value: -11.0332
- type: nauc_map_at_1_diff1
value: -5.2508
- type: nauc_map_at_3_max
value: -30.4968
- type: nauc_map_at_3_std
value: -11.138
- type: nauc_map_at_3_diff1
value: -0.8447
- type: nauc_map_at_5_max
value: -24.7543
- type: nauc_map_at_5_std
value: -10.302
- type: nauc_map_at_5_diff1
value: -10.0762
- type: nauc_map_at_10_max
value: -20.420099999999998
- type: nauc_map_at_10_std
value: -10.485
- type: nauc_map_at_10_diff1
value: -10.3134
- type: nauc_map_at_20_max
value: -20.8606
- type: nauc_map_at_20_std
value: -6.3984
- type: nauc_map_at_20_diff1
value: -10.8605
- type: nauc_map_at_100_max
value: -22.6385
- type: nauc_map_at_100_std
value: 3.8738
- type: nauc_map_at_100_diff1
value: -12.9055
- type: nauc_map_at_1000_max
value: -23.0823
- type: nauc_map_at_1000_std
value: 8.6942
- type: nauc_map_at_1000_diff1
value: -13.1715
- type: nauc_recall_at_1_max
value: -41.9612
- type: nauc_recall_at_1_std
value: -11.0332
- type: nauc_recall_at_1_diff1
value: -5.2508
- type: nauc_recall_at_3_max
value: -25.9715
- type: nauc_recall_at_3_std
value: -14.9623
- type: nauc_recall_at_3_diff1
value: -4.2583
- type: nauc_recall_at_5_max
value: -24.5848
- type: nauc_recall_at_5_std
value: -14.258299999999998
- type: nauc_recall_at_5_diff1
value: -13.1162
- type: nauc_recall_at_10_max
value: -22.3834
- type: nauc_recall_at_10_std
value: -15.274199999999999
- type: nauc_recall_at_10_diff1
value: -10.8836
- type: nauc_recall_at_20_max
value: -22.8634
- type: nauc_recall_at_20_std
value: -4.8215
- type: nauc_recall_at_20_diff1
value: -11.1747
- type: nauc_recall_at_100_max
value: -25.9537
- type: nauc_recall_at_100_std
value: 29.75
- type: nauc_recall_at_100_diff1
value: -15.512799999999999
- type: nauc_recall_at_1000_max
value: -18.9449
- type: nauc_recall_at_1000_std
value: 69.619
- type: nauc_recall_at_1000_diff1
value: -5.629300000000001
- type: nauc_precision_at_1_max
value: -33.7627
- type: nauc_precision_at_1_std
value: 1.8065000000000002
- type: nauc_precision_at_1_diff1
value: 5.3592
- type: nauc_precision_at_3_max
value: -30.7992
- type: nauc_precision_at_3_std
value: -6.285399999999999
- type: nauc_precision_at_3_diff1
value: 1.1098000000000001
- type: nauc_precision_at_5_max
value: -27.8949
- type: nauc_precision_at_5_std
value: -1.8754
- type: nauc_precision_at_5_diff1
value: -8.0528
- type: nauc_precision_at_10_max
value: -19.659299999999998
- type: nauc_precision_at_10_std
value: -0.9809999999999999
- type: nauc_precision_at_10_diff1
value: -2.0972999999999997
- type: nauc_precision_at_20_max
value: -25.810899999999997
- type: nauc_precision_at_20_std
value: 19.5577
- type: nauc_precision_at_20_diff1
value: -8.879199999999999
- type: nauc_precision_at_100_max
value: -21.1488
- type: nauc_precision_at_100_std
value: 65.00200000000001
- type: nauc_precision_at_100_diff1
value: -11.740499999999999
- type: nauc_precision_at_1000_max
value: 20.7392
- type: nauc_precision_at_1000_std
value: 38.2851
- type: nauc_precision_at_1000_diff1
value: 17.4954
- type: nauc_mrr_at_1_max
value: -33.7627
- type: nauc_mrr_at_1_std
value: 1.8065000000000002
- type: nauc_mrr_at_1_diff1
value: 5.3592
- type: nauc_mrr_at_3_max
value: -39.837
- type: nauc_mrr_at_3_std
value: -5.3861
- type: nauc_mrr_at_3_diff1
value: -4.1776
- type: nauc_mrr_at_5_max
value: -39.756099999999996
- type: nauc_mrr_at_5_std
value: -5.3674
- type: nauc_mrr_at_5_diff1
value: -2.4693
- type: nauc_mrr_at_10_max
value: -37.7379
- type: nauc_mrr_at_10_std
value: -6.2844
- type: nauc_mrr_at_10_diff1
value: -0.6525000000000001
- type: nauc_mrr_at_20_max
value: -38.4522
- type: nauc_mrr_at_20_std
value: -5.0927
- type: nauc_mrr_at_20_diff1
value: -0.2814
- type: nauc_mrr_at_100_max
value: -38.1599
- type: nauc_mrr_at_100_std
value: -5.2147
- type: nauc_mrr_at_100_diff1
value: -0.7001000000000001
- type: nauc_mrr_at_1000_max
value: -38.1599
- type: nauc_mrr_at_1000_std
value: -5.2147
- type: nauc_mrr_at_1000_diff1
value: -0.7001000000000001
- type: main_score
value: 24.029
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 62.9395
- type: f1
value: 47.7133
- type: f1_weighted
value: 71.0525
- type: ap
value: 10.306600000000001
- type: ap_weighted
value: 10.306600000000001
- type: main_score
value: 62.9395
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 52.8721
- type: f1
value: 53.034800000000004
- type: f1_weighted
value: 52.4319
- type: main_score
value: 52.8721
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 44.9227
- type: v_measure_std
value: 1.1638000000000002
- type: main_score
value: 44.9227
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: similarity_accuracy
value: 82.04090000000001
- type: similarity_accuracy_threshold
value: 86.6147
- type: similarity_f1
value: 57.258399999999995
- type: similarity_f1_threshold
value: 82.9233
- type: similarity_precision
value: 52.1456
- type: similarity_recall
value: 63.4828
- type: similarity_ap
value: 60.0317
- type: cosine_accuracy
value: 82.04090000000001
- type: cosine_accuracy_threshold
value: 86.6147
- type: cosine_f1
value: 57.258399999999995
- type: cosine_f1_threshold
value: 82.9233
- type: cosine_precision
value: 52.1456
- type: cosine_recall
value: 63.4828
- type: cosine_ap
value: 60.0317
- type: manhattan_accuracy
value: 81.9574
- type: manhattan_accuracy_threshold
value: 794.4433
- type: manhattan_f1
value: 57.1936
- type: manhattan_f1_threshold
value: 898.9445
- type: manhattan_precision
value: 51.91480000000001
- type: manhattan_recall
value: 63.6675
- type: manhattan_ap
value: 59.9255
- type: euclidean_accuracy
value: 82.04090000000001
- type: euclidean_accuracy_threshold
value: 51.7403
- type: euclidean_f1
value: 57.258399999999995
- type: euclidean_f1_threshold
value: 58.440999999999995
- type: euclidean_precision
value: 52.1456
- type: euclidean_recall
value: 63.4828
- type: euclidean_ap
value: 60.0317
- type: dot_accuracy
value: 82.04090000000001
- type: dot_accuracy_threshold
value: 86.6147
- type: dot_f1
value: 57.258399999999995
- type: dot_f1_threshold
value: 82.9233
- type: dot_precision
value: 52.1456
- type: dot_recall
value: 63.4828
- type: dot_ap
value: 60.0317
- type: max_accuracy
value: 82.04090000000001
- type: max_f1
value: 57.258399999999995
- type: max_precision
value: 52.1456
- type: max_recall
value: 63.6675
- type: max_ap
value: 60.0317
- type: main_score
value: 60.0317
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 87.3035
- type: similarity_accuracy_threshold
value: 85.4123
- type: similarity_f1
value: 74.5555
- type: similarity_f1_threshold
value: 83.7581
- type: similarity_precision
value: 72.55369999999999
- type: similarity_recall
value: 76.6708
- type: similarity_ap
value: 82.42930000000001
- type: cosine_accuracy
value: 87.3035
- type: cosine_accuracy_threshold
value: 85.4123
- type: cosine_f1
value: 74.5555
- type: cosine_f1_threshold
value: 83.7581
- type: cosine_precision
value: 72.55369999999999
- type: cosine_recall
value: 76.6708
- type: cosine_ap
value: 82.42930000000001
- type: manhattan_accuracy
value: 87.3249
- type: manhattan_accuracy_threshold
value: 831.9304999999999
- type: manhattan_f1
value: 74.8665
- type: manhattan_f1_threshold
value: 893.9980999999999
- type: manhattan_precision
value: 70.8502
- type: manhattan_recall
value: 79.3656
- type: manhattan_ap
value: 82.5792
- type: euclidean_accuracy
value: 87.3035
- type: euclidean_accuracy_threshold
value: 54.014300000000006
- type: euclidean_f1
value: 74.5555
- type: euclidean_f1_threshold
value: 56.9946
- type: euclidean_precision
value: 72.55369999999999
- type: euclidean_recall
value: 76.6708
- type: euclidean_ap
value: 82.42920000000001
- type: dot_accuracy
value: 87.3035
- type: dot_accuracy_threshold
value: 85.4123
- type: dot_f1
value: 74.5555
- type: dot_f1_threshold
value: 83.7581
- type: dot_precision
value: 72.55369999999999
- type: dot_recall
value: 76.6708
- type: dot_ap
value: 82.42920000000001
- type: max_accuracy
value: 87.3249
- type: max_f1
value: 74.8665
- type: max_precision
value: 72.55369999999999
- type: max_recall
value: 79.3656
- type: max_ap
value: 82.5792
- type: main_score
value: 82.5792
---
# Granite-Embedding-30m-English
**Model Summary:**
Granite-Embedding-30m-English is a 30M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning, knowledge distillation and model merging for improved performance.
- **Developers:** Granite Embedding Team, IBM
- **GitHub Repository:** [ibm-granite/granite-embedding-models](https://github.com/ibm-granite/granite-embedding-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** Coming Soon
- **Release Date**: December 18th, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
**Supported Languages:**
English.
**Intended use:**
The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
**Usage with Sentence Transformers:**
The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
```shell
pip install sentence_transformers
```
The model can then be used to encode pairs of text and find the similarity between their representations
```python
from sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-30m-english"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
input_passages = [
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
```
**Usage with Huggingface Transformers:**
This is a simple example of how to use the Granite-Embedding-30m-English model with the Transformers library and PyTorch.
First, install the required libraries
```shell
pip install transformers torch
```
The model can then be used to encode pairs of text
```python
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-30m-english"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode queries
with torch.no_grad():
# Queries
model_output = model(**tokenized_queries)
# Perform pooling. granite-embedding-30m-english uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
```
**Evaluation:**
Granite-Embedding-30M-English is twice as fast as other models with similar embedding dimensions, while maintaining competitive performance. The performance of the Granite-Embedding-30M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below.
| Model | Paramters (M)| Embedding Dimension | MTEB Retrieval (15) | CoIR (10) |
|---------------------------------|:------------:|:-------------------:|:-------------------: |:----------:|
|granite-embedding-30m-english |30 |384 |49.1 |47.0 |
**Model Architecture:**
Granite-Embedding-30m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
| :--------- | :-------:| :--------: | :-----:| :-----:|
| Embedding size | **384** | 768 | 384 | 768 |
| Number of layers | **6** | 12 | 6 | 12 |
| Number of attention heads | **12** | 12 | 12 | 12 |
| Intermediate size | **1536** | 3072 | 1536 | 3072 |
| Activation Function | **GeLU** | GeLU | GeLU | GeLU |
| Vocabulary Size | **50265**| 50265 | 250002 | 250002 |
| Max. Sequence Length | **512** | 512 | 512 | 512 |
| # Parameters | **30M** | 125M | 107M | 278M |
**Training Data:**
Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
| **Dataset** | **Num. Pairs** |
|----------------------------------------------------|:---------------:|
| SPECTER citation triplets | 684,100 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Stack Exchange Duplicate questions (titles+bodies) | 250,460 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| PAQ (Question, Answer) pairs | 64,371,441 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title+Body, Answer) pairs | 187,195 |
| S2ORC Citation pairs (Titles) | 52,603,982 |
| S2ORC (Title, Abstract) | 41,769,185 |
| S2ORC (Citations, abstracts) | 52,603,982 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| SearchQA | 582,261 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| Arxiv | 2,358,545 |
| Wikipedia | 20,745,403 |
| PubMed | 20,000,000 |
| Miracl En Pairs | 9,016 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: Query-Wikipedia Passage | 1,879,093 |
| Synthetic: Fact Verification | 9,888 |
| IBM Internal Triples | 40,290 |
| IBM Internal Title-Body Pairs | 1,524,586 |
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
**Infrastructure:**
We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
**Ethical Considerations and Limitations:**
The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-30m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
**Resources**
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
<!-- ## Citation
```
@misc{granite-embedding-models,
author = {author 1, author2, ...},
title = {},
journal = {},
volume = {},
year = {2024},
url = {https://arxiv.org/abs/0000.00000},
}
``` --> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
abhinand/MedEmbed-large-v0.1 | abhinand | null | [
"sentence-transformers",
"safetensors",
"bert",
"medembed",
"medical-embedding",
"clinical-embedding",
"information-retrieval",
"en",
"dataset:MedicalQARetrieval",
"dataset:NFCorpus",
"dataset:PublicHealthQA",
"dataset:TRECCOVID",
"dataset:ArguAna",
"base_model:BAAI/bge-large-en-v1.5",
"base_model:finetune:BAAI/bge-large-en-v1.5",
"license:apache-2.0",
"region:us"
] | 2024-10-20T11:43:03 | 2024-10-21T06:49:18 | 85,805 | 18 | ---
base_model:
- BAAI/bge-large-en-v1.5
datasets:
- MedicalQARetrieval
- NFCorpus
- PublicHealthQA
- TRECCOVID
- ArguAna
language: en
license: apache-2.0
metrics:
- nDCG
- MAP
- Recall
- Precision
- MRR
tags:
- medembed
- medical-embedding
- clinical-embedding
- information-retrieval
- sentence-transformers
---
# MedEmbed: Specialized Embedding Model for Medical and Clinical Information Retrieval
## Model Description
MedEmbed is a family of embedding models fine-tuned specifically for medical and clinical data, designed to enhance performance in healthcare-related natural language processing (NLP) tasks, particularly information retrieval.
**GitHub Repo:** [https://github.com/abhinand5/MedEmbed](https://github.com/abhinand5/MedEmbed)
**Technical Blog Post:** [https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir](https://huggingface.co/blog/abhinand/medembed-finetuned-embedding-models-for-medical-ir)
## Intended Use
This model is intended for use in medical and clinical contexts to improve information retrieval, question answering, and semantic search tasks. It can be integrated into healthcare systems, research tools, and medical literature databases to enhance search capabilities and information access.
## Training Data
The model was trained using a simple yet effective synthetic data generation pipeline:
1. Source: Clinical notes from PubMed Central (PMC)
2. Processing: [LLaMA 3.1 70B](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) model used to generate query-response pairs
3. Augmentation: Negative sampling for challenging examples
4. Format: Triplets (query, positive response, negative response) for contrastive learning
## Performance
MedEmbed consistently outperforms general-purpose embedding models across various medical NLP benchmarks:
- ArguAna
- MedicalQARetrieval
- NFCorpus
- PublicHealthQA
- TRECCOVID
Specific performance metrics (nDCG, MAP, Recall, Precision, MRR) are available in the full documentation.
## Limitations
While highly effective for medical and clinical data, this model may not generalize well to non-medical domains. It should be used with caution in general-purpose NLP tasks.
## Ethical Considerations
Users should be aware of potential biases in medical data and the ethical implications of AI in healthcare. This model should be used as a tool to assist, not replace, human expertise in medical decision-making.
## Citation
If you use this model in your research, please cite:
```bibtex
@software{balachandran2024medembed,
author = {Balachandran, Abhinand},
title = {MedEmbed: Medical-Focused Embedding Models},
year = {2024},
url = {https://github.com/abhinand5/MedEmbed}
}
```
For more detailed information, visit our GitHub repository. | [
"QUESTION_ANSWERING"
] | [
"MEDICAL DATA"
] |
EleutherAI/pythia-70m | EleutherAI | null | [
"gpt-neox",
"pytorch",
"safetensors",
"gpt_neox",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"region:us"
] | 2023-02-13T14:54:51 | 2023-11-21T19:04:09 | 85,506 | 64 | ---
datasets:
- EleutherAI/pile
language:
- en
library_name: gpt-neox
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-70M
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-70M for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-70M as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-70M has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-70M will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-70M to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-70M may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-70M.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-70M.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure>
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-70m)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 25.28 |
| ARC (25-shot) | 21.59 |
| HellaSwag (10-shot) | 27.29 |
| MMLU (5-shot) | 25.9 |
| TruthfulQA (0-shot) | 47.06 |
| Winogrande (5-shot) | 51.46 |
| GSM8K (5-shot) | 0.3 |
| DROP (3-shot) | 3.33 | | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
EleutherAI/pythia-1.4b | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-09T14:08:20 | 2023-07-09T16:01:57 | 83,953 | 23 | ---
datasets:
- EleutherAI/the_pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-1.4B
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-1.4B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-1.4B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-1.4B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-1.4B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-1.4B to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-1.4B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-1.4B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-1.4B.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
Snowflake/snowflake-arctic-embed-l-v2.0 | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"af",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"cs",
"cy",
"da",
"de",
"el",
"en",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"gl",
"gu",
"he",
"hi",
"hr",
"ht",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ky",
"lo",
"lt",
"lv",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"pa",
"pl",
"pt",
"qu",
"ro",
"ru",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"uk",
"ur",
"vi",
"yo",
"zh",
"arxiv:2412.04506",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-11-08T16:55:31 | 2024-12-14T00:18:38 | 78,528 | 132 | ---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-arctic-embed-l-v2.0
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 67.039
- type: f1
value: 55.1806
- type: f1_weighted
value: 73.41149999999999
- type: ap
value: 17.9914
- type: ap_weighted
value: 17.9914
- type: main_score
value: 67.039
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 65.59700000000001
- type: f1
value: 60.244299999999996
- type: f1_weighted
value: 68.9975
- type: ap
value: 29.762100000000004
- type: ap_weighted
value: 29.762100000000004
- type: main_score
value: 65.59700000000001
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 74.2565
- type: f1
value: 74.0291
- type: f1_weighted
value: 74.0291
- type: ap
value: 68.7595
- type: ap_weighted
value: 68.7595
- type: main_score
value: 74.2565
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 34.946
- type: f1
value: 34.2853
- type: f1_weighted
value: 34.2853
- type: main_score
value: 34.946
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: ndcg_at_1
value: 33.286
- type: ndcg_at_3
value: 49.051
- type: ndcg_at_5
value: 54.107000000000006
- type: ndcg_at_10
value: 59.146
- type: ndcg_at_20
value: 60.897999999999996
- type: ndcg_at_100
value: 61.78399999999999
- type: ndcg_at_1000
value: 61.845000000000006
- type: map_at_1
value: 33.286
- type: map_at_3
value: 45.14
- type: map_at_5
value: 47.939
- type: map_at_10
value: 50.046
- type: map_at_20
value: 50.56
- type: map_at_100
value: 50.708
- type: map_at_1000
value: 50.712
- type: recall_at_1
value: 33.286
- type: recall_at_3
value: 60.38400000000001
- type: recall_at_5
value: 72.688
- type: recall_at_10
value: 88.122
- type: recall_at_20
value: 94.808
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.286
- type: precision_at_3
value: 20.128
- type: precision_at_5
value: 14.538
- type: precision_at_10
value: 8.812000000000001
- type: precision_at_20
value: 4.74
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 33.926
- type: mrr_at_3
value: 45.3414
- type: mrr_at_5
value: 48.1828
- type: mrr_at_10
value: 50.270700000000005
- type: mrr_at_20
value: 50.7844
- type: mrr_at_100
value: 50.9259
- type: mrr_at_1000
value: 50.9294
- type: nauc_ndcg_at_1_max
value: -10.305
- type: nauc_ndcg_at_1_std
value: -15.674199999999999
- type: nauc_ndcg_at_1_diff1
value: 18.6355
- type: nauc_ndcg_at_3_max
value: -7.744
- type: nauc_ndcg_at_3_std
value: -16.894000000000002
- type: nauc_ndcg_at_3_diff1
value: 15.4469
- type: nauc_ndcg_at_5_max
value: -6.4887
- type: nauc_ndcg_at_5_std
value: -16.1382
- type: nauc_ndcg_at_5_diff1
value: 13.8214
- type: nauc_ndcg_at_10_max
value: -7.616499999999999
- type: nauc_ndcg_at_10_std
value: -15.8073
- type: nauc_ndcg_at_10_diff1
value: 13.7678
- type: nauc_ndcg_at_20_max
value: -6.9801
- type: nauc_ndcg_at_20_std
value: -15.068699999999998
- type: nauc_ndcg_at_20_diff1
value: 14.2013
- type: nauc_ndcg_at_100_max
value: -7.5221
- type: nauc_ndcg_at_100_std
value: -15.417200000000001
- type: nauc_ndcg_at_100_diff1
value: 15.1072
- type: nauc_ndcg_at_1000_max
value: -7.6931
- type: nauc_ndcg_at_1000_std
value: -15.5367
- type: nauc_ndcg_at_1000_diff1
value: 15.001700000000001
- type: nauc_map_at_1_max
value: -10.305
- type: nauc_map_at_1_std
value: -15.674199999999999
- type: nauc_map_at_1_diff1
value: 18.6355
- type: nauc_map_at_3_max
value: -8.4505
- type: nauc_map_at_3_std
value: -16.5487
- type: nauc_map_at_3_diff1
value: 15.965599999999998
- type: nauc_map_at_5_max
value: -7.8429
- type: nauc_map_at_5_std
value: -16.1332
- type: nauc_map_at_5_diff1
value: 15.0893
- type: nauc_map_at_10_max
value: -8.3186
- type: nauc_map_at_10_std
value: -15.979399999999998
- type: nauc_map_at_10_diff1
value: 15.136199999999999
- type: nauc_map_at_20_max
value: -8.1697
- type: nauc_map_at_20_std
value: -15.8241
- type: nauc_map_at_20_diff1
value: 15.260599999999998
- type: nauc_map_at_100_max
value: -8.2285
- type: nauc_map_at_100_std
value: -15.8624
- type: nauc_map_at_100_diff1
value: 15.412600000000001
- type: nauc_map_at_1000_max
value: -8.2359
- type: nauc_map_at_1000_std
value: -15.867
- type: nauc_map_at_1000_diff1
value: 15.408
- type: nauc_recall_at_1_max
value: -10.305
- type: nauc_recall_at_1_std
value: -15.674199999999999
- type: nauc_recall_at_1_diff1
value: 18.6355
- type: nauc_recall_at_3_max
value: -5.5097
- type: nauc_recall_at_3_std
value: -17.9896
- type: nauc_recall_at_3_diff1
value: 13.9525
- type: nauc_recall_at_5_max
value: -0.9383
- type: nauc_recall_at_5_std
value: -16.035
- type: nauc_recall_at_5_diff1
value: 8.8431
- type: nauc_recall_at_10_max
value: -2.8548
- type: nauc_recall_at_10_std
value: -14.1203
- type: nauc_recall_at_10_diff1
value: 3.2265
- type: nauc_recall_at_20_max
value: 14.2043
- type: nauc_recall_at_20_std
value: 2.1298999999999997
- type: nauc_recall_at_20_diff1
value: -1.9900000000000002
- type: nauc_recall_at_100_max
value: 44.0173
- type: nauc_recall_at_100_std
value: 42.131800000000005
- type: nauc_recall_at_100_diff1
value: 29.9983
- type: nauc_recall_at_1000_max
value: 25.9434
- type: nauc_recall_at_1000_std
value: 53.9252
- type: nauc_recall_at_1000_diff1
value: -0.9778
- type: nauc_precision_at_1_max
value: -10.305
- type: nauc_precision_at_1_std
value: -15.674199999999999
- type: nauc_precision_at_1_diff1
value: 18.6355
- type: nauc_precision_at_3_max
value: -5.5097
- type: nauc_precision_at_3_std
value: -17.9896
- type: nauc_precision_at_3_diff1
value: 13.9525
- type: nauc_precision_at_5_max
value: -0.9383
- type: nauc_precision_at_5_std
value: -16.035
- type: nauc_precision_at_5_diff1
value: 8.8431
- type: nauc_precision_at_10_max
value: -2.8548
- type: nauc_precision_at_10_std
value: -14.1203
- type: nauc_precision_at_10_diff1
value: 3.2265
- type: nauc_precision_at_20_max
value: 14.2043
- type: nauc_precision_at_20_std
value: 2.1298999999999997
- type: nauc_precision_at_20_diff1
value: -1.9900000000000002
- type: nauc_precision_at_100_max
value: 44.0173
- type: nauc_precision_at_100_std
value: 42.131800000000005
- type: nauc_precision_at_100_diff1
value: 29.9983
- type: nauc_precision_at_1000_max
value: 25.9434
- type: nauc_precision_at_1000_std
value: 53.9252
- type: nauc_precision_at_1000_diff1
value: -0.9778
- type: nauc_mrr_at_1_max
value: -9.833
- type: nauc_mrr_at_1_std
value: -14.8351
- type: nauc_mrr_at_1_diff1
value: 16.7604
- type: nauc_mrr_at_3_max
value: -9.0116
- type: nauc_mrr_at_3_std
value: -16.296
- type: nauc_mrr_at_3_diff1
value: 14.178199999999999
- type: nauc_mrr_at_5_max
value: -8.308300000000001
- type: nauc_mrr_at_5_std
value: -15.751999999999999
- type: nauc_mrr_at_5_diff1
value: 13.306299999999998
- type: nauc_mrr_at_10_max
value: -8.7962
- type: nauc_mrr_at_10_std
value: -15.688099999999999
- type: nauc_mrr_at_10_diff1
value: 13.2589
- type: nauc_mrr_at_20_max
value: -8.6773
- type: nauc_mrr_at_20_std
value: -15.479499999999998
- type: nauc_mrr_at_20_diff1
value: 13.354
- type: nauc_mrr_at_100_max
value: -8.7533
- type: nauc_mrr_at_100_std
value: -15.553600000000001
- type: nauc_mrr_at_100_diff1
value: 13.4796
- type: nauc_mrr_at_1000_max
value: -8.7608
- type: nauc_mrr_at_1000_std
value: -15.5582
- type: nauc_mrr_at_1000_diff1
value: 13.4748
- type: main_score
value: 59.146
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.9715
- type: v_measure_std
value: 13.4325
- type: main_score
value: 43.9715
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 34.775800000000004
- type: v_measure_std
value: 13.922799999999999
- type: main_score
value: 34.775800000000004
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 63.3521
- type: mrr
value: 77.5965
- type: nAUC_map_max
value: 21.2353
- type: nAUC_map_std
value: 17.002100000000002
- type: nAUC_map_diff1
value: 3.8135000000000003
- type: nAUC_mrr_max
value: 35.058299999999996
- type: nAUC_mrr_std
value: 20.432
- type: nAUC_mrr_diff1
value: 9.2584
- type: main_score
value: 63.3521
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: pearson
value: 89.8072
- type: spearman
value: 87.2875
- type: cosine_pearson
value: 89.8072
- type: cosine_spearman
value: 87.2875
- type: manhattan_pearson
value: 87.9173
- type: manhattan_spearman
value: 86.7327
- type: euclidean_pearson
value: 88.21600000000001
- type: euclidean_spearman
value: 87.2875
- type: main_score
value: 87.2875
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.8149
- type: f1
value: 81.2226
- type: f1_weighted
value: 81.2226
- type: main_score
value: 81.8149
- task:
type: Clustering
dataset:
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type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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type: Clustering
dataset:
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type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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type: mteb/cqadupstack-android
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revision: f46a197baaae43b4f621051089b82a364682dfeb
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dataset:
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type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
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revision: 4885aa143210c98657558c04aaf3dc47cfb54340
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type: Classification
dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 72.5444
- type: f1
value: 72.4069
- type: f1_weighted
value: 72.4069
- type: ap
value: 66.8419
- type: ap_weighted
value: 66.8419
- type: main_score
value: 72.5444
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO (default)
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
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value: 25.516
- type: ndcg_at_3
value: 36.687999999999995
- type: ndcg_at_5
value: 40.864
- type: ndcg_at_10
value: 44.856
- type: ndcg_at_20
value: 47.3
- type: ndcg_at_100
value: 50.062
- type: ndcg_at_1000
value: 51.085
- type: map_at_1
value: 24.782
- type: map_at_3
value: 33.668
- type: map_at_5
value: 36.010999999999996
- type: map_at_10
value: 37.702000000000005
- type: map_at_20
value: 38.391
- type: map_at_100
value: 38.798
- type: map_at_1000
value: 38.841
- type: recall_at_1
value: 24.782
- type: recall_at_3
value: 44.722
- type: recall_at_5
value: 54.769999999999996
- type: recall_at_10
value: 66.842
- type: recall_at_20
value: 76.319
- type: recall_at_100
value: 90.761
- type: recall_at_1000
value: 98.48
- type: precision_at_1
value: 25.516
- type: precision_at_3
value: 15.506
- type: precision_at_5
value: 11.413
- type: precision_at_10
value: 6.99
- type: precision_at_20
value: 4.009
- type: precision_at_100
value: 0.959
- type: precision_at_1000
value: 0.105
- type: mrr_at_1
value: 25.5014
- type: mrr_at_3
value: 34.3553
- type: mrr_at_5
value: 36.666199999999996
- type: mrr_at_10
value: 38.3084
- type: mrr_at_20
value: 38.9663
- type: mrr_at_100
value: 39.341300000000004
- type: mrr_at_1000
value: 39.3785
- type: nauc_ndcg_at_1_max
value: 4.2138
- type: nauc_ndcg_at_1_std
value: -24.7801
- type: nauc_ndcg_at_1_diff1
value: 37.758399999999995
- type: nauc_ndcg_at_3_max
value: 5.2536
- type: nauc_ndcg_at_3_std
value: -29.642200000000003
- type: nauc_ndcg_at_3_diff1
value: 32.1639
- type: nauc_ndcg_at_5_max
value: 5.0839
- type: nauc_ndcg_at_5_std
value: -31.3077
- type: nauc_ndcg_at_5_diff1
value: 31.5135
- type: nauc_ndcg_at_10_max
value: 6.2542
- type: nauc_ndcg_at_10_std
value: -30.8439
- type: nauc_ndcg_at_10_diff1
value: 31.461299999999998
- type: nauc_ndcg_at_20_max
value: 6.5669
- type: nauc_ndcg_at_20_std
value: -29.6288
- type: nauc_ndcg_at_20_diff1
value: 31.590200000000003
- type: nauc_ndcg_at_100_max
value: 6.691800000000001
- type: nauc_ndcg_at_100_std
value: -28.1768
- type: nauc_ndcg_at_100_diff1
value: 32.1699
- type: nauc_ndcg_at_1000_max
value: 6.451700000000001
- type: nauc_ndcg_at_1000_std
value: -28.2093
- type: nauc_ndcg_at_1000_diff1
value: 32.3573
- type: nauc_map_at_1_max
value: 4.1941
- type: nauc_map_at_1_std
value: -24.9531
- type: nauc_map_at_1_diff1
value: 38.099
- type: nauc_map_at_3_max
value: 4.9883999999999995
- type: nauc_map_at_3_std
value: -28.7062
- type: nauc_map_at_3_diff1
value: 33.5696
- type: nauc_map_at_5_max
value: 4.8525
- type: nauc_map_at_5_std
value: -29.6601
- type: nauc_map_at_5_diff1
value: 33.2144
- type: nauc_map_at_10_max
value: 5.3533
- type: nauc_map_at_10_std
value: -29.4529
- type: nauc_map_at_10_diff1
value: 33.219300000000004
- type: nauc_map_at_20_max
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- type: nauc_map_at_20_std
value: -29.1294
- type: nauc_map_at_20_diff1
value: 33.2747
- type: nauc_map_at_100_max
value: 5.4547
- type: nauc_map_at_100_std
value: -28.8978
- type: nauc_map_at_100_diff1
value: 33.3505
- type: nauc_map_at_1000_max
value: 5.4512
- type: nauc_map_at_1000_std
value: -28.8844
- type: nauc_map_at_1000_diff1
value: 33.356700000000004
- type: nauc_recall_at_1_max
value: 4.1941
- type: nauc_recall_at_1_std
value: -24.9531
- type: nauc_recall_at_1_diff1
value: 38.099
- type: nauc_recall_at_3_max
value: 5.884799999999999
- type: nauc_recall_at_3_std
value: -32.317
- type: nauc_recall_at_3_diff1
value: 28.284399999999998
- type: nauc_recall_at_5_max
value: 5.4525
- type: nauc_recall_at_5_std
value: -36.4055
- type: nauc_recall_at_5_diff1
value: 26.384200000000003
- type: nauc_recall_at_10_max
value: 9.403400000000001
- type: nauc_recall_at_10_std
value: -35.9112
- type: nauc_recall_at_10_diff1
value: 25.2415
- type: nauc_recall_at_20_max
value: 12.0952
- type: nauc_recall_at_20_std
value: -30.778299999999998
- type: nauc_recall_at_20_diff1
value: 24.1866
- type: nauc_recall_at_100_max
value: 19.6413
- type: nauc_recall_at_100_std
value: -11.9243
- type: nauc_recall_at_100_diff1
value: 24.6153
- type: nauc_recall_at_1000_max
value: 48.1206
- type: nauc_recall_at_1000_std
value: 48.0062
- type: nauc_recall_at_1000_diff1
value: 16.2543
- type: nauc_precision_at_1_max
value: 4.2138
- type: nauc_precision_at_1_std
value: -24.7801
- type: nauc_precision_at_1_diff1
value: 37.758399999999995
- type: nauc_precision_at_3_max
value: 5.7985
- type: nauc_precision_at_3_std
value: -31.749899999999997
- type: nauc_precision_at_3_diff1
value: 27.373399999999997
- type: nauc_precision_at_5_max
value: 5.390000000000001
- type: nauc_precision_at_5_std
value: -35.0586
- type: nauc_precision_at_5_diff1
value: 25.100099999999998
- type: nauc_precision_at_10_max
value: 9.248199999999999
- type: nauc_precision_at_10_std
value: -32.244299999999996
- type: nauc_precision_at_10_diff1
value: 22.5684
- type: nauc_precision_at_20_max
value: 11.495099999999999
- type: nauc_precision_at_20_std
value: -24.226300000000002
- type: nauc_precision_at_20_diff1
value: 19.6528
- type: nauc_precision_at_100_max
value: 14.3649
- type: nauc_precision_at_100_std
value: 0.0593
- type: nauc_precision_at_100_diff1
value: 10.9596
- type: nauc_precision_at_1000_max
value: 10.9512
- type: nauc_precision_at_1000_std
value: 18.288
- type: nauc_precision_at_1000_diff1
value: -3.5423000000000004
- type: nauc_mrr_at_1_max
value: 4.2204
- type: nauc_mrr_at_1_std
value: -24.7703
- type: nauc_mrr_at_1_diff1
value: 37.8126
- type: nauc_mrr_at_3_max
value: 5.0668
- type: nauc_mrr_at_3_std
value: -28.2677
- type: nauc_mrr_at_3_diff1
value: 33.3724
- type: nauc_mrr_at_5_max
value: 5.0481
- type: nauc_mrr_at_5_std
value: -29.133
- type: nauc_mrr_at_5_diff1
value: 33.0415
- type: nauc_mrr_at_10_max
value: 5.5038
- type: nauc_mrr_at_10_std
value: -28.886200000000002
- type: nauc_mrr_at_10_diff1
value: 33.0593
- type: nauc_mrr_at_20_max
value: 5.5467
- type: nauc_mrr_at_20_std
value: -28.5678
- type: nauc_mrr_at_20_diff1
value: 33.0916
- type: nauc_mrr_at_100_max
value: 5.5636
- type: nauc_mrr_at_100_std
value: -28.3877
- type: nauc_mrr_at_100_diff1
value: 33.1799
- type: nauc_mrr_at_1000_max
value: 5.557
- type: nauc_mrr_at_1000_std
value: -28.3796
- type: nauc_mrr_at_1000_diff1
value: 33.184999999999995
- type: main_score
value: 44.856
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.5317
- type: f1
value: 93.1956
- type: f1_weighted
value: 93.5431
- type: main_score
value: 93.5317
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.7907
- type: f1
value: 48.2877
- type: f1_weighted
value: 70.3225
- type: main_score
value: 67.7907
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 71.456
- type: f1
value: 68.2268
- type: f1_weighted
value: 70.4722
- type: main_score
value: 71.456
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 76.21719999999999
- type: f1
value: 75.14189999999999
- type: f1_weighted
value: 76.0733
- type: main_score
value: 76.21719999999999
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P (default)
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.3917
- type: v_measure_std
value: 1.4778
- type: main_score
value: 31.3917
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S (default)
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.2408
- type: v_measure_std
value: 1.1622999999999999
- type: main_score
value: 28.2408
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: map
value: 29.5796
- type: mrr
value: 30.3081
- type: nAUC_map_max
value: -24.9194
- type: nAUC_map_std
value: -9.042
- type: nAUC_map_diff1
value: 12.1611
- type: nAUC_mrr_max
value: -19.3867
- type: nAUC_mrr_std
value: -6.3873
- type: nAUC_mrr_diff1
value: 11.8078
- type: main_score
value: 29.5796
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus (default)
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: ndcg_at_1
value: 45.046
- type: ndcg_at_3
value: 41.704
- type: ndcg_at_5
value: 39.296
- type: ndcg_at_10
value: 35.343999999999994
- type: ndcg_at_20
value: 32.525999999999996
- type: ndcg_at_100
value: 31.352999999999998
- type: ndcg_at_1000
value: 39.772
- type: map_at_1
value: 5.833
- type: map_at_3
value: 9.953
- type: map_at_5
value: 11.549
- type: map_at_10
value: 13.38
- type: map_at_20
value: 14.706
- type: map_at_100
value: 16.422
- type: map_at_1000
value: 17.777
- type: recall_at_1
value: 5.833
- type: recall_at_3
value: 11.112
- type: recall_at_5
value: 13.834
- type: recall_at_10
value: 16.961000000000002
- type: recall_at_20
value: 20.294999999999998
- type: recall_at_100
value: 30.253000000000004
- type: recall_at_1000
value: 60.902
- type: precision_at_1
value: 46.44
- type: precision_at_3
value: 39.009
- type: precision_at_5
value: 33.745999999999995
- type: precision_at_10
value: 25.635
- type: precision_at_20
value: 18.576
- type: precision_at_100
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- type: precision_at_1000
value: 2.037
- type: mrr_at_1
value: 46.7492
- type: mrr_at_3
value: 54.6956
- type: mrr_at_5
value: 55.8875
- type: mrr_at_10
value: 56.3913
- type: mrr_at_20
value: 56.6265
- type: mrr_at_100
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- type: mrr_at_1000
value: 56.8573
- type: nauc_ndcg_at_1_max
value: 43.3685
- type: nauc_ndcg_at_1_std
value: 21.6124
- type: nauc_ndcg_at_1_diff1
value: 29.0317
- type: nauc_ndcg_at_3_max
value: 39.8155
- type: nauc_ndcg_at_3_std
value: 23.2206
- type: nauc_ndcg_at_3_diff1
value: 20.7425
- type: nauc_ndcg_at_5_max
value: 40.951
- type: nauc_ndcg_at_5_std
value: 24.7184
- type: nauc_ndcg_at_5_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
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- type: nauc_ndcg_at_10_diff1
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- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_20_std
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- type: nauc_map_at_1_std
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- type: nauc_map_at_1_diff1
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- type: nauc_map_at_3_std
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- type: nauc_map_at_3_diff1
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- type: nauc_map_at_5_max
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- type: nauc_map_at_5_std
value: -4.136
- type: nauc_map_at_5_diff1
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- type: nauc_map_at_10_max
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- type: nauc_map_at_10_std
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- type: nauc_map_at_10_diff1
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- type: nauc_map_at_20_max
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- type: nauc_map_at_20_std
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- type: nauc_map_at_20_diff1
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- type: nauc_map_at_100_max
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- type: nauc_map_at_100_std
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- type: nauc_map_at_1000_max
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- type: nauc_map_at_1000_std
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- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_1_std
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- type: nauc_recall_at_1_diff1
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value: 24.1569
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value: 48.732
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value: 31.899100000000004
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value: 24.4177
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value: 48.9748
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value: 32.2053
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value: 24.0317
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value: 49.0832
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value: 32.0994
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value: 23.9777
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value: 49.1731
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value: 32.3179
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value: 24.081
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value: 49.1387
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value: 32.2738
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value: 24.063200000000002
- type: main_score
value: 35.343999999999994
- task:
type: Retrieval
dataset:
name: MTEB NQ (default)
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: ndcg_at_1
value: 44.93
- type: ndcg_at_3
value: 56.003
- type: ndcg_at_5
value: 60.150000000000006
- type: ndcg_at_10
value: 63.673
- type: ndcg_at_20
value: 65.211
- type: ndcg_at_100
value: 66.686
- type: ndcg_at_1000
value: 67.009
- type: map_at_1
value: 40.035
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- type: nauc_map_at_5_diff1
value: 21.4395
- type: nauc_map_at_10_max
value: 30.417300000000004
- type: nauc_map_at_10_std
value: 11.615300000000001
- type: nauc_map_at_10_diff1
value: 20.624000000000002
- type: nauc_map_at_20_max
value: 31.479200000000002
- type: nauc_map_at_20_std
value: 13.808699999999998
- type: nauc_map_at_20_diff1
value: 20.413
- type: nauc_map_at_100_max
value: 32.2613
- type: nauc_map_at_100_std
value: 15.5692
- type: nauc_map_at_100_diff1
value: 20.5465
- type: nauc_map_at_1000_max
value: 32.2476
- type: nauc_map_at_1000_std
value: 15.7471
- type: nauc_map_at_1000_diff1
value: 20.4622
- type: nauc_recall_at_1_max
value: 23.5069
- type: nauc_recall_at_1_std
value: 3.8736
- type: nauc_recall_at_1_diff1
value: 28.231
- type: nauc_recall_at_3_max
value: 27.970299999999998
- type: nauc_recall_at_3_std
value: 10.2171
- type: nauc_recall_at_3_diff1
value: 19.403699999999997
- type: nauc_recall_at_5_max
value: 28.4521
- type: nauc_recall_at_5_std
value: 12.2105
- type: nauc_recall_at_5_diff1
value: 17.5747
- type: nauc_recall_at_10_max
value: 30.6955
- type: nauc_recall_at_10_std
value: 19.096
- type: nauc_recall_at_10_diff1
value: 15.3116
- type: nauc_recall_at_20_max
value: 32.1047
- type: nauc_recall_at_20_std
value: 24.823600000000003
- type: nauc_recall_at_20_diff1
value: 14.257700000000002
- type: nauc_recall_at_100_max
value: 33.6062
- type: nauc_recall_at_100_std
value: 33.8641
- type: nauc_recall_at_100_diff1
value: 14.5145
- type: nauc_recall_at_1000_max
value: 26.848300000000002
- type: nauc_recall_at_1000_std
value: 38.5884
- type: nauc_recall_at_1000_diff1
value: 5.6408
- type: nauc_precision_at_1_max
value: 23.7575
- type: nauc_precision_at_1_std
value: 4.1697
- type: nauc_precision_at_1_diff1
value: 28.3995
- type: nauc_precision_at_3_max
value: 28.2504
- type: nauc_precision_at_3_std
value: 10.6227
- type: nauc_precision_at_3_diff1
value: 19.5683
- type: nauc_precision_at_5_max
value: 28.8134
- type: nauc_precision_at_5_std
value: 12.518899999999999
- type: nauc_precision_at_5_diff1
value: 17.8036
- type: nauc_precision_at_10_max
value: 30.9813
- type: nauc_precision_at_10_std
value: 19.3506
- type: nauc_precision_at_10_diff1
value: 15.512
- type: nauc_precision_at_20_max
value: 32.6743
- type: nauc_precision_at_20_std
value: 24.9974
- type: nauc_precision_at_20_diff1
value: 14.794099999999998
- type: nauc_precision_at_100_max
value: 34.413700000000006
- type: nauc_precision_at_100_std
value: 34.0889
- type: nauc_precision_at_100_diff1
value: 15.252699999999999
- type: nauc_precision_at_1000_max
value: 27.3954
- type: nauc_precision_at_1000_std
value: 37.8895
- type: nauc_precision_at_1000_diff1
value: 6.587999999999999
- type: nauc_mrr_at_1_max
value: 23.7575
- type: nauc_mrr_at_1_std
value: 4.1697
- type: nauc_mrr_at_1_diff1
value: 28.3995
- type: nauc_mrr_at_3_max
value: 26.8324
- type: nauc_mrr_at_3_std
value: 8.646700000000001
- type: nauc_mrr_at_3_diff1
value: 25.5754
- type: nauc_mrr_at_5_max
value: 26.8274
- type: nauc_mrr_at_5_std
value: 8.911
- type: nauc_mrr_at_5_diff1
value: 25.106
- type: nauc_mrr_at_10_max
value: 27.073399999999996
- type: nauc_mrr_at_10_std
value: 9.7624
- type: nauc_mrr_at_10_diff1
value: 24.9405
- type: nauc_mrr_at_20_max
value: 27.1229
- type: nauc_mrr_at_20_std
value: 10.0676
- type: nauc_mrr_at_20_diff1
value: 24.8122
- type: nauc_mrr_at_100_max
value: 27.1391
- type: nauc_mrr_at_100_std
value: 9.9628
- type: nauc_mrr_at_100_diff1
value: 24.9507
- type: nauc_mrr_at_1000_max
value: 27.114
- type: nauc_mrr_at_1000_std
value: 9.9537
- type: nauc_mrr_at_1000_diff1
value: 24.9421
- type: main_score
value: 20.238999999999997
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: pearson
value: 79.5908
- type: spearman
value: 73.9888
- type: cosine_pearson
value: 79.5908
- type: cosine_spearman
value: 73.9888
- type: manhattan_pearson
value: 77.0623
- type: manhattan_spearman
value: 73.7724
- type: euclidean_pearson
value: 77.30890000000001
- type: euclidean_spearman
value: 73.9888
- type: main_score
value: 73.9888
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: pearson
value: 74.0752
- type: spearman
value: 71.22699999999999
- type: cosine_pearson
value: 74.0752
- type: cosine_spearman
value: 71.22699999999999
- type: manhattan_pearson
value: 70.6037
- type: manhattan_spearman
value: 70.9916
- type: euclidean_pearson
value: 70.922
- type: euclidean_spearman
value: 71.22699999999999
- type: main_score
value: 71.22699999999999
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: pearson
value: 77.8946
- type: spearman
value: 80.4405
- type: cosine_pearson
value: 77.8946
- type: cosine_spearman
value: 80.4405
- type: manhattan_pearson
value: 79.6856
- type: manhattan_spearman
value: 80.1236
- type: euclidean_pearson
value: 80.0315
- type: euclidean_spearman
value: 80.44059999999999
- type: main_score
value: 80.4405
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: pearson
value: 76.2196
- type: spearman
value: 75.10419999999999
- type: cosine_pearson
value: 76.2196
- type: cosine_spearman
value: 75.10419999999999
- type: manhattan_pearson
value: 75.4647
- type: manhattan_spearman
value: 74.81179999999999
- type: euclidean_pearson
value: 75.8091
- type: euclidean_spearman
value: 75.10419999999999
- type: main_score
value: 75.10419999999999
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: pearson
value: 81.2455
- type: spearman
value: 82.8681
- type: cosine_pearson
value: 81.2455
- type: cosine_spearman
value: 82.8681
- type: manhattan_pearson
value: 82.4327
- type: manhattan_spearman
value: 82.7513
- type: euclidean_pearson
value: 82.5635
- type: euclidean_spearman
value: 82.8681
- type: main_score
value: 82.8681
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: pearson
value: 81.6322
- type: spearman
value: 83.487
- type: cosine_pearson
value: 81.6322
- type: cosine_spearman
value: 83.487
- type: manhattan_pearson
value: 83.0048
- type: manhattan_spearman
value: 83.4064
- type: euclidean_pearson
value: 83.0938
- type: euclidean_spearman
value: 83.487
- type: main_score
value: 83.487
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 81.1124
- type: spearman
value: 84.5436
- type: cosine_pearson
value: 81.1124
- type: cosine_spearman
value: 84.5436
- type: manhattan_pearson
value: 83.5158
- type: manhattan_spearman
value: 84.596
- type: euclidean_pearson
value: 83.4429
- type: euclidean_spearman
value: 84.5436
- type: main_score
value: 84.5436
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 62.0001
- type: spearman
value: 63.631099999999996
- type: cosine_pearson
value: 62.0001
- type: cosine_spearman
value: 63.631099999999996
- type: manhattan_pearson
value: 62.239599999999996
- type: manhattan_spearman
value: 62.892199999999995
- type: euclidean_pearson
value: 62.9809
- type: euclidean_spearman
value: 63.631099999999996
- type: main_score
value: 63.631099999999996
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 75.1556
- type: spearman
value: 76.8807
- type: cosine_pearson
value: 75.1556
- type: cosine_spearman
value: 76.8807
- type: manhattan_pearson
value: 76.2428
- type: manhattan_spearman
value: 76.8101
- type: euclidean_pearson
value: 76.107
- type: euclidean_spearman
value: 76.8807
- type: main_score
value: 76.8807
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 69.85719999999999
- type: spearman
value: 71.0489
- type: cosine_pearson
value: 69.85719999999999
- type: cosine_spearman
value: 71.0489
- type: manhattan_pearson
value: 71.08449999999999
- type: manhattan_spearman
value: 71.0051
- type: euclidean_pearson
value: 71.19760000000001
- type: euclidean_spearman
value: 71.0489
- type: main_score
value: 71.0489
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 76.1131
- type: spearman
value: 78.2714
- type: cosine_pearson
value: 76.1131
- type: cosine_spearman
value: 78.2714
- type: manhattan_pearson
value: 76.70270000000001
- type: manhattan_spearman
value: 77.7803
- type: euclidean_pearson
value: 77.14269999999999
- type: euclidean_spearman
value: 78.2714
- type: main_score
value: 78.2714
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 74.49719999999999
- type: spearman
value: 76.2747
- type: cosine_pearson
value: 74.49719999999999
- type: cosine_spearman
value: 76.2747
- type: manhattan_pearson
value: 75.071
- type: manhattan_spearman
value: 75.8969
- type: euclidean_pearson
value: 75.289
- type: euclidean_spearman
value: 76.2747
- type: main_score
value: 76.2747
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 76.7073
- type: spearman
value: 79.3107
- type: cosine_pearson
value: 76.7073
- type: cosine_spearman
value: 79.3107
- type: manhattan_pearson
value: 77.9578
- type: manhattan_spearman
value: 79.3195
- type: euclidean_pearson
value: 77.7386
- type: euclidean_spearman
value: 79.3107
- type: main_score
value: 79.3107
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: pearson
value: 60.5826
- type: spearman
value: 61.0502
- type: cosine_pearson
value: 60.5826
- type: cosine_spearman
value: 61.0502
- type: manhattan_pearson
value: 61.202
- type: manhattan_spearman
value: 61.2039
- type: euclidean_pearson
value: 61.1915
- type: euclidean_spearman
value: 61.0502
- type: main_score
value: 61.0502
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 69.2521
- type: spearman
value: 68.06219999999999
- type: cosine_pearson
value: 69.2521
- type: cosine_spearman
value: 68.06219999999999
- type: manhattan_pearson
value: 70.5115
- type: manhattan_spearman
value: 67.8705
- type: euclidean_pearson
value: 70.68480000000001
- type: euclidean_spearman
value: 68.06219999999999
- type: main_score
value: 68.06219999999999
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 77.97500000000001
- type: spearman
value: 76.848
- type: cosine_pearson
value: 77.97500000000001
- type: cosine_spearman
value: 76.848
- type: manhattan_pearson
value: 76.4098
- type: manhattan_spearman
value: 76.6188
- type: euclidean_pearson
value: 77.17500000000001
- type: euclidean_spearman
value: 76.848
- type: main_score
value: 76.848
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 71.3604
- type: spearman
value: 70.7891
- type: cosine_pearson
value: 71.3604
- type: cosine_spearman
value: 70.7891
- type: manhattan_pearson
value: 73.0185
- type: manhattan_spearman
value: 70.79299999999999
- type: euclidean_pearson
value: 73.17620000000001
- type: euclidean_spearman
value: 70.7891
- type: main_score
value: 70.7891
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 77.58239999999999
- type: spearman
value: 78.5907
- type: cosine_pearson
value: 77.58239999999999
- type: cosine_spearman
value: 78.5907
- type: manhattan_pearson
value: 79.25720000000001
- type: manhattan_spearman
value: 78.6249
- type: euclidean_pearson
value: 79.3724
- type: euclidean_spearman
value: 78.5907
- type: main_score
value: 78.5907
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: pearson
value: 63.324000000000005
- type: spearman
value: 55.1099
- type: cosine_pearson
value: 63.324000000000005
- type: cosine_spearman
value: 55.1099
- type: manhattan_pearson
value: 67.3128
- type: manhattan_spearman
value: 56.340199999999996
- type: euclidean_pearson
value: 67.12089999999999
- type: euclidean_spearman
value: 55.1099
- type: main_score
value: 55.1099
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: pearson
value: 78.02329999999999
- type: spearman
value: 79.1887
- type: cosine_pearson
value: 78.02329999999999
- type: cosine_spearman
value: 79.1887
- type: manhattan_pearson
value: 78.8951
- type: manhattan_spearman
value: 78.9444
- type: euclidean_pearson
value: 79.1499
- type: euclidean_spearman
value: 79.1888
- type: main_score
value: 79.1887
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.7501
- type: mrr
value: 93.9748
- type: nAUC_map_max
value: 54.495599999999996
- type: nAUC_map_std
value: 70.0377
- type: nAUC_map_diff1
value: 6.0146999999999995
- type: nAUC_mrr_max
value: 81.1486
- type: nAUC_mrr_std
value: 78.3478
- type: nAUC_mrr_diff1
value: 50.7613
- type: main_score
value: 78.7501
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_3
value: 66.022
- type: ndcg_at_5
value: 68.508
- type: ndcg_at_10
value: 70.586
- type: ndcg_at_20
value: 71.714
- type: ndcg_at_100
value: 72.81
- type: ndcg_at_1000
value: 73.482
- type: map_at_1
value: 55.594
- type: map_at_3
value: 63.2
- type: map_at_5
value: 64.996
- type: map_at_10
value: 65.988
- type: map_at_20
value: 66.347
- type: map_at_100
value: 66.526
- type: map_at_1000
value: 66.547
- type: recall_at_1
value: 55.594
- type: recall_at_3
value: 71.22800000000001
- type: recall_at_5
value: 77.078
- type: recall_at_10
value: 83.172
- type: recall_at_20
value: 87.422
- type: recall_at_100
value: 93.167
- type: recall_at_1000
value: 98.667
- type: precision_at_1
value: 58.667
- type: precision_at_3
value: 25.778000000000002
- type: precision_at_5
value: 17.333000000000002
- type: precision_at_10
value: 9.433
- type: precision_at_20
value: 4.967
- type: precision_at_100
value: 1.06
- type: precision_at_1000
value: 0.11199999999999999
- type: mrr_at_1
value: 58.666700000000006
- type: mrr_at_3
value: 65.3889
- type: mrr_at_5
value: 66.62219999999999
- type: mrr_at_10
value: 67.3364
- type: mrr_at_20
value: 67.6046
- type: mrr_at_100
value: 67.73320000000001
- type: mrr_at_1000
value: 67.7526
- type: nauc_ndcg_at_1_max
value: 60.2511
- type: nauc_ndcg_at_1_std
value: 12.422
- type: nauc_ndcg_at_1_diff1
value: 74.4289
- type: nauc_ndcg_at_3_max
value: 60.2109
- type: nauc_ndcg_at_3_std
value: 11.0152
- type: nauc_ndcg_at_3_diff1
value: 71.0436
- type: nauc_ndcg_at_5_max
value: 62.690999999999995
- type: nauc_ndcg_at_5_std
value: 13.585700000000001
- type: nauc_ndcg_at_5_diff1
value: 70.4007
- type: nauc_ndcg_at_10_max
value: 62.740899999999996
- type: nauc_ndcg_at_10_std
value: 13.980400000000001
- type: nauc_ndcg_at_10_diff1
value: 70.0506
- type: nauc_ndcg_at_20_max
value: 62.271699999999996
- type: nauc_ndcg_at_20_std
value: 15.9756
- type: nauc_ndcg_at_20_diff1
value: 70.3237
- type: nauc_ndcg_at_100_max
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dataset:
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
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type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
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type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
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dataset:
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type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
name: MTEB Touche2020 (default)
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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- type: mrr_at_1000
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- type: nauc_ndcg_at_10_std
value: -17.9067
- type: nauc_ndcg_at_10_diff1
value: -9.3129
- type: nauc_ndcg_at_20_max
value: -24.479400000000002
- type: nauc_ndcg_at_20_std
value: -16.06
- type: nauc_ndcg_at_20_diff1
value: -10.57
- type: nauc_ndcg_at_100_max
value: -20.9167
- type: nauc_ndcg_at_100_std
value: 9.6051
- type: nauc_ndcg_at_100_diff1
value: -0.2363
- type: nauc_ndcg_at_1000_max
value: -13.6708
- type: nauc_ndcg_at_1000_std
value: 17.956
- type: nauc_ndcg_at_1000_diff1
value: -2.5696
- type: nauc_map_at_1_max
value: -14.276900000000001
- type: nauc_map_at_1_std
value: -31.3091
- type: nauc_map_at_1_diff1
value: -1.4354
- type: nauc_map_at_3_max
value: -21.7098
- type: nauc_map_at_3_std
value: -32.112899999999996
- type: nauc_map_at_3_diff1
value: -8.846
- type: nauc_map_at_5_max
value: -16.700200000000002
- type: nauc_map_at_5_std
value: -32.643499999999996
- type: nauc_map_at_5_diff1
value: -13.9766
- type: nauc_map_at_10_max
value: -13.415199999999999
- type: nauc_map_at_10_std
value: -28.459200000000003
- type: nauc_map_at_10_diff1
value: -12.4042
- type: nauc_map_at_20_max
value: -17.8629
- type: nauc_map_at_20_std
value: -24.5837
- type: nauc_map_at_20_diff1
value: -14.9642
- type: nauc_map_at_100_max
value: -15.6478
- type: nauc_map_at_100_std
value: -11.4237
- type: nauc_map_at_100_diff1
value: -11.542
- type: nauc_map_at_1000_max
value: -15.2149
- type: nauc_map_at_1000_std
value: -8.0384
- type: nauc_map_at_1000_diff1
value: -12.984000000000002
- type: nauc_recall_at_1_max
value: -14.276900000000001
- type: nauc_recall_at_1_std
value: -31.3091
- type: nauc_recall_at_1_diff1
value: -1.4354
- type: nauc_recall_at_3_max
value: -23.021900000000002
- type: nauc_recall_at_3_std
value: -30.2834
- type: nauc_recall_at_3_diff1
value: -11.4226
- type: nauc_recall_at_5_max
value: -20.596600000000002
- type: nauc_recall_at_5_std
value: -33.219300000000004
- type: nauc_recall_at_5_diff1
value: -17.718999999999998
- type: nauc_recall_at_10_max
value: -16.1214
- type: nauc_recall_at_10_std
value: -23.9041
- type: nauc_recall_at_10_diff1
value: -11.047
- type: nauc_recall_at_20_max
value: -25.603399999999997
- type: nauc_recall_at_20_std
value: -15.8105
- type: nauc_recall_at_20_diff1
value: -14.546000000000001
- type: nauc_recall_at_100_max
value: -16.389400000000002
- type: nauc_recall_at_100_std
value: 28.5141
- type: nauc_recall_at_100_diff1
value: 6.1868
- type: nauc_recall_at_1000_max
value: 11.022
- type: nauc_recall_at_1000_std
value: 68.0021
- type: nauc_recall_at_1000_diff1
value: 8.426
- type: nauc_precision_at_1_max
value: -17.1625
- type: nauc_precision_at_1_std
value: -27.9451
- type: nauc_precision_at_1_diff1
value: 1.0831
- type: nauc_precision_at_3_max
value: -17.2798
- type: nauc_precision_at_3_std
value: -20.347199999999997
- type: nauc_precision_at_3_diff1
value: -5.2689
- type: nauc_precision_at_5_max
value: -19.6408
- type: nauc_precision_at_5_std
value: -24.157
- type: nauc_precision_at_5_diff1
value: -20.274900000000002
- type: nauc_precision_at_10_max
value: -11.8033
- type: nauc_precision_at_10_std
value: -7.2727
- type: nauc_precision_at_10_diff1
value: -9.3776
- type: nauc_precision_at_20_max
value: -20.1541
- type: nauc_precision_at_20_std
value: 9.0645
- type: nauc_precision_at_20_diff1
value: -16.1323
- type: nauc_precision_at_100_max
value: 0.3701
- type: nauc_precision_at_100_std
value: 67.6941
- type: nauc_precision_at_100_diff1
value: 8.0336
- type: nauc_precision_at_1000_max
value: 38.8632
- type: nauc_precision_at_1000_std
value: 38.0504
- type: nauc_precision_at_1000_diff1
value: 0.5907
- type: nauc_mrr_at_1_max
value: -17.1625
- type: nauc_mrr_at_1_std
value: -27.9451
- type: nauc_mrr_at_1_diff1
value: 1.0831
- type: nauc_mrr_at_3_max
value: -20.479300000000002
- type: nauc_mrr_at_3_std
value: -21.9225
- type: nauc_mrr_at_3_diff1
value: -1.5211000000000001
- type: nauc_mrr_at_5_max
value: -24.8175
- type: nauc_mrr_at_5_std
value: -23.805
- type: nauc_mrr_at_5_diff1
value: -7.9258
- type: nauc_mrr_at_10_max
value: -22.53
- type: nauc_mrr_at_10_std
value: -21.9391
- type: nauc_mrr_at_10_diff1
value: -5.7533
- type: nauc_mrr_at_20_max
value: -22.7064
- type: nauc_mrr_at_20_std
value: -22.4697
- type: nauc_mrr_at_20_diff1
value: -5.7068
- type: nauc_mrr_at_100_max
value: -23.0016
- type: nauc_mrr_at_100_std
value: -22.488
- type: nauc_mrr_at_100_diff1
value: -5.3738
- type: nauc_mrr_at_1000_max
value: -23.0016
- type: nauc_mrr_at_1000_std
value: -22.488
- type: nauc_mrr_at_1000_diff1
value: -5.3738
- type: main_score
value: 29.499
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 65.8643
- type: f1
value: 50.6764
- type: f1_weighted
value: 73.2472
- type: ap
value: 12.2658
- type: ap_weighted
value: 12.2658
- type: main_score
value: 65.8643
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.6633
- type: f1
value: 59.935700000000004
- type: f1_weighted
value: 59.0249
- type: main_score
value: 59.6633
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 43.2311
- type: v_measure_std
value: 2.3994999999999997
- type: main_score
value: 43.2311
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: similarity_accuracy
value: 83.8469
- type: similarity_accuracy_threshold
value: 77.6695
- type: similarity_f1
value: 62.3159
- type: similarity_f1_threshold
value: 71.6554
- type: similarity_precision
value: 59.114599999999996
- type: similarity_recall
value: 65.8839
- type: similarity_ap
value: 67.00930000000001
- type: cosine_accuracy
value: 83.8469
- type: cosine_accuracy_threshold
value: 77.6695
- type: cosine_f1
value: 62.3159
- type: cosine_f1_threshold
value: 71.6554
- type: cosine_precision
value: 59.114599999999996
- type: cosine_recall
value: 65.8839
- type: cosine_ap
value: 67.00930000000001
- type: manhattan_accuracy
value: 83.7694
- type: manhattan_accuracy_threshold
value: 1677.8293999999999
- type: manhattan_f1
value: 62.1324
- type: manhattan_f1_threshold
value: 1848.6641
- type: manhattan_precision
value: 61.839999999999996
- type: manhattan_recall
value: 62.4274
- type: manhattan_ap
value: 66.8849
- type: euclidean_accuracy
value: 83.8469
- type: euclidean_accuracy_threshold
value: 66.8288
- type: euclidean_f1
value: 62.3159
- type: euclidean_f1_threshold
value: 75.2922
- type: euclidean_precision
value: 59.114599999999996
- type: euclidean_recall
value: 65.8839
- type: euclidean_ap
value: 67.00930000000001
- type: dot_accuracy
value: 83.8469
- type: dot_accuracy_threshold
value: 77.6695
- type: dot_f1
value: 62.3159
- type: dot_f1_threshold
value: 71.6554
- type: dot_precision
value: 59.114599999999996
- type: dot_recall
value: 65.8839
- type: dot_ap
value: 67.00930000000001
- type: max_accuracy
value: 83.8469
- type: max_f1
value: 62.3159
- type: max_precision
value: 61.839999999999996
- type: max_recall
value: 65.8839
- type: max_ap
value: 67.00930000000001
- type: main_score
value: 67.00930000000001
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: similarity_accuracy
value: 88.8811
- type: similarity_accuracy_threshold
value: 71.1053
- type: similarity_f1
value: 77.9005
- type: similarity_f1_threshold
value: 67.5068
- type: similarity_precision
value: 75.5609
- type: similarity_recall
value: 80.3896
- type: similarity_ap
value: 85.459
- type: cosine_accuracy
value: 88.8811
- type: cosine_accuracy_threshold
value: 71.1053
- type: cosine_f1
value: 77.9005
- type: cosine_f1_threshold
value: 67.5068
- type: cosine_precision
value: 75.5609
- type: cosine_recall
value: 80.3896
- type: cosine_ap
value: 85.459
- type: manhattan_accuracy
value: 88.8598
- type: manhattan_accuracy_threshold
value: 1928.9173
- type: manhattan_f1
value: 77.9172
- type: manhattan_f1_threshold
value: 2007.8883999999998
- type: manhattan_precision
value: 76.29310000000001
- type: manhattan_recall
value: 79.6119
- type: manhattan_ap
value: 85.4464
- type: euclidean_accuracy
value: 88.8811
- type: euclidean_accuracy_threshold
value: 76.0193
- type: euclidean_f1
value: 77.9005
- type: euclidean_f1_threshold
value: 80.6141
- type: euclidean_precision
value: 75.5609
- type: euclidean_recall
value: 80.3896
- type: euclidean_ap
value: 85.459
- type: dot_accuracy
value: 88.8811
- type: dot_accuracy_threshold
value: 71.1053
- type: dot_f1
value: 77.9005
- type: dot_f1_threshold
value: 67.5068
- type: dot_precision
value: 75.5609
- type: dot_recall
value: 80.3896
- type: dot_ap
value: 85.459
- type: max_accuracy
value: 88.8811
- type: max_f1
value: 77.9172
- type: max_precision
value: 76.29310000000001
- type: max_recall
value: 80.3896
- type: max_ap
value: 85.459
- type: main_score
value: 85.459
---
<h1 align="center">Snowflake's Arctic-embed-l-v2.0</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=18f5b1a3-da66-4f25-92d3-21da829509c3" />
## News
- 12/11/2024: Release of [Technical Report](https://arxiv.org/abs/2412.04506)
- 12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind.
## Models
Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency.
Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English.
Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.
Key Features:
1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale.
3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
4. Drop-In Replacement: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc.
5. Long Context Support: arctic-embed-l-v2.0 builds on [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which can support a context window of up to 8192 via the use of RoPE.
### Quality Benchmarks
Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF).
You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed.
| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** |
| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
| snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
| me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
| gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality.
Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.
| Model | | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance |
|---|---|:---:|:---:|:---:|:---:|:---:|---|---|---|
| snowflake-arctic-l-v2.0 | 1024 | 55.6 | N/A | 55.8 | N/A | 52.9 | N/A | 54.3 | N/A |
| snowflake-arctic-l-v2.0 | 256 | 54.3 | -0.18% | 54.3 | -2.70% | 51.9 | -1.81% | 53.4 | -1.53% |
## Usage
### Using Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
model = SentenceTransformer(model_name)
# Define the queries and documents
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
# Compute embeddings: use `prompt_name="query"` to encode queries!
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Compute cosine similarity scores
scores = model.similarity(query_embeddings, document_embeddings)
# Output the results
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Huggingface Transformers
You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
model.eval()
query_prefix = 'query: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
This should produce the following scores
```
Query: what is snowflake?
tensor(0.2715) The Data Cloud!
tensor(0.0661) Mexico City of Course!
Query: Where can I get the best tacos?
tensor(0.2797) Mexico City of Course!
tensor(0.1250) The Data Cloud!
```
### Using Huggingface Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model for retrieval, as follows:
```js
import { pipeline, dot } from '@huggingface/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
dtype: 'q8',
});
// Generate sentence embeddings
const sentences = [
'query: what is snowflake?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.24783534471401417, 0.05313122704326892]
```
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
minishlab/M2V_base_output | minishlab | null | [
"model2vec",
"onnx",
"safetensors",
"embeddings",
"static-embeddings",
"mteb",
"sentence-transformers",
"en",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:quantized:BAAI/bge-base-en-v1.5",
"license:mit",
"model-index",
"region:us"
] | 2024-09-19T18:03:34 | 2025-01-21T19:16:22 | 76,963 | 10 | ---
base_model:
- BAAI/bge-base-en-v1.5
language:
- en
library_name: model2vec
license: mit
tags:
- embeddings
- static-embeddings
- mteb
- sentence-transformers
model-index:
- name: M2V_base_output
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 69.1904047976012
- type: ap
value: 19.610682715583142
- type: ap_weighted
value: 19.610682715583142
- type: f1
value: 57.14831247701502
- type: f1_weighted
value: 75.0407024695743
- type: main_score
value: 69.1904047976012
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.1044776119403
- type: ap
value: 33.83428171392154
- type: ap_weighted
value: 33.83428171392154
- type: f1
value: 65.18431700199532
- type: f1_weighted
value: 73.90467162513829
- type: main_score
value: 71.1044776119403
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 67.328075
- type: ap
value: 62.26238067958846
- type: ap_weighted
value: 62.26238067958846
- type: f1
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type: mteb/amazon_reviews_multi
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
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split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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- type: ndcg_at_10
value: 9.668000000000001
- type: ndcg_at_100
value: 15.015999999999998
- type: ndcg_at_1000
value: 20.015
- type: ndcg_at_20
value: 11.436
- type: ndcg_at_3
value: 8.924
- type: ndcg_at_5
value: 7.911
- type: precision_at_1
value: 11.799999999999999
- type: precision_at_10
value: 5.050000000000001
- type: precision_at_100
value: 1.291
- type: precision_at_1000
value: 0.251
- type: precision_at_20
value: 3.56
- type: precision_at_3
value: 8.133
- type: precision_at_5
value: 6.88
- type: recall_at_1
value: 2.395
- type: recall_at_10
value: 10.232
- type: recall_at_100
value: 26.172
- type: recall_at_1000
value: 50.917
- type: recall_at_20
value: 14.421999999999999
- type: recall_at_3
value: 4.935
- type: recall_at_5
value: 6.973
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 73.8523071648734
- type: cosine_spearman
value: 65.43442849067297
- type: euclidean_pearson
value: 66.70464173822097
- type: euclidean_spearman
value: 60.82604439637834
- type: main_score
value: 65.43442849067297
- type: manhattan_pearson
value: 66.58172841322595
- type: manhattan_spearman
value: 61.202424661616796
- type: pearson
value: 73.8523071648734
- type: spearman
value: 65.43442849067297
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 66.23949905692108
- type: cosine_spearman
value: 59.97334423570035
- type: euclidean_pearson
value: 53.93367474754671
- type: euclidean_spearman
value: 49.65643891073131
- type: main_score
value: 59.97334423570035
- type: manhattan_pearson
value: 52.50090747870868
- type: manhattan_spearman
value: 48.726772969833064
- type: pearson
value: 66.23949905692108
- type: spearman
value: 59.97334423570035
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 70.87351220452432
- type: cosine_spearman
value: 71.81863685179427
- type: euclidean_pearson
value: 59.249945757203946
- type: euclidean_spearman
value: 60.053057494316796
- type: main_score
value: 71.81863685179427
- type: manhattan_pearson
value: 59.798731614026714
- type: manhattan_spearman
value: 60.31075071097369
- type: pearson
value: 70.87351220452432
- type: spearman
value: 71.81863685179427
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cosine_pearson
value: 69.03600787240593
- type: cosine_spearman
value: 66.99860396187162
- type: euclidean_pearson
value: 58.61094669791067
- type: euclidean_spearman
value: 58.286341788544995
- type: main_score
value: 66.99860396187162
- type: manhattan_pearson
value: 58.665872206618964
- type: manhattan_spearman
value: 58.30408154246083
- type: pearson
value: 69.03600787240593
- type: spearman
value: 66.99860396187162
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cosine_pearson
value: 74.45269985909863
- type: cosine_spearman
value: 75.4907813361932
- type: euclidean_pearson
value: 58.68237542933832
- type: euclidean_spearman
value: 61.08891047408572
- type: main_score
value: 75.4907813361932
- type: manhattan_pearson
value: 59.32028954908928
- type: manhattan_spearman
value: 61.38980243849822
- type: pearson
value: 74.45269985909863
- type: spearman
value: 75.4907813361932
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cosine_pearson
value: 64.2309456558779
- type: cosine_spearman
value: 66.97205823920407
- type: euclidean_pearson
value: 52.471209393825134
- type: euclidean_spearman
value: 55.05667213079255
- type: main_score
value: 66.97205823920407
- type: manhattan_pearson
value: 52.4566691722933
- type: manhattan_spearman
value: 54.98149865449457
- type: pearson
value: 64.2309456558779
- type: spearman
value: 66.97205823920407
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 21.06202710190164
- type: cosine_spearman
value: 18.26963771909619
- type: euclidean_pearson
value: -10.937704538162821
- type: euclidean_spearman
value: -13.838045200730331
- type: main_score
value: 18.26963771909619
- type: manhattan_pearson
value: -9.194548970239005
- type: manhattan_spearman
value: -12.642533487235347
- type: pearson
value: 21.06202710190164
- type: spearman
value: 18.26963771909619
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 9.974655940103192
- type: cosine_spearman
value: 6.625332823012507
- type: euclidean_pearson
value: -6.193994464373409
- type: euclidean_spearman
value: -13.09777719442545
- type: main_score
value: 6.625332823012507
- type: manhattan_pearson
value: -7.596649200902214
- type: manhattan_spearman
value: -14.341067466786914
- type: pearson
value: 9.974655940103192
- type: spearman
value: 6.625332823012507
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 3.939829923076509
- type: cosine_spearman
value: 1.5988688581594497
- type: euclidean_pearson
value: -10.456279294578557
- type: euclidean_spearman
value: -9.811244215059508
- type: main_score
value: 1.5988688581594497
- type: manhattan_pearson
value: -10.913654400994407
- type: manhattan_spearman
value: -8.604616012491228
- type: pearson
value: 3.939829923076509
- type: spearman
value: 1.5988688581594497
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 17.28499679216241
- type: cosine_spearman
value: 14.621483811474079
- type: euclidean_pearson
value: -16.874097134885233
- type: euclidean_spearman
value: -16.68311783384881
- type: main_score
value: 14.621483811474079
- type: manhattan_pearson
value: -17.639738926102574
- type: manhattan_spearman
value: -16.66416708388087
- type: pearson
value: 17.28499679216241
- type: spearman
value: 14.621483811474079
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 78.99251283215277
- type: cosine_spearman
value: 80.61049377743727
- type: euclidean_pearson
value: 66.17827666954877
- type: euclidean_spearman
value: 67.45271515314245
- type: main_score
value: 80.61049377743727
- type: manhattan_pearson
value: 66.23284409257823
- type: manhattan_spearman
value: 67.666247437264
- type: pearson
value: 78.99251283215277
- type: spearman
value: 80.61049377743727
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: -1.931391285281735
- type: cosine_spearman
value: -3.321078837897458
- type: euclidean_pearson
value: -21.683857378409378
- type: euclidean_spearman
value: -24.244038106560804
- type: main_score
value: -3.321078837897458
- type: manhattan_pearson
value: -22.19415161015049
- type: manhattan_spearman
value: -22.71872700697092
- type: pearson
value: -1.931391285281735
- type: spearman
value: -3.321078837897458
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 21.215714201927316
- type: cosine_spearman
value: 16.647983989080657
- type: euclidean_pearson
value: -17.529579365480654
- type: euclidean_spearman
value: -17.98599150405874
- type: main_score
value: 16.647983989080657
- type: manhattan_pearson
value: -17.041217222851987
- type: manhattan_spearman
value: -17.099688376247617
- type: pearson
value: 21.215714201927316
- type: spearman
value: 16.647983989080657
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 25.55717236376004
- type: cosine_spearman
value: 21.120437860825668
- type: euclidean_pearson
value: -13.532867255677811
- type: euclidean_spearman
value: -14.067414622756136
- type: main_score
value: 21.120437860825668
- type: manhattan_pearson
value: -14.812251264524642
- type: manhattan_spearman
value: -14.777202854314126
- type: pearson
value: 25.55717236376004
- type: spearman
value: 21.120437860825668
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 45.445485581559176
- type: cosine_spearman
value: 57.81995941896327
- type: euclidean_pearson
value: 46.45758835829159
- type: euclidean_spearman
value: 57.15291591278634
- type: main_score
value: 57.81995941896327
- type: manhattan_pearson
value: 45.38976415067536
- type: manhattan_spearman
value: 56.412461810883244
- type: pearson
value: 45.445485581559176
- type: spearman
value: 57.81995941896327
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 9.618696238808342
- type: cosine_spearman
value: 11.05047267189447
- type: euclidean_pearson
value: 10.475166065910297
- type: euclidean_spearman
value: 11.515497306325212
- type: main_score
value: 11.05047267189447
- type: manhattan_pearson
value: 11.677707905016238
- type: manhattan_spearman
value: 13.47068609853333
- type: pearson
value: 9.618696238808342
- type: spearman
value: 11.05047267189447
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 9.219640350559175
- type: cosine_spearman
value: 15.424812621979203
- type: euclidean_pearson
value: 27.079648075136692
- type: euclidean_spearman
value: 15.127881072012025
- type: main_score
value: 15.424812621979203
- type: manhattan_pearson
value: 29.948405026370768
- type: manhattan_spearman
value: 11.450097312769431
- type: pearson
value: 9.219640350559175
- type: spearman
value: 15.424812621979203
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 2.016891027432069
- type: cosine_spearman
value: 9.065694923749145
- type: euclidean_pearson
value: -0.2317575485284492
- type: euclidean_spearman
value: 1.478447144326562
- type: main_score
value: 9.065694923749145
- type: manhattan_pearson
value: 1.2210552984769953
- type: manhattan_spearman
value: 1.0797490938939034
- type: pearson
value: 2.016891027432069
- type: spearman
value: 9.065694923749145
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 20.30265778022666
- type: cosine_spearman
value: 27.04088495025885
- type: euclidean_pearson
value: 21.92624711333554
- type: euclidean_spearman
value: 30.314966090982715
- type: main_score
value: 27.04088495025885
- type: manhattan_pearson
value: 22.449954374970556
- type: manhattan_spearman
value: 33.98792612061501
- type: pearson
value: 20.30265778022666
- type: spearman
value: 27.04088495025885
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 67.58098869120114
- type: cosine_spearman
value: 67.2453123773366
- type: euclidean_pearson
value: 58.23603604808463
- type: euclidean_spearman
value: 58.623631847217
- type: main_score
value: 67.2453123773366
- type: manhattan_pearson
value: 58.368136302971195
- type: manhattan_spearman
value: 58.837841919175105
- type: pearson
value: 67.58098869120114
- type: spearman
value: 67.2453123773366
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 68.53428785087402
- type: map
value: 68.53428785087402
- type: mrr
value: 88.53875880836665
- type: nAUC_map_diff1
value: 11.778449408360105
- type: nAUC_map_max
value: 55.710378394122195
- type: nAUC_map_std
value: 66.15614923206279
- type: nAUC_mrr_diff1
value: 47.35327285304558
- type: nAUC_mrr_max
value: 74.15113781105075
- type: nAUC_mrr_std
value: 70.40747046150474
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 44.018
- type: map_at_1
value: 30.778
- type: map_at_10
value: 39.095
- type: map_at_100
value: 40.136
- type: map_at_1000
value: 40.19
- type: map_at_20
value: 39.695
- type: map_at_3
value: 36.25
- type: map_at_5
value: 37.942
- type: mrr_at_1
value: 32.33333333333333
- type: mrr_at_10
value: 40.46640211640211
- type: mrr_at_100
value: 41.3527413808237
- type: mrr_at_1000
value: 41.402308015811776
- type: mrr_at_20
value: 40.9920777608471
- type: mrr_at_3
value: 37.999999999999986
- type: mrr_at_5
value: 39.46666666666666
- type: nauc_map_at_1000_diff1
value: 51.57525678345129
- type: nauc_map_at_1000_max
value: 35.72906391653508
- type: nauc_map_at_1000_std
value: -1.672862325664642
- type: nauc_map_at_100_diff1
value: 51.57482414972323
- type: nauc_map_at_100_max
value: 35.714681767398474
- type: nauc_map_at_100_std
value: -1.6459806802624475
- type: nauc_map_at_10_diff1
value: 51.142890340689064
- type: nauc_map_at_10_max
value: 35.78128552943207
- type: nauc_map_at_10_std
value: -2.1957957240897907
- type: nauc_map_at_1_diff1
value: 57.59762900453854
- type: nauc_map_at_1_max
value: 36.479602157030534
- type: nauc_map_at_1_std
value: -4.834289532948042
- type: nauc_map_at_20_diff1
value: 51.47980323079124
- type: nauc_map_at_20_max
value: 35.585900524174406
- type: nauc_map_at_20_std
value: -1.7680354064625985
- type: nauc_map_at_3_diff1
value: 51.012766710346625
- type: nauc_map_at_3_max
value: 34.8262662118054
- type: nauc_map_at_3_std
value: -2.8168593560801045
- type: nauc_map_at_5_diff1
value: 50.836092917622864
- type: nauc_map_at_5_max
value: 35.32174769825645
- type: nauc_map_at_5_std
value: -3.113242921586995
- type: nauc_mrr_at_1000_diff1
value: 53.10217120766699
- type: nauc_mrr_at_1000_max
value: 37.46657201878918
- type: nauc_mrr_at_1000_std
value: 1.9085047586195323
- type: nauc_mrr_at_100_diff1
value: 53.10038602820947
- type: nauc_mrr_at_100_max
value: 37.461065885458225
- type: nauc_mrr_at_100_std
value: 1.9403756850021763
- type: nauc_mrr_at_10_diff1
value: 52.71420660954082
- type: nauc_mrr_at_10_max
value: 37.62806428278671
- type: nauc_mrr_at_10_std
value: 1.9517437711674281
- type: nauc_mrr_at_1_diff1
value: 59.730007702616675
- type: nauc_mrr_at_1_max
value: 38.85146416502298
- type: nauc_mrr_at_1_std
value: -0.46260223776596965
- type: nauc_mrr_at_20_diff1
value: 53.041376670418906
- type: nauc_mrr_at_20_max
value: 37.45508852907037
- type: nauc_mrr_at_20_std
value: 1.9843723810434797
- type: nauc_mrr_at_3_diff1
value: 52.716388196194494
- type: nauc_mrr_at_3_max
value: 36.76096106397856
- type: nauc_mrr_at_3_std
value: 1.716782555536502
- type: nauc_mrr_at_5_diff1
value: 52.61598345028188
- type: nauc_mrr_at_5_max
value: 37.26316036644959
- type: nauc_mrr_at_5_std
value: 1.3757366695050894
- type: nauc_ndcg_at_1000_diff1
value: 51.342395628428314
- type: nauc_ndcg_at_1000_max
value: 37.22548194348463
- type: nauc_ndcg_at_1000_std
value: 1.6360986297119697
- type: nauc_ndcg_at_100_diff1
value: 51.12772923293346
- type: nauc_ndcg_at_100_max
value: 37.08162525770745
- type: nauc_ndcg_at_100_std
value: 2.1437445417460146
- type: nauc_ndcg_at_10_diff1
value: 49.48104920841383
- type: nauc_ndcg_at_10_max
value: 36.98553295749576
- type: nauc_ndcg_at_10_std
value: 0.7074029546666143
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dataset:
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
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dataset:
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type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
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dataset:
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type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
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metrics:
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type: mteb/summeval
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type: mteb/trec-covid
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dataset:
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type: mteb/touche2020
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split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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value: -37.87305582981634
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value: 14.285999999999998
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value: 12.417
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value: 21.564
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value: 34.264
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value: 13.997000000000002
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value: 13.161999999999999
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value: 16.326999999999998
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value: 1.2
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value: 8.763
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value: 31.584
- type: recall_at_1000
value: 70.519
- type: recall_at_20
value: 14.379
- type: recall_at_3
value: 3.229
- type: recall_at_5
value: 5.079000000000001
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 63.04199218749999
- type: ap
value: 10.379917199607485
- type: ap_weighted
value: 10.379917199607485
- type: f1
value: 47.876568123841864
- type: f1_weighted
value: 71.2370937104015
- type: main_score
value: 63.04199218749999
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 49.442558007923026
- type: f1
value: 49.60441043943531
- type: f1_weighted
value: 48.96898929345838
- type: main_score
value: 49.442558007923026
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 21.127920450161458
- type: v_measure
value: 21.127920450161458
- type: v_measure_std
value: 1.5027840050520012
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 82.18394230196103
- type: cosine_accuracy_threshold
value: 70.92341184616089
- type: cosine_ap
value: 59.78262740579837
- type: cosine_f1
value: 56.536101934874935
- type: cosine_f1_threshold
value: 63.08426856994629
- type: cosine_precision
value: 51.13102859581733
- type: cosine_recall
value: 63.21899736147757
- type: dot_accuracy
value: 78.2559456398641
- type: dot_accuracy_threshold
value: 75122.66235351562
- type: dot_ap
value: 42.7554645305854
- type: dot_f1
value: 46.84298752095361
- type: dot_f1_threshold
value: 47930.230712890625
- type: dot_precision
value: 36.19746689694876
- type: dot_recall
value: 66.35883905013192
- type: euclidean_accuracy
value: 80.41962210168684
- type: euclidean_accuracy_threshold
value: 2041.592025756836
- type: euclidean_ap
value: 53.9382918676684
- type: euclidean_f1
value: 53.007111003977336
- type: euclidean_f1_threshold
value: 2444.729995727539
- type: euclidean_precision
value: 48.79076991346794
- type: euclidean_recall
value: 58.02110817941952
- type: main_score
value: 59.78262740579837
- type: manhattan_accuracy
value: 80.65208320915539
- type: manhattan_accuracy_threshold
value: 26017.153930664062
- type: manhattan_ap
value: 54.628314460914396
- type: manhattan_f1
value: 53.78151260504202
- type: manhattan_f1_threshold
value: 30961.737060546875
- type: manhattan_precision
value: 47.208931419457734
- type: manhattan_recall
value: 62.48021108179419
- type: max_accuracy
value: 82.18394230196103
- type: max_ap
value: 59.78262740579837
- type: max_f1
value: 56.536101934874935
- type: max_precision
value: 51.13102859581733
- type: max_recall
value: 66.35883905013192
- type: similarity_accuracy
value: 82.18394230196103
- type: similarity_accuracy_threshold
value: 70.92341184616089
- type: similarity_ap
value: 59.78262740579837
- type: similarity_f1
value: 56.536101934874935
- type: similarity_f1_threshold
value: 63.08426856994629
- type: similarity_precision
value: 51.13102859581733
- type: similarity_recall
value: 63.21899736147757
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 86.35269918888501
- type: cosine_accuracy_threshold
value: 65.62063097953796
- type: cosine_ap
value: 79.86337146522463
- type: cosine_f1
value: 72.03383314109958
- type: cosine_f1_threshold
value: 62.217533588409424
- type: cosine_precision
value: 71.93979419444018
- type: cosine_recall
value: 72.12811826301201
- type: dot_accuracy
value: 82.84045484534482
- type: dot_accuracy_threshold
value: 35566.62902832031
- type: dot_ap
value: 69.69127356271262
- type: dot_f1
value: 64.93162154619034
- type: dot_f1_threshold
value: 28885.244750976562
- type: dot_precision
value: 59.36463383516203
- type: dot_recall
value: 71.65075454265477
- type: euclidean_accuracy
value: 83.63022470601933
- type: euclidean_accuracy_threshold
value: 1693.5848236083984
- type: euclidean_ap
value: 71.73555972139718
- type: euclidean_f1
value: 63.8556476722812
- type: euclidean_f1_threshold
value: 1923.9103317260742
- type: euclidean_precision
value: 62.26497914990124
- type: euclidean_recall
value: 65.52971974129966
- type: main_score
value: 79.86337146522463
- type: manhattan_accuracy
value: 83.70978383203322
- type: manhattan_accuracy_threshold
value: 21348.568725585938
- type: manhattan_ap
value: 72.01847359087003
- type: manhattan_f1
value: 64.34136401773942
- type: manhattan_f1_threshold
value: 23113.516235351562
- type: manhattan_precision
value: 66.8715222988124
- type: manhattan_recall
value: 61.99568832768709
- type: max_accuracy
value: 86.35269918888501
- type: max_ap
value: 79.86337146522463
- type: max_f1
value: 72.03383314109958
- type: max_precision
value: 71.93979419444018
- type: max_recall
value: 72.12811826301201
- type: similarity_accuracy
value: 86.35269918888501
- type: similarity_accuracy_threshold
value: 65.62063097953796
- type: similarity_ap
value: 79.86337146522463
- type: similarity_f1
value: 72.03383314109958
- type: similarity_f1_threshold
value: 62.217533588409424
- type: similarity_precision
value: 71.93979419444018
- type: similarity_recall
value: 72.12811826301201
---
# minishlab/M2V_base_output Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the [baai/bge-base-en-v1.5](https://huggingface.co/baai/bge-base-en-v1.5) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/M2V_base_output")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
Alternatively, you can distill your own model using the `distill` method:
```python
from model2vec.distill import distill
# Choose a Sentence Transformer model
model_name = "BAAI/bge-base-en-v1.5"
# Distill the model
m2v_model = distill(model_name=model_name, pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
BAAI/bge-small-zh-v1.5 | BAAI | feature-extraction | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"zh",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-09-12T05:22:29 | 2023-10-12T03:35:59 | 72,966 | 53 | ---
language:
- zh
license: mit
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](./FlagEmbedding/llm_embedder/README.md), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
sdadas/mmlw-roberta-base | sdadas | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"pl",
"arxiv:2402.13350",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-11-17T19:04:53 | 2024-10-24T13:05:51 | 72,641 | 3 | ---
language: pl
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
widget:
- source_sentence: 'zapytanie: Jak dożyć 100 lat?'
sentences:
- Trzeba zdrowo się odżywiać i uprawiać sport.
- Trzeba pić alkohol, imprezować i jeździć szybkimi autami.
- Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego
handlu.
model-index:
- name: mmlw-roberta-base
results:
- task:
type: Clustering
dataset:
name: MTEB 8TagsClustering
type: PL-MTEB/8tags-clustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 33.08463724780795
- task:
type: Classification
dataset:
name: MTEB AllegroReviews
type: PL-MTEB/allegro-reviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 40.25844930417495
- type: f1
value: 35.59685265418916
- task:
type: Retrieval
dataset:
name: MTEB ArguAna-PL
type: arguana-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.073
- type: map_at_10
value: 50.223
- type: map_at_100
value: 50.942
- type: map_at_1000
value: 50.94499999999999
- type: map_at_3
value: 45.721000000000004
- type: map_at_5
value: 48.413000000000004
- type: mrr_at_1
value: 34.424
- type: mrr_at_10
value: 50.68899999999999
- type: mrr_at_100
value: 51.437999999999995
- type: mrr_at_1000
value: 51.441
- type: mrr_at_3
value: 46.219
- type: mrr_at_5
value: 48.921
- type: ndcg_at_1
value: 33.073
- type: ndcg_at_10
value: 59.021
- type: ndcg_at_100
value: 61.902
- type: ndcg_at_1000
value: 61.983999999999995
- type: ndcg_at_3
value: 49.818
- type: ndcg_at_5
value: 54.644999999999996
- type: precision_at_1
value: 33.073
- type: precision_at_10
value: 8.684
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.555
- type: precision_at_5
value: 14.666
- type: recall_at_1
value: 33.073
- type: recall_at_10
value: 86.842
- type: recall_at_100
value: 99.004
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.663999999999994
- type: recall_at_5
value: 73.329
- task:
type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 68.11
- type: ap
value: 20.916633959031266
- type: f1
value: 56.85804802205465
- task:
type: PairClassification
dataset:
name: MTEB CDSC-E
type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 89.2
- type: cos_sim_ap
value: 79.1041156765933
- type: cos_sim_f1
value: 70.0
- type: cos_sim_precision
value: 74.11764705882354
- type: cos_sim_recall
value: 66.3157894736842
- type: dot_accuracy
value: 88.2
- type: dot_ap
value: 72.57183688228149
- type: dot_f1
value: 67.16417910447761
- type: dot_precision
value: 63.67924528301887
- type: dot_recall
value: 71.05263157894737
- type: euclidean_accuracy
value: 89.3
- type: euclidean_ap
value: 79.01345533432428
- type: euclidean_f1
value: 70.19498607242339
- type: euclidean_precision
value: 74.55621301775149
- type: euclidean_recall
value: 66.3157894736842
- type: manhattan_accuracy
value: 89.3
- type: manhattan_ap
value: 79.01671381791259
- type: manhattan_f1
value: 70.0280112044818
- type: manhattan_precision
value: 74.8502994011976
- type: manhattan_recall
value: 65.78947368421053
- type: max_accuracy
value: 89.3
- type: max_ap
value: 79.1041156765933
- type: max_f1
value: 70.19498607242339
- task:
type: STS
dataset:
name: MTEB CDSC-R
type: PL-MTEB/cdscr-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 91.79559442663039
- type: cos_sim_spearman
value: 92.5438168962641
- type: euclidean_pearson
value: 92.02981265332856
- type: euclidean_spearman
value: 92.5548245733484
- type: manhattan_pearson
value: 91.95296287979178
- type: manhattan_spearman
value: 92.50279516120241
- task:
type: Retrieval
dataset:
name: MTEB DBPedia-PL
type: dbpedia-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.829999999999999
- type: map_at_10
value: 16.616
- type: map_at_100
value: 23.629
- type: map_at_1000
value: 25.235999999999997
- type: map_at_3
value: 12.485
- type: map_at_5
value: 14.077
- type: mrr_at_1
value: 61.75000000000001
- type: mrr_at_10
value: 69.852
- type: mrr_at_100
value: 70.279
- type: mrr_at_1000
value: 70.294
- type: mrr_at_3
value: 68.375
- type: mrr_at_5
value: 69.187
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 36.217
- type: ndcg_at_100
value: 41.235
- type: ndcg_at_1000
value: 48.952
- type: ndcg_at_3
value: 41.669
- type: ndcg_at_5
value: 38.285000000000004
- type: precision_at_1
value: 61.5
- type: precision_at_10
value: 28.499999999999996
- type: precision_at_100
value: 9.572
- type: precision_at_1000
value: 2.025
- type: precision_at_3
value: 44.083
- type: precision_at_5
value: 36.3
- type: recall_at_1
value: 7.829999999999999
- type: recall_at_10
value: 21.462999999999997
- type: recall_at_100
value: 47.095
- type: recall_at_1000
value: 71.883
- type: recall_at_3
value: 13.891
- type: recall_at_5
value: 16.326999999999998
- task:
type: Retrieval
dataset:
name: MTEB FiQA-PL
type: fiqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.950000000000003
- type: map_at_10
value: 27.422
- type: map_at_100
value: 29.146
- type: map_at_1000
value: 29.328
- type: map_at_3
value: 23.735999999999997
- type: map_at_5
value: 25.671
- type: mrr_at_1
value: 33.796
- type: mrr_at_10
value: 42.689
- type: mrr_at_100
value: 43.522
- type: mrr_at_1000
value: 43.563
- type: mrr_at_3
value: 40.226
- type: mrr_at_5
value: 41.685
- type: ndcg_at_1
value: 33.642
- type: ndcg_at_10
value: 35.008
- type: ndcg_at_100
value: 41.839
- type: ndcg_at_1000
value: 45.035
- type: ndcg_at_3
value: 31.358999999999998
- type: ndcg_at_5
value: 32.377
- type: precision_at_1
value: 33.642
- type: precision_at_10
value: 9.937999999999999
- type: precision_at_100
value: 1.685
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 21.142
- type: precision_at_5
value: 15.586
- type: recall_at_1
value: 16.950000000000003
- type: recall_at_10
value: 42.286
- type: recall_at_100
value: 68.51899999999999
- type: recall_at_1000
value: 87.471
- type: recall_at_3
value: 28.834
- type: recall_at_5
value: 34.274
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA-PL
type: hotpotqa-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.711
- type: map_at_10
value: 57.867999999999995
- type: map_at_100
value: 58.77
- type: map_at_1000
value: 58.836999999999996
- type: map_at_3
value: 54.400999999999996
- type: map_at_5
value: 56.564
- type: mrr_at_1
value: 75.449
- type: mrr_at_10
value: 81.575
- type: mrr_at_100
value: 81.783
- type: mrr_at_1000
value: 81.792
- type: mrr_at_3
value: 80.50399999999999
- type: mrr_at_5
value: 81.172
- type: ndcg_at_1
value: 75.422
- type: ndcg_at_10
value: 66.635
- type: ndcg_at_100
value: 69.85
- type: ndcg_at_1000
value: 71.179
- type: ndcg_at_3
value: 61.648
- type: ndcg_at_5
value: 64.412
- type: precision_at_1
value: 75.422
- type: precision_at_10
value: 13.962
- type: precision_at_100
value: 1.649
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 39.172000000000004
- type: precision_at_5
value: 25.691000000000003
- type: recall_at_1
value: 37.711
- type: recall_at_10
value: 69.811
- type: recall_at_100
value: 82.471
- type: recall_at_1000
value: 91.29
- type: recall_at_3
value: 58.757999999999996
- type: recall_at_5
value: 64.227
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO-PL
type: msmarco-pl
config: default
split: validation
revision: None
metrics:
- type: map_at_1
value: 17.033
- type: map_at_10
value: 27.242
- type: map_at_100
value: 28.451999999999998
- type: map_at_1000
value: 28.515
- type: map_at_3
value: 24.046
- type: map_at_5
value: 25.840999999999998
- type: mrr_at_1
value: 17.493
- type: mrr_at_10
value: 27.67
- type: mrr_at_100
value: 28.823999999999998
- type: mrr_at_1000
value: 28.881
- type: mrr_at_3
value: 24.529999999999998
- type: mrr_at_5
value: 26.27
- type: ndcg_at_1
value: 17.479
- type: ndcg_at_10
value: 33.048
- type: ndcg_at_100
value: 39.071
- type: ndcg_at_1000
value: 40.739999999999995
- type: ndcg_at_3
value: 26.493
- type: ndcg_at_5
value: 29.701
- type: precision_at_1
value: 17.479
- type: precision_at_10
value: 5.324
- type: precision_at_100
value: 0.8380000000000001
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 11.408999999999999
- type: precision_at_5
value: 8.469999999999999
- type: recall_at_1
value: 17.033
- type: recall_at_10
value: 50.929
- type: recall_at_100
value: 79.262
- type: recall_at_1000
value: 92.239
- type: recall_at_3
value: 33.06
- type: recall_at_5
value: 40.747
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (pl)
type: mteb/amazon_massive_intent
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.31002017484867
- type: f1
value: 69.61603671063031
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.52790854068594
- type: f1
value: 75.4053872472259
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus-PL
type: nfcorpus-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.877000000000001
- type: map_at_10
value: 12.817
- type: map_at_100
value: 16.247
- type: map_at_1000
value: 17.683
- type: map_at_3
value: 9.334000000000001
- type: map_at_5
value: 10.886999999999999
- type: mrr_at_1
value: 45.201
- type: mrr_at_10
value: 52.7
- type: mrr_at_100
value: 53.425999999999995
- type: mrr_at_1000
value: 53.461000000000006
- type: mrr_at_3
value: 50.464
- type: mrr_at_5
value: 51.827
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 34.144999999999996
- type: ndcg_at_100
value: 31.556
- type: ndcg_at_1000
value: 40.265
- type: ndcg_at_3
value: 38.07
- type: ndcg_at_5
value: 36.571
- type: precision_at_1
value: 44.272
- type: precision_at_10
value: 25.697
- type: precision_at_100
value: 8.077
- type: precision_at_1000
value: 2.084
- type: precision_at_3
value: 36.016999999999996
- type: precision_at_5
value: 31.703
- type: recall_at_1
value: 5.877000000000001
- type: recall_at_10
value: 16.986
- type: recall_at_100
value: 32.719
- type: recall_at_1000
value: 63.763000000000005
- type: recall_at_3
value: 10.292
- type: recall_at_5
value: 12.886000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ-PL
type: nq-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.476
- type: map_at_10
value: 38.67
- type: map_at_100
value: 39.784000000000006
- type: map_at_1000
value: 39.831
- type: map_at_3
value: 34.829
- type: map_at_5
value: 37.025000000000006
- type: mrr_at_1
value: 28.621000000000002
- type: mrr_at_10
value: 41.13
- type: mrr_at_100
value: 42.028
- type: mrr_at_1000
value: 42.059999999999995
- type: mrr_at_3
value: 37.877
- type: mrr_at_5
value: 39.763999999999996
- type: ndcg_at_1
value: 28.563
- type: ndcg_at_10
value: 45.654
- type: ndcg_at_100
value: 50.695
- type: ndcg_at_1000
value: 51.873999999999995
- type: ndcg_at_3
value: 38.359
- type: ndcg_at_5
value: 42.045
- type: precision_at_1
value: 28.563
- type: precision_at_10
value: 7.6450000000000005
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 17.458000000000002
- type: precision_at_5
value: 12.613
- type: recall_at_1
value: 25.476
- type: recall_at_10
value: 64.484
- type: recall_at_100
value: 86.96199999999999
- type: recall_at_1000
value: 95.872
- type: recall_at_3
value: 45.527
- type: recall_at_5
value: 54.029
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 65.87315377932232
- type: ap
value: 76.41966964416534
- type: f1
value: 63.64417488639012
- task:
type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 87.7
- type: cos_sim_ap
value: 92.81319372631636
- type: cos_sim_f1
value: 90.04048582995952
- type: cos_sim_precision
value: 88.11410459587957
- type: cos_sim_recall
value: 92.05298013245033
- type: dot_accuracy
value: 75.0
- type: dot_ap
value: 83.63089957943261
- type: dot_f1
value: 80.76923076923077
- type: dot_precision
value: 75.43103448275862
- type: dot_recall
value: 86.9205298013245
- type: euclidean_accuracy
value: 87.7
- type: euclidean_ap
value: 92.94772245932825
- type: euclidean_f1
value: 90.10458567980692
- type: euclidean_precision
value: 87.63693270735524
- type: euclidean_recall
value: 92.71523178807946
- type: manhattan_accuracy
value: 87.8
- type: manhattan_ap
value: 92.95330512127123
- type: manhattan_f1
value: 90.08130081300813
- type: manhattan_precision
value: 88.49840255591054
- type: manhattan_recall
value: 91.72185430463577
- type: max_accuracy
value: 87.8
- type: max_ap
value: 92.95330512127123
- type: max_f1
value: 90.10458567980692
- task:
type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 96.19666048237477
- type: cos_sim_ap
value: 98.61237969571302
- type: cos_sim_f1
value: 93.77845220030349
- type: cos_sim_precision
value: 93.35347432024169
- type: cos_sim_recall
value: 94.20731707317073
- type: dot_accuracy
value: 94.89795918367348
- type: dot_ap
value: 97.02853491357943
- type: dot_f1
value: 91.85185185185186
- type: dot_precision
value: 89.33717579250721
- type: dot_recall
value: 94.51219512195121
- type: euclidean_accuracy
value: 96.38218923933209
- type: euclidean_ap
value: 98.58145584134218
- type: euclidean_f1
value: 94.04580152671755
- type: euclidean_precision
value: 94.18960244648318
- type: euclidean_recall
value: 93.90243902439023
- type: manhattan_accuracy
value: 96.47495361781077
- type: manhattan_ap
value: 98.6108221024781
- type: manhattan_f1
value: 94.18960244648318
- type: manhattan_precision
value: 94.47852760736197
- type: manhattan_recall
value: 93.90243902439023
- type: max_accuracy
value: 96.47495361781077
- type: max_ap
value: 98.61237969571302
- type: max_f1
value: 94.18960244648318
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 71.73130193905818
- type: f1
value: 71.17731918813324
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 46.59919028340081
- type: f1
value: 37.216392949948954
- task:
type: Retrieval
dataset:
name: MTEB Quora-PL
type: quora-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.134
- type: map_at_10
value: 80.19
- type: map_at_100
value: 80.937
- type: map_at_1000
value: 80.95599999999999
- type: map_at_3
value: 77.074
- type: map_at_5
value: 79.054
- type: mrr_at_1
value: 75.88000000000001
- type: mrr_at_10
value: 83.226
- type: mrr_at_100
value: 83.403
- type: mrr_at_1000
value: 83.406
- type: mrr_at_3
value: 82.03200000000001
- type: mrr_at_5
value: 82.843
- type: ndcg_at_1
value: 75.94
- type: ndcg_at_10
value: 84.437
- type: ndcg_at_100
value: 86.13
- type: ndcg_at_1000
value: 86.29299999999999
- type: ndcg_at_3
value: 81.07799999999999
- type: ndcg_at_5
value: 83.0
- type: precision_at_1
value: 75.94
- type: precision_at_10
value: 12.953999999999999
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 35.61
- type: precision_at_5
value: 23.652
- type: recall_at_1
value: 66.134
- type: recall_at_10
value: 92.991
- type: recall_at_100
value: 99.003
- type: recall_at_1000
value: 99.86
- type: recall_at_3
value: 83.643
- type: recall_at_5
value: 88.81099999999999
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: scidocs-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.183
- type: map_at_10
value: 10.626
- type: map_at_100
value: 12.485
- type: map_at_1000
value: 12.793
- type: map_at_3
value: 7.531000000000001
- type: map_at_5
value: 9.037
- type: mrr_at_1
value: 20.5
- type: mrr_at_10
value: 30.175
- type: mrr_at_100
value: 31.356
- type: mrr_at_1000
value: 31.421
- type: mrr_at_3
value: 26.900000000000002
- type: mrr_at_5
value: 28.689999999999998
- type: ndcg_at_1
value: 20.599999999999998
- type: ndcg_at_10
value: 17.84
- type: ndcg_at_100
value: 25.518
- type: ndcg_at_1000
value: 31.137999999999998
- type: ndcg_at_3
value: 16.677
- type: ndcg_at_5
value: 14.641000000000002
- type: precision_at_1
value: 20.599999999999998
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 2.048
- type: precision_at_1000
value: 0.33999999999999997
- type: precision_at_3
value: 15.533
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.183
- type: recall_at_10
value: 18.862000000000002
- type: recall_at_100
value: 41.592
- type: recall_at_1000
value: 69.037
- type: recall_at_3
value: 9.443
- type: recall_at_5
value: 13.028
- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 86.32286995515696
- type: cos_sim_ap
value: 82.04302619416443
- type: cos_sim_f1
value: 74.95572086432874
- type: cos_sim_precision
value: 74.55954897815363
- type: cos_sim_recall
value: 75.35612535612536
- type: dot_accuracy
value: 83.9176518548716
- type: dot_ap
value: 76.8608733580272
- type: dot_f1
value: 72.31936654569449
- type: dot_precision
value: 67.36324523663184
- type: dot_recall
value: 78.06267806267806
- type: euclidean_accuracy
value: 86.32286995515696
- type: euclidean_ap
value: 81.9648986659308
- type: euclidean_f1
value: 74.93796526054591
- type: euclidean_precision
value: 74.59421312632321
- type: euclidean_recall
value: 75.28490028490027
- type: manhattan_accuracy
value: 86.30248675091724
- type: manhattan_ap
value: 81.92853980116878
- type: manhattan_f1
value: 74.80968858131489
- type: manhattan_precision
value: 72.74562584118439
- type: manhattan_recall
value: 76.99430199430199
- type: max_accuracy
value: 86.32286995515696
- type: max_ap
value: 82.04302619416443
- type: max_f1
value: 74.95572086432874
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 83.07566183637853
- type: cos_sim_spearman
value: 79.20198022242548
- type: euclidean_pearson
value: 81.27875473517936
- type: euclidean_spearman
value: 79.21560102311153
- type: manhattan_pearson
value: 81.21559474880459
- type: manhattan_spearman
value: 79.1537846814979
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 36.39657573900194
- type: cos_sim_spearman
value: 40.36403461037013
- type: euclidean_pearson
value: 29.143416004776316
- type: euclidean_spearman
value: 40.43197841306375
- type: manhattan_pearson
value: 29.18632337290767
- type: manhattan_spearman
value: 40.50563343395481
- task:
type: Retrieval
dataset:
name: MTEB SciFact-PL
type: scifact-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.428
- type: map_at_10
value: 60.423
- type: map_at_100
value: 61.037
- type: map_at_1000
value: 61.065999999999995
- type: map_at_3
value: 56.989000000000004
- type: map_at_5
value: 59.041999999999994
- type: mrr_at_1
value: 52.666999999999994
- type: mrr_at_10
value: 61.746
- type: mrr_at_100
value: 62.273
- type: mrr_at_1000
value: 62.300999999999995
- type: mrr_at_3
value: 59.278
- type: mrr_at_5
value: 60.611000000000004
- type: ndcg_at_1
value: 52.333
- type: ndcg_at_10
value: 65.75
- type: ndcg_at_100
value: 68.566
- type: ndcg_at_1000
value: 69.314
- type: ndcg_at_3
value: 59.768
- type: ndcg_at_5
value: 62.808
- type: precision_at_1
value: 52.333
- type: precision_at_10
value: 9.167
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 23.778
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 49.428
- type: recall_at_10
value: 81.07799999999999
- type: recall_at_100
value: 93.93299999999999
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 65.061
- type: recall_at_5
value: 72.667
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID-PL
type: trec-covid-pl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22100000000000003
- type: map_at_10
value: 1.788
- type: map_at_100
value: 9.937
- type: map_at_1000
value: 24.762999999999998
- type: map_at_3
value: 0.579
- type: map_at_5
value: 0.947
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 88.067
- type: mrr_at_100
value: 88.067
- type: mrr_at_1000
value: 88.067
- type: mrr_at_3
value: 87.667
- type: mrr_at_5
value: 88.067
- type: ndcg_at_1
value: 76.0
- type: ndcg_at_10
value: 71.332
- type: ndcg_at_100
value: 54.80500000000001
- type: ndcg_at_1000
value: 49.504999999999995
- type: ndcg_at_3
value: 73.693
- type: ndcg_at_5
value: 73.733
- type: precision_at_1
value: 82.0
- type: precision_at_10
value: 76.8
- type: precision_at_100
value: 56.68
- type: precision_at_1000
value: 22.236
- type: precision_at_3
value: 78.667
- type: precision_at_5
value: 79.2
- type: recall_at_1
value: 0.22100000000000003
- type: recall_at_10
value: 2.033
- type: recall_at_100
value: 13.431999999999999
- type: recall_at_1000
value: 46.913
- type: recall_at_3
value: 0.625
- type: recall_at_5
value: 1.052
---
<h1 align="center">MMLW-roberta-base</h1>
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish.
This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning.
It transforms texts to 768 dimensional vectors.
The model was initialized with Polish RoBERTa checkpoint, and then trained with [multilingual knowledge distillation method](https://aclanthology.org/2020.emnlp-main.365/) on a diverse corpus of 60 million Polish-English text pairs. We utilised [English FlagEmbeddings (BGE)](https://huggingface.co/BAAI/bge-base-en) as teacher models for distillation.
## Usage (Sentence-Transformers)
⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix **"zapytanie: "** ⚠️
You can use the model like this with [sentence-transformers](https://www.SBERT.net):
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
query_prefix = "zapytanie: "
answer_prefix = ""
queries = [query_prefix + "Jak dożyć 100 lat?"]
answers = [
answer_prefix + "Trzeba zdrowo się odżywiać i uprawiać sport.",
answer_prefix + "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.",
answer_prefix + "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
]
model = SentenceTransformer("sdadas/mmlw-roberta-base")
queries_emb = model.encode(queries, convert_to_tensor=True, show_progress_bar=False)
answers_emb = model.encode(answers, convert_to_tensor=True, show_progress_bar=False)
best_answer = cos_sim(queries_emb, answers_emb).argmax().item()
print(answers[best_answer])
# Trzeba zdrowo się odżywiać i uprawiać sport.
```
## Evaluation Results
- The model achieves an **Average Score** of **61.05** on the Polish Massive Text Embedding Benchmark (MTEB). See [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for detailed results.
- The model achieves **NDCG@10** of **53.60** on the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) for detailed results.
## Acknowledgements
This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.
## Citation
```bibtex
@article{dadas2024pirb,
title={{PIRB}: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods},
author={Sławomir Dadas and Michał Perełkiewicz and Rafał Poświata},
year={2024},
eprint={2402.13350},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
"SEMANTIC_SIMILARITY"
] | [
"SCIFACT"
] |
BAAI/llm-embedder | BAAI | feature-extraction | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-09T09:46:10 | 2023-11-14T10:11:55 | 68,786 | 118 | ---
license: mit
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
<span style="#FF69B4;"> **Hiring:** We're seeking experienced NLP researchers and intern students focusing on dense retrieval and retrieval-augmented LLMs. If you're interested, please feel free to reach out to us via email at [email protected].</span>
FlagEmbedding can map any text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, and semantic search.
And it can also be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages in a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from the embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For example, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 documents to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you can also download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
**1. How to fine-tune bge embedding model?**
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- In general, larger hyper-parameter `per_device_train_batch_size` brings better performance. You can expand it by enabling `--fp16`, `--deepspeed df_config.json` (df_config.json can refer to [ds_config.json](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/ds_config.json), `--gradient_checkpointing`, etc.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples of using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pair data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
For more training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
For more details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
### Our Contributors:
<a href="https://github.com/FlagOpen/FlagEmbedding/graphs/contributors">
<img src="https://contrib.rocks/image?repo=FlagOpen/FlagEmbedding" />
</a>
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{llm_embedder,
title={Retrieve Anything To Augment Large Language Models},
author={Peitian Zhang and Shitao Xiao and Zheng Liu and Zhicheng Dou and Jian-Yun Nie},
year={2023},
eprint={2310.07554},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
| [
"SEMANTIC_SIMILARITY",
"SUMMARIZATION"
] | [
"BEAR"
] |
beademiguelperez/sentence-transformers-multilingual-e5-small | beademiguelperez | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"mteb",
"Sentence Transformers",
"sentence-similarity",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"id",
"is",
"it",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lo",
"lt",
"lv",
"mg",
"mk",
"ml",
"mn",
"mr",
"ms",
"my",
"ne",
"nl",
"no",
"om",
"or",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sa",
"sd",
"si",
"sk",
"sl",
"so",
"sq",
"sr",
"su",
"sv",
"sw",
"ta",
"te",
"th",
"tl",
"tr",
"ug",
"uk",
"ur",
"uz",
"vi",
"xh",
"yi",
"zh",
"arxiv:2402.05672",
"arxiv:2108.08787",
"arxiv:2104.08663",
"arxiv:2210.07316",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-25T14:48:23 | 2024-03-25T14:56:59 | 68,126 | 0 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
model-index:
- name: multilingual-e5-small
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 36.9996434842022
- type: f1
value: 67.95453679103099
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.64882226980728
- type: ap
value: 82.11942130026586
- type: f1
value: 69.87963421606715
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.8095952023988
- type: ap
value: 24.46869495579561
- type: f1
value: 63.00108480037597
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 64.186295503212
- type: ap
value: 15.496804690197042
- type: f1
value: 52.07153895475031
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 88.699325
- type: ap
value: 85.27039559917269
- type: f1
value: 88.65556295032513
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.69799999999999
- type: f1
value: 43.73187348654165
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.245999999999995
- type: f1
value: 39.3863530637684
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.394
- type: f1
value: 39.301223469483446
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.864
- type: f1
value: 37.97974261868003
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.682
- type: f1
value: 37.07399369768313
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.504
- type: f1
value: 36.62317273874278
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.061
- type: map_at_10
value: 31.703
- type: map_at_100
value: 32.967
- type: map_at_1000
value: 33.001000000000005
- type: map_at_3
value: 27.466
- type: map_at_5
value: 29.564
- type: mrr_at_1
value: 19.559
- type: mrr_at_10
value: 31.874999999999996
- type: mrr_at_100
value: 33.146
- type: mrr_at_1000
value: 33.18
- type: mrr_at_3
value: 27.667
- type: mrr_at_5
value: 29.74
- type: ndcg_at_1
value: 19.061
- type: ndcg_at_10
value: 39.062999999999995
- type: ndcg_at_100
value: 45.184000000000005
- type: ndcg_at_1000
value: 46.115
- type: ndcg_at_3
value: 30.203000000000003
- type: ndcg_at_5
value: 33.953
- type: precision_at_1
value: 19.061
- type: precision_at_10
value: 6.279999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 12.706999999999999
- type: precision_at_5
value: 9.431000000000001
- type: recall_at_1
value: 19.061
- type: recall_at_10
value: 62.802
- type: recall_at_100
value: 91.323
- type: recall_at_1000
value: 98.72
- type: recall_at_3
value: 38.122
- type: recall_at_5
value: 47.155
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.22266660528253
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 30.79980849482483
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 57.8790068352054
- type: mrr
value: 71.78791276436706
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 82.36328364043163
- type: cos_sim_spearman
value: 82.26211536195868
- type: euclidean_pearson
value: 80.3183865039173
- type: euclidean_spearman
value: 79.88495276296132
- type: manhattan_pearson
value: 80.14484480692127
- type: manhattan_spearman
value: 80.39279565980743
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0375782881002
- type: f1
value: 97.86012526096033
- type: precision
value: 97.77139874739039
- type: recall
value: 98.0375782881002
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 93.35241030156286
- type: f1
value: 92.66050333846944
- type: precision
value: 92.3306919069631
- type: recall
value: 93.35241030156286
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 94.0699688257707
- type: f1
value: 93.50236693222492
- type: precision
value: 93.22791825424315
- type: recall
value: 94.0699688257707
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 89.25750394944708
- type: f1
value: 88.79234684921889
- type: precision
value: 88.57293312269616
- type: recall
value: 89.25750394944708
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.41558441558442
- type: f1
value: 79.25886487487219
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.747820820329736
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 27.045143830596146
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.252999999999997
- type: map_at_10
value: 31.655916666666666
- type: map_at_100
value: 32.680749999999996
- type: map_at_1000
value: 32.79483333333334
- type: map_at_3
value: 29.43691666666666
- type: map_at_5
value: 30.717416666666665
- type: mrr_at_1
value: 28.602750000000004
- type: mrr_at_10
value: 35.56875
- type: mrr_at_100
value: 36.3595
- type: mrr_at_1000
value: 36.427749999999996
- type: mrr_at_3
value: 33.586166666666664
- type: mrr_at_5
value: 34.73641666666666
- type: ndcg_at_1
value: 28.602750000000004
- type: ndcg_at_10
value: 36.06933333333334
- type: ndcg_at_100
value: 40.70141666666667
- type: ndcg_at_1000
value: 43.24341666666667
- type: ndcg_at_3
value: 32.307916666666664
- type: ndcg_at_5
value: 34.129999999999995
- type: precision_at_1
value: 28.602750000000004
- type: precision_at_10
value: 6.097666666666667
- type: precision_at_100
value: 0.9809166666666668
- type: precision_at_1000
value: 0.13766666666666663
- type: precision_at_3
value: 14.628166666666667
- type: precision_at_5
value: 10.266916666666667
- type: recall_at_1
value: 24.252999999999997
- type: recall_at_10
value: 45.31916666666667
- type: recall_at_100
value: 66.03575000000001
- type: recall_at_1000
value: 83.94708333333334
- type: recall_at_3
value: 34.71941666666666
- type: recall_at_5
value: 39.46358333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.024000000000001
- type: map_at_10
value: 15.644
- type: map_at_100
value: 17.154
- type: map_at_1000
value: 17.345
- type: map_at_3
value: 13.028
- type: map_at_5
value: 14.251
- type: mrr_at_1
value: 19.674
- type: mrr_at_10
value: 29.826999999999998
- type: mrr_at_100
value: 30.935000000000002
- type: mrr_at_1000
value: 30.987
- type: mrr_at_3
value: 26.645000000000003
- type: mrr_at_5
value: 28.29
- type: ndcg_at_1
value: 19.674
- type: ndcg_at_10
value: 22.545
- type: ndcg_at_100
value: 29.207
- type: ndcg_at_1000
value: 32.912
- type: ndcg_at_3
value: 17.952
- type: ndcg_at_5
value: 19.363
- type: precision_at_1
value: 19.674
- type: precision_at_10
value: 7.212000000000001
- type: precision_at_100
value: 1.435
- type: precision_at_1000
value: 0.212
- type: precision_at_3
value: 13.507
- type: precision_at_5
value: 10.397
- type: recall_at_1
value: 9.024000000000001
- type: recall_at_10
value: 28.077999999999996
- type: recall_at_100
value: 51.403
- type: recall_at_1000
value: 72.406
- type: recall_at_3
value: 16.768
- type: recall_at_5
value: 20.737
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.012
- type: map_at_10
value: 17.138
- type: map_at_100
value: 24.146
- type: map_at_1000
value: 25.622
- type: map_at_3
value: 12.552
- type: map_at_5
value: 14.435
- type: mrr_at_1
value: 62.25000000000001
- type: mrr_at_10
value: 71.186
- type: mrr_at_100
value: 71.504
- type: mrr_at_1000
value: 71.514
- type: mrr_at_3
value: 69.333
- type: mrr_at_5
value: 70.408
- type: ndcg_at_1
value: 49.75
- type: ndcg_at_10
value: 37.76
- type: ndcg_at_100
value: 42.071
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 41.644
- type: ndcg_at_5
value: 39.812999999999995
- type: precision_at_1
value: 62.25000000000001
- type: precision_at_10
value: 30.15
- type: precision_at_100
value: 9.753
- type: precision_at_1000
value: 1.9189999999999998
- type: precision_at_3
value: 45.667
- type: precision_at_5
value: 39.15
- type: recall_at_1
value: 8.012
- type: recall_at_10
value: 22.599
- type: recall_at_100
value: 48.068
- type: recall_at_1000
value: 71.328
- type: recall_at_3
value: 14.043
- type: recall_at_5
value: 17.124
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 42.455
- type: f1
value: 37.59462649781862
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.092
- type: map_at_10
value: 69.586
- type: map_at_100
value: 69.968
- type: map_at_1000
value: 69.982
- type: map_at_3
value: 67.48100000000001
- type: map_at_5
value: 68.915
- type: mrr_at_1
value: 62.166
- type: mrr_at_10
value: 73.588
- type: mrr_at_100
value: 73.86399999999999
- type: mrr_at_1000
value: 73.868
- type: mrr_at_3
value: 71.6
- type: mrr_at_5
value: 72.99
- type: ndcg_at_1
value: 62.166
- type: ndcg_at_10
value: 75.27199999999999
- type: ndcg_at_100
value: 76.816
- type: ndcg_at_1000
value: 77.09700000000001
- type: ndcg_at_3
value: 71.36
- type: ndcg_at_5
value: 73.785
- type: precision_at_1
value: 62.166
- type: precision_at_10
value: 9.716
- type: precision_at_100
value: 1.065
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 28.278
- type: precision_at_5
value: 18.343999999999998
- type: recall_at_1
value: 58.092
- type: recall_at_10
value: 88.73400000000001
- type: recall_at_100
value: 95.195
- type: recall_at_1000
value: 97.04599999999999
- type: recall_at_3
value: 78.45
- type: recall_at_5
value: 84.316
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.649
- type: map_at_10
value: 26.457000000000004
- type: map_at_100
value: 28.169
- type: map_at_1000
value: 28.352
- type: map_at_3
value: 23.305
- type: map_at_5
value: 25.169000000000004
- type: mrr_at_1
value: 32.407000000000004
- type: mrr_at_10
value: 40.922
- type: mrr_at_100
value: 41.931000000000004
- type: mrr_at_1000
value: 41.983
- type: mrr_at_3
value: 38.786
- type: mrr_at_5
value: 40.205999999999996
- type: ndcg_at_1
value: 32.407000000000004
- type: ndcg_at_10
value: 33.314
- type: ndcg_at_100
value: 40.312
- type: ndcg_at_1000
value: 43.685
- type: ndcg_at_3
value: 30.391000000000002
- type: ndcg_at_5
value: 31.525
- type: precision_at_1
value: 32.407000000000004
- type: precision_at_10
value: 8.966000000000001
- type: precision_at_100
value: 1.6019999999999999
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 20.165
- type: precision_at_5
value: 14.722
- type: recall_at_1
value: 16.649
- type: recall_at_10
value: 39.117000000000004
- type: recall_at_100
value: 65.726
- type: recall_at_1000
value: 85.784
- type: recall_at_3
value: 27.914
- type: recall_at_5
value: 33.289
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.253
- type: map_at_10
value: 56.16799999999999
- type: map_at_100
value: 57.06099999999999
- type: map_at_1000
value: 57.126
- type: map_at_3
value: 52.644999999999996
- type: map_at_5
value: 54.909
- type: mrr_at_1
value: 72.505
- type: mrr_at_10
value: 79.66
- type: mrr_at_100
value: 79.869
- type: mrr_at_1000
value: 79.88
- type: mrr_at_3
value: 78.411
- type: mrr_at_5
value: 79.19800000000001
- type: ndcg_at_1
value: 72.505
- type: ndcg_at_10
value: 65.094
- type: ndcg_at_100
value: 68.219
- type: ndcg_at_1000
value: 69.515
- type: ndcg_at_3
value: 59.99
- type: ndcg_at_5
value: 62.909000000000006
- type: precision_at_1
value: 72.505
- type: precision_at_10
value: 13.749
- type: precision_at_100
value: 1.619
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.357
- type: precision_at_5
value: 25.313000000000002
- type: recall_at_1
value: 36.253
- type: recall_at_10
value: 68.744
- type: recall_at_100
value: 80.925
- type: recall_at_1000
value: 89.534
- type: recall_at_3
value: 57.535000000000004
- type: recall_at_5
value: 63.282000000000004
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.82239999999999
- type: ap
value: 75.65895781725314
- type: f1
value: 80.75880969095746
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.624
- type: map_at_10
value: 34.075
- type: map_at_100
value: 35.229
- type: map_at_1000
value: 35.276999999999994
- type: map_at_3
value: 30.245
- type: map_at_5
value: 32.42
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 34.638000000000005
- type: mrr_at_100
value: 35.744
- type: mrr_at_1000
value: 35.787
- type: mrr_at_3
value: 30.891000000000002
- type: mrr_at_5
value: 33.042
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 40.991
- type: ndcg_at_100
value: 46.563
- type: ndcg_at_1000
value: 47.743
- type: ndcg_at_3
value: 33.198
- type: ndcg_at_5
value: 37.069
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 14.216999999999999
- type: precision_at_5
value: 10.487
- type: recall_at_1
value: 21.624
- type: recall_at_10
value: 62.303
- type: recall_at_100
value: 88.124
- type: recall_at_1000
value: 97.08
- type: recall_at_3
value: 41.099999999999994
- type: recall_at_5
value: 50.381
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.06703146374831
- type: f1
value: 90.86867815863172
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 87.46970977740209
- type: f1
value: 86.36832872036588
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.26951300867245
- type: f1
value: 88.93561193959502
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 84.22799874725963
- type: f1
value: 84.30490069236556
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 86.02007888131948
- type: f1
value: 85.39376041027991
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.34900542495481
- type: f1
value: 85.39859673336713
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.078431372549
- type: f1
value: 53.45071102002276
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 65.85798816568047
- type: f1
value: 46.53112748993529
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.96864576384256
- type: f1
value: 45.966703022829506
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 61.31537738803633
- type: f1
value: 45.52601712835461
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.29616349946218
- type: f1
value: 47.24166485726613
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
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type: Classification
dataset:
name: MTEB MassiveScenarioClassification (he)
type: mteb/amazon_massive_scenario
config: he
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.806321452589096
- type: f1
value: 63.273048243765054
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hi)
type: mteb/amazon_massive_scenario
config: hi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.68997982515133
- type: f1
value: 66.54703855381324
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hu)
type: mteb/amazon_massive_scenario
config: hu
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.46940147948891
- type: f1
value: 65.91017343463396
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (hy)
type: mteb/amazon_massive_scenario
config: hy
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.49899125756556
- type: f1
value: 57.90333469917769
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (id)
type: mteb/amazon_massive_scenario
config: id
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.9219905850706
- type: f1
value: 67.23169403762938
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (is)
type: mteb/amazon_massive_scenario
config: is
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.486213853396094
- type: f1
value: 54.85282355583758
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (it)
type: mteb/amazon_massive_scenario
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.04169468728985
- type: f1
value: 68.83833333320462
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ja)
type: mteb/amazon_massive_scenario
config: ja
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.88702084734365
- type: f1
value: 74.04474735232299
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (jv)
type: mteb/amazon_massive_scenario
config: jv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.63416274377943
- type: f1
value: 55.11332211687954
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ka)
type: mteb/amazon_massive_scenario
config: ka
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.23604572965702
- type: f1
value: 50.86529813991055
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (km)
type: mteb/amazon_massive_scenario
config: km
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 46.62407531943511
- type: f1
value: 43.63485467164535
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (kn)
type: mteb/amazon_massive_scenario
config: kn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.15601882985878
- type: f1
value: 57.522837510959924
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ko)
type: mteb/amazon_massive_scenario
config: ko
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.84532616005382
- type: f1
value: 69.60021127179697
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (lv)
type: mteb/amazon_massive_scenario
config: lv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 56.65770006724949
- type: f1
value: 55.84219135523227
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 66.53665097511768
- type: f1
value: 65.09087787792639
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.31405514458642
- type: f1
value: 58.06135303831491
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.88231338264964
- type: f1
value: 62.751099407787926
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.86012104909213
- type: f1
value: 56.29118323058282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.37390719569602
- type: f1
value: 66.27922244885102
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.8675184936113
- type: f1
value: 70.22146529932019
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.2212508406187
- type: f1
value: 67.77454802056282
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.18090114324143
- type: f1
value: 68.03737625431621
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.65030262273034
- type: f1
value: 63.792945486912856
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 69.48217888365838
- type: f1
value: 69.96028997292197
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.17821116341627
- type: f1
value: 59.3935969827171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.86146603900471
- type: f1
value: 60.133692735032376
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.89441829186282
- type: f1
value: 70.03064076194089
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 58.15063887020847
- type: f1
value: 56.23326278499678
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 59.43846671149966
- type: f1
value: 57.70440450281974
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8507061197041
- type: f1
value: 59.22916396061171
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.65568258238063
- type: f1
value: 69.90736239440633
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 60.8843308675185
- type: f1
value: 59.30332663713599
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.05312710154674
- type: f1
value: 67.44024062594775
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 62.111634162743776
- type: f1
value: 60.89083013084519
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.44115669132482
- type: f1
value: 67.92227541674552
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.4687289845326
- type: f1
value: 74.16376793486025
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 68.31876260928043
- type: f1
value: 68.5246745215607
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.90431696479766
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.259158476693774
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.28445330838555
- type: mrr
value: 31.15758529581164
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.353
- type: map_at_10
value: 11.565
- type: map_at_100
value: 14.097000000000001
- type: map_at_1000
value: 15.354999999999999
- type: map_at_3
value: 8.749
- type: map_at_5
value: 9.974
- type: mrr_at_1
value: 42.105
- type: mrr_at_10
value: 50.589
- type: mrr_at_100
value: 51.187000000000005
- type: mrr_at_1000
value: 51.233
- type: mrr_at_3
value: 48.246
- type: mrr_at_5
value: 49.546
- type: ndcg_at_1
value: 40.402
- type: ndcg_at_10
value: 31.009999999999998
- type: ndcg_at_100
value: 28.026
- type: ndcg_at_1000
value: 36.905
- type: ndcg_at_3
value: 35.983
- type: ndcg_at_5
value: 33.764
- type: precision_at_1
value: 42.105
- type: precision_at_10
value: 22.786
- type: precision_at_100
value: 6.916
- type: precision_at_1000
value: 1.981
- type: precision_at_3
value: 33.333
- type: precision_at_5
value: 28.731
- type: recall_at_1
value: 5.353
- type: recall_at_10
value: 15.039
- type: recall_at_100
value: 27.348
- type: recall_at_1000
value: 59.453
- type: recall_at_3
value: 9.792
- type: recall_at_5
value: 11.882
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.852
- type: map_at_10
value: 48.924
- type: map_at_100
value: 49.854
- type: map_at_1000
value: 49.886
- type: map_at_3
value: 44.9
- type: map_at_5
value: 47.387
- type: mrr_at_1
value: 38.035999999999994
- type: mrr_at_10
value: 51.644
- type: mrr_at_100
value: 52.339
- type: mrr_at_1000
value: 52.35999999999999
- type: mrr_at_3
value: 48.421
- type: mrr_at_5
value: 50.468999999999994
- type: ndcg_at_1
value: 38.007000000000005
- type: ndcg_at_10
value: 56.293000000000006
- type: ndcg_at_100
value: 60.167
- type: ndcg_at_1000
value: 60.916000000000004
- type: ndcg_at_3
value: 48.903999999999996
- type: ndcg_at_5
value: 52.978
- type: precision_at_1
value: 38.007000000000005
- type: precision_at_10
value: 9.041
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.084
- type: precision_at_5
value: 15.608
- type: recall_at_1
value: 33.852
- type: recall_at_10
value: 75.893
- type: recall_at_100
value: 92.589
- type: recall_at_1000
value: 98.153
- type: recall_at_3
value: 56.969
- type: recall_at_5
value: 66.283
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.174
- type: map_at_10
value: 82.891
- type: map_at_100
value: 83.545
- type: map_at_1000
value: 83.56700000000001
- type: map_at_3
value: 79.944
- type: map_at_5
value: 81.812
- type: mrr_at_1
value: 79.67999999999999
- type: mrr_at_10
value: 86.279
- type: mrr_at_100
value: 86.39
- type: mrr_at_1000
value: 86.392
- type: mrr_at_3
value: 85.21
- type: mrr_at_5
value: 85.92999999999999
- type: ndcg_at_1
value: 79.69000000000001
- type: ndcg_at_10
value: 86.929
- type: ndcg_at_100
value: 88.266
- type: ndcg_at_1000
value: 88.428
- type: ndcg_at_3
value: 83.899
- type: ndcg_at_5
value: 85.56700000000001
- type: precision_at_1
value: 79.69000000000001
- type: precision_at_10
value: 13.161000000000001
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.603
- type: precision_at_5
value: 24.138
- type: recall_at_1
value: 69.174
- type: recall_at_10
value: 94.529
- type: recall_at_100
value: 99.15
- type: recall_at_1000
value: 99.925
- type: recall_at_3
value: 85.86200000000001
- type: recall_at_5
value: 90.501
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 39.13064340585255
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 58.97884249325877
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.4680000000000004
- type: map_at_10
value: 7.865
- type: map_at_100
value: 9.332
- type: map_at_1000
value: 9.587
- type: map_at_3
value: 5.800000000000001
- type: map_at_5
value: 6.8790000000000004
- type: mrr_at_1
value: 17.0
- type: mrr_at_10
value: 25.629
- type: mrr_at_100
value: 26.806
- type: mrr_at_1000
value: 26.889000000000003
- type: mrr_at_3
value: 22.8
- type: mrr_at_5
value: 24.26
- type: ndcg_at_1
value: 17.0
- type: ndcg_at_10
value: 13.895
- type: ndcg_at_100
value: 20.491999999999997
- type: ndcg_at_1000
value: 25.759999999999998
- type: ndcg_at_3
value: 13.347999999999999
- type: ndcg_at_5
value: 11.61
- type: precision_at_1
value: 17.0
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 1.669
- type: precision_at_1000
value: 0.294
- type: precision_at_3
value: 12.3
- type: precision_at_5
value: 10.02
- type: recall_at_1
value: 3.4680000000000004
- type: recall_at_10
value: 14.363000000000001
- type: recall_at_100
value: 33.875
- type: recall_at_1000
value: 59.711999999999996
- type: recall_at_3
value: 7.483
- type: recall_at_5
value: 10.173
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.04084311714061
- type: cos_sim_spearman
value: 77.51342467443078
- type: euclidean_pearson
value: 80.0321166028479
- type: euclidean_spearman
value: 77.29249114733226
- type: manhattan_pearson
value: 80.03105964262431
- type: manhattan_spearman
value: 77.22373689514794
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.1680158034387
- type: cos_sim_spearman
value: 76.55983344071117
- type: euclidean_pearson
value: 79.75266678300143
- type: euclidean_spearman
value: 75.34516823467025
- type: manhattan_pearson
value: 79.75959151517357
- type: manhattan_spearman
value: 75.42330344141912
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 76.48898993209346
- type: cos_sim_spearman
value: 76.96954120323366
- type: euclidean_pearson
value: 76.94139109279668
- type: euclidean_spearman
value: 76.85860283201711
- type: manhattan_pearson
value: 76.6944095091912
- type: manhattan_spearman
value: 76.61096912972553
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 77.85082366246944
- type: cos_sim_spearman
value: 75.52053350101731
- type: euclidean_pearson
value: 77.1165845070926
- type: euclidean_spearman
value: 75.31216065884388
- type: manhattan_pearson
value: 77.06193941833494
- type: manhattan_spearman
value: 75.31003701700112
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.36305246526497
- type: cos_sim_spearman
value: 87.11704613927415
- type: euclidean_pearson
value: 86.04199125810939
- type: euclidean_spearman
value: 86.51117572414263
- type: manhattan_pearson
value: 86.0805106816633
- type: manhattan_spearman
value: 86.52798366512229
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.18536255599724
- type: cos_sim_spearman
value: 83.63377151025418
- type: euclidean_pearson
value: 83.24657467993141
- type: euclidean_spearman
value: 84.02751481993825
- type: manhattan_pearson
value: 83.11941806582371
- type: manhattan_spearman
value: 83.84251281019304
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 78.95816528475514
- type: cos_sim_spearman
value: 78.86607380120462
- type: euclidean_pearson
value: 78.51268699230545
- type: euclidean_spearman
value: 79.11649316502229
- type: manhattan_pearson
value: 78.32367302808157
- type: manhattan_spearman
value: 78.90277699624637
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.89126914997624
- type: cos_sim_spearman
value: 73.0296921832678
- type: euclidean_pearson
value: 71.50385903677738
- type: euclidean_spearman
value: 73.13368899716289
- type: manhattan_pearson
value: 71.47421463379519
- type: manhattan_spearman
value: 73.03383242946575
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 59.22923684492637
- type: cos_sim_spearman
value: 57.41013211368396
- type: euclidean_pearson
value: 61.21107388080905
- type: euclidean_spearman
value: 60.07620768697254
- type: manhattan_pearson
value: 59.60157142786555
- type: manhattan_spearman
value: 59.14069604103739
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 76.24345978774299
- type: cos_sim_spearman
value: 77.24225743830719
- type: euclidean_pearson
value: 76.66226095469165
- type: euclidean_spearman
value: 77.60708820493146
- type: manhattan_pearson
value: 76.05303324760429
- type: manhattan_spearman
value: 76.96353149912348
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.50879160160852
- type: cos_sim_spearman
value: 86.43594662965224
- type: euclidean_pearson
value: 86.06846012826577
- type: euclidean_spearman
value: 86.02041395794136
- type: manhattan_pearson
value: 86.10916255616904
- type: manhattan_spearman
value: 86.07346068198953
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 58.39803698977196
- type: cos_sim_spearman
value: 55.96910950423142
- type: euclidean_pearson
value: 58.17941175613059
- type: euclidean_spearman
value: 55.03019330522745
- type: manhattan_pearson
value: 57.333358138183286
- type: manhattan_spearman
value: 54.04614023149965
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 70.98304089637197
- type: cos_sim_spearman
value: 72.44071656215888
- type: euclidean_pearson
value: 72.19224359033983
- type: euclidean_spearman
value: 73.89871188913025
- type: manhattan_pearson
value: 71.21098311547406
- type: manhattan_spearman
value: 72.93405764824821
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 85.99792397466308
- type: cos_sim_spearman
value: 84.83824377879495
- type: euclidean_pearson
value: 85.70043288694438
- type: euclidean_spearman
value: 84.70627558703686
- type: manhattan_pearson
value: 85.89570850150801
- type: manhattan_spearman
value: 84.95806105313007
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 72.21850322994712
- type: cos_sim_spearman
value: 72.28669398117248
- type: euclidean_pearson
value: 73.40082510412948
- type: euclidean_spearman
value: 73.0326539281865
- type: manhattan_pearson
value: 71.8659633964841
- type: manhattan_spearman
value: 71.57817425823303
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 75.80921368595645
- type: cos_sim_spearman
value: 77.33209091229315
- type: euclidean_pearson
value: 76.53159540154829
- type: euclidean_spearman
value: 78.17960842810093
- type: manhattan_pearson
value: 76.13530186637601
- type: manhattan_spearman
value: 78.00701437666875
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 74.74980608267349
- type: cos_sim_spearman
value: 75.37597374318821
- type: euclidean_pearson
value: 74.90506081911661
- type: euclidean_spearman
value: 75.30151613124521
- type: manhattan_pearson
value: 74.62642745918002
- type: manhattan_spearman
value: 75.18619716592303
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.632662289205584
- type: cos_sim_spearman
value: 60.938543391610914
- type: euclidean_pearson
value: 62.113200529767056
- type: euclidean_spearman
value: 61.410312633261164
- type: manhattan_pearson
value: 61.75494698945686
- type: manhattan_spearman
value: 60.92726195322362
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 45.283470551557244
- type: cos_sim_spearman
value: 53.44833015864201
- type: euclidean_pearson
value: 41.17892011120893
- type: euclidean_spearman
value: 53.81441383126767
- type: manhattan_pearson
value: 41.17482200420659
- type: manhattan_spearman
value: 53.82180269276363
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 60.5069165306236
- type: cos_sim_spearman
value: 66.87803259033826
- type: euclidean_pearson
value: 63.5428979418236
- type: euclidean_spearman
value: 66.9293576586897
- type: manhattan_pearson
value: 63.59789526178922
- type: manhattan_spearman
value: 66.86555009875066
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 28.23026196280264
- type: cos_sim_spearman
value: 35.79397812652861
- type: euclidean_pearson
value: 17.828102102767353
- type: euclidean_spearman
value: 35.721501145568894
- type: manhattan_pearson
value: 17.77134274219677
- type: manhattan_spearman
value: 35.98107902846267
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 56.51946541393812
- type: cos_sim_spearman
value: 63.714686006214485
- type: euclidean_pearson
value: 58.32104651305898
- type: euclidean_spearman
value: 62.237110895702216
- type: manhattan_pearson
value: 58.579416468759185
- type: manhattan_spearman
value: 62.459738981727
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 48.76009839569795
- type: cos_sim_spearman
value: 56.65188431953149
- type: euclidean_pearson
value: 50.997682160915595
- type: euclidean_spearman
value: 55.99910008818135
- type: manhattan_pearson
value: 50.76220659606342
- type: manhattan_spearman
value: 55.517347595391456
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 51.232731157702425
- type: cos_sim_spearman
value: 59.89531877658345
- type: euclidean_pearson
value: 49.937914570348376
- type: euclidean_spearman
value: 60.220905659334036
- type: manhattan_pearson
value: 50.00987996844193
- type: manhattan_spearman
value: 60.081341480977926
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.717524559088005
- type: cos_sim_spearman
value: 66.83570886252286
- type: euclidean_pearson
value: 58.41338625505467
- type: euclidean_spearman
value: 66.68991427704938
- type: manhattan_pearson
value: 58.78638572916807
- type: manhattan_spearman
value: 66.58684161046335
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.2962042954962
- type: cos_sim_spearman
value: 76.58255504852025
- type: euclidean_pearson
value: 75.70983192778257
- type: euclidean_spearman
value: 77.4547684870542
- type: manhattan_pearson
value: 75.75565853870485
- type: manhattan_spearman
value: 76.90208974949428
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.47396266924846
- type: cos_sim_spearman
value: 56.492267162048606
- type: euclidean_pearson
value: 55.998505203070195
- type: euclidean_spearman
value: 56.46447012960222
- type: manhattan_pearson
value: 54.873172394430995
- type: manhattan_spearman
value: 56.58111534551218
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.87177267688686
- type: cos_sim_spearman
value: 74.57160943395763
- type: euclidean_pearson
value: 70.88330406826788
- type: euclidean_spearman
value: 74.29767636038422
- type: manhattan_pearson
value: 71.38245248369536
- type: manhattan_spearman
value: 74.53102232732175
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.80225656959544
- type: cos_sim_spearman
value: 76.52646173725735
- type: euclidean_pearson
value: 73.95710720200799
- type: euclidean_spearman
value: 76.54040031984111
- type: manhattan_pearson
value: 73.89679971946774
- type: manhattan_spearman
value: 76.60886958161574
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.70844249898789
- type: cos_sim_spearman
value: 72.68571783670241
- type: euclidean_pearson
value: 72.38800772441031
- type: euclidean_spearman
value: 72.86804422703312
- type: manhattan_pearson
value: 71.29840508203515
- type: manhattan_spearman
value: 71.86264441749513
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 58.647478923935694
- type: cos_sim_spearman
value: 63.74453623540931
- type: euclidean_pearson
value: 59.60138032437505
- type: euclidean_spearman
value: 63.947930832166065
- type: manhattan_pearson
value: 58.59735509491861
- type: manhattan_spearman
value: 62.082503844627404
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 65.8722516867162
- type: cos_sim_spearman
value: 71.81208592523012
- type: euclidean_pearson
value: 67.95315252165956
- type: euclidean_spearman
value: 73.00749822046009
- type: manhattan_pearson
value: 68.07884688638924
- type: manhattan_spearman
value: 72.34210325803069
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 54.5405814240949
- type: cos_sim_spearman
value: 60.56838649023775
- type: euclidean_pearson
value: 53.011731611314104
- type: euclidean_spearman
value: 58.533194841668426
- type: manhattan_pearson
value: 53.623067729338494
- type: manhattan_spearman
value: 58.018756154446926
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 13.611046866216112
- type: cos_sim_spearman
value: 28.238192909158492
- type: euclidean_pearson
value: 22.16189199885129
- type: euclidean_spearman
value: 35.012895679076564
- type: manhattan_pearson
value: 21.969771178698387
- type: manhattan_spearman
value: 32.456985088607475
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 74.58077407011655
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 74.64613843596234
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 75.15335973101396
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.0739825531578
- type: cos_sim_spearman
value: 84.01057479311115
- type: euclidean_pearson
value: 83.85453227433344
- type: euclidean_spearman
value: 84.01630226898655
- type: manhattan_pearson
value: 83.75323603028978
- type: manhattan_spearman
value: 83.89677983727685
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.12945623123957
- type: mrr
value: 93.87738713719106
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.983000000000004
- type: map_at_10
value: 62.946000000000005
- type: map_at_100
value: 63.514
- type: map_at_1000
value: 63.554
- type: map_at_3
value: 60.183
- type: map_at_5
value: 61.672000000000004
- type: mrr_at_1
value: 55.667
- type: mrr_at_10
value: 64.522
- type: mrr_at_100
value: 64.957
- type: mrr_at_1000
value: 64.995
- type: mrr_at_3
value: 62.388999999999996
- type: mrr_at_5
value: 63.639
- type: ndcg_at_1
value: 55.667
- type: ndcg_at_10
value: 67.704
- type: ndcg_at_100
value: 70.299
- type: ndcg_at_1000
value: 71.241
- type: ndcg_at_3
value: 62.866
- type: ndcg_at_5
value: 65.16999999999999
- type: precision_at_1
value: 55.667
- type: precision_at_10
value: 9.033
- type: precision_at_100
value: 1.053
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 16.133
- type: recall_at_1
value: 52.983000000000004
- type: recall_at_10
value: 80.656
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 67.744
- type: recall_at_5
value: 73.433
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72772277227723
- type: cos_sim_ap
value: 92.17845897992215
- type: cos_sim_f1
value: 85.9746835443038
- type: cos_sim_precision
value: 87.07692307692308
- type: cos_sim_recall
value: 84.89999999999999
- type: dot_accuracy
value: 99.3039603960396
- type: dot_ap
value: 60.70244020124878
- type: dot_f1
value: 59.92742353551063
- type: dot_precision
value: 62.21743810548978
- type: dot_recall
value: 57.8
- type: euclidean_accuracy
value: 99.71683168316832
- type: euclidean_ap
value: 91.53997039964659
- type: euclidean_f1
value: 84.88372093023257
- type: euclidean_precision
value: 90.02242152466367
- type: euclidean_recall
value: 80.30000000000001
- type: manhattan_accuracy
value: 99.72376237623763
- type: manhattan_ap
value: 91.80756777790289
- type: manhattan_f1
value: 85.48468106479157
- type: manhattan_precision
value: 85.8728557013118
- type: manhattan_recall
value: 85.1
- type: max_accuracy
value: 99.72772277227723
- type: max_ap
value: 92.17845897992215
- type: max_f1
value: 85.9746835443038
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.52464042600003
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 32.071631948736
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.19552407604654
- type: mrr
value: 49.95269130379425
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.345293033095427
- type: cos_sim_spearman
value: 29.976931423258403
- type: dot_pearson
value: 27.047078008958408
- type: dot_spearman
value: 27.75894368380218
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.706
- type: map_at_100
value: 9.634
- type: map_at_1000
value: 23.665
- type: map_at_3
value: 0.5950000000000001
- type: map_at_5
value: 0.95
- type: mrr_at_1
value: 86.0
- type: mrr_at_10
value: 91.8
- type: mrr_at_100
value: 91.8
- type: mrr_at_1000
value: 91.8
- type: mrr_at_3
value: 91.0
- type: mrr_at_5
value: 91.8
- type: ndcg_at_1
value: 80.0
- type: ndcg_at_10
value: 72.573
- type: ndcg_at_100
value: 53.954
- type: ndcg_at_1000
value: 47.760999999999996
- type: ndcg_at_3
value: 76.173
- type: ndcg_at_5
value: 75.264
- type: precision_at_1
value: 86.0
- type: precision_at_10
value: 76.4
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.802
- type: precision_at_3
value: 81.333
- type: precision_at_5
value: 80.4
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 1.925
- type: recall_at_100
value: 12.762
- type: recall_at_1000
value: 44.946000000000005
- type: recall_at_3
value: 0.634
- type: recall_at_5
value: 1.051
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.0
- type: f1
value: 88.55666666666666
- type: precision
value: 87.46166666666667
- type: recall
value: 91.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.22543352601156
- type: f1
value: 51.03220478943021
- type: precision
value: 48.8150289017341
- type: recall
value: 57.22543352601156
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.58536585365854
- type: f1
value: 39.66870798578116
- type: precision
value: 37.416085946573745
- type: recall
value: 46.58536585365854
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 86.77999999999999
- type: precision
value: 85.45333333333332
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.58333333333331
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.3
- type: precision
value: 89.31666666666668
- type: recall
value: 92.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.9
- type: f1
value: 83.67190476190476
- type: precision
value: 82.23333333333332
- type: recall
value: 86.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50.0
- type: f1
value: 42.23229092632078
- type: precision
value: 39.851634683724235
- type: recall
value: 50.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.3
- type: f1
value: 70.86190476190477
- type: precision
value: 68.68777777777777
- type: recall
value: 76.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 57.073170731707314
- type: f1
value: 50.658958927251604
- type: precision
value: 48.26480836236933
- type: recall
value: 57.073170731707314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.2
- type: f1
value: 62.156507936507936
- type: precision
value: 59.84964285714286
- type: recall
value: 68.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.52126366950182
- type: f1
value: 72.8496210148701
- type: precision
value: 70.92171498003819
- type: recall
value: 77.52126366950182
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.78260869565217
- type: f1
value: 65.32422360248447
- type: precision
value: 63.063067367415194
- type: recall
value: 70.78260869565217
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.43478260869566
- type: f1
value: 73.02608695652172
- type: precision
value: 70.63768115942028
- type: recall
value: 78.43478260869566
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 60.9
- type: f1
value: 55.309753694581275
- type: precision
value: 53.130476190476195
- type: recall
value: 60.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.89999999999999
- type: f1
value: 67.92023809523809
- type: precision
value: 65.82595238095237
- type: recall
value: 72.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 46.80337756332931
- type: f1
value: 39.42174900558496
- type: precision
value: 36.97101116280851
- type: recall
value: 46.80337756332931
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.8
- type: f1
value: 86.79
- type: precision
value: 85.375
- type: recall
value: 89.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.199999999999996
- type: f1
value: 39.95484348984349
- type: precision
value: 37.561071428571424
- type: recall
value: 47.199999999999996
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.8
- type: f1
value: 84.68190476190475
- type: precision
value: 83.275
- type: recall
value: 87.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 48.76190476190476
- type: f1
value: 42.14965986394558
- type: precision
value: 39.96743626743626
- type: recall
value: 48.76190476190476
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.10000000000001
- type: f1
value: 59.58580086580086
- type: precision
value: 57.150238095238095
- type: recall
value: 66.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.3
- type: f1
value: 84.0
- type: precision
value: 82.48666666666666
- type: recall
value: 87.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 87.79523809523809
- type: precision
value: 86.6
- type: recall
value: 90.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.0
- type: f1
value: 83.81
- type: precision
value: 82.36666666666666
- type: recall
value: 87.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.9
- type: f1
value: 57.76533189033189
- type: precision
value: 55.50595238095239
- type: recall
value: 63.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 76.1
- type: f1
value: 71.83690476190478
- type: precision
value: 70.04928571428573
- type: recall
value: 76.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 66.3
- type: f1
value: 59.32626984126984
- type: precision
value: 56.62535714285713
- type: recall
value: 66.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.60000000000001
- type: f1
value: 87.96333333333334
- type: precision
value: 86.73333333333333
- type: recall
value: 90.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.10000000000001
- type: precision
value: 90.16666666666666
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.71428571428571
- type: f1
value: 82.29142600436403
- type: precision
value: 80.8076626877166
- type: recall
value: 85.71428571428571
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.88888888888889
- type: f1
value: 85.7834757834758
- type: precision
value: 84.43732193732193
- type: recall
value: 88.88888888888889
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.5
- type: f1
value: 85.67190476190476
- type: precision
value: 84.43333333333332
- type: recall
value: 88.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.72727272727273
- type: f1
value: 78.21969696969695
- type: precision
value: 76.18181818181819
- type: recall
value: 82.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 61.0062893081761
- type: f1
value: 55.13976240391334
- type: precision
value: 52.92112499659669
- type: recall
value: 61.0062893081761
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.86666666666666
- type: precision
value: 85.69166666666668
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nov-eng)
type: mteb/tatoeba-bitext-mining
config: nov-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.54085603112841
- type: f1
value: 68.56031128404669
- type: precision
value: 66.53047989623866
- type: recall
value: 73.54085603112841
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gsw-eng)
type: mteb/tatoeba-bitext-mining
config: gsw-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.58974358974359
- type: f1
value: 36.45299145299145
- type: precision
value: 33.81155881155882
- type: recall
value: 43.58974358974359
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nds-eng)
type: mteb/tatoeba-bitext-mining
config: nds-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 59.599999999999994
- type: f1
value: 53.264689754689755
- type: precision
value: 50.869166666666665
- type: recall
value: 59.599999999999994
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ukr-eng)
type: mteb/tatoeba-bitext-mining
config: ukr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.2
- type: f1
value: 81.61666666666665
- type: precision
value: 80.02833333333335
- type: recall
value: 85.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uzb-eng)
type: mteb/tatoeba-bitext-mining
config: uzb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 63.78504672897196
- type: f1
value: 58.00029669188548
- type: precision
value: 55.815809968847354
- type: recall
value: 63.78504672897196
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metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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metrics:
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dataset:
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metrics:
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value: 91.4
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dataset:
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metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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value: 22.7
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91.4
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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metrics:
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dataset:
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metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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metrics:
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dataset:
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metrics:
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metrics:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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config: dtp-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 9.1
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type: BitextMining
dataset:
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 80.9651474530831
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.9
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 19.64071856287425
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type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 90.2
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value: 90.2
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type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.98591549295775
- type: f1
value: 49.94718309859154
- type: precision
value: 47.77864154624717
- type: recall
value: 55.98591549295775
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.07692307692307
- type: f1
value: 66.74358974358974
- type: precision
value: 64.06837606837607
- type: recall
value: 73.07692307692307
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.25
- type: precision
value: 92.43333333333332
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 37.78705636743215
- type: f1
value: 31.63899658680452
- type: precision
value: 29.72264397629742
- type: recall
value: 37.78705636743215
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 21.6
- type: f1
value: 16.91697302697303
- type: precision
value: 15.71225147075147
- type: recall
value: 21.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.01628664495115
- type: f1
value: 81.38514037536838
- type: precision
value: 79.83170466883823
- type: recall
value: 85.01628664495115
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.39999999999999
- type: f1
value: 79.96380952380952
- type: precision
value: 78.48333333333333
- type: recall
value: 83.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 83.2
- type: f1
value: 79.26190476190476
- type: precision
value: 77.58833333333334
- type: recall
value: 83.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.59055118110236
- type: f1
value: 71.66854143232096
- type: precision
value: 70.30183727034121
- type: recall
value: 75.59055118110236
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.5
- type: f1
value: 59.26095238095238
- type: precision
value: 56.81909090909092
- type: recall
value: 65.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 55.26315789473685
- type: f1
value: 47.986523325858506
- type: precision
value: 45.33950006595436
- type: recall
value: 55.26315789473685
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 82.89999999999999
- type: f1
value: 78.835
- type: precision
value: 77.04761904761905
- type: recall
value: 82.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 43.269230769230774
- type: f1
value: 36.20421245421245
- type: precision
value: 33.57371794871795
- type: recall
value: 43.269230769230774
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.0
- type: f1
value: 84.70666666666666
- type: precision
value: 83.23166666666665
- type: recall
value: 88.0
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.4
- type: f1
value: 72.54666666666667
- type: precision
value: 70.54318181818181
- type: recall
value: 77.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 78.60000000000001
- type: f1
value: 74.1588888888889
- type: precision
value: 72.30250000000001
- type: recall
value: 78.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 72.40566037735849
- type: f1
value: 66.82587328813744
- type: precision
value: 64.75039308176099
- type: recall
value: 72.40566037735849
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 73.8
- type: f1
value: 68.56357142857144
- type: precision
value: 66.3178822055138
- type: recall
value: 73.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.78832116788321
- type: f1
value: 89.3552311435523
- type: precision
value: 88.20559610705597
- type: recall
value: 91.78832116788321
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.05085581085581
- type: precision
value: 66.955
- type: recall
value: 74.3
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.896
- type: map_at_10
value: 8.993
- type: map_at_100
value: 14.133999999999999
- type: map_at_1000
value: 15.668000000000001
- type: map_at_3
value: 5.862
- type: map_at_5
value: 7.17
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 42.931000000000004
- type: mrr_at_100
value: 44.81
- type: mrr_at_1000
value: 44.81
- type: mrr_at_3
value: 38.435
- type: mrr_at_5
value: 41.701
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 21.163
- type: ndcg_at_100
value: 33.306000000000004
- type: ndcg_at_1000
value: 45.275999999999996
- type: ndcg_at_3
value: 25.685999999999996
- type: ndcg_at_5
value: 23.732
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 17.755000000000003
- type: precision_at_100
value: 6.938999999999999
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 23.265
- type: recall_at_1
value: 2.896
- type: recall_at_10
value: 13.333999999999998
- type: recall_at_100
value: 43.517
- type: recall_at_1000
value: 79.836
- type: recall_at_3
value: 6.306000000000001
- type: recall_at_5
value: 8.825
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.3874
- type: ap
value: 13.829909072469423
- type: f1
value: 53.54534203543492
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.62026032823995
- type: f1
value: 62.85251350485221
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 33.21527881409797
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.97943613280086
- type: cos_sim_ap
value: 70.75454316885921
- type: cos_sim_f1
value: 65.38274012676743
- type: cos_sim_precision
value: 60.761214318078835
- type: cos_sim_recall
value: 70.76517150395777
- type: dot_accuracy
value: 79.0546581629612
- type: dot_ap
value: 47.3197121792147
- type: dot_f1
value: 49.20106524633821
- type: dot_precision
value: 42.45499808502489
- type: dot_recall
value: 58.49604221635884
- type: euclidean_accuracy
value: 85.08076533349228
- type: euclidean_ap
value: 70.95016106374474
- type: euclidean_f1
value: 65.43987900176455
- type: euclidean_precision
value: 62.64478764478765
- type: euclidean_recall
value: 68.49604221635884
- type: manhattan_accuracy
value: 84.93771234428085
- type: manhattan_ap
value: 70.63668388755362
- type: manhattan_f1
value: 65.23895401262398
- type: manhattan_precision
value: 56.946084218811485
- type: manhattan_recall
value: 76.35883905013192
- type: max_accuracy
value: 85.08076533349228
- type: max_ap
value: 70.95016106374474
- type: max_f1
value: 65.43987900176455
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.69096130709822
- type: cos_sim_ap
value: 84.82526278228542
- type: cos_sim_f1
value: 77.65485060585536
- type: cos_sim_precision
value: 75.94582658619167
- type: cos_sim_recall
value: 79.44256236526024
- type: dot_accuracy
value: 80.97954748321496
- type: dot_ap
value: 64.81642914145866
- type: dot_f1
value: 60.631996987229975
- type: dot_precision
value: 54.5897293631712
- type: dot_recall
value: 68.17831844779796
- type: euclidean_accuracy
value: 88.6987231730508
- type: euclidean_ap
value: 84.80003825477253
- type: euclidean_f1
value: 77.67194179854496
- type: euclidean_precision
value: 75.7128235122094
- type: euclidean_recall
value: 79.73514012935017
- type: manhattan_accuracy
value: 88.62692591298949
- type: manhattan_ap
value: 84.80451408255276
- type: manhattan_f1
value: 77.69888949572183
- type: manhattan_precision
value: 73.70311528631622
- type: manhattan_recall
value: 82.15275639051433
- type: max_accuracy
value: 88.6987231730508
- type: max_ap
value: 84.82526278228542
- type: max_f1
value: 77.69888949572183
---
## Multilingual-E5-small
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 12 layers and the embedding size is 384.
## Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-small')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-small')
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```
## Supported Languages
This model is initialized from [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.
## Training Details
**Initialization**: [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
**First stage**: contrastive pre-training with weak supervision
| Dataset | Weak supervision | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
**Second stage**: supervised fine-tuning
| Dataset | Language | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
For all labeled datasets, we only use its training set for fine-tuning.
For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
| | |
| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
## MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
## Support for Sentence Transformers
Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-small')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```
Package requirements
`pip install sentence_transformers~=2.2.2`
Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
## FAQ
**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
**2. Why are my reproduced results slightly different from reported in the model card?**
Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity,
what matters is the relative order of the scores instead of the absolute values,
so this should not be an issue.
## Citation
If you find our paper or models helpful, please consider cite as follows:
```
@article{wang2024multilingual,
title={Multilingual E5 Text Embeddings: A Technical Report},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2402.05672},
year={2024}
}
```
## Limitations
Long texts will be truncated to at most 512 tokens. | [
"SEMANTIC_SIMILARITY",
"TRANSLATION",
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Alibaba-NLP/gme-Qwen2-VL-2B-Instruct | Alibaba-NLP | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"mteb",
"transformers",
"Qwen2-VL",
"sentence-similarity",
"vidore",
"en",
"zh",
"arxiv:2412.16855",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-21T03:45:36 | 2025-01-21T11:54:11 | 65,883 | 47 | ---
base_model:
- Qwen/Qwen2-VL-2B-Instruct
language:
- en
- zh
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2-VL
- sentence-similarity
- vidore
model-index:
- name: external
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cos_sim_pearson
value: 61.03190209456061
- type: cos_sim_spearman
value: 67.54853383020948
- type: euclidean_pearson
value: 65.38958681599493
- type: euclidean_spearman
value: 67.54853383020948
- type: manhattan_pearson
value: 65.25341659273157
- type: manhattan_spearman
value: 67.34190190683134
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cos_sim_pearson
value: 50.83794357648487
- type: cos_sim_spearman
value: 54.03230997664373
- type: euclidean_pearson
value: 55.2072028123375
- type: euclidean_spearman
value: 54.032311102613264
- type: manhattan_pearson
value: 55.05163232251946
- type: manhattan_spearman
value: 53.81272176804127
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.55223880597015
- type: ap
value: 35.01515316721116
- type: f1
value: 66.44086070814382
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.75819999999999
- type: ap
value: 95.51009242092881
- type: f1
value: 96.75713119357414
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.971999999999994
- type: f1
value: 60.50745575187704
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.49
- type: f1
value: 51.576550662258434
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 36.272999999999996
- type: map_at_10
value: 52.782
- type: map_at_100
value: 53.339999999999996
- type: map_at_1000
value: 53.342999999999996
- type: map_at_3
value: 48.4
- type: map_at_5
value: 50.882000000000005
- type: mrr_at_1
value: 36.984
- type: mrr_at_10
value: 53.052
- type: mrr_at_100
value: 53.604
- type: mrr_at_1000
value: 53.607000000000006
- type: mrr_at_3
value: 48.613
- type: mrr_at_5
value: 51.159
- type: ndcg_at_1
value: 36.272999999999996
- type: ndcg_at_10
value: 61.524
- type: ndcg_at_100
value: 63.796
- type: ndcg_at_1000
value: 63.869
- type: ndcg_at_3
value: 52.456
- type: ndcg_at_5
value: 56.964000000000006
- type: precision_at_1
value: 36.272999999999996
- type: precision_at_10
value: 8.926
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.407999999999998
- type: precision_at_5
value: 15.049999999999999
- type: recall_at_1
value: 36.272999999999996
- type: recall_at_10
value: 89.25999999999999
- type: recall_at_100
value: 98.933
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 64.225
- type: recall_at_5
value: 75.249
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 52.45236368396085
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 46.83781937870832
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.653430349851746
- type: mrr
value: 74.28736314470387
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.18568151905953
- type: cos_sim_spearman
value: 86.47666922475281
- type: euclidean_pearson
value: 87.25416218056225
- type: euclidean_spearman
value: 86.47666922475281
- type: manhattan_pearson
value: 87.04960508086356
- type: manhattan_spearman
value: 86.73992823533615
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cos_sim_pearson
value: 75.7464284612374
- type: cos_sim_spearman
value: 77.71894224189296
- type: euclidean_pearson
value: 77.63454068918787
- type: euclidean_spearman
value: 77.71894224189296
- type: manhattan_pearson
value: 77.58744810404339
- type: manhattan_spearman
value: 77.63293552726073
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.2435064935065
- type: f1
value: 79.44078343737895
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 44.68220155432257
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 40.666150477589284
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 44.23533333311907
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 43.01114481307774
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 86.4349853821696
- type: mrr
value: 88.80150793650795
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 87.56417400982208
- type: mrr
value: 89.85813492063491
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 30.623
- type: map_at_10
value: 40.482
- type: map_at_100
value: 41.997
- type: map_at_1000
value: 42.135
- type: map_at_3
value: 37.754
- type: map_at_5
value: 39.031
- type: mrr_at_1
value: 37.482
- type: mrr_at_10
value: 46.311
- type: mrr_at_100
value: 47.211999999999996
- type: mrr_at_1000
value: 47.27
- type: mrr_at_3
value: 44.157999999999994
- type: mrr_at_5
value: 45.145
- type: ndcg_at_1
value: 37.482
- type: ndcg_at_10
value: 46.142
- type: ndcg_at_100
value: 51.834
- type: ndcg_at_1000
value: 54.164
- type: ndcg_at_3
value: 42.309000000000005
- type: ndcg_at_5
value: 43.485
- type: precision_at_1
value: 37.482
- type: precision_at_10
value: 8.455
- type: precision_at_100
value: 1.3780000000000001
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.172
- type: precision_at_5
value: 13.705
- type: recall_at_1
value: 30.623
- type: recall_at_10
value: 56.77100000000001
- type: recall_at_100
value: 80.034
- type: recall_at_1000
value: 94.62899999999999
- type: recall_at_3
value: 44.663000000000004
- type: recall_at_5
value: 48.692
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 27.941
- type: map_at_10
value: 38.437
- type: map_at_100
value: 39.625
- type: map_at_1000
value: 39.753
- type: map_at_3
value: 35.388999999999996
- type: map_at_5
value: 37.113
- type: mrr_at_1
value: 34.522000000000006
- type: mrr_at_10
value: 43.864999999999995
- type: mrr_at_100
value: 44.533
- type: mrr_at_1000
value: 44.580999999999996
- type: mrr_at_3
value: 41.55
- type: mrr_at_5
value: 42.942
- type: ndcg_at_1
value: 34.522000000000006
- type: ndcg_at_10
value: 44.330000000000005
- type: ndcg_at_100
value: 48.61
- type: ndcg_at_1000
value: 50.712999999999994
- type: ndcg_at_3
value: 39.834
- type: ndcg_at_5
value: 42.016
- type: precision_at_1
value: 34.522000000000006
- type: precision_at_10
value: 8.471
- type: precision_at_100
value: 1.3379999999999999
- type: precision_at_1000
value: 0.182
- type: precision_at_3
value: 19.363
- type: precision_at_5
value: 13.898
- type: recall_at_1
value: 27.941
- type: recall_at_10
value: 55.336
- type: recall_at_100
value: 73.51100000000001
- type: recall_at_1000
value: 86.636
- type: recall_at_3
value: 42.54
- type: recall_at_5
value: 48.392
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 32.681
- type: map_at_10
value: 45.48
- type: map_at_100
value: 46.542
- type: map_at_1000
value: 46.604
- type: map_at_3
value: 42.076
- type: map_at_5
value: 44.076
- type: mrr_at_1
value: 37.492
- type: mrr_at_10
value: 48.746
- type: mrr_at_100
value: 49.485
- type: mrr_at_1000
value: 49.517
- type: mrr_at_3
value: 45.998
- type: mrr_at_5
value: 47.681000000000004
- type: ndcg_at_1
value: 37.492
- type: ndcg_at_10
value: 51.778999999999996
- type: ndcg_at_100
value: 56.294
- type: ndcg_at_1000
value: 57.58
- type: ndcg_at_3
value: 45.856
- type: ndcg_at_5
value: 48.968
- type: precision_at_1
value: 37.492
- type: precision_at_10
value: 8.620999999999999
- type: precision_at_100
value: 1.189
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 20.773
- type: precision_at_5
value: 14.596
- type: recall_at_1
value: 32.681
- type: recall_at_10
value: 67.196
- type: recall_at_100
value: 87.027
- type: recall_at_1000
value: 96.146
- type: recall_at_3
value: 51.565000000000005
- type: recall_at_5
value: 59.123999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 22.421
- type: map_at_10
value: 30.127
- type: map_at_100
value: 31.253999999999998
- type: map_at_1000
value: 31.344
- type: map_at_3
value: 27.673
- type: map_at_5
value: 29.182000000000002
- type: mrr_at_1
value: 24.068
- type: mrr_at_10
value: 31.857000000000003
- type: mrr_at_100
value: 32.808
- type: mrr_at_1000
value: 32.881
- type: mrr_at_3
value: 29.397000000000002
- type: mrr_at_5
value: 30.883
- type: ndcg_at_1
value: 24.068
- type: ndcg_at_10
value: 34.642
- type: ndcg_at_100
value: 40.327
- type: ndcg_at_1000
value: 42.55
- type: ndcg_at_3
value: 29.868
- type: ndcg_at_5
value: 32.461
- type: precision_at_1
value: 24.068
- type: precision_at_10
value: 5.390000000000001
- type: precision_at_100
value: 0.873
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 12.692999999999998
- type: precision_at_5
value: 9.107
- type: recall_at_1
value: 22.421
- type: recall_at_10
value: 46.846
- type: recall_at_100
value: 73.409
- type: recall_at_1000
value: 90.06
- type: recall_at_3
value: 34.198
- type: recall_at_5
value: 40.437
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 16.494
- type: map_at_10
value: 24.4
- type: map_at_100
value: 25.718999999999998
- type: map_at_1000
value: 25.840000000000003
- type: map_at_3
value: 21.731
- type: map_at_5
value: 23.247999999999998
- type: mrr_at_1
value: 20.274
- type: mrr_at_10
value: 28.866000000000003
- type: mrr_at_100
value: 29.889
- type: mrr_at_1000
value: 29.957
- type: mrr_at_3
value: 26.284999999999997
- type: mrr_at_5
value: 27.79
- type: ndcg_at_1
value: 20.274
- type: ndcg_at_10
value: 29.666999999999998
- type: ndcg_at_100
value: 36.095
- type: ndcg_at_1000
value: 38.87
- type: ndcg_at_3
value: 24.672
- type: ndcg_at_5
value: 27.106
- type: precision_at_1
value: 20.274
- type: precision_at_10
value: 5.5969999999999995
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.023
- type: precision_at_5
value: 8.98
- type: recall_at_1
value: 16.494
- type: recall_at_10
value: 41.400999999999996
- type: recall_at_100
value: 69.811
- type: recall_at_1000
value: 89.422
- type: recall_at_3
value: 27.834999999999997
- type: recall_at_5
value: 33.774
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 26.150000000000002
- type: map_at_10
value: 36.012
- type: map_at_100
value: 37.377
- type: map_at_1000
value: 37.497
- type: map_at_3
value: 32.712
- type: map_at_5
value: 34.475
- type: mrr_at_1
value: 32.05
- type: mrr_at_10
value: 41.556
- type: mrr_at_100
value: 42.451
- type: mrr_at_1000
value: 42.498000000000005
- type: mrr_at_3
value: 38.659
- type: mrr_at_5
value: 40.314
- type: ndcg_at_1
value: 32.05
- type: ndcg_at_10
value: 42.132
- type: ndcg_at_100
value: 48.028999999999996
- type: ndcg_at_1000
value: 50.229
- type: ndcg_at_3
value: 36.622
- type: ndcg_at_5
value: 39.062000000000005
- type: precision_at_1
value: 32.05
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.269
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 17.355999999999998
- type: precision_at_5
value: 12.474
- type: recall_at_1
value: 26.150000000000002
- type: recall_at_10
value: 55.205000000000005
- type: recall_at_100
value: 80.2
- type: recall_at_1000
value: 94.524
- type: recall_at_3
value: 39.322
- type: recall_at_5
value: 45.761
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 23.741
- type: map_at_10
value: 33.51
- type: map_at_100
value: 34.882999999999996
- type: map_at_1000
value: 34.995
- type: map_at_3
value: 30.514000000000003
- type: map_at_5
value: 32.085
- type: mrr_at_1
value: 28.653000000000002
- type: mrr_at_10
value: 38.059
- type: mrr_at_100
value: 39.050000000000004
- type: mrr_at_1000
value: 39.107
- type: mrr_at_3
value: 35.445
- type: mrr_at_5
value: 36.849
- type: ndcg_at_1
value: 28.653000000000002
- type: ndcg_at_10
value: 39.186
- type: ndcg_at_100
value: 45.301
- type: ndcg_at_1000
value: 47.547
- type: ndcg_at_3
value: 34.103
- type: ndcg_at_5
value: 36.239
- type: precision_at_1
value: 28.653000000000002
- type: precision_at_10
value: 7.295
- type: precision_at_100
value: 1.2189999999999999
- type: precision_at_1000
value: 0.159
- type: precision_at_3
value: 16.438
- type: precision_at_5
value: 11.804
- type: recall_at_1
value: 23.741
- type: recall_at_10
value: 51.675000000000004
- type: recall_at_100
value: 78.13799999999999
- type: recall_at_1000
value: 93.12700000000001
- type: recall_at_3
value: 37.033
- type: recall_at_5
value: 42.793
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 25.281666666666663
- type: map_at_10
value: 34.080666666666666
- type: map_at_100
value: 35.278749999999995
- type: map_at_1000
value: 35.40183333333333
- type: map_at_3
value: 31.45316666666667
- type: map_at_5
value: 32.92716666666667
- type: mrr_at_1
value: 29.78783333333333
- type: mrr_at_10
value: 38.077333333333335
- type: mrr_at_100
value: 38.936499999999995
- type: mrr_at_1000
value: 39.000249999999994
- type: mrr_at_3
value: 35.7735
- type: mrr_at_5
value: 37.07683333333334
- type: ndcg_at_1
value: 29.78783333333333
- type: ndcg_at_10
value: 39.18300000000001
- type: ndcg_at_100
value: 44.444750000000006
- type: ndcg_at_1000
value: 46.90316666666667
- type: ndcg_at_3
value: 34.69308333333333
- type: ndcg_at_5
value: 36.80316666666666
- type: precision_at_1
value: 29.78783333333333
- type: precision_at_10
value: 6.820749999999999
- type: precision_at_100
value: 1.1224166666666666
- type: precision_at_1000
value: 0.1525
- type: precision_at_3
value: 15.936333333333335
- type: precision_at_5
value: 11.282333333333334
- type: recall_at_1
value: 25.281666666666663
- type: recall_at_10
value: 50.282
- type: recall_at_100
value: 73.54558333333334
- type: recall_at_1000
value: 90.64241666666666
- type: recall_at_3
value: 37.800999999999995
- type: recall_at_5
value: 43.223000000000006
- type: map_at_1
value: 19.07
- type: map_at_10
value: 26.608999999999998
- type: map_at_100
value: 27.625
- type: map_at_1000
value: 27.743000000000002
- type: map_at_3
value: 24.532999999999998
- type: map_at_5
value: 25.671
- type: mrr_at_1
value: 20.518
- type: mrr_at_10
value: 28.541
- type: mrr_at_100
value: 29.453000000000003
- type: mrr_at_1000
value: 29.536
- type: mrr_at_3
value: 26.71
- type: mrr_at_5
value: 27.708
- type: ndcg_at_1
value: 20.518
- type: ndcg_at_10
value: 30.855
- type: ndcg_at_100
value: 35.973
- type: ndcg_at_1000
value: 38.827
- type: ndcg_at_3
value: 26.868
- type: ndcg_at_5
value: 28.74
- type: precision_at_1
value: 20.518
- type: precision_at_10
value: 4.843
- type: precision_at_100
value: 0.799
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.645
- type: precision_at_5
value: 8.133
- type: recall_at_1
value: 19.07
- type: recall_at_10
value: 41.925000000000004
- type: recall_at_100
value: 65.68
- type: recall_at_1000
value: 86.713
- type: recall_at_3
value: 31.251
- type: recall_at_5
value: 35.653
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 23.452
- type: map_at_10
value: 30.231
- type: map_at_100
value: 31.227
- type: map_at_1000
value: 31.338
- type: map_at_3
value: 28.083000000000002
- type: map_at_5
value: 29.125
- type: mrr_at_1
value: 25.613000000000003
- type: mrr_at_10
value: 32.62
- type: mrr_at_100
value: 33.469
- type: mrr_at_1000
value: 33.554
- type: mrr_at_3
value: 30.368000000000002
- type: mrr_at_5
value: 31.502999999999997
- type: ndcg_at_1
value: 25.613000000000003
- type: ndcg_at_10
value: 34.441
- type: ndcg_at_100
value: 39.253
- type: ndcg_at_1000
value: 42.105
- type: ndcg_at_3
value: 30.183
- type: ndcg_at_5
value: 31.917
- type: precision_at_1
value: 25.613000000000003
- type: precision_at_10
value: 5.367999999999999
- type: precision_at_100
value: 0.848
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.73
- type: precision_at_5
value: 8.773
- type: recall_at_1
value: 23.452
- type: recall_at_10
value: 45.021
- type: recall_at_100
value: 66.563
- type: recall_at_1000
value: 87.713
- type: recall_at_3
value: 33.433
- type: recall_at_5
value: 37.637
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 16.11
- type: map_at_10
value: 22.832
- type: map_at_100
value: 23.829
- type: map_at_1000
value: 23.959
- type: map_at_3
value: 20.66
- type: map_at_5
value: 21.851000000000003
- type: mrr_at_1
value: 19.408
- type: mrr_at_10
value: 26.354
- type: mrr_at_100
value: 27.237000000000002
- type: mrr_at_1000
value: 27.32
- type: mrr_at_3
value: 24.243000000000002
- type: mrr_at_5
value: 25.430000000000003
- type: ndcg_at_1
value: 19.408
- type: ndcg_at_10
value: 27.239
- type: ndcg_at_100
value: 32.286
- type: ndcg_at_1000
value: 35.498000000000005
- type: ndcg_at_3
value: 23.244
- type: ndcg_at_5
value: 25.080999999999996
- type: precision_at_1
value: 19.408
- type: precision_at_10
value: 4.917
- type: precision_at_100
value: 0.874
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.863
- type: precision_at_5
value: 7.887
- type: recall_at_1
value: 16.11
- type: recall_at_10
value: 37.075
- type: recall_at_100
value: 60.251999999999995
- type: recall_at_1000
value: 83.38600000000001
- type: recall_at_3
value: 25.901999999999997
- type: recall_at_5
value: 30.612000000000002
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 25.941
- type: map_at_10
value: 33.711999999999996
- type: map_at_100
value: 34.926
- type: map_at_1000
value: 35.05
- type: map_at_3
value: 31.075000000000003
- type: map_at_5
value: 32.611000000000004
- type: mrr_at_1
value: 30.784
- type: mrr_at_10
value: 38.079
- type: mrr_at_100
value: 39.018
- type: mrr_at_1000
value: 39.09
- type: mrr_at_3
value: 35.603
- type: mrr_at_5
value: 36.988
- type: ndcg_at_1
value: 30.784
- type: ndcg_at_10
value: 38.586
- type: ndcg_at_100
value: 44.205
- type: ndcg_at_1000
value: 46.916000000000004
- type: ndcg_at_3
value: 33.899
- type: ndcg_at_5
value: 36.11
- type: precision_at_1
value: 30.784
- type: precision_at_10
value: 6.409
- type: precision_at_100
value: 1.034
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 15.112
- type: precision_at_5
value: 10.728
- type: recall_at_1
value: 25.941
- type: recall_at_10
value: 49.242999999999995
- type: recall_at_100
value: 73.85000000000001
- type: recall_at_1000
value: 92.782
- type: recall_at_3
value: 36.204
- type: recall_at_5
value: 41.908
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 24.401999999999997
- type: map_at_10
value: 33.195
- type: map_at_100
value: 34.699999999999996
- type: map_at_1000
value: 34.946
- type: map_at_3
value: 30.570999999999998
- type: map_at_5
value: 32.0
- type: mrr_at_1
value: 28.656
- type: mrr_at_10
value: 37.039
- type: mrr_at_100
value: 38.049
- type: mrr_at_1000
value: 38.108
- type: mrr_at_3
value: 34.717
- type: mrr_at_5
value: 36.07
- type: ndcg_at_1
value: 28.656
- type: ndcg_at_10
value: 38.557
- type: ndcg_at_100
value: 44.511
- type: ndcg_at_1000
value: 47.346
- type: ndcg_at_3
value: 34.235
- type: ndcg_at_5
value: 36.260999999999996
- type: precision_at_1
value: 28.656
- type: precision_at_10
value: 7.312
- type: precision_at_100
value: 1.451
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 15.942
- type: precision_at_5
value: 11.66
- type: recall_at_1
value: 24.401999999999997
- type: recall_at_10
value: 48.791000000000004
- type: recall_at_100
value: 76.211
- type: recall_at_1000
value: 93.92
- type: recall_at_3
value: 36.975
- type: recall_at_5
value: 42.01
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 18.762
- type: map_at_10
value: 32.412
- type: map_at_100
value: 34.506
- type: map_at_1000
value: 34.678
- type: map_at_3
value: 27.594
- type: map_at_5
value: 30.128
- type: mrr_at_1
value: 42.345
- type: mrr_at_10
value: 54.443
- type: mrr_at_100
value: 55.05799999999999
- type: mrr_at_1000
value: 55.076
- type: mrr_at_3
value: 51.553000000000004
- type: mrr_at_5
value: 53.269
- type: ndcg_at_1
value: 42.345
- type: ndcg_at_10
value: 42.304
- type: ndcg_at_100
value: 49.425000000000004
- type: ndcg_at_1000
value: 52.123
- type: ndcg_at_3
value: 36.271
- type: ndcg_at_5
value: 38.216
- type: precision_at_1
value: 42.345
- type: precision_at_10
value: 12.808
- type: precision_at_100
value: 2.062
- type: precision_at_1000
value: 0.258
- type: precision_at_3
value: 26.840000000000003
- type: precision_at_5
value: 20.052
- type: recall_at_1
value: 18.762
- type: recall_at_10
value: 47.976
- type: recall_at_100
value: 71.86
- type: recall_at_1000
value: 86.61999999999999
- type: recall_at_3
value: 32.708999999999996
- type: recall_at_5
value: 39.151
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: map_at_1
value: 24.871
- type: map_at_10
value: 37.208999999999996
- type: map_at_100
value: 38.993
- type: map_at_1000
value: 39.122
- type: map_at_3
value: 33.2
- type: map_at_5
value: 35.33
- type: mrr_at_1
value: 37.884
- type: mrr_at_10
value: 46.189
- type: mrr_at_100
value: 47.147
- type: mrr_at_1000
value: 47.195
- type: mrr_at_3
value: 43.728
- type: mrr_at_5
value: 44.994
- type: ndcg_at_1
value: 37.884
- type: ndcg_at_10
value: 43.878
- type: ndcg_at_100
value: 51.002
- type: ndcg_at_1000
value: 53.161
- type: ndcg_at_3
value: 38.729
- type: ndcg_at_5
value: 40.628
- type: precision_at_1
value: 37.884
- type: precision_at_10
value: 9.75
- type: precision_at_100
value: 1.558
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 21.964
- type: precision_at_5
value: 15.719
- type: recall_at_1
value: 24.871
- type: recall_at_10
value: 54.615
- type: recall_at_100
value: 84.276
- type: recall_at_1000
value: 98.578
- type: recall_at_3
value: 38.936
- type: recall_at_5
value: 45.061
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cos_sim_accuracy
value: 76.12748045700542
- type: cos_sim_ap
value: 84.47948419710998
- type: cos_sim_f1
value: 77.88108108108108
- type: cos_sim_precision
value: 72.43112809169516
- type: cos_sim_recall
value: 84.21790974982464
- type: dot_accuracy
value: 76.12748045700542
- type: dot_ap
value: 84.4933237839786
- type: dot_f1
value: 77.88108108108108
- type: dot_precision
value: 72.43112809169516
- type: dot_recall
value: 84.21790974982464
- type: euclidean_accuracy
value: 76.12748045700542
- type: euclidean_ap
value: 84.47947997540409
- type: euclidean_f1
value: 77.88108108108108
- type: euclidean_precision
value: 72.43112809169516
- type: euclidean_recall
value: 84.21790974982464
- type: manhattan_accuracy
value: 75.40589296452195
- type: manhattan_ap
value: 83.74383956930585
- type: manhattan_f1
value: 77.0983342289092
- type: manhattan_precision
value: 71.34049323786795
- type: manhattan_recall
value: 83.86719663315408
- type: max_accuracy
value: 76.12748045700542
- type: max_ap
value: 84.4933237839786
- type: max_f1
value: 77.88108108108108
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: 1271c7809071a13532e05f25fb53511ffce77117
metrics:
- type: map_at_1
value: 66.781
- type: map_at_10
value: 74.539
- type: map_at_100
value: 74.914
- type: map_at_1000
value: 74.921
- type: map_at_3
value: 72.734
- type: map_at_5
value: 73.788
- type: mrr_at_1
value: 66.913
- type: mrr_at_10
value: 74.543
- type: mrr_at_100
value: 74.914
- type: mrr_at_1000
value: 74.921
- type: mrr_at_3
value: 72.831
- type: mrr_at_5
value: 73.76899999999999
- type: ndcg_at_1
value: 67.018
- type: ndcg_at_10
value: 78.34299999999999
- type: ndcg_at_100
value: 80.138
- type: ndcg_at_1000
value: 80.322
- type: ndcg_at_3
value: 74.667
- type: ndcg_at_5
value: 76.518
- type: precision_at_1
value: 67.018
- type: precision_at_10
value: 9.115
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.906000000000002
- type: precision_at_5
value: 17.092
- type: recall_at_1
value: 66.781
- type: recall_at_10
value: 90.253
- type: recall_at_100
value: 98.52499999999999
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 80.05799999999999
- type: recall_at_5
value: 84.615
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.685
- type: map_at_10
value: 21.65
- type: map_at_100
value: 30.952
- type: map_at_1000
value: 33.049
- type: map_at_3
value: 14.953
- type: map_at_5
value: 17.592
- type: mrr_at_1
value: 72.0
- type: mrr_at_10
value: 78.054
- type: mrr_at_100
value: 78.41900000000001
- type: mrr_at_1000
value: 78.425
- type: mrr_at_3
value: 76.5
- type: mrr_at_5
value: 77.28699999999999
- type: ndcg_at_1
value: 61.25000000000001
- type: ndcg_at_10
value: 46.306000000000004
- type: ndcg_at_100
value: 50.867
- type: ndcg_at_1000
value: 58.533
- type: ndcg_at_3
value: 50.857
- type: ndcg_at_5
value: 48.283
- type: precision_at_1
value: 72.0
- type: precision_at_10
value: 37.3
- type: precision_at_100
value: 11.95
- type: precision_at_1000
value: 2.528
- type: precision_at_3
value: 53.583000000000006
- type: precision_at_5
value: 46.6
- type: recall_at_1
value: 9.685
- type: recall_at_10
value: 27.474999999999998
- type: recall_at_100
value: 56.825
- type: recall_at_1000
value: 81.792
- type: recall_at_3
value: 15.939
- type: recall_at_5
value: 19.853
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
metrics:
- type: map_at_1
value: 24.528
- type: map_at_10
value: 76.304
- type: map_at_100
value: 79.327
- type: map_at_1000
value: 79.373
- type: map_at_3
value: 52.035
- type: map_at_5
value: 66.074
- type: mrr_at_1
value: 86.05000000000001
- type: mrr_at_10
value: 90.74
- type: mrr_at_100
value: 90.809
- type: mrr_at_1000
value: 90.81099999999999
- type: mrr_at_3
value: 90.30799999999999
- type: mrr_at_5
value: 90.601
- type: ndcg_at_1
value: 86.05000000000001
- type: ndcg_at_10
value: 84.518
- type: ndcg_at_100
value: 87.779
- type: ndcg_at_1000
value: 88.184
- type: ndcg_at_3
value: 82.339
- type: ndcg_at_5
value: 81.613
- type: precision_at_1
value: 86.05000000000001
- type: precision_at_10
value: 40.945
- type: precision_at_100
value: 4.787
- type: precision_at_1000
value: 0.48900000000000005
- type: precision_at_3
value: 74.117
- type: precision_at_5
value: 62.86000000000001
- type: recall_at_1
value: 24.528
- type: recall_at_10
value: 86.78
- type: recall_at_100
value: 97.198
- type: recall_at_1000
value: 99.227
- type: recall_at_3
value: 54.94799999999999
- type: recall_at_5
value: 72.053
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
metrics:
- type: map_at_1
value: 52.1
- type: map_at_10
value: 62.502
- type: map_at_100
value: 63.026
- type: map_at_1000
value: 63.04
- type: map_at_3
value: 59.782999999999994
- type: map_at_5
value: 61.443000000000005
- type: mrr_at_1
value: 52.1
- type: mrr_at_10
value: 62.502
- type: mrr_at_100
value: 63.026
- type: mrr_at_1000
value: 63.04
- type: mrr_at_3
value: 59.782999999999994
- type: mrr_at_5
value: 61.443000000000005
- type: ndcg_at_1
value: 52.1
- type: ndcg_at_10
value: 67.75999999999999
- type: ndcg_at_100
value: 70.072
- type: ndcg_at_1000
value: 70.441
- type: ndcg_at_3
value: 62.28
- type: ndcg_at_5
value: 65.25800000000001
- type: precision_at_1
value: 52.1
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 0.946
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.166999999999998
- type: precision_at_5
value: 15.340000000000002
- type: recall_at_1
value: 52.1
- type: recall_at_10
value: 84.3
- type: recall_at_100
value: 94.6
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 69.5
- type: recall_at_5
value: 76.7
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 62.805000000000014
- type: f1
value: 56.401757250989384
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 83.734
- type: map_at_10
value: 90.089
- type: map_at_100
value: 90.274
- type: map_at_1000
value: 90.286
- type: map_at_3
value: 89.281
- type: map_at_5
value: 89.774
- type: mrr_at_1
value: 90.039
- type: mrr_at_10
value: 94.218
- type: mrr_at_100
value: 94.24
- type: mrr_at_1000
value: 94.24
- type: mrr_at_3
value: 93.979
- type: mrr_at_5
value: 94.137
- type: ndcg_at_1
value: 90.039
- type: ndcg_at_10
value: 92.597
- type: ndcg_at_100
value: 93.147
- type: ndcg_at_1000
value: 93.325
- type: ndcg_at_3
value: 91.64999999999999
- type: ndcg_at_5
value: 92.137
- type: precision_at_1
value: 90.039
- type: precision_at_10
value: 10.809000000000001
- type: precision_at_100
value: 1.133
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 34.338
- type: precision_at_5
value: 21.089
- type: recall_at_1
value: 83.734
- type: recall_at_10
value: 96.161
- type: recall_at_100
value: 98.137
- type: recall_at_1000
value: 99.182
- type: recall_at_3
value: 93.551
- type: recall_at_5
value: 94.878
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 24.529999999999998
- type: map_at_10
value: 37.229
- type: map_at_100
value: 39.333
- type: map_at_1000
value: 39.491
- type: map_at_3
value: 32.177
- type: map_at_5
value: 35.077999999999996
- type: mrr_at_1
value: 45.678999999999995
- type: mrr_at_10
value: 53.952
- type: mrr_at_100
value: 54.727000000000004
- type: mrr_at_1000
value: 54.761
- type: mrr_at_3
value: 51.568999999999996
- type: mrr_at_5
value: 52.973000000000006
- type: ndcg_at_1
value: 45.678999999999995
- type: ndcg_at_10
value: 45.297
- type: ndcg_at_100
value: 52.516
- type: ndcg_at_1000
value: 55.16
- type: ndcg_at_3
value: 40.569
- type: ndcg_at_5
value: 42.49
- type: precision_at_1
value: 45.678999999999995
- type: precision_at_10
value: 12.269
- type: precision_at_100
value: 1.9709999999999999
- type: precision_at_1000
value: 0.244
- type: precision_at_3
value: 25.72
- type: precision_at_5
value: 19.66
- type: recall_at_1
value: 24.529999999999998
- type: recall_at_10
value: 51.983999999999995
- type: recall_at_100
value: 78.217
- type: recall_at_1000
value: 94.104
- type: recall_at_3
value: 36.449999999999996
- type: recall_at_5
value: 43.336999999999996
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 41.519
- type: map_at_10
value: 64.705
- type: map_at_100
value: 65.554
- type: map_at_1000
value: 65.613
- type: map_at_3
value: 61.478
- type: map_at_5
value: 63.55800000000001
- type: mrr_at_1
value: 83.038
- type: mrr_at_10
value: 87.82900000000001
- type: mrr_at_100
value: 87.96000000000001
- type: mrr_at_1000
value: 87.96300000000001
- type: mrr_at_3
value: 87.047
- type: mrr_at_5
value: 87.546
- type: ndcg_at_1
value: 83.038
- type: ndcg_at_10
value: 72.928
- type: ndcg_at_100
value: 75.778
- type: ndcg_at_1000
value: 76.866
- type: ndcg_at_3
value: 68.46600000000001
- type: ndcg_at_5
value: 71.036
- type: precision_at_1
value: 83.038
- type: precision_at_10
value: 15.040999999999999
- type: precision_at_100
value: 1.7260000000000002
- type: precision_at_1000
value: 0.187
- type: precision_at_3
value: 43.597
- type: precision_at_5
value: 28.188999999999997
- type: recall_at_1
value: 41.519
- type: recall_at_10
value: 75.20599999999999
- type: recall_at_100
value: 86.3
- type: recall_at_1000
value: 93.437
- type: recall_at_3
value: 65.39500000000001
- type: recall_at_5
value: 70.473
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 52.04309349749903
- type: f1
value: 39.91893257315586
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.0428
- type: ap
value: 94.48278082595033
- type: f1
value: 96.0409595432081
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
metrics:
- type: accuracy
value: 85.60975609756099
- type: ap
value: 54.30148799475452
- type: f1
value: 80.55899583002706
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
metrics:
- type: cos_sim_pearson
value: 66.44418108776416
- type: cos_sim_spearman
value: 72.79912770347306
- type: euclidean_pearson
value: 71.11194894579198
- type: euclidean_spearman
value: 72.79912104971427
- type: manhattan_pearson
value: 70.96800061808604
- type: manhattan_spearman
value: 72.63525186107175
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 27.9616280919871
- type: mrr
value: 26.544047619047618
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
metrics:
- type: map_at_1
value: 68.32300000000001
- type: map_at_10
value: 77.187
- type: map_at_100
value: 77.496
- type: map_at_1000
value: 77.503
- type: map_at_3
value: 75.405
- type: map_at_5
value: 76.539
- type: mrr_at_1
value: 70.616
- type: mrr_at_10
value: 77.703
- type: mrr_at_100
value: 77.97699999999999
- type: mrr_at_1000
value: 77.984
- type: mrr_at_3
value: 76.139
- type: mrr_at_5
value: 77.125
- type: ndcg_at_1
value: 70.616
- type: ndcg_at_10
value: 80.741
- type: ndcg_at_100
value: 82.123
- type: ndcg_at_1000
value: 82.32300000000001
- type: ndcg_at_3
value: 77.35600000000001
- type: ndcg_at_5
value: 79.274
- type: precision_at_1
value: 70.616
- type: precision_at_10
value: 9.696
- type: precision_at_100
value: 1.038
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.026000000000003
- type: precision_at_5
value: 18.433
- type: recall_at_1
value: 68.32300000000001
- type: recall_at_10
value: 91.186
- type: recall_at_100
value: 97.439
- type: recall_at_1000
value: 99.004
- type: recall_at_3
value: 82.218
- type: recall_at_5
value: 86.797
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.496000000000002
- type: map_at_10
value: 33.82
- type: map_at_100
value: 35.013
- type: map_at_1000
value: 35.063
- type: map_at_3
value: 29.910999999999998
- type: map_at_5
value: 32.086
- type: mrr_at_1
value: 22.092
- type: mrr_at_10
value: 34.404
- type: mrr_at_100
value: 35.534
- type: mrr_at_1000
value: 35.577999999999996
- type: mrr_at_3
value: 30.544
- type: mrr_at_5
value: 32.711
- type: ndcg_at_1
value: 22.092
- type: ndcg_at_10
value: 40.877
- type: ndcg_at_100
value: 46.619
- type: ndcg_at_1000
value: 47.823
- type: ndcg_at_3
value: 32.861000000000004
- type: ndcg_at_5
value: 36.769
- type: precision_at_1
value: 22.092
- type: precision_at_10
value: 6.54
- type: precision_at_100
value: 0.943
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.069
- type: precision_at_5
value: 10.424
- type: recall_at_1
value: 21.496000000000002
- type: recall_at_10
value: 62.67
- type: recall_at_100
value: 89.24499999999999
- type: recall_at_1000
value: 98.312
- type: recall_at_3
value: 40.796
- type: recall_at_5
value: 50.21600000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.74555403556772
- type: f1
value: 95.61381879323093
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 85.82763337893297
- type: f1
value: 63.17139719465236
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 78.51714862138535
- type: f1
value: 76.3995118440293
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.78143913920646
- type: f1
value: 72.6141122227626
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.03698722259583
- type: f1
value: 79.36511484240766
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.98722259583053
- type: f1
value: 76.5974920207624
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: map_at_1
value: 51.800000000000004
- type: map_at_10
value: 57.938
- type: map_at_100
value: 58.494
- type: map_at_1000
value: 58.541
- type: map_at_3
value: 56.617
- type: map_at_5
value: 57.302
- type: mrr_at_1
value: 51.800000000000004
- type: mrr_at_10
value: 57.938
- type: mrr_at_100
value: 58.494
- type: mrr_at_1000
value: 58.541
- type: mrr_at_3
value: 56.617
- type: mrr_at_5
value: 57.302
- type: ndcg_at_1
value: 51.800000000000004
- type: ndcg_at_10
value: 60.891
- type: ndcg_at_100
value: 63.897000000000006
- type: ndcg_at_1000
value: 65.231
- type: ndcg_at_3
value: 58.108000000000004
- type: ndcg_at_5
value: 59.343
- type: precision_at_1
value: 51.800000000000004
- type: precision_at_10
value: 7.02
- type: precision_at_100
value: 0.8500000000000001
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 20.8
- type: precision_at_5
value: 13.08
- type: recall_at_1
value: 51.800000000000004
- type: recall_at_10
value: 70.19999999999999
- type: recall_at_100
value: 85.0
- type: recall_at_1000
value: 95.7
- type: recall_at_3
value: 62.4
- type: recall_at_5
value: 65.4
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 38.68901889835701
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 38.0740589898848
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.41312482460189
- type: mrr
value: 34.713530863302495
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 80.39333333333335
- type: f1
value: 80.42683132366277
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.232
- type: map_at_10
value: 13.442000000000002
- type: map_at_100
value: 17.443
- type: map_at_1000
value: 19.1
- type: map_at_3
value: 9.794
- type: map_at_5
value: 11.375
- type: mrr_at_1
value: 50.15500000000001
- type: mrr_at_10
value: 58.628
- type: mrr_at_100
value: 59.077
- type: mrr_at_1000
value: 59.119
- type: mrr_at_3
value: 56.914
- type: mrr_at_5
value: 57.921
- type: ndcg_at_1
value: 48.762
- type: ndcg_at_10
value: 37.203
- type: ndcg_at_100
value: 34.556
- type: ndcg_at_1000
value: 43.601
- type: ndcg_at_3
value: 43.004
- type: ndcg_at_5
value: 40.181
- type: precision_at_1
value: 50.15500000000001
- type: precision_at_10
value: 27.276
- type: precision_at_100
value: 8.981
- type: precision_at_1000
value: 2.228
- type: precision_at_3
value: 39.628
- type: precision_at_5
value: 33.808
- type: recall_at_1
value: 6.232
- type: recall_at_10
value: 18.137
- type: recall_at_100
value: 36.101
- type: recall_at_1000
value: 68.733
- type: recall_at_3
value: 10.978
- type: recall_at_5
value: 13.718
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 35.545
- type: map_at_10
value: 52.083
- type: map_at_100
value: 52.954
- type: map_at_1000
value: 52.96999999999999
- type: map_at_3
value: 47.508
- type: map_at_5
value: 50.265
- type: mrr_at_1
value: 40.122
- type: mrr_at_10
value: 54.567
- type: mrr_at_100
value: 55.19199999999999
- type: mrr_at_1000
value: 55.204
- type: mrr_at_3
value: 51.043000000000006
- type: mrr_at_5
value: 53.233
- type: ndcg_at_1
value: 40.122
- type: ndcg_at_10
value: 60.012
- type: ndcg_at_100
value: 63.562
- type: ndcg_at_1000
value: 63.94
- type: ndcg_at_3
value: 51.681
- type: ndcg_at_5
value: 56.154
- type: precision_at_1
value: 40.122
- type: precision_at_10
value: 9.774
- type: precision_at_100
value: 1.176
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 23.426
- type: precision_at_5
value: 16.686
- type: recall_at_1
value: 35.545
- type: recall_at_10
value: 81.557
- type: recall_at_100
value: 96.729
- type: recall_at_1000
value: 99.541
- type: recall_at_3
value: 60.185
- type: recall_at_5
value: 70.411
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cos_sim_accuracy
value: 70.7634001082837
- type: cos_sim_ap
value: 74.97527385556558
- type: cos_sim_f1
value: 72.77277277277277
- type: cos_sim_precision
value: 69.17221693625119
- type: cos_sim_recall
value: 76.76874340021119
- type: dot_accuracy
value: 70.7634001082837
- type: dot_ap
value: 74.97527385556558
- type: dot_f1
value: 72.77277277277277
- type: dot_precision
value: 69.17221693625119
- type: dot_recall
value: 76.76874340021119
- type: euclidean_accuracy
value: 70.7634001082837
- type: euclidean_ap
value: 74.97527385556558
- type: euclidean_f1
value: 72.77277277277277
- type: euclidean_precision
value: 69.17221693625119
- type: euclidean_recall
value: 76.76874340021119
- type: manhattan_accuracy
value: 69.89713048186248
- type: manhattan_ap
value: 74.25943370061067
- type: manhattan_f1
value: 72.17268887846082
- type: manhattan_precision
value: 64.94932432432432
- type: manhattan_recall
value: 81.20380147835269
- type: max_accuracy
value: 70.7634001082837
- type: max_ap
value: 74.97527385556558
- type: max_f1
value: 72.77277277277277
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 92.92000000000002
- type: ap
value: 91.98475625106201
- type: f1
value: 92.91841470541901
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cos_sim_pearson
value: 41.23764415526825
- type: cos_sim_spearman
value: 46.872669471694664
- type: euclidean_pearson
value: 46.434144530918566
- type: euclidean_spearman
value: 46.872669471694664
- type: manhattan_pearson
value: 46.39678126910133
- type: manhattan_spearman
value: 46.55877754642116
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cos_sim_pearson
value: 28.77503601696299
- type: cos_sim_spearman
value: 31.818095557325606
- type: euclidean_pearson
value: 29.811479220397125
- type: euclidean_spearman
value: 31.817046821577673
- type: manhattan_pearson
value: 29.901628633314214
- type: manhattan_spearman
value: 31.991472038092084
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 68.908
- type: map_at_10
value: 83.19
- type: map_at_100
value: 83.842
- type: map_at_1000
value: 83.858
- type: map_at_3
value: 80.167
- type: map_at_5
value: 82.053
- type: mrr_at_1
value: 79.46
- type: mrr_at_10
value: 86.256
- type: mrr_at_100
value: 86.37
- type: mrr_at_1000
value: 86.371
- type: mrr_at_3
value: 85.177
- type: mrr_at_5
value: 85.908
- type: ndcg_at_1
value: 79.5
- type: ndcg_at_10
value: 87.244
- type: ndcg_at_100
value: 88.532
- type: ndcg_at_1000
value: 88.626
- type: ndcg_at_3
value: 84.161
- type: ndcg_at_5
value: 85.835
- type: precision_at_1
value: 79.5
- type: precision_at_10
value: 13.339
- type: precision_at_100
value: 1.53
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.97
- type: precision_at_5
value: 24.384
- type: recall_at_1
value: 68.908
- type: recall_at_10
value: 95.179
- type: recall_at_100
value: 99.579
- type: recall_at_1000
value: 99.964
- type: recall_at_3
value: 86.424
- type: recall_at_5
value: 91.065
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 65.17897847862794
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 66.22194961632586
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.668
- type: map_at_10
value: 13.921
- type: map_at_100
value: 16.391
- type: map_at_1000
value: 16.749
- type: map_at_3
value: 10.001999999999999
- type: map_at_5
value: 11.974
- type: mrr_at_1
value: 27.800000000000004
- type: mrr_at_10
value: 39.290000000000006
- type: mrr_at_100
value: 40.313
- type: mrr_at_1000
value: 40.355999999999995
- type: mrr_at_3
value: 35.667
- type: mrr_at_5
value: 37.742
- type: ndcg_at_1
value: 27.800000000000004
- type: ndcg_at_10
value: 23.172
- type: ndcg_at_100
value: 32.307
- type: ndcg_at_1000
value: 38.048
- type: ndcg_at_3
value: 22.043
- type: ndcg_at_5
value: 19.287000000000003
- type: precision_at_1
value: 27.800000000000004
- type: precision_at_10
value: 11.95
- type: precision_at_100
value: 2.5260000000000002
- type: precision_at_1000
value: 0.38999999999999996
- type: precision_at_3
value: 20.433
- type: precision_at_5
value: 16.84
- type: recall_at_1
value: 5.668
- type: recall_at_10
value: 24.22
- type: recall_at_100
value: 51.217
- type: recall_at_1000
value: 79.10000000000001
- type: recall_at_3
value: 12.443
- type: recall_at_5
value: 17.068
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.83535239748218
- type: cos_sim_spearman
value: 73.98553311584509
- type: euclidean_pearson
value: 79.57336200069007
- type: euclidean_spearman
value: 73.98553926018461
- type: manhattan_pearson
value: 79.02277757114132
- type: manhattan_spearman
value: 73.52350678760683
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 81.99055838690317
- type: cos_sim_spearman
value: 72.05290668592296
- type: euclidean_pearson
value: 81.7130610313565
- type: euclidean_spearman
value: 72.0529066787229
- type: manhattan_pearson
value: 82.09213883730894
- type: manhattan_spearman
value: 72.5171577483134
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.4685161191763
- type: cos_sim_spearman
value: 84.4847436140129
- type: euclidean_pearson
value: 84.05016757016948
- type: euclidean_spearman
value: 84.48474353891532
- type: manhattan_pearson
value: 83.83064062713048
- type: manhattan_spearman
value: 84.30431591842805
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.00171021092486
- type: cos_sim_spearman
value: 77.91329577609622
- type: euclidean_pearson
value: 81.49758593915315
- type: euclidean_spearman
value: 77.91329577609622
- type: manhattan_pearson
value: 81.23255996803785
- type: manhattan_spearman
value: 77.80027024941825
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.62608607472492
- type: cos_sim_spearman
value: 87.62293916855751
- type: euclidean_pearson
value: 87.04313886714989
- type: euclidean_spearman
value: 87.62293907119869
- type: manhattan_pearson
value: 86.97266321040769
- type: manhattan_spearman
value: 87.61807042381702
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 80.8012095789289
- type: cos_sim_spearman
value: 81.91868918081325
- type: euclidean_pearson
value: 81.2267973811213
- type: euclidean_spearman
value: 81.91868918081325
- type: manhattan_pearson
value: 81.0173457901168
- type: manhattan_spearman
value: 81.79743115887055
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.39698537303725
- type: cos_sim_spearman
value: 88.78668529808967
- type: euclidean_pearson
value: 88.78863351718252
- type: euclidean_spearman
value: 88.78668529808967
- type: manhattan_pearson
value: 88.41678215762478
- type: manhattan_spearman
value: 88.3827998418763
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 68.49024974161408
- type: cos_sim_spearman
value: 69.19917146180619
- type: euclidean_pearson
value: 70.48882819806336
- type: euclidean_spearman
value: 69.19917146180619
- type: manhattan_pearson
value: 70.86827961779932
- type: manhattan_spearman
value: 69.38456983992613
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 67.41628669863584
- type: cos_sim_spearman
value: 67.87238206703478
- type: euclidean_pearson
value: 67.67834985311778
- type: euclidean_spearman
value: 67.87238206703478
- type: manhattan_pearson
value: 68.23423896742973
- type: manhattan_spearman
value: 68.27069260687092
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cos_sim_pearson
value: 77.31628954400037
- type: cos_sim_spearman
value: 76.83296022489624
- type: euclidean_pearson
value: 76.69680425261211
- type: euclidean_spearman
value: 76.83287843321102
- type: manhattan_pearson
value: 76.65603163327958
- type: manhattan_spearman
value: 76.80803503360451
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.31376078795105
- type: cos_sim_spearman
value: 83.3985199217591
- type: euclidean_pearson
value: 84.06630133719332
- type: euclidean_spearman
value: 83.3985199217591
- type: manhattan_pearson
value: 83.7896654474364
- type: manhattan_spearman
value: 83.1885039212299
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.83161002188668
- type: mrr
value: 96.19253114351153
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 48.132999999999996
- type: map_at_10
value: 58.541
- type: map_at_100
value: 59.34
- type: map_at_1000
value: 59.367999999999995
- type: map_at_3
value: 55.191
- type: map_at_5
value: 57.084
- type: mrr_at_1
value: 51.0
- type: mrr_at_10
value: 59.858
- type: mrr_at_100
value: 60.474000000000004
- type: mrr_at_1000
value: 60.501000000000005
- type: mrr_at_3
value: 57.111000000000004
- type: mrr_at_5
value: 58.694
- type: ndcg_at_1
value: 51.0
- type: ndcg_at_10
value: 63.817
- type: ndcg_at_100
value: 67.229
- type: ndcg_at_1000
value: 67.94
- type: ndcg_at_3
value: 57.896
- type: ndcg_at_5
value: 60.785999999999994
- type: precision_at_1
value: 51.0
- type: precision_at_10
value: 8.933
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 23.111
- type: precision_at_5
value: 15.733
- type: recall_at_1
value: 48.132999999999996
- type: recall_at_10
value: 78.922
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 62.806
- type: recall_at_5
value: 70.078
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.88415841584158
- type: cos_sim_ap
value: 97.72557886493401
- type: cos_sim_f1
value: 94.1294530858003
- type: cos_sim_precision
value: 94.46122860020141
- type: cos_sim_recall
value: 93.8
- type: dot_accuracy
value: 99.88415841584158
- type: dot_ap
value: 97.72557439066108
- type: dot_f1
value: 94.1294530858003
- type: dot_precision
value: 94.46122860020141
- type: dot_recall
value: 93.8
- type: euclidean_accuracy
value: 99.88415841584158
- type: euclidean_ap
value: 97.72557439066108
- type: euclidean_f1
value: 94.1294530858003
- type: euclidean_precision
value: 94.46122860020141
- type: euclidean_recall
value: 93.8
- type: manhattan_accuracy
value: 99.88514851485148
- type: manhattan_ap
value: 97.73324334051959
- type: manhattan_f1
value: 94.1825476429288
- type: manhattan_precision
value: 94.46680080482898
- type: manhattan_recall
value: 93.89999999999999
- type: max_accuracy
value: 99.88514851485148
- type: max_ap
value: 97.73324334051959
- type: max_f1
value: 94.1825476429288
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 72.8168026381278
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 44.30948635130784
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.11268548719803
- type: mrr
value: 55.08079747050335
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.82885852096243
- type: cos_sim_spearman
value: 30.800770979226076
- type: dot_pearson
value: 30.82885608827704
- type: dot_spearman
value: 30.800770979226076
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 66.73038448968596
- type: mrr
value: 77.26510193334836
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: map_at_1
value: 28.157
- type: map_at_10
value: 79.00399999999999
- type: map_at_100
value: 82.51899999999999
- type: map_at_1000
value: 82.577
- type: map_at_3
value: 55.614
- type: map_at_5
value: 68.292
- type: mrr_at_1
value: 91.167
- type: mrr_at_10
value: 93.391
- type: mrr_at_100
value: 93.467
- type: mrr_at_1000
value: 93.47
- type: mrr_at_3
value: 93.001
- type: mrr_at_5
value: 93.254
- type: ndcg_at_1
value: 91.167
- type: ndcg_at_10
value: 86.155
- type: ndcg_at_100
value: 89.425
- type: ndcg_at_1000
value: 89.983
- type: ndcg_at_3
value: 87.516
- type: ndcg_at_5
value: 86.148
- type: precision_at_1
value: 91.167
- type: precision_at_10
value: 42.697
- type: precision_at_100
value: 5.032
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 76.45100000000001
- type: precision_at_5
value: 64.051
- type: recall_at_1
value: 28.157
- type: recall_at_10
value: 84.974
- type: recall_at_100
value: 95.759
- type: recall_at_1000
value: 98.583
- type: recall_at_3
value: 57.102
- type: recall_at_5
value: 71.383
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 55.031
- type: f1
value: 53.07992810732314
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.27
- type: map_at_100
value: 7.993
- type: map_at_1000
value: 20.934
- type: map_at_3
value: 0.469
- type: map_at_5
value: 0.716
- type: mrr_at_1
value: 76.0
- type: mrr_at_10
value: 84.967
- type: mrr_at_100
value: 84.967
- type: mrr_at_1000
value: 84.967
- type: mrr_at_3
value: 83.667
- type: mrr_at_5
value: 84.967
- type: ndcg_at_1
value: 69.0
- type: ndcg_at_10
value: 59.243
- type: ndcg_at_100
value: 48.784
- type: ndcg_at_1000
value: 46.966
- type: ndcg_at_3
value: 64.14
- type: ndcg_at_5
value: 61.60600000000001
- type: precision_at_1
value: 76.0
- type: precision_at_10
value: 62.6
- type: precision_at_100
value: 50.18
- type: precision_at_1000
value: 21.026
- type: precision_at_3
value: 68.667
- type: precision_at_5
value: 66.0
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.582
- type: recall_at_100
value: 11.988
- type: recall_at_1000
value: 44.994
- type: recall_at_3
value: 0.515
- type: recall_at_5
value: 0.844
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 72.80915114296552
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 70.86374654127641
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.3009999999999997
- type: map_at_10
value: 11.566
- type: map_at_100
value: 17.645
- type: map_at_1000
value: 19.206
- type: map_at_3
value: 6.986000000000001
- type: map_at_5
value: 8.716
- type: mrr_at_1
value: 42.857
- type: mrr_at_10
value: 58.287
- type: mrr_at_100
value: 59.111000000000004
- type: mrr_at_1000
value: 59.111000000000004
- type: mrr_at_3
value: 55.102
- type: mrr_at_5
value: 57.449
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 29.059
- type: ndcg_at_100
value: 40.629
- type: ndcg_at_1000
value: 51.446000000000005
- type: ndcg_at_3
value: 36.254999999999995
- type: ndcg_at_5
value: 32.216
- type: precision_at_1
value: 42.857
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 8.041
- type: precision_at_1000
value: 1.551
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 30.203999999999997
- type: recall_at_1
value: 3.3009999999999997
- type: recall_at_10
value: 17.267
- type: recall_at_100
value: 49.36
- type: recall_at_1000
value: 83.673
- type: recall_at_3
value: 8.049000000000001
- type: recall_at_5
value: 11.379999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 88.7576
- type: ap
value: 35.52110634325751
- type: f1
value: 74.14476947482417
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 73.52009054895304
- type: f1
value: 73.81407409876577
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.35358706465052
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.65619598259522
- type: cos_sim_ap
value: 65.824087818991
- type: cos_sim_f1
value: 61.952620244077536
- type: cos_sim_precision
value: 56.676882661996494
- type: cos_sim_recall
value: 68.311345646438
- type: dot_accuracy
value: 83.65619598259522
- type: dot_ap
value: 65.82406256999921
- type: dot_f1
value: 61.952620244077536
- type: dot_precision
value: 56.676882661996494
- type: dot_recall
value: 68.311345646438
- type: euclidean_accuracy
value: 83.65619598259522
- type: euclidean_ap
value: 65.82409143427542
- type: euclidean_f1
value: 61.952620244077536
- type: euclidean_precision
value: 56.676882661996494
- type: euclidean_recall
value: 68.311345646438
- type: manhattan_accuracy
value: 83.4296954163438
- type: manhattan_ap
value: 65.20662449614932
- type: manhattan_f1
value: 61.352885525070946
- type: manhattan_precision
value: 55.59365623660523
- type: manhattan_recall
value: 68.44327176781002
- type: max_accuracy
value: 83.65619598259522
- type: max_ap
value: 65.82409143427542
- type: max_f1
value: 61.952620244077536
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 87.90119144642372
- type: cos_sim_ap
value: 84.04753852793387
- type: cos_sim_f1
value: 76.27737226277372
- type: cos_sim_precision
value: 73.86757068667052
- type: cos_sim_recall
value: 78.84970742223591
- type: dot_accuracy
value: 87.90119144642372
- type: dot_ap
value: 84.04753668117337
- type: dot_f1
value: 76.27737226277372
- type: dot_precision
value: 73.86757068667052
- type: dot_recall
value: 78.84970742223591
- type: euclidean_accuracy
value: 87.90119144642372
- type: euclidean_ap
value: 84.04754553468206
- type: euclidean_f1
value: 76.27737226277372
- type: euclidean_precision
value: 73.86757068667052
- type: euclidean_recall
value: 78.84970742223591
- type: manhattan_accuracy
value: 87.87014398261343
- type: manhattan_ap
value: 84.05164646221583
- type: manhattan_f1
value: 76.31392706820128
- type: manhattan_precision
value: 73.91586694566708
- type: manhattan_recall
value: 78.87280566676932
- type: max_accuracy
value: 87.90119144642372
- type: max_ap
value: 84.05164646221583
- type: max_f1
value: 76.31392706820128
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
metrics:
- type: map_at_1
value: 63.6
- type: map_at_10
value: 72.673
- type: map_at_100
value: 73.05199999999999
- type: map_at_1000
value: 73.057
- type: map_at_3
value: 70.833
- type: map_at_5
value: 72.05799999999999
- type: mrr_at_1
value: 63.6
- type: mrr_at_10
value: 72.673
- type: mrr_at_100
value: 73.05199999999999
- type: mrr_at_1000
value: 73.057
- type: mrr_at_3
value: 70.833
- type: mrr_at_5
value: 72.05799999999999
- type: ndcg_at_1
value: 63.6
- type: ndcg_at_10
value: 76.776
- type: ndcg_at_100
value: 78.52900000000001
- type: ndcg_at_1000
value: 78.696
- type: ndcg_at_3
value: 73.093
- type: ndcg_at_5
value: 75.288
- type: precision_at_1
value: 63.6
- type: precision_at_10
value: 8.95
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 26.533
- type: precision_at_5
value: 16.98
- type: recall_at_1
value: 63.6
- type: recall_at_10
value: 89.5
- type: recall_at_100
value: 97.5
- type: recall_at_1000
value: 98.9
- type: recall_at_3
value: 79.60000000000001
- type: recall_at_5
value: 84.89999999999999
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 89.39999999999999
- type: ap
value: 75.52087544076016
- type: f1
value: 87.7629629899278
---
<p align="center">
<img src="images/gme_logo.png" alt="GME Logo" style="width: 100%; max-width: 450px;">
</p>
<p align="center"><b>GME: General Multimodal Embedding</b></p>
## GME-Qwen2-VL-2B
We are excited to present `GME-Qwen2VL` series of unified **multimodal embedding models**,
which are based on the advanced [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) multimodal large language models (MLLMs).
The `GME` models support three types of input: **text**, **image**, and **image-text pair**, all of which can produce universal vector representations and have powerful retrieval performance.
**Key Enhancements of GME Models**:
- **Unified Multimodal Representation**: GME models can process both single-modal and combined-modal inputs, resulting in a unified vector representation. This enables versatile retrieval scenarios (Any2Any Search), supporting tasks such as text retrieval, image retrieval from text, and image-to-image searches.
- **High Performance**: Achieves state-of-the-art (SOTA) results in our universal multimodal retrieval benchmark (**UMRB**) and demonstrate strong evaluation scores in the Multimodal Textual Evaluation Benchmark (**MTEB**).
- **Dynamic Image Resolution**: Benefiting from `Qwen2-VL` and our training data, GME models support dynamic resolution image input.
- **Strong Visual Retrieval Performance**: Enhanced by the Qwen2-VL model series, our models excel in visual document retrieval tasks that require a nuanced understanding of document screenshots.
This capability is particularly beneficial for complex document understanding scenarios,
such as multimodal retrieval-augmented generation (RAG) applications focused on academic papers.
**Developed by**: Tongyi Lab, Alibaba Group
**Paper**: [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](http://arxiv.org/abs/2412.16855)
## Model List
| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| MTEB-zh | UMRB |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gme-Qwen2-VL-2B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | 65.27 | 66.92 | 64.45 |
|[`gme-Qwen2-VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 69.73 | 67.44 |
## Usage
**Use with custom code**
```python
# You can find the script gme_inference.py in https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/blob/main/gme_inference.py
from gme_inference import GmeQwen2VL
texts = [
"What kind of car is this?",
"The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
]
images = [
'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
]
gme = GmeQwen2VL("Alibaba-NLP/gme-Qwen2-VL-2B-Instruct")
# Single-modal embedding
e_text = gme.get_text_embeddings(texts=texts)
e_image = gme.get_image_embeddings(images=images)
print((e_text * e_image).sum(-1))
## tensor([0.2281, 0.6001], dtype=torch.float16)
# How to set embedding instruction
e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
# If is_query=False, we always use the default instruction.
e_corpus = gme.get_image_embeddings(images=images, is_query=False)
print((e_query * e_corpus).sum(-1))
## tensor([0.2433, 0.7051], dtype=torch.float16)
# Fused-modal embedding
e_fused = gme.get_fused_embeddings(texts=texts, images=images)
print((e_fused[0] * e_fused[1]).sum())
## tensor(0.6108, dtype=torch.float16)
```
## Evaluation
We validated the performance on our universal multimodal retrieval benchmark (**UMRB**) among others.
| | | Single-modal | | Cross-modal | | | Fused-modal | | | | Avg. |
|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|
| | | T→T (16) | I→I (1) | T→I (4) | T→VD (10) | I→T (4) | T→IT (2) | IT→T (5) | IT→I (2) | IT→IT (3) | (47) |
| VISTA | 0.2B | 55.15 | **31.98** | 32.88 | 10.12 | 31.23 | 45.81 | 53.32 | 8.97 | 26.26 | 37.32 |
| CLIP-SF | 0.4B | 39.75 | 31.42 | 59.05 | 24.09 | 62.95 | 66.41 | 53.32 | 34.9 | 55.65 | 43.66 |
| One-Peace | 4B | 43.54 | 31.27 | 61.38 | 42.9 | 65.59 | 42.72 | 28.29 | 6.73 | 23.41 | 42.01 |
| DSE | 4.2B | 48.94 | 27.92 | 40.75 | 78.21 | 52.54 | 49.62 | 35.44 | 8.36 | 40.18 | 50.04 |
| E5-V | 8.4B | 52.41 | 27.36 | 46.56 | 41.22 | 47.95 | 54.13 | 32.9 | 23.17 | 7.23 | 42.52 |
| **[GME-Qwen2-VL-2B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct)** | 2.2B | 55.93 | 29.86 | 57.36 | 87.84 | 61.93 | 76.47 | 64.58 | 37.02 | 66.47 | 64.45 |
| **[GME-Qwen2-VL-7B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct)** | 8.3B | **58.19** | 31.89 | **61.35** | **89.92** | **65.83** | **80.94** | **66.18** | **42.56** | **73.62** | **67.44** |
The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.
**More detailed experimental results can be found in the [paper](http://arxiv.org/abs/2412.16855)**.
## Community support
### Fine-tuning
GME models can be fine-tuned by SWIFT：
```shell
pip install ms-swift -U
```
```shell
# MAX_PIXELS settings to reduce memory usage
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
MAX_PIXELS=1003520 \
USE_HF=1 \
NPROC_PER_NODE=$nproc_per_node \
swift sft \
--model Alibaba-NLP/gme-Qwen2-VL-2B-Instruct \
--train_type lora \
--dataset 'HuggingFaceM4/TextCaps:emb' \
--torch_dtype bfloat16 \
--num_train_epochs 1 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
--eval_steps 100 \
--save_steps 100 \
--eval_strategy steps \
--save_total_limit 5 \
--logging_steps 5 \
--output_dir output \
--lazy_tokenize true \
--warmup_ratio 0.05 \
--learning_rate 5e-6 \
--deepspeed zero3 \
--dataloader_num_workers 4 \
--task_type embedding \
--loss_type infonce \
--dataloader_drop_last true
```
## Limitations
- **Single Image Input**: In `Qwen2-VL`, an image could be converted into a very large number of visual tokens. We limit the number of visual tokens to 1024 to obtain a good training efficiency.
Due to the lack of relevant data, our models and evaluations retain one single image.
- **English-only Training**: Our models are trained on english data only. Although the `Qwen2-VL` models are multilingual, the multilingual-multimodal embedding performance are not guaranteed.
We will extend to multi-image input, image-text interleaved data as well as multilingual data in the future version.
## Redistribution and Use
We encourage and value diverse applications of GME models and continuous enhancements to the models themselves.
- If you distribute or make GME models (or any derivative works) available, or if you create a product or service (including another AI model) that incorporates them, you must prominently display `Built with GME` on your website, user interface, blog post, About page, or product documentation.
- If you utilize GME models or their outputs to develop, train, fine-tune, or improve an AI model that is distributed or made available, you must prefix the name of any such AI model with `GME`.
## Cloud API Services
In addition to the open-source [GME](https://huggingface.co/collections/Alibaba-NLP/gme-models-67667e092da3491f630964d6) series models, GME series models are also available as commercial API services on Alibaba Cloud.
- [MultiModal Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/multimodal-embedding-api-reference?spm=a2c4g.11186623.0.0.321c1d1cqmoJ5C): The `multimodal-embedding-v1` model service is available.
Note that the models behind the commercial APIs are not entirely identical to the open-source models.
## Hiring
We have open positions for Research Interns and Full-Time Researchers to join our team at Tongyi Lab.
We are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems.
Our team is located in the vibrant cities of Beijing and Hangzhou, offering a collaborative and dynamic work environment where you can contribute to cutting-edge advancements in artificial intelligence and machine learning.
If you are driven by curiosity and eager to make a meaningful impact through your work, we would love to hear from you. Please submit your resume along with a brief introduction to <a href="mailto:[email protected]">[email protected]</a>.
## Citation
If you find our paper or models helpful, please consider cite:
```
@misc{zhang2024gme,
title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs},
author={Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
year={2024},
eprint={2412.16855},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={http://arxiv.org/abs/2412.16855},
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-1b | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:the_pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-03-10T21:42:46 | 2023-07-09T16:05:58 | 64,849 | 37 | ---
datasets:
- the_pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-1B
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-1B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-1B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-1B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-1B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-1B to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-1B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-1B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-1B.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
Snowflake/snowflake-arctic-embed-m-v1.5 | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"gguf",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"arxiv:2412.04506",
"arxiv:2407.18887",
"arxiv:2405.05374",
"arxiv:2205.13147",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-03T18:46:29 | 2024-12-13T22:45:35 | 63,055 | 57 | ---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-arctic-embed-m-v1.5
results:
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type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
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value: -19.567415651090702
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value: -15.365511906408983
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value: 40.816
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value: 31.426
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value: 41.558
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value: 53.042
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value: 31.108999999999998
- type: ndcg_at_3
value: 35.518
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value: 33.235
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 27.551
- type: precision_at_100
value: 8.204
- type: precision_at_1000
value: 1.582
- type: precision_at_20
value: 19.796
- type: precision_at_3
value: 36.735
- type: precision_at_5
value: 33.061
- type: recall_at_1
value: 3.4709999999999996
- type: recall_at_10
value: 19.563
- type: recall_at_100
value: 50.3
- type: recall_at_1000
value: 85.13199999999999
- type: recall_at_20
value: 26.738
- type: recall_at_3
value: 7.8420000000000005
- type: recall_at_5
value: 11.994
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 68.29850746268657
- type: ap
value: 30.109785890841966
- type: ap_weighted
value: 30.109785890841966
- type: f1
value: 61.76875915202924
- type: f1_weighted
value: 71.32073190458556
- type: main_score
value: 68.29850746268657
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 90.3068
- type: ap
value: 86.17914339624038
- type: ap_weighted
value: 86.17914339624038
- type: f1
value: 90.29716826358077
- type: f1_weighted
value: 90.29716826358077
- type: main_score
value: 90.3068
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.272000000000006
- type: f1
value: 45.57042543386915
- type: f1_weighted
value: 45.57042543386915
- type: main_score
value: 46.272000000000006
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 44.9469238081379
- type: v_measure
value: 44.9469238081379
- type: v_measure_std
value: 13.26811262671461
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 34.12071448053325
- type: v_measure
value: 34.12071448053325
- type: v_measure_std
value: 13.7019879046405
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions (default)
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: main_score
value: 61.597667288657846
- type: map
value: 61.597667288657846
- type: mrr
value: 75.57940904893813
- type: nAUC_map_diff1
value: 8.745172077340095
- type: nAUC_map_max
value: 20.114863024035493
- type: nAUC_map_std
value: 15.991351189572192
- type: nAUC_mrr_diff1
value: 20.781369244159983
- type: nAUC_mrr_max
value: 30.78542570228559
- type: nAUC_mrr_std
value: 19.861484857303676
- task:
type: STS
dataset:
name: MTEB BIOSSES (default)
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cosine_pearson
value: 88.55587996301419
- type: cosine_spearman
value: 86.40317357420093
- type: euclidean_pearson
value: 86.93771958250231
- type: euclidean_spearman
value: 86.40317357420093
- type: main_score
value: 86.40317357420093
- type: manhattan_pearson
value: 86.92196577117366
- type: manhattan_spearman
value: 85.79834051556095
- type: pearson
value: 88.55587996301419
- type: spearman
value: 86.40317357420093
- task:
type: Classification
dataset:
name: MTEB Banking77Classification (default)
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.0064935064935
- type: f1
value: 79.29524254086299
- type: f1_weighted
value: 79.295242540863
- type: main_score
value: 80.0064935064935
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P (default)
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 35.27186813341181
- type: v_measure
value: 35.27186813341181
- type: v_measure_std
value: 0.8621482145872432
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S (default)
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 28.411805064852295
- type: v_measure
value: 28.411805064852295
- type: v_measure_std
value: 0.7194290078011281
- task:
type: Classification
dataset:
name: MTEB EmotionClassification (default)
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 43.675
- type: f1
value: 40.15061931375577
- type: f1_weighted
value: 45.714186572727066
- type: main_score
value: 43.675
- task:
type: Classification
dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 84.35640000000001
- type: ap
value: 79.07507736685174
- type: ap_weighted
value: 79.07507736685174
- type: f1
value: 84.32288494833531
- type: f1_weighted
value: 84.32288494833531
- type: main_score
value: 84.35640000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.35658914728684
- type: f1
value: 90.86877537911086
- type: f1_weighted
value: 91.3282092774443
- type: main_score
value: 91.35658914728684
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 60.63611491108071
- type: f1
value: 42.78886482112741
- type: f1_weighted
value: 63.44208631840539
- type: main_score
value: 60.63611491108071
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 66.68796234028245
- type: f1
value: 64.44940791000278
- type: f1_weighted
value: 65.77554417406792
- type: main_score
value: 66.68796234028245
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 73.0598520511096
- type: f1
value: 72.14267273884774
- type: f1_weighted
value: 72.93345180137516
- type: main_score
value: 73.0598520511096
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P (default)
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: main_score
value: 31.143081341699606
- type: v_measure
value: 31.143081341699606
- type: v_measure_std
value: 1.5578716347076906
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S (default)
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: main_score
value: 27.010818869829556
- type: v_measure
value: 27.010818869829556
- type: v_measure_std
value: 1.1771554540819378
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking (default)
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: main_score
value: 30.20503776754942
- type: map
value: 30.20503776754942
- type: mrr
value: 31.076636002733437
- type: nAUC_map_diff1
value: 7.290568655287842
- type: nAUC_map_max
value: -21.381599355932945
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value: -7.709920607543168
- type: nAUC_mrr_diff1
value: 7.558397329284913
- type: nAUC_mrr_max
value: -15.981397186427607
- type: nAUC_mrr_std
value: -4.870495243168834
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 51.85893476633338
- type: v_measure
value: 51.85893476633338
- type: v_measure_std
value: 4.704770139385852
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 61.8124222918822
- type: v_measure
value: 61.8124222918822
- type: v_measure_std
value: 11.994472578100165
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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value: 77.63310776935984
- type: cosine_spearman
value: 69.86468291111039
- type: euclidean_pearson
value: 73.91537077798837
- type: euclidean_spearman
value: 69.86468376650203
- type: main_score
value: 69.86468291111039
- type: manhattan_pearson
value: 73.68616048370464
- type: manhattan_spearman
value: 69.76232036206659
- type: pearson
value: 77.63310776935984
- type: spearman
value: 69.86468291111039
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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value: 57.71716838245049
- type: cosine_spearman
value: 61.797855543446424
- type: euclidean_pearson
value: 58.22958675325848
- type: euclidean_spearman
value: 61.797855543446424
- type: main_score
value: 61.797855543446424
- type: manhattan_pearson
value: 57.63117544997929
- type: manhattan_spearman
value: 61.3629404350085
- type: pearson
value: 57.71716838245049
- type: spearman
value: 61.797855543446424
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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value: 82.30260026790903
- type: cosine_spearman
value: 82.66959813070869
- type: euclidean_pearson
value: 82.08383017580783
- type: euclidean_spearman
value: 82.66959813070869
- type: main_score
value: 82.66959813070869
- type: manhattan_pearson
value: 81.77991451392153
- type: manhattan_spearman
value: 82.3652534745606
- type: pearson
value: 82.30260026790903
- type: spearman
value: 82.66959813070869
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 71.50608384084478
- type: cosine_spearman
value: 68.94968064977785
- type: euclidean_pearson
value: 70.73381299949564
- type: euclidean_spearman
value: 68.94968064977785
- type: main_score
value: 68.94968064977785
- type: manhattan_pearson
value: 70.5385486953787
- type: manhattan_spearman
value: 68.82132770672365
- type: pearson
value: 71.50608384084478
- type: spearman
value: 68.94968064977785
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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value: 73.66969825874907
- type: cosine_spearman
value: 75.55374982088381
- type: euclidean_pearson
value: 75.9339313749594
- type: euclidean_spearman
value: 75.55374982088381
- type: main_score
value: 75.55374982088381
- type: manhattan_pearson
value: 75.88287553383817
- type: manhattan_spearman
value: 75.50729812977688
- type: pearson
value: 73.66969825874907
- type: spearman
value: 75.55374982088381
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 74.5954724414016
- type: cosine_spearman
value: 77.2688820850505
- type: euclidean_pearson
value: 77.19866353971555
- type: euclidean_spearman
value: 77.2688820850505
- type: main_score
value: 77.2688820850505
- type: manhattan_pearson
value: 77.27072603680978
- type: manhattan_spearman
value: 77.29408453673607
- type: pearson
value: 74.5954724414016
- type: spearman
value: 77.2688820850505
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 71.52588722654055
- type: cosine_spearman
value: 74.97235736456061
- type: euclidean_pearson
value: 74.51952528854038
- type: euclidean_spearman
value: 74.97235736456061
- type: main_score
value: 74.97235736456061
- type: manhattan_pearson
value: 74.48272300884209
- type: manhattan_spearman
value: 74.80633649415176
- type: pearson
value: 71.52588722654055
- type: spearman
value: 74.97235736456061
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 68.80031120401976
- type: cosine_spearman
value: 69.07945196478491
- type: euclidean_pearson
value: 68.99674496430792
- type: euclidean_spearman
value: 69.07945196478491
- type: main_score
value: 69.07945196478491
- type: manhattan_pearson
value: 69.00236107775687
- type: manhattan_spearman
value: 68.98064879049272
- type: pearson
value: 68.80031120401976
- type: spearman
value: 69.07945196478491
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 65.6898007230089
- type: cosine_spearman
value: 69.72386211803668
- type: euclidean_pearson
value: 69.04523003701475
- type: euclidean_spearman
value: 69.72386211803668
- type: main_score
value: 69.72386211803668
- type: manhattan_pearson
value: 68.80479743770702
- type: manhattan_spearman
value: 69.43264575177459
- type: pearson
value: 65.6898007230089
- type: spearman
value: 69.72386211803668
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 79.74088066874383
- type: map
value: 79.74088066874383
- type: mrr
value: 94.47697455050397
- type: nAUC_map_diff1
value: 8.036086256905502
- type: nAUC_map_max
value: 54.88199803816819
- type: nAUC_map_std
value: 69.16267942176574
- type: nAUC_mrr_diff1
value: 50.020738477678115
- type: nAUC_mrr_max
value: 83.28922770326483
- type: nAUC_mrr_std
value: 83.63973501802224
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions (default)
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.83861386138614
- type: cosine_accuracy_threshold
value: 74.75666999816895
- type: cosine_ap
value: 96.15132792066652
- type: cosine_f1
value: 91.84890656063618
- type: cosine_f1_threshold
value: 71.70594930648804
- type: cosine_precision
value: 91.30434782608695
- type: cosine_recall
value: 92.4
- type: dot_accuracy
value: 99.83861386138614
- type: dot_accuracy_threshold
value: 74.75666999816895
- type: dot_ap
value: 96.15132792066653
- type: dot_f1
value: 91.84890656063618
- type: dot_f1_threshold
value: 71.70596122741699
- type: dot_precision
value: 91.30434782608695
- type: dot_recall
value: 92.4
- type: euclidean_accuracy
value: 99.83861386138614
- type: euclidean_accuracy_threshold
value: 71.05395793914795
- type: euclidean_ap
value: 96.15132792066652
- type: euclidean_f1
value: 91.84890656063618
- type: euclidean_f1_threshold
value: 75.22505521774292
- type: euclidean_precision
value: 91.30434782608695
- type: euclidean_recall
value: 92.4
- type: main_score
value: 96.15132792066653
- type: manhattan_accuracy
value: 99.83564356435643
- type: manhattan_accuracy_threshold
value: 1547.6950645446777
- type: manhattan_ap
value: 96.06151211452136
- type: manhattan_f1
value: 91.61676646706587
- type: manhattan_f1_threshold
value: 1626.3608932495117
- type: manhattan_precision
value: 91.43426294820716
- type: manhattan_recall
value: 91.8
- type: max_ap
value: 96.15132792066653
- type: max_f1
value: 91.84890656063618
- type: max_precision
value: 91.43426294820716
- type: max_recall
value: 92.4
- type: similarity_accuracy
value: 99.83861386138614
- type: similarity_accuracy_threshold
value: 74.75666999816895
- type: similarity_ap
value: 96.15132792066652
- type: similarity_f1
value: 91.84890656063618
- type: similarity_f1_threshold
value: 71.70594930648804
- type: similarity_precision
value: 91.30434782608695
- type: similarity_recall
value: 92.4
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering (default)
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 61.24120328328453
- type: v_measure
value: 61.24120328328453
- type: v_measure_std
value: 3.9946560691100372
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P (default)
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 33.808268374864745
- type: v_measure
value: 33.808268374864745
- type: v_measure_std
value: 1.2212188701887239
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions (default)
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 52.19806018468037
- type: map
value: 52.19806018468037
- type: mrr
value: 52.98921462524404
- type: nAUC_map_diff1
value: 37.41443156995912
- type: nAUC_map_max
value: 9.410262727675603
- type: nAUC_map_std
value: 8.7094185014992
- type: nAUC_mrr_diff1
value: 37.78202772392581
- type: nAUC_mrr_max
value: 10.517635536565816
- type: nAUC_mrr_std
value: 8.509423813772491
- task:
type: Summarization
dataset:
name: MTEB SummEval (default)
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cosine_pearson
value: 30.48413700430812
- type: cosine_spearman
value: 30.357162200875816
- type: dot_pearson
value: 30.484140144824938
- type: dot_spearman
value: 30.357162200875816
- type: main_score
value: 30.357162200875816
- type: pearson
value: 30.48413700430812
- type: spearman
value: 30.357162200875816
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 66.8359375
- type: ap
value: 12.482653786025985
- type: ap_weighted
value: 12.482653786025985
- type: f1
value: 51.328608527332385
- type: f1_weighted
value: 74.07974463955398
- type: main_score
value: 66.8359375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 53.907753254103
- type: f1
value: 54.22707647269581
- type: f1_weighted
value: 53.611822984407695
- type: main_score
value: 53.907753254103
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 38.1364789307295
- type: v_measure
value: 38.1364789307295
- type: v_measure_std
value: 2.0731634966352077
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 82.66674614054956
- type: cosine_accuracy_threshold
value: 79.80123162269592
- type: cosine_ap
value: 63.28209719072804
- type: cosine_f1
value: 60.16389710903711
- type: cosine_f1_threshold
value: 72.22893834114075
- type: cosine_precision
value: 52.90232185748599
- type: cosine_recall
value: 69.73614775725594
- type: dot_accuracy
value: 82.66674614054956
- type: dot_accuracy_threshold
value: 79.8012375831604
- type: dot_ap
value: 63.282103870645166
- type: dot_f1
value: 60.16389710903711
- type: dot_f1_threshold
value: 72.22894430160522
- type: dot_precision
value: 52.90232185748599
- type: dot_recall
value: 69.73614775725594
- type: euclidean_accuracy
value: 82.66674614054956
- type: euclidean_accuracy_threshold
value: 63.55905532836914
- type: euclidean_ap
value: 63.282095399953164
- type: euclidean_f1
value: 60.16389710903711
- type: euclidean_f1_threshold
value: 74.5265781879425
- type: euclidean_precision
value: 52.90232185748599
- type: euclidean_recall
value: 69.73614775725594
- type: main_score
value: 63.282103870645166
- type: manhattan_accuracy
value: 82.74423317637242
- type: manhattan_accuracy_threshold
value: 1415.380859375
- type: manhattan_ap
value: 63.26931757839598
- type: manhattan_f1
value: 60.11014948859166
- type: manhattan_f1_threshold
value: 1632.522201538086
- type: manhattan_precision
value: 52.359506559624045
- type: manhattan_recall
value: 70.55408970976254
- type: max_ap
value: 63.282103870645166
- type: max_f1
value: 60.16389710903711
- type: max_precision
value: 52.90232185748599
- type: max_recall
value: 70.55408970976254
- type: similarity_accuracy
value: 82.66674614054956
- type: similarity_accuracy_threshold
value: 79.80123162269592
- type: similarity_ap
value: 63.28209719072804
- type: similarity_f1
value: 60.16389710903711
- type: similarity_f1_threshold
value: 72.22893834114075
- type: similarity_precision
value: 52.90232185748599
- type: similarity_recall
value: 69.73614775725594
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 88.10105949470253
- type: cosine_accuracy_threshold
value: 68.95147562026978
- type: cosine_ap
value: 84.65516103854583
- type: cosine_f1
value: 76.54581123301605
- type: cosine_f1_threshold
value: 63.92929553985596
- type: cosine_precision
value: 72.46526344751685
- type: cosine_recall
value: 81.11333538651063
- type: dot_accuracy
value: 88.10105949470253
- type: dot_accuracy_threshold
value: 68.95147562026978
- type: dot_ap
value: 84.65516301437592
- type: dot_f1
value: 76.54581123301605
- type: dot_f1_threshold
value: 63.92928957939148
- type: dot_precision
value: 72.46526344751685
- type: dot_recall
value: 81.11333538651063
- type: euclidean_accuracy
value: 88.10105949470253
- type: euclidean_accuracy_threshold
value: 78.80169153213501
- type: euclidean_ap
value: 84.65517268264233
- type: euclidean_f1
value: 76.54581123301605
- type: euclidean_f1_threshold
value: 84.93610620498657
- type: euclidean_precision
value: 72.46526344751685
- type: euclidean_recall
value: 81.11333538651063
- type: main_score
value: 84.65517268264233
- type: manhattan_accuracy
value: 88.08941669577366
- type: manhattan_accuracy_threshold
value: 1739.3169403076172
- type: manhattan_ap
value: 84.64592398855694
- type: manhattan_f1
value: 76.62890540443034
- type: manhattan_f1_threshold
value: 1861.344337463379
- type: manhattan_precision
value: 72.09775967413442
- type: manhattan_recall
value: 81.76778564829073
- type: max_ap
value: 84.65517268264233
- type: max_f1
value: 76.62890540443034
- type: max_precision
value: 72.46526344751685
- type: max_recall
value: 81.76778564829073
- type: similarity_accuracy
value: 88.10105949470253
- type: similarity_accuracy_threshold
value: 68.95147562026978
- type: similarity_ap
value: 84.65516103854583
- type: similarity_f1
value: 76.54581123301605
- type: similarity_f1_threshold
value: 63.92929553985596
- type: similarity_precision
value: 72.46526344751685
- type: similarity_recall
value: 81.11333538651063
---
<h1 align="center">Snowflake's Arctic-embed-m-v1.5</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#this-model>This Model</a> |
<a href=#usage>Usage</a> |
<a href="#faq">FAQ</a> |
<a href="#contact">Contact</a> |
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=8ab1f2d9-8425-4212-9bf3-717f7ac637e4" />
## News
12/11/2024: Release of [Technical Report for 2.0 model](https://arxiv.org/abs/2412.04506)
12/04/2024: Release of [L-2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [M-2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0)
07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
05/10/2024: Release of the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
04/16/2024: Original release the `snowflake-arctic-embed` family of text embedding models.
## This Model
This model is an updated version of [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) designed to improve embedding vector compressibility. This model achieves a slightly higher performance overall without compression, and it is additionally capable of retaining most of its retrieval quality even down to 128 byte embedding vectors through a combination of [Matryoshka Representation Learning (MRL)](https://arxiv.org/abs/2205.13147) and uniform scalar quanitization.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|:------------------------------------------------------------------------------------------------|:---------------------------------|
| [snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) | 55.14 |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.91 |
Compared to several other models trained with MRL to produce 256-dimensional embedding vectors, `snowflake-arctic-embed-m-v1.5` retains a higher degree of original model quality and delivers better retrieval quality on the MTEB Retrieval benchmark.
| Model | Model Parameters | MTEB Retrieval Score at 256 Dimensions (fraction of arctic-embed-m-v1.5) |
|:------------------------------|:-------------------|:---------------------------------------------------------------------------|
| Snowflake arctic-embed-m-v1.5 | 109M | 54.2 (100%) |
| Google gecko | 1200M | 52.4 (97%) |
| OpenAI text-embedding-3-large | Not Published | 51.7 (95%) |
| Nomic nomic-embed-text-v1.5 | 138M | 50.8 (94%) |
Additionally, this model was designed to pair well with a corpus-independent scalar quantization scheme to achieve great performance even in as little as 128 bytes per vector (24x compression compared to 768 dimensional vectors stored in float32).
| Model Version | Dimensionality | Scalar Quantization | Bytes Per Vector (fraction of baseline) | MTEB Retrieval Score (fraction of baseline) | Vectors Per GB (improvement over baseline) |
|:----------------|-----------------:|:----------------------|:------------------------------------------|:----------------------------------------------|:---------------------------------------------|
| v1 | 768 | None (float32) | 3072 (100%) | 54.9 (100%) | 0.33M (1.0x) |
| v1 | 768 | int8 | 768 (25%) | 54.9 (100%) | 1.3M (4x) |
| v1.5 | 768 | int8 | 768 (25%) | 55.1 (100%) | 1.3M (4x) |
| v1.5 | 256 | int8 | 256 (8.3%) | 54.2 (99%) | 3.9M (12x) |
| v1.5 | 256 | int4 | 128 (4.2%) | 53.7 (98%) | 7.8M (24x) |
NOTE: Good uniform scalar quantization ranges to use with this model (and which were used in the eval above), are -0.18 to +0.18 for 4bit and -0.3 to +0.3 for 8bit. For a detailed walkthrough of using integer quantization with `snowflake-arctic-embed-m-v1.5`, check out our [example notebook on GitHub](https://github.com/Snowflake-Labs/arctic-embed/tree/main/compressed_embeddings_examples/score_arctic_embed_m_v1dot5_with_quantization.ipynb).
## Usage
### Using Sentence Transformers
You can use the sentence-transformers package to use any of the snowflake-arctic-embed models. Here's an example for `snowflake-arctic-embed-m-v1.5`.
```python
import torch
from sentence_transformers import SentenceTransformer
from torch.nn.functional import normalize
# Model constant.
MODEL_ID = "Snowflake/snowflake-arctic-embed-m-v1.5"
# Your queries and docs.
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
# Load the model.
model = SentenceTransformer(MODEL_ID)
# Generate text embeddings.
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
# Scores via dotproduct.
scores = query_embeddings @ document_embeddings.T
# Pretty-print the results.
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
print(f'Query: "{query}"')
for document, score in doc_score_pairs:
print(f'Score: {score:.4f} | Document: "{document}"')
print()
#### OUTPUT ####
# Query: "what is snowflake?"
# Score: 0.3521 | Document: "The Data Cloud!"
# Score: 0.2358 | Document: "Mexico City of Course!"
# Query: "Where can I get the best tacos?"
# Score: 0.3884 | Document: "Mexico City of Course!"
# Score: 0.2389 | Document: "The Data Cloud!"
#
#### Variation: Truncated Embeddings ####
query_embeddings_256 = normalize(torch.from_numpy(query_embeddings)[:, :256])
document_embeddings_256 = normalize(torch.from_numpy(document_embeddings)[:, :256])
scores_256 = query_embeddings_256 @ document_embeddings_256.T
# Pretty-print the results.
for query, query_scores in zip(queries, scores_256):
doc_score_pairs = sorted(zip(documents, query_scores), key=lambda x: x[1], reverse=True)
print(f'Query: "{query}"')
for document, score in doc_score_pairs:
print(f'Score: {score:.4f} | Document: "{document}"')
print()
#### OUTPUT ####
# Query: "what is snowflake?"
# Score: 0.3852 | Document: "The Data Cloud!"
# Score: 0.2721 | Document: "Mexico City of Course!"
# Query: "Where can I get the best tacos?"
# Score: 0.4337 | Document: "Mexico City of Course!"
# Score: 0.2886 | Document: "The Data Cloud!"
#
```
### Using Huggingface transformers
You can use the transformers package to use an snowflake-arctic-embed model, too. For optimal retrieval quality, remember to use the CLS token for embeddings and to use the query prefix below (just on the query).
```python
import torch
from torch.nn.functional import normalize
from transformers import AutoModel, AutoTokenizer
# Model constants.
MODEL_ID = "Snowflake/snowflake-arctic-embed-m-v1.5"
QUERY_PREFIX = 'Represent this sentence for searching relevant passages: '
# Your queries and docs.
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
# Load the model and tokenizer.
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModel.from_pretrained(MODEL_ID, add_pooling_layer=False)
model.eval()
# Add query prefix and tokenize queries and docs.
queries_with_prefix = [f"{QUERY_PREFIX}{q}" for q in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Use the model to generate text embeddings.
with torch.inference_mode():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# Remember to normalize embeddings.
query_embeddings = normalize(query_embeddings)
document_embeddings = normalize(document_embeddings)
# Scores via dotproduct.
scores = query_embeddings @ document_embeddings.T
# Pretty-print the results.
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
print(f'Query: "{query}"')
for document, score in doc_score_pairs:
print(f'Score: {score:.4f} | Document: "{document}"')
print()
#### OUTPUT ####
# Query: "what is snowflake?"
# Score: 0.3521 | Document: "The Data Cloud!"
# Score: 0.2358 | Document: "Mexico City of Course!"
# Query: "Where can I get the best tacos?"
# Score: 0.3884 | Document: "Mexico City of Course!"
# Score: 0.2389 | Document: "The Data Cloud!"
#
#### Variation: Truncated Embeddings ####
query_embeddings_256 = normalize(query_embeddings[:, :256])
document_embeddings_256 = normalize(document_embeddings[:, :256])
scores_256 = query_embeddings_256 @ document_embeddings_256.T
# Pretty-print the results.
for query, query_scores in zip(queries, scores_256):
doc_score_pairs = sorted(zip(documents, query_scores), key=lambda x: x[1], reverse=True)
print(f'Query: "{query}"')
for document, score in doc_score_pairs:
print(f'Score: {score:.4f} | Document: "{document}"')
print()
#### OUTPUT ####
# Query: "what is snowflake?"
# Score: 0.3852 | Document: "The Data Cloud!"
# Score: 0.2721 | Document: "Mexico City of Course!"
# Query: "Where can I get the best tacos?"
# Score: 0.4337 | Document: "Mexico City of Course!"
# Score: 0.2886 | Document: "The Data Cloud!"
#
```
### Using Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.15664823859882132, 0.24481869975470627]
```
### Compressing to 128 bytes
This model is designed to generate embeddings which compress well down to 128 bytes via a two-part compression scheme:
1. Truncation and renormalization to 256 dimensions (a la Matryoskha Representation Learning, see [the original paper for reference](https://arxiv.org/abs/2205.13147)).
2. 4-bit uniform scalar quantization of all 256 values to the same range (-0.18 to +0.18).
- For 8-bit uniform scalar quantization, the slightly wider range -0.3 to +0.3 tends to work slightly better given how much more granular 8-bit quantization is.
For an in-depth examples, check out our [arctic-embed GitHub repositiory](https://github.com/Snowflake-Labs/arctic-embed).
## FAQ
TBD
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
## Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
OrcaDB/gte-base-en-v1.5 | OrcaDB | sentence-similarity | [
"transformers",
"safetensors",
"new",
"feature-extraction",
"sentence-transformers",
"gte",
"mteb",
"transformers.js",
"sentence-similarity",
"custom_code",
"en",
"arxiv:2407.19669",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-11-08T19:45:52 | 2024-11-08T22:34:51 | 62,202 | 0 | ---
language:
- en
library_name: transformers
license: apache-2.0
tags:
- sentence-transformers
- gte
- mteb
- transformers.js
- sentence-similarity
model-index:
- name: gte-base-en-v1.5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.7910447761194
- type: ap
value: 37.053785713650626
- type: f1
value: 68.51101510998551
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.016875
- type: ap
value: 89.17750268426342
- type: f1
value: 92.9970977240524
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.312000000000005
- type: f1
value: 52.98175784163017
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 38.193
- type: map_at_10
value: 54.848
- type: map_at_100
value: 55.388000000000005
- type: map_at_1000
value: 55.388999999999996
- type: map_at_3
value: 50.427
- type: map_at_5
value: 53.105000000000004
- type: mrr_at_1
value: 39.047
- type: mrr_at_10
value: 55.153
- type: mrr_at_100
value: 55.686
- type: mrr_at_1000
value: 55.688
- type: mrr_at_3
value: 50.676
- type: mrr_at_5
value: 53.417
- type: ndcg_at_1
value: 38.193
- type: ndcg_at_10
value: 63.486
- type: ndcg_at_100
value: 65.58
- type: ndcg_at_1000
value: 65.61
- type: ndcg_at_3
value: 54.494
- type: ndcg_at_5
value: 59.339
- type: precision_at_1
value: 38.193
- type: precision_at_10
value: 9.075
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.096
- type: precision_at_5
value: 15.619
- type: recall_at_1
value: 38.193
- type: recall_at_10
value: 90.754
- type: recall_at_100
value: 99.431
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.28699999999999
- type: recall_at_5
value: 78.094
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.508221208908964
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.04668382560096
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.828759903716815
- type: mrr
value: 74.37343358395991
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.03673698773017
- type: cos_sim_spearman
value: 83.6470866785058
- type: euclidean_pearson
value: 82.64048673096565
- type: euclidean_spearman
value: 83.63142367101115
- type: manhattan_pearson
value: 82.71493099760228
- type: manhattan_spearman
value: 83.60491704294326
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.73376623376623
- type: f1
value: 86.70294049278262
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.31923804167062
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.552547125348454
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 30.567
- type: map_at_10
value: 41.269
- type: map_at_100
value: 42.689
- type: map_at_1000
value: 42.84
- type: map_at_3
value: 37.567
- type: map_at_5
value: 39.706
- type: mrr_at_1
value: 37.053000000000004
- type: mrr_at_10
value: 46.900999999999996
- type: mrr_at_100
value: 47.662
- type: mrr_at_1000
value: 47.713
- type: mrr_at_3
value: 43.801
- type: mrr_at_5
value: 45.689
- type: ndcg_at_1
value: 37.053000000000004
- type: ndcg_at_10
value: 47.73
- type: ndcg_at_100
value: 53.128
- type: ndcg_at_1000
value: 55.300000000000004
- type: ndcg_at_3
value: 42.046
- type: ndcg_at_5
value: 44.782
- type: precision_at_1
value: 37.053000000000004
- type: precision_at_10
value: 9.142
- type: precision_at_100
value: 1.485
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.535
- type: recall_at_1
value: 30.567
- type: recall_at_10
value: 60.602999999999994
- type: recall_at_100
value: 83.22800000000001
- type: recall_at_1000
value: 96.696
- type: recall_at_3
value: 44.336999999999996
- type: recall_at_5
value: 51.949
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 28.538000000000004
- type: map_at_10
value: 38.757999999999996
- type: map_at_100
value: 40.129
- type: map_at_1000
value: 40.262
- type: map_at_3
value: 35.866
- type: map_at_5
value: 37.417
- type: mrr_at_1
value: 36.051
- type: mrr_at_10
value: 44.868
- type: mrr_at_100
value: 45.568999999999996
- type: mrr_at_1000
value: 45.615
- type: mrr_at_3
value: 42.558
- type: mrr_at_5
value: 43.883
- type: ndcg_at_1
value: 36.051
- type: ndcg_at_10
value: 44.584
- type: ndcg_at_100
value: 49.356
- type: ndcg_at_1000
value: 51.39
- type: ndcg_at_3
value: 40.389
- type: ndcg_at_5
value: 42.14
- type: precision_at_1
value: 36.051
- type: precision_at_10
value: 8.446
- type: precision_at_100
value: 1.411
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 19.639
- type: precision_at_5
value: 13.796
- type: recall_at_1
value: 28.538000000000004
- type: recall_at_10
value: 54.99000000000001
- type: recall_at_100
value: 75.098
- type: recall_at_1000
value: 87.848
- type: recall_at_3
value: 42.236000000000004
- type: recall_at_5
value: 47.377
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 37.188
- type: map_at_10
value: 50.861000000000004
- type: map_at_100
value: 51.917
- type: map_at_1000
value: 51.964999999999996
- type: map_at_3
value: 47.144000000000005
- type: map_at_5
value: 49.417
- type: mrr_at_1
value: 42.571
- type: mrr_at_10
value: 54.086999999999996
- type: mrr_at_100
value: 54.739000000000004
- type: mrr_at_1000
value: 54.762
- type: mrr_at_3
value: 51.285000000000004
- type: mrr_at_5
value: 53.0
- type: ndcg_at_1
value: 42.571
- type: ndcg_at_10
value: 57.282
- type: ndcg_at_100
value: 61.477000000000004
- type: ndcg_at_1000
value: 62.426
- type: ndcg_at_3
value: 51.0
- type: ndcg_at_5
value: 54.346000000000004
- type: precision_at_1
value: 42.571
- type: precision_at_10
value: 9.467
- type: precision_at_100
value: 1.2550000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 23.114
- type: precision_at_5
value: 16.250999999999998
- type: recall_at_1
value: 37.188
- type: recall_at_10
value: 73.068
- type: recall_at_100
value: 91.203
- type: recall_at_1000
value: 97.916
- type: recall_at_3
value: 56.552
- type: recall_at_5
value: 64.567
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 25.041000000000004
- type: map_at_10
value: 33.86
- type: map_at_100
value: 34.988
- type: map_at_1000
value: 35.064
- type: map_at_3
value: 31.049
- type: map_at_5
value: 32.845
- type: mrr_at_1
value: 26.893
- type: mrr_at_10
value: 35.594
- type: mrr_at_100
value: 36.617
- type: mrr_at_1000
value: 36.671
- type: mrr_at_3
value: 33.051
- type: mrr_at_5
value: 34.61
- type: ndcg_at_1
value: 26.893
- type: ndcg_at_10
value: 38.674
- type: ndcg_at_100
value: 44.178
- type: ndcg_at_1000
value: 46.089999999999996
- type: ndcg_at_3
value: 33.485
- type: ndcg_at_5
value: 36.402
- type: precision_at_1
value: 26.893
- type: precision_at_10
value: 5.989
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 14.2
- type: precision_at_5
value: 10.26
- type: recall_at_1
value: 25.041000000000004
- type: recall_at_10
value: 51.666000000000004
- type: recall_at_100
value: 76.896
- type: recall_at_1000
value: 91.243
- type: recall_at_3
value: 38.035999999999994
- type: recall_at_5
value: 44.999
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 15.909999999999998
- type: map_at_10
value: 23.901
- type: map_at_100
value: 25.165
- type: map_at_1000
value: 25.291000000000004
- type: map_at_3
value: 21.356
- type: map_at_5
value: 22.816
- type: mrr_at_1
value: 20.025000000000002
- type: mrr_at_10
value: 28.382
- type: mrr_at_100
value: 29.465000000000003
- type: mrr_at_1000
value: 29.535
- type: mrr_at_3
value: 25.933
- type: mrr_at_5
value: 27.332
- type: ndcg_at_1
value: 20.025000000000002
- type: ndcg_at_10
value: 29.099000000000004
- type: ndcg_at_100
value: 35.127
- type: ndcg_at_1000
value: 38.096000000000004
- type: ndcg_at_3
value: 24.464
- type: ndcg_at_5
value: 26.709
- type: precision_at_1
value: 20.025000000000002
- type: precision_at_10
value: 5.398
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.774
- type: precision_at_5
value: 8.632
- type: recall_at_1
value: 15.909999999999998
- type: recall_at_10
value: 40.672000000000004
- type: recall_at_100
value: 66.855
- type: recall_at_1000
value: 87.922
- type: recall_at_3
value: 28.069
- type: recall_at_5
value: 33.812
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 30.175
- type: map_at_10
value: 41.36
- type: map_at_100
value: 42.701
- type: map_at_1000
value: 42.817
- type: map_at_3
value: 37.931
- type: map_at_5
value: 39.943
- type: mrr_at_1
value: 35.611
- type: mrr_at_10
value: 46.346
- type: mrr_at_100
value: 47.160000000000004
- type: mrr_at_1000
value: 47.203
- type: mrr_at_3
value: 43.712
- type: mrr_at_5
value: 45.367000000000004
- type: ndcg_at_1
value: 35.611
- type: ndcg_at_10
value: 47.532000000000004
- type: ndcg_at_100
value: 53.003
- type: ndcg_at_1000
value: 55.007
- type: ndcg_at_3
value: 42.043
- type: ndcg_at_5
value: 44.86
- type: precision_at_1
value: 35.611
- type: precision_at_10
value: 8.624
- type: precision_at_100
value: 1.332
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 20.083000000000002
- type: precision_at_5
value: 14.437
- type: recall_at_1
value: 30.175
- type: recall_at_10
value: 60.5
- type: recall_at_100
value: 83.399
- type: recall_at_1000
value: 96.255
- type: recall_at_3
value: 45.448
- type: recall_at_5
value: 52.432
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 22.467000000000002
- type: map_at_10
value: 33.812999999999995
- type: map_at_100
value: 35.248000000000005
- type: map_at_1000
value: 35.359
- type: map_at_3
value: 30.316
- type: map_at_5
value: 32.233000000000004
- type: mrr_at_1
value: 28.310999999999996
- type: mrr_at_10
value: 38.979
- type: mrr_at_100
value: 39.937
- type: mrr_at_1000
value: 39.989999999999995
- type: mrr_at_3
value: 36.244
- type: mrr_at_5
value: 37.871
- type: ndcg_at_1
value: 28.310999999999996
- type: ndcg_at_10
value: 40.282000000000004
- type: ndcg_at_100
value: 46.22
- type: ndcg_at_1000
value: 48.507
- type: ndcg_at_3
value: 34.596
- type: ndcg_at_5
value: 37.267
- type: precision_at_1
value: 28.310999999999996
- type: precision_at_10
value: 7.831
- type: precision_at_100
value: 1.257
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 17.275
- type: precision_at_5
value: 12.556999999999999
- type: recall_at_1
value: 22.467000000000002
- type: recall_at_10
value: 54.14099999999999
- type: recall_at_100
value: 79.593
- type: recall_at_1000
value: 95.063
- type: recall_at_3
value: 38.539
- type: recall_at_5
value: 45.403
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 24.18591666666667
- type: map_at_10
value: 33.84258333333333
- type: map_at_100
value: 35.11391666666666
- type: map_at_1000
value: 35.23258333333333
- type: map_at_3
value: 30.764249999999997
- type: map_at_5
value: 32.52333333333334
- type: mrr_at_1
value: 28.54733333333333
- type: mrr_at_10
value: 37.81725
- type: mrr_at_100
value: 38.716499999999996
- type: mrr_at_1000
value: 38.77458333333333
- type: mrr_at_3
value: 35.157833333333336
- type: mrr_at_5
value: 36.69816666666667
- type: ndcg_at_1
value: 28.54733333333333
- type: ndcg_at_10
value: 39.51508333333334
- type: ndcg_at_100
value: 44.95316666666666
- type: ndcg_at_1000
value: 47.257083333333334
- type: ndcg_at_3
value: 34.205833333333324
- type: ndcg_at_5
value: 36.78266666666667
- type: precision_at_1
value: 28.54733333333333
- type: precision_at_10
value: 7.082583333333334
- type: precision_at_100
value: 1.1590833333333332
- type: precision_at_1000
value: 0.15516666666666662
- type: precision_at_3
value: 15.908750000000001
- type: precision_at_5
value: 11.505416666666669
- type: recall_at_1
value: 24.18591666666667
- type: recall_at_10
value: 52.38758333333333
- type: recall_at_100
value: 76.13666666666667
- type: recall_at_1000
value: 91.99066666666667
- type: recall_at_3
value: 37.78333333333334
- type: recall_at_5
value: 44.30141666666666
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 21.975
- type: map_at_10
value: 29.781000000000002
- type: map_at_100
value: 30.847
- type: map_at_1000
value: 30.94
- type: map_at_3
value: 27.167
- type: map_at_5
value: 28.633999999999997
- type: mrr_at_1
value: 24.387
- type: mrr_at_10
value: 32.476
- type: mrr_at_100
value: 33.337
- type: mrr_at_1000
value: 33.403
- type: mrr_at_3
value: 29.881999999999998
- type: mrr_at_5
value: 31.339
- type: ndcg_at_1
value: 24.387
- type: ndcg_at_10
value: 34.596
- type: ndcg_at_100
value: 39.635
- type: ndcg_at_1000
value: 42.079
- type: ndcg_at_3
value: 29.516
- type: ndcg_at_5
value: 31.959
- type: precision_at_1
value: 24.387
- type: precision_at_10
value: 5.6129999999999995
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 12.73
- type: precision_at_5
value: 9.171999999999999
- type: recall_at_1
value: 21.975
- type: recall_at_10
value: 46.826
- type: recall_at_100
value: 69.554
- type: recall_at_1000
value: 87.749
- type: recall_at_3
value: 33.016
- type: recall_at_5
value: 38.97
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 15.614
- type: map_at_10
value: 22.927
- type: map_at_100
value: 24.185000000000002
- type: map_at_1000
value: 24.319
- type: map_at_3
value: 20.596
- type: map_at_5
value: 21.854000000000003
- type: mrr_at_1
value: 18.858
- type: mrr_at_10
value: 26.535999999999998
- type: mrr_at_100
value: 27.582
- type: mrr_at_1000
value: 27.665
- type: mrr_at_3
value: 24.295
- type: mrr_at_5
value: 25.532
- type: ndcg_at_1
value: 18.858
- type: ndcg_at_10
value: 27.583000000000002
- type: ndcg_at_100
value: 33.635
- type: ndcg_at_1000
value: 36.647
- type: ndcg_at_3
value: 23.348
- type: ndcg_at_5
value: 25.257
- type: precision_at_1
value: 18.858
- type: precision_at_10
value: 5.158
- type: precision_at_100
value: 0.964
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 11.092
- type: precision_at_5
value: 8.1
- type: recall_at_1
value: 15.614
- type: recall_at_10
value: 37.916
- type: recall_at_100
value: 65.205
- type: recall_at_1000
value: 86.453
- type: recall_at_3
value: 26.137
- type: recall_at_5
value: 31.087999999999997
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 23.078000000000003
- type: map_at_10
value: 31.941999999999997
- type: map_at_100
value: 33.196999999999996
- type: map_at_1000
value: 33.303
- type: map_at_3
value: 28.927000000000003
- type: map_at_5
value: 30.707
- type: mrr_at_1
value: 26.866
- type: mrr_at_10
value: 35.557
- type: mrr_at_100
value: 36.569
- type: mrr_at_1000
value: 36.632
- type: mrr_at_3
value: 32.897999999999996
- type: mrr_at_5
value: 34.437
- type: ndcg_at_1
value: 26.866
- type: ndcg_at_10
value: 37.372
- type: ndcg_at_100
value: 43.248
- type: ndcg_at_1000
value: 45.632
- type: ndcg_at_3
value: 31.852999999999998
- type: ndcg_at_5
value: 34.582
- type: precision_at_1
value: 26.866
- type: precision_at_10
value: 6.511
- type: precision_at_100
value: 1.078
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 14.582999999999998
- type: precision_at_5
value: 10.634
- type: recall_at_1
value: 23.078000000000003
- type: recall_at_10
value: 50.334
- type: recall_at_100
value: 75.787
- type: recall_at_1000
value: 92.485
- type: recall_at_3
value: 35.386
- type: recall_at_5
value: 42.225
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 22.203999999999997
- type: map_at_10
value: 31.276
- type: map_at_100
value: 32.844
- type: map_at_1000
value: 33.062999999999995
- type: map_at_3
value: 27.733999999999998
- type: map_at_5
value: 29.64
- type: mrr_at_1
value: 27.272999999999996
- type: mrr_at_10
value: 36.083
- type: mrr_at_100
value: 37.008
- type: mrr_at_1000
value: 37.076
- type: mrr_at_3
value: 33.004
- type: mrr_at_5
value: 34.664
- type: ndcg_at_1
value: 27.272999999999996
- type: ndcg_at_10
value: 37.763000000000005
- type: ndcg_at_100
value: 43.566
- type: ndcg_at_1000
value: 46.356
- type: ndcg_at_3
value: 31.673000000000002
- type: ndcg_at_5
value: 34.501
- type: precision_at_1
value: 27.272999999999996
- type: precision_at_10
value: 7.470000000000001
- type: precision_at_100
value: 1.502
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 14.756
- type: precision_at_5
value: 11.225
- type: recall_at_1
value: 22.203999999999997
- type: recall_at_10
value: 51.437999999999995
- type: recall_at_100
value: 76.845
- type: recall_at_1000
value: 94.38600000000001
- type: recall_at_3
value: 34.258
- type: recall_at_5
value: 41.512
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 17.474
- type: map_at_10
value: 26.362999999999996
- type: map_at_100
value: 27.456999999999997
- type: map_at_1000
value: 27.567999999999998
- type: map_at_3
value: 23.518
- type: map_at_5
value: 25.068
- type: mrr_at_1
value: 18.669
- type: mrr_at_10
value: 27.998
- type: mrr_at_100
value: 28.953
- type: mrr_at_1000
value: 29.03
- type: mrr_at_3
value: 25.230999999999998
- type: mrr_at_5
value: 26.654
- type: ndcg_at_1
value: 18.669
- type: ndcg_at_10
value: 31.684
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.555
- type: ndcg_at_3
value: 26.057000000000002
- type: ndcg_at_5
value: 28.587
- type: precision_at_1
value: 18.669
- type: precision_at_10
value: 5.3420000000000005
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 11.583
- type: precision_at_5
value: 8.466
- type: recall_at_1
value: 17.474
- type: recall_at_10
value: 46.497
- type: recall_at_100
value: 69.977
- type: recall_at_1000
value: 89.872
- type: recall_at_3
value: 31.385999999999996
- type: recall_at_5
value: 37.283
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 17.173
- type: map_at_10
value: 30.407
- type: map_at_100
value: 32.528
- type: map_at_1000
value: 32.698
- type: map_at_3
value: 25.523
- type: map_at_5
value: 28.038
- type: mrr_at_1
value: 38.958
- type: mrr_at_10
value: 51.515
- type: mrr_at_100
value: 52.214000000000006
- type: mrr_at_1000
value: 52.237
- type: mrr_at_3
value: 48.502
- type: mrr_at_5
value: 50.251000000000005
- type: ndcg_at_1
value: 38.958
- type: ndcg_at_10
value: 40.355000000000004
- type: ndcg_at_100
value: 47.68
- type: ndcg_at_1000
value: 50.370000000000005
- type: ndcg_at_3
value: 33.946
- type: ndcg_at_5
value: 36.057
- type: precision_at_1
value: 38.958
- type: precision_at_10
value: 12.508
- type: precision_at_100
value: 2.054
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 25.581
- type: precision_at_5
value: 19.256999999999998
- type: recall_at_1
value: 17.173
- type: recall_at_10
value: 46.967
- type: recall_at_100
value: 71.47200000000001
- type: recall_at_1000
value: 86.238
- type: recall_at_3
value: 30.961
- type: recall_at_5
value: 37.539
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 8.999
- type: map_at_10
value: 18.989
- type: map_at_100
value: 26.133
- type: map_at_1000
value: 27.666
- type: map_at_3
value: 13.918
- type: map_at_5
value: 16.473
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.161
- type: mrr_at_100
value: 74.516
- type: mrr_at_1000
value: 74.524
- type: mrr_at_3
value: 72.875
- type: mrr_at_5
value: 73.613
- type: ndcg_at_1
value: 54.37499999999999
- type: ndcg_at_10
value: 39.902
- type: ndcg_at_100
value: 44.212
- type: ndcg_at_1000
value: 51.62
- type: ndcg_at_3
value: 45.193
- type: ndcg_at_5
value: 42.541000000000004
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 30.425
- type: precision_at_100
value: 9.754999999999999
- type: precision_at_1000
value: 2.043
- type: precision_at_3
value: 48.25
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.999
- type: recall_at_10
value: 24.133
- type: recall_at_100
value: 49.138999999999996
- type: recall_at_1000
value: 72.639
- type: recall_at_3
value: 15.287999999999998
- type: recall_at_5
value: 19.415
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.38999999999999
- type: f1
value: 41.444205512055234
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 87.35000000000001
- type: map_at_10
value: 92.837
- type: map_at_100
value: 92.996
- type: map_at_1000
value: 93.006
- type: map_at_3
value: 92.187
- type: map_at_5
value: 92.595
- type: mrr_at_1
value: 93.864
- type: mrr_at_10
value: 96.723
- type: mrr_at_100
value: 96.72500000000001
- type: mrr_at_1000
value: 96.72500000000001
- type: mrr_at_3
value: 96.64
- type: mrr_at_5
value: 96.71499999999999
- type: ndcg_at_1
value: 93.864
- type: ndcg_at_10
value: 94.813
- type: ndcg_at_100
value: 95.243
- type: ndcg_at_1000
value: 95.38600000000001
- type: ndcg_at_3
value: 94.196
- type: ndcg_at_5
value: 94.521
- type: precision_at_1
value: 93.864
- type: precision_at_10
value: 10.951
- type: precision_at_100
value: 1.1400000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 35.114000000000004
- type: precision_at_5
value: 21.476
- type: recall_at_1
value: 87.35000000000001
- type: recall_at_10
value: 96.941
- type: recall_at_100
value: 98.397
- type: recall_at_1000
value: 99.21600000000001
- type: recall_at_3
value: 95.149
- type: recall_at_5
value: 96.131
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 24.476
- type: map_at_10
value: 40.11
- type: map_at_100
value: 42.229
- type: map_at_1000
value: 42.378
- type: map_at_3
value: 34.512
- type: map_at_5
value: 38.037
- type: mrr_at_1
value: 47.839999999999996
- type: mrr_at_10
value: 57.053
- type: mrr_at_100
value: 57.772
- type: mrr_at_1000
value: 57.799
- type: mrr_at_3
value: 54.552
- type: mrr_at_5
value: 56.011
- type: ndcg_at_1
value: 47.839999999999996
- type: ndcg_at_10
value: 48.650999999999996
- type: ndcg_at_100
value: 55.681000000000004
- type: ndcg_at_1000
value: 57.979
- type: ndcg_at_3
value: 43.923
- type: ndcg_at_5
value: 46.037
- type: precision_at_1
value: 47.839999999999996
- type: precision_at_10
value: 13.395000000000001
- type: precision_at_100
value: 2.0660000000000003
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 29.064
- type: precision_at_5
value: 22.006
- type: recall_at_1
value: 24.476
- type: recall_at_10
value: 56.216
- type: recall_at_100
value: 81.798
- type: recall_at_1000
value: 95.48299999999999
- type: recall_at_3
value: 39.357
- type: recall_at_5
value: 47.802
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 42.728
- type: map_at_10
value: 57.737
- type: map_at_100
value: 58.531
- type: map_at_1000
value: 58.594
- type: map_at_3
value: 54.869
- type: map_at_5
value: 56.55
- type: mrr_at_1
value: 85.456
- type: mrr_at_10
value: 90.062
- type: mrr_at_100
value: 90.159
- type: mrr_at_1000
value: 90.16
- type: mrr_at_3
value: 89.37899999999999
- type: mrr_at_5
value: 89.81
- type: ndcg_at_1
value: 85.456
- type: ndcg_at_10
value: 67.755
- type: ndcg_at_100
value: 70.341
- type: ndcg_at_1000
value: 71.538
- type: ndcg_at_3
value: 63.735
- type: ndcg_at_5
value: 65.823
- type: precision_at_1
value: 85.456
- type: precision_at_10
value: 13.450000000000001
- type: precision_at_100
value: 1.545
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 38.861000000000004
- type: precision_at_5
value: 24.964
- type: recall_at_1
value: 42.728
- type: recall_at_10
value: 67.252
- type: recall_at_100
value: 77.265
- type: recall_at_1000
value: 85.246
- type: recall_at_3
value: 58.292
- type: recall_at_5
value: 62.41100000000001
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 87.4836
- type: ap
value: 82.29552224030336
- type: f1
value: 87.42791432227448
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 23.015
- type: map_at_10
value: 35.621
- type: map_at_100
value: 36.809
- type: map_at_1000
value: 36.853
- type: map_at_3
value: 31.832
- type: map_at_5
value: 34.006
- type: mrr_at_1
value: 23.738999999999997
- type: mrr_at_10
value: 36.309999999999995
- type: mrr_at_100
value: 37.422
- type: mrr_at_1000
value: 37.461
- type: mrr_at_3
value: 32.592999999999996
- type: mrr_at_5
value: 34.736
- type: ndcg_at_1
value: 23.724999999999998
- type: ndcg_at_10
value: 42.617
- type: ndcg_at_100
value: 48.217999999999996
- type: ndcg_at_1000
value: 49.309
- type: ndcg_at_3
value: 34.905
- type: ndcg_at_5
value: 38.769
- type: precision_at_1
value: 23.724999999999998
- type: precision_at_10
value: 6.689
- type: precision_at_100
value: 0.9480000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.89
- type: precision_at_5
value: 10.897
- type: recall_at_1
value: 23.015
- type: recall_at_10
value: 64.041
- type: recall_at_100
value: 89.724
- type: recall_at_1000
value: 98.00999999999999
- type: recall_at_3
value: 43.064
- type: recall_at_5
value: 52.31099999999999
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.49794801641588
- type: f1
value: 96.28931114498003
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 82.81121751025992
- type: f1
value: 63.18740125901853
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.66644250168123
- type: f1
value: 74.93211186867839
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 81.77202420981843
- type: f1
value: 81.63681969283554
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.596687684870645
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.26965660101405
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.33619694846802
- type: mrr
value: 32.53719657720334
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.0729999999999995
- type: map_at_10
value: 13.245999999999999
- type: map_at_100
value: 16.747999999999998
- type: map_at_1000
value: 18.163
- type: map_at_3
value: 10.064
- type: map_at_5
value: 11.513
- type: mrr_at_1
value: 49.536
- type: mrr_at_10
value: 58.092
- type: mrr_at_100
value: 58.752
- type: mrr_at_1000
value: 58.78
- type: mrr_at_3
value: 56.398
- type: mrr_at_5
value: 57.389
- type: ndcg_at_1
value: 47.059
- type: ndcg_at_10
value: 35.881
- type: ndcg_at_100
value: 32.751999999999995
- type: ndcg_at_1000
value: 41.498000000000005
- type: ndcg_at_3
value: 42.518
- type: ndcg_at_5
value: 39.550999999999995
- type: precision_at_1
value: 49.536
- type: precision_at_10
value: 26.316
- type: precision_at_100
value: 8.084
- type: precision_at_1000
value: 2.081
- type: precision_at_3
value: 39.938
- type: precision_at_5
value: 34.056
- type: recall_at_1
value: 6.0729999999999995
- type: recall_at_10
value: 16.593
- type: recall_at_100
value: 32.883
- type: recall_at_1000
value: 64.654
- type: recall_at_3
value: 11.174000000000001
- type: recall_at_5
value: 13.528
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 30.043
- type: map_at_10
value: 45.318999999999996
- type: map_at_100
value: 46.381
- type: map_at_1000
value: 46.412
- type: map_at_3
value: 40.941
- type: map_at_5
value: 43.662
- type: mrr_at_1
value: 33.98
- type: mrr_at_10
value: 47.870000000000005
- type: mrr_at_100
value: 48.681999999999995
- type: mrr_at_1000
value: 48.703
- type: mrr_at_3
value: 44.341
- type: mrr_at_5
value: 46.547
- type: ndcg_at_1
value: 33.98
- type: ndcg_at_10
value: 52.957
- type: ndcg_at_100
value: 57.434
- type: ndcg_at_1000
value: 58.103
- type: ndcg_at_3
value: 44.896
- type: ndcg_at_5
value: 49.353
- type: precision_at_1
value: 33.98
- type: precision_at_10
value: 8.786
- type: precision_at_100
value: 1.1280000000000001
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 20.577
- type: precision_at_5
value: 14.942
- type: recall_at_1
value: 30.043
- type: recall_at_10
value: 73.593
- type: recall_at_100
value: 93.026
- type: recall_at_1000
value: 97.943
- type: recall_at_3
value: 52.955
- type: recall_at_5
value: 63.132
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.808
- type: map_at_10
value: 84.675
- type: map_at_100
value: 85.322
- type: map_at_1000
value: 85.33800000000001
- type: map_at_3
value: 81.68900000000001
- type: map_at_5
value: 83.543
- type: mrr_at_1
value: 81.5
- type: mrr_at_10
value: 87.59700000000001
- type: mrr_at_100
value: 87.705
- type: mrr_at_1000
value: 87.70599999999999
- type: mrr_at_3
value: 86.607
- type: mrr_at_5
value: 87.289
- type: ndcg_at_1
value: 81.51
- type: ndcg_at_10
value: 88.41799999999999
- type: ndcg_at_100
value: 89.644
- type: ndcg_at_1000
value: 89.725
- type: ndcg_at_3
value: 85.49900000000001
- type: ndcg_at_5
value: 87.078
- type: precision_at_1
value: 81.51
- type: precision_at_10
value: 13.438
- type: precision_at_100
value: 1.532
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.363
- type: precision_at_5
value: 24.57
- type: recall_at_1
value: 70.808
- type: recall_at_10
value: 95.575
- type: recall_at_100
value: 99.667
- type: recall_at_1000
value: 99.98899999999999
- type: recall_at_3
value: 87.223
- type: recall_at_5
value: 91.682
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 58.614831329137715
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 66.86580408560826
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.093
- type: map_at_10
value: 13.014000000000001
- type: map_at_100
value: 15.412999999999998
- type: map_at_1000
value: 15.756999999999998
- type: map_at_3
value: 9.216000000000001
- type: map_at_5
value: 11.036999999999999
- type: mrr_at_1
value: 25.1
- type: mrr_at_10
value: 37.133
- type: mrr_at_100
value: 38.165
- type: mrr_at_1000
value: 38.198
- type: mrr_at_3
value: 33.217
- type: mrr_at_5
value: 35.732
- type: ndcg_at_1
value: 25.1
- type: ndcg_at_10
value: 21.918000000000003
- type: ndcg_at_100
value: 30.983
- type: ndcg_at_1000
value: 36.629
- type: ndcg_at_3
value: 20.544999999999998
- type: ndcg_at_5
value: 18.192
- type: precision_at_1
value: 25.1
- type: precision_at_10
value: 11.44
- type: precision_at_100
value: 2.459
- type: precision_at_1000
value: 0.381
- type: precision_at_3
value: 19.267
- type: precision_at_5
value: 16.16
- type: recall_at_1
value: 5.093
- type: recall_at_10
value: 23.215
- type: recall_at_100
value: 49.902
- type: recall_at_1000
value: 77.403
- type: recall_at_3
value: 11.733
- type: recall_at_5
value: 16.372999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.9365442977452
- type: cos_sim_spearman
value: 79.36960687383745
- type: euclidean_pearson
value: 79.6045204840714
- type: euclidean_spearman
value: 79.26382712751337
- type: manhattan_pearson
value: 79.4805084789529
- type: manhattan_spearman
value: 79.21847863209523
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.27906192961453
- type: cos_sim_spearman
value: 74.38364712099211
- type: euclidean_pearson
value: 78.54358927241223
- type: euclidean_spearman
value: 74.22185560806376
- type: manhattan_pearson
value: 78.50904327377751
- type: manhattan_spearman
value: 74.2627500781748
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 84.66863742649639
- type: cos_sim_spearman
value: 84.70630905216271
- type: euclidean_pearson
value: 84.64498334705334
- type: euclidean_spearman
value: 84.87204770690148
- type: manhattan_pearson
value: 84.65774227976077
- type: manhattan_spearman
value: 84.91251851797985
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.1577763924467
- type: cos_sim_spearman
value: 80.10314039230198
- type: euclidean_pearson
value: 81.51346991046043
- type: euclidean_spearman
value: 80.08678485109435
- type: manhattan_pearson
value: 81.57058914661894
- type: manhattan_spearman
value: 80.1516230725106
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.40310839662533
- type: cos_sim_spearman
value: 87.16293477217867
- type: euclidean_pearson
value: 86.50688711184775
- type: euclidean_spearman
value: 87.08651444923031
- type: manhattan_pearson
value: 86.54674677557857
- type: manhattan_spearman
value: 87.15079017870971
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.32886275207817
- type: cos_sim_spearman
value: 85.0190460590732
- type: euclidean_pearson
value: 84.42553652784679
- type: euclidean_spearman
value: 85.20027364279328
- type: manhattan_pearson
value: 84.42926246281078
- type: manhattan_spearman
value: 85.20187419804306
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.76732216967812
- type: cos_sim_spearman
value: 90.63701653633909
- type: euclidean_pearson
value: 90.26678186114682
- type: euclidean_spearman
value: 90.67288073455427
- type: manhattan_pearson
value: 90.20772020584582
- type: manhattan_spearman
value: 90.60764863983702
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 69.09280387698125
- type: cos_sim_spearman
value: 68.62743151172162
- type: euclidean_pearson
value: 69.89386398104689
- type: euclidean_spearman
value: 68.71191066733556
- type: manhattan_pearson
value: 69.92516500604872
- type: manhattan_spearman
value: 68.80452846992576
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.13178592019887
- type: cos_sim_spearman
value: 86.03947178806887
- type: euclidean_pearson
value: 85.87029414285313
- type: euclidean_spearman
value: 86.04960843306998
- type: manhattan_pearson
value: 85.92946858580146
- type: manhattan_spearman
value: 86.12575341860442
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.16657063002837
- type: mrr
value: 95.73671063867141
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 63.510999999999996
- type: map_at_10
value: 72.76899999999999
- type: map_at_100
value: 73.303
- type: map_at_1000
value: 73.32499999999999
- type: map_at_3
value: 70.514
- type: map_at_5
value: 71.929
- type: mrr_at_1
value: 66.333
- type: mrr_at_10
value: 73.75
- type: mrr_at_100
value: 74.119
- type: mrr_at_1000
value: 74.138
- type: mrr_at_3
value: 72.222
- type: mrr_at_5
value: 73.122
- type: ndcg_at_1
value: 66.333
- type: ndcg_at_10
value: 76.774
- type: ndcg_at_100
value: 78.78500000000001
- type: ndcg_at_1000
value: 79.254
- type: ndcg_at_3
value: 73.088
- type: ndcg_at_5
value: 75.002
- type: precision_at_1
value: 66.333
- type: precision_at_10
value: 9.833
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.222
- type: precision_at_5
value: 18.333
- type: recall_at_1
value: 63.510999999999996
- type: recall_at_10
value: 87.98899999999999
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 77.86699999999999
- type: recall_at_5
value: 82.73899999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.78514851485149
- type: cos_sim_ap
value: 94.94214383862038
- type: cos_sim_f1
value: 89.02255639097744
- type: cos_sim_precision
value: 89.2462311557789
- type: cos_sim_recall
value: 88.8
- type: dot_accuracy
value: 99.78217821782178
- type: dot_ap
value: 94.69965247836805
- type: dot_f1
value: 88.78695208970439
- type: dot_precision
value: 90.54054054054053
- type: dot_recall
value: 87.1
- type: euclidean_accuracy
value: 99.78118811881188
- type: euclidean_ap
value: 94.9865187695411
- type: euclidean_f1
value: 88.99950223992036
- type: euclidean_precision
value: 88.60257680872151
- type: euclidean_recall
value: 89.4
- type: manhattan_accuracy
value: 99.78811881188119
- type: manhattan_ap
value: 95.0021236766459
- type: manhattan_f1
value: 89.12071535022356
- type: manhattan_precision
value: 88.54886475814413
- type: manhattan_recall
value: 89.7
- type: max_accuracy
value: 99.78811881188119
- type: max_ap
value: 95.0021236766459
- type: max_f1
value: 89.12071535022356
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.93190546593995
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 37.602808534760655
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.29214480978073
- type: mrr
value: 53.123169722434426
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.967800769650022
- type: cos_sim_spearman
value: 31.168490040206926
- type: dot_pearson
value: 30.888603021128553
- type: dot_spearman
value: 31.028241262520385
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22300000000000003
- type: map_at_10
value: 1.781
- type: map_at_100
value: 9.905999999999999
- type: map_at_1000
value: 23.455000000000002
- type: map_at_3
value: 0.569
- type: map_at_5
value: 0.918
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 91.067
- type: mrr_at_100
value: 91.067
- type: mrr_at_1000
value: 91.067
- type: mrr_at_3
value: 90.667
- type: mrr_at_5
value: 91.067
- type: ndcg_at_1
value: 78.0
- type: ndcg_at_10
value: 73.13499999999999
- type: ndcg_at_100
value: 55.32
- type: ndcg_at_1000
value: 49.532
- type: ndcg_at_3
value: 73.715
- type: ndcg_at_5
value: 72.74199999999999
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 78.8
- type: precision_at_100
value: 56.32
- type: precision_at_1000
value: 21.504
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 78.0
- type: recall_at_1
value: 0.22300000000000003
- type: recall_at_10
value: 2.049
- type: recall_at_100
value: 13.553
- type: recall_at_1000
value: 46.367999999999995
- type: recall_at_3
value: 0.604
- type: recall_at_5
value: 1.015
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.0380000000000003
- type: map_at_10
value: 10.188
- type: map_at_100
value: 16.395
- type: map_at_1000
value: 18.024
- type: map_at_3
value: 6.236
- type: map_at_5
value: 7.276000000000001
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 46.292
- type: mrr_at_100
value: 47.446
- type: mrr_at_1000
value: 47.446
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 44.32
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 25.219
- type: ndcg_at_100
value: 37.802
- type: ndcg_at_1000
value: 49.274
- type: ndcg_at_3
value: 28.605999999999998
- type: ndcg_at_5
value: 26.21
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 21.837
- type: precision_at_100
value: 7.776
- type: precision_at_1000
value: 1.522
- type: precision_at_3
value: 28.571
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 3.0380000000000003
- type: recall_at_10
value: 16.298000000000002
- type: recall_at_100
value: 48.712
- type: recall_at_1000
value: 83.16799999999999
- type: recall_at_3
value: 7.265000000000001
- type: recall_at_5
value: 9.551
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 83.978
- type: ap
value: 24.751887949330015
- type: f1
value: 66.8685134049279
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.573288058856825
- type: f1
value: 61.973261751726604
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.75483298792469
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.36824223639506
- type: cos_sim_ap
value: 75.53126388573047
- type: cos_sim_f1
value: 67.9912831688245
- type: cos_sim_precision
value: 66.11817501869858
- type: cos_sim_recall
value: 69.9736147757256
- type: dot_accuracy
value: 86.39804494248078
- type: dot_ap
value: 75.27598891718046
- type: dot_f1
value: 67.91146284159763
- type: dot_precision
value: 63.90505003490807
- type: dot_recall
value: 72.45382585751979
- type: euclidean_accuracy
value: 86.36228169517793
- type: euclidean_ap
value: 75.51438087434647
- type: euclidean_f1
value: 68.02370523061066
- type: euclidean_precision
value: 66.46525679758308
- type: euclidean_recall
value: 69.65699208443272
- type: manhattan_accuracy
value: 86.46361089586935
- type: manhattan_ap
value: 75.50800785730111
- type: manhattan_f1
value: 67.9220437187253
- type: manhattan_precision
value: 67.79705573080967
- type: manhattan_recall
value: 68.04749340369392
- type: max_accuracy
value: 86.46361089586935
- type: max_ap
value: 75.53126388573047
- type: max_f1
value: 68.02370523061066
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.80350836341057
- type: cos_sim_ap
value: 85.51101933260743
- type: cos_sim_f1
value: 77.9152271629704
- type: cos_sim_precision
value: 75.27815662910056
- type: cos_sim_recall
value: 80.74376347397599
- type: dot_accuracy
value: 88.84425815966158
- type: dot_ap
value: 85.49726945962519
- type: dot_f1
value: 77.94445269567801
- type: dot_precision
value: 75.27251864601261
- type: dot_recall
value: 80.81305820757623
- type: euclidean_accuracy
value: 88.80350836341057
- type: euclidean_ap
value: 85.4882880790211
- type: euclidean_f1
value: 77.87063284615103
- type: euclidean_precision
value: 74.61022927689595
- type: euclidean_recall
value: 81.42901139513397
- type: manhattan_accuracy
value: 88.7161873714441
- type: manhattan_ap
value: 85.45753871906821
- type: manhattan_f1
value: 77.8686401480111
- type: manhattan_precision
value: 74.95903683123174
- type: manhattan_recall
value: 81.01324299353249
- type: max_accuracy
value: 88.84425815966158
- type: max_ap
value: 85.51101933260743
- type: max_f1
value: 77.94445269567801
---
<!-- **English** | [中文](./README_zh.md) -->
# gte-base-en-v1.5
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
- **Model type:** Text Embeddings
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
<!-- - **Demo [optional]:** [More Information Needed] -->
### Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-base-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
Use with `sentence-transformers`:
```python
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
Use with `transformers.js`:
```js
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]
```
## Training Details
### Training Data
- Masked language modeling (MLM): `c4-en`
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
### Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
The model first undergoes preliminary MLM pre-training on shorter lengths.
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-base): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
- CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
- Fine-tuning: TODO
## Evaluation
### MTEB
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 434 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
### LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
## Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@misc{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
year={2024},
eprint={2407.19669},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.19669},
}
@misc{li2023gte,
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
year={2023},
eprint={2308.03281},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.03281},
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-160m-deduped | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-08T21:50:19 | 2023-07-09T16:04:57 | 61,847 | 3 | ---
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-160M-deduped
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-160M-deduped for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-160M-deduped as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-160M-deduped has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-160M-deduped will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-160M-deduped to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-160M-deduped may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-160M-deduped.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
Pythia-160M-deduped was trained on the Pile **after the dataset has been globally
deduplicated**.<br>
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
EleutherAI/pythia-6.9b | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-14T04:18:48 | 2025-03-10T18:30:39 | 60,726 | 50 | ---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-6.9B
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-6.9B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-6.9B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-6.9B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-6.9B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-6.9B to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-6.9B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-6.9B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-6.9B.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
TaylorAI/gte-tiny | TaylorAI | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-05T20:30:20 | 2023-10-07T05:20:49 | 59,999 | 136 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: gte_tiny
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.76119402985076
- type: ap
value: 34.63659287952359
- type: f1
value: 65.88939512571113
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 86.61324999999998
- type: ap
value: 81.7476302802319
- type: f1
value: 86.5863470912001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.61000000000001
- type: f1
value: 42.2217180000715
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.377999999999997
- type: map_at_10
value: 44.565
- type: map_at_100
value: 45.48
- type: map_at_1000
value: 45.487
- type: map_at_3
value: 39.841
- type: map_at_5
value: 42.284
- type: mrr_at_1
value: 29.445
- type: mrr_at_10
value: 44.956
- type: mrr_at_100
value: 45.877
- type: mrr_at_1000
value: 45.884
- type: mrr_at_3
value: 40.209
- type: mrr_at_5
value: 42.719
- type: ndcg_at_1
value: 28.377999999999997
- type: ndcg_at_10
value: 53.638
- type: ndcg_at_100
value: 57.354000000000006
- type: ndcg_at_1000
value: 57.513000000000005
- type: ndcg_at_3
value: 43.701
- type: ndcg_at_5
value: 48.114000000000004
- type: precision_at_1
value: 28.377999999999997
- type: precision_at_10
value: 8.272
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.303
- type: precision_at_5
value: 13.129
- type: recall_at_1
value: 28.377999999999997
- type: recall_at_10
value: 82.717
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 54.908
- type: recall_at_5
value: 65.647
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.637318326729876
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 36.01134479855804
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 59.82917555338909
- type: mrr
value: 74.7888361254012
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.1657730995964
- type: cos_sim_spearman
value: 86.62787748941281
- type: euclidean_pearson
value: 85.48127914481798
- type: euclidean_spearman
value: 86.48148861167424
- type: manhattan_pearson
value: 85.07496934780823
- type: manhattan_spearman
value: 86.39473964708843
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.73051948051948
- type: f1
value: 81.66368364988331
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.18623707448217
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.12697757150375
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.160000000000004
- type: map_at_10
value: 40.474
- type: map_at_100
value: 41.905
- type: map_at_1000
value: 42.041000000000004
- type: map_at_3
value: 37.147000000000006
- type: map_at_5
value: 38.873999999999995
- type: mrr_at_1
value: 36.91
- type: mrr_at_10
value: 46.495999999999995
- type: mrr_at_100
value: 47.288000000000004
- type: mrr_at_1000
value: 47.339999999999996
- type: mrr_at_3
value: 43.777
- type: mrr_at_5
value: 45.257999999999996
- type: ndcg_at_1
value: 36.91
- type: ndcg_at_10
value: 46.722
- type: ndcg_at_100
value: 51.969
- type: ndcg_at_1000
value: 54.232
- type: ndcg_at_3
value: 41.783
- type: ndcg_at_5
value: 43.797000000000004
- type: precision_at_1
value: 36.91
- type: precision_at_10
value: 9.013
- type: precision_at_100
value: 1.455
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 20.124
- type: precision_at_5
value: 14.363000000000001
- type: recall_at_1
value: 29.160000000000004
- type: recall_at_10
value: 58.521
- type: recall_at_100
value: 80.323
- type: recall_at_1000
value: 95.13000000000001
- type: recall_at_3
value: 44.205
- type: recall_at_5
value: 49.97
- type: map_at_1
value: 27.750000000000004
- type: map_at_10
value: 36.39
- type: map_at_100
value: 37.5
- type: map_at_1000
value: 37.625
- type: map_at_3
value: 33.853
- type: map_at_5
value: 35.397
- type: mrr_at_1
value: 34.14
- type: mrr_at_10
value: 41.841
- type: mrr_at_100
value: 42.469
- type: mrr_at_1000
value: 42.521
- type: mrr_at_3
value: 39.724
- type: mrr_at_5
value: 40.955999999999996
- type: ndcg_at_1
value: 34.14
- type: ndcg_at_10
value: 41.409
- type: ndcg_at_100
value: 45.668
- type: ndcg_at_1000
value: 47.916
- type: ndcg_at_3
value: 37.836
- type: ndcg_at_5
value: 39.650999999999996
- type: precision_at_1
value: 34.14
- type: precision_at_10
value: 7.739
- type: precision_at_100
value: 1.2630000000000001
- type: precision_at_1000
value: 0.173
- type: precision_at_3
value: 18.217
- type: precision_at_5
value: 12.854
- type: recall_at_1
value: 27.750000000000004
- type: recall_at_10
value: 49.882
- type: recall_at_100
value: 68.556
- type: recall_at_1000
value: 83.186
- type: recall_at_3
value: 39.047
- type: recall_at_5
value: 44.458
- type: map_at_1
value: 36.879
- type: map_at_10
value: 48.878
- type: map_at_100
value: 49.918
- type: map_at_1000
value: 49.978
- type: map_at_3
value: 45.867999999999995
- type: map_at_5
value: 47.637
- type: mrr_at_1
value: 42.696
- type: mrr_at_10
value: 52.342
- type: mrr_at_100
value: 53.044000000000004
- type: mrr_at_1000
value: 53.077
- type: mrr_at_3
value: 50.01
- type: mrr_at_5
value: 51.437
- type: ndcg_at_1
value: 42.696
- type: ndcg_at_10
value: 54.469
- type: ndcg_at_100
value: 58.664
- type: ndcg_at_1000
value: 59.951
- type: ndcg_at_3
value: 49.419999999999995
- type: ndcg_at_5
value: 52.007000000000005
- type: precision_at_1
value: 42.696
- type: precision_at_10
value: 8.734
- type: precision_at_100
value: 1.1769999999999998
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 22.027
- type: precision_at_5
value: 15.135000000000002
- type: recall_at_1
value: 36.879
- type: recall_at_10
value: 67.669
- type: recall_at_100
value: 85.822
- type: recall_at_1000
value: 95.092
- type: recall_at_3
value: 54.157999999999994
- type: recall_at_5
value: 60.436
- type: map_at_1
value: 22.942
- type: map_at_10
value: 31.741999999999997
- type: map_at_100
value: 32.721000000000004
- type: map_at_1000
value: 32.809
- type: map_at_3
value: 29.17
- type: map_at_5
value: 30.714000000000002
- type: mrr_at_1
value: 24.746000000000002
- type: mrr_at_10
value: 33.517
- type: mrr_at_100
value: 34.451
- type: mrr_at_1000
value: 34.522000000000006
- type: mrr_at_3
value: 31.148999999999997
- type: mrr_at_5
value: 32.606
- type: ndcg_at_1
value: 24.746000000000002
- type: ndcg_at_10
value: 36.553000000000004
- type: ndcg_at_100
value: 41.53
- type: ndcg_at_1000
value: 43.811
- type: ndcg_at_3
value: 31.674000000000003
- type: ndcg_at_5
value: 34.241
- type: precision_at_1
value: 24.746000000000002
- type: precision_at_10
value: 5.684
- type: precision_at_100
value: 0.859
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.597000000000001
- type: precision_at_5
value: 9.672
- type: recall_at_1
value: 22.942
- type: recall_at_10
value: 49.58
- type: recall_at_100
value: 72.614
- type: recall_at_1000
value: 89.89200000000001
- type: recall_at_3
value: 36.552
- type: recall_at_5
value: 42.702
- type: map_at_1
value: 15.345
- type: map_at_10
value: 22.428
- type: map_at_100
value: 23.756
- type: map_at_1000
value: 23.872
- type: map_at_3
value: 20.212
- type: map_at_5
value: 21.291
- type: mrr_at_1
value: 19.279
- type: mrr_at_10
value: 27.1
- type: mrr_at_100
value: 28.211000000000002
- type: mrr_at_1000
value: 28.279
- type: mrr_at_3
value: 24.813
- type: mrr_at_5
value: 25.889
- type: ndcg_at_1
value: 19.279
- type: ndcg_at_10
value: 27.36
- type: ndcg_at_100
value: 33.499
- type: ndcg_at_1000
value: 36.452
- type: ndcg_at_3
value: 23.233999999999998
- type: ndcg_at_5
value: 24.806
- type: precision_at_1
value: 19.279
- type: precision_at_10
value: 5.149
- type: precision_at_100
value: 0.938
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.360000000000001
- type: precision_at_5
value: 8.035
- type: recall_at_1
value: 15.345
- type: recall_at_10
value: 37.974999999999994
- type: recall_at_100
value: 64.472
- type: recall_at_1000
value: 85.97200000000001
- type: recall_at_3
value: 26.203
- type: recall_at_5
value: 30.485
- type: map_at_1
value: 26.362000000000002
- type: map_at_10
value: 36.406
- type: map_at_100
value: 37.726
- type: map_at_1000
value: 37.84
- type: map_at_3
value: 33.425
- type: map_at_5
value: 35.043
- type: mrr_at_1
value: 32.146
- type: mrr_at_10
value: 41.674
- type: mrr_at_100
value: 42.478
- type: mrr_at_1000
value: 42.524
- type: mrr_at_3
value: 38.948
- type: mrr_at_5
value: 40.415
- type: ndcg_at_1
value: 32.146
- type: ndcg_at_10
value: 42.374
- type: ndcg_at_100
value: 47.919
- type: ndcg_at_1000
value: 50.013
- type: ndcg_at_3
value: 37.29
- type: ndcg_at_5
value: 39.531
- type: precision_at_1
value: 32.146
- type: precision_at_10
value: 7.767
- type: precision_at_100
value: 1.236
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.965999999999998
- type: precision_at_5
value: 12.742999999999999
- type: recall_at_1
value: 26.362000000000002
- type: recall_at_10
value: 54.98800000000001
- type: recall_at_100
value: 78.50200000000001
- type: recall_at_1000
value: 92.146
- type: recall_at_3
value: 40.486
- type: recall_at_5
value: 46.236
- type: map_at_1
value: 24.417
- type: map_at_10
value: 33.161
- type: map_at_100
value: 34.357
- type: map_at_1000
value: 34.473
- type: map_at_3
value: 30.245
- type: map_at_5
value: 31.541999999999998
- type: mrr_at_1
value: 29.909000000000002
- type: mrr_at_10
value: 38.211
- type: mrr_at_100
value: 39.056999999999995
- type: mrr_at_1000
value: 39.114
- type: mrr_at_3
value: 35.769
- type: mrr_at_5
value: 36.922
- type: ndcg_at_1
value: 29.909000000000002
- type: ndcg_at_10
value: 38.694
- type: ndcg_at_100
value: 44.057
- type: ndcg_at_1000
value: 46.6
- type: ndcg_at_3
value: 33.822
- type: ndcg_at_5
value: 35.454
- type: precision_at_1
value: 29.909000000000002
- type: precision_at_10
value: 7.180000000000001
- type: precision_at_100
value: 1.153
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 16.134
- type: precision_at_5
value: 11.256
- type: recall_at_1
value: 24.417
- type: recall_at_10
value: 50.260000000000005
- type: recall_at_100
value: 73.55699999999999
- type: recall_at_1000
value: 91.216
- type: recall_at_3
value: 35.971
- type: recall_at_5
value: 40.793
- type: map_at_1
value: 24.266916666666663
- type: map_at_10
value: 32.75025
- type: map_at_100
value: 33.91341666666667
- type: map_at_1000
value: 34.031749999999995
- type: map_at_3
value: 30.166416666666674
- type: map_at_5
value: 31.577000000000005
- type: mrr_at_1
value: 28.828166666666664
- type: mrr_at_10
value: 36.80991666666667
- type: mrr_at_100
value: 37.67075
- type: mrr_at_1000
value: 37.733
- type: mrr_at_3
value: 34.513416666666664
- type: mrr_at_5
value: 35.788
- type: ndcg_at_1
value: 28.828166666666664
- type: ndcg_at_10
value: 37.796
- type: ndcg_at_100
value: 42.94783333333333
- type: ndcg_at_1000
value: 45.38908333333333
- type: ndcg_at_3
value: 33.374750000000006
- type: ndcg_at_5
value: 35.379666666666665
- type: precision_at_1
value: 28.828166666666664
- type: precision_at_10
value: 6.615749999999999
- type: precision_at_100
value: 1.0848333333333333
- type: precision_at_1000
value: 0.1484166666666667
- type: precision_at_3
value: 15.347833333333332
- type: precision_at_5
value: 10.848916666666666
- type: recall_at_1
value: 24.266916666666663
- type: recall_at_10
value: 48.73458333333333
- type: recall_at_100
value: 71.56341666666667
- type: recall_at_1000
value: 88.63091666666668
- type: recall_at_3
value: 36.31208333333333
- type: recall_at_5
value: 41.55633333333333
- type: map_at_1
value: 23.497
- type: map_at_10
value: 30.249
- type: map_at_100
value: 30.947000000000003
- type: map_at_1000
value: 31.049
- type: map_at_3
value: 28.188000000000002
- type: map_at_5
value: 29.332
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.182
- type: mrr_at_100
value: 33.794999999999995
- type: mrr_at_1000
value: 33.873
- type: mrr_at_3
value: 31.263
- type: mrr_at_5
value: 32.428000000000004
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.252
- type: ndcg_at_100
value: 38.083
- type: ndcg_at_1000
value: 40.682
- type: ndcg_at_3
value: 30.464999999999996
- type: ndcg_at_5
value: 32.282
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.2909999999999995
- type: precision_at_100
value: 0.788
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.037
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.497
- type: recall_at_10
value: 43.813
- type: recall_at_100
value: 61.88399999999999
- type: recall_at_1000
value: 80.926
- type: recall_at_3
value: 33.332
- type: recall_at_5
value: 37.862
- type: map_at_1
value: 16.073
- type: map_at_10
value: 22.705000000000002
- type: map_at_100
value: 23.703
- type: map_at_1000
value: 23.833
- type: map_at_3
value: 20.593
- type: map_at_5
value: 21.7
- type: mrr_at_1
value: 19.683
- type: mrr_at_10
value: 26.39
- type: mrr_at_100
value: 27.264
- type: mrr_at_1000
value: 27.349
- type: mrr_at_3
value: 24.409
- type: mrr_at_5
value: 25.474000000000004
- type: ndcg_at_1
value: 19.683
- type: ndcg_at_10
value: 27.014
- type: ndcg_at_100
value: 31.948
- type: ndcg_at_1000
value: 35.125
- type: ndcg_at_3
value: 23.225
- type: ndcg_at_5
value: 24.866
- type: precision_at_1
value: 19.683
- type: precision_at_10
value: 4.948
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.943
- type: precision_at_5
value: 7.86
- type: recall_at_1
value: 16.073
- type: recall_at_10
value: 36.283
- type: recall_at_100
value: 58.745999999999995
- type: recall_at_1000
value: 81.711
- type: recall_at_3
value: 25.637
- type: recall_at_5
value: 29.919
- type: map_at_1
value: 25.776
- type: map_at_10
value: 33.317
- type: map_at_100
value: 34.437
- type: map_at_1000
value: 34.54
- type: map_at_3
value: 30.706
- type: map_at_5
value: 32.202999999999996
- type: mrr_at_1
value: 30.224
- type: mrr_at_10
value: 37.34
- type: mrr_at_100
value: 38.268
- type: mrr_at_1000
value: 38.335
- type: mrr_at_3
value: 35.075
- type: mrr_at_5
value: 36.348
- type: ndcg_at_1
value: 30.224
- type: ndcg_at_10
value: 38.083
- type: ndcg_at_100
value: 43.413000000000004
- type: ndcg_at_1000
value: 45.856
- type: ndcg_at_3
value: 33.437
- type: ndcg_at_5
value: 35.661
- type: precision_at_1
value: 30.224
- type: precision_at_10
value: 6.1850000000000005
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 14.646
- type: precision_at_5
value: 10.428999999999998
- type: recall_at_1
value: 25.776
- type: recall_at_10
value: 48.787000000000006
- type: recall_at_100
value: 72.04899999999999
- type: recall_at_1000
value: 89.339
- type: recall_at_3
value: 36.192
- type: recall_at_5
value: 41.665
- type: map_at_1
value: 23.156
- type: map_at_10
value: 30.886000000000003
- type: map_at_100
value: 32.551
- type: map_at_1000
value: 32.769
- type: map_at_3
value: 28.584
- type: map_at_5
value: 29.959999999999997
- type: mrr_at_1
value: 28.260999999999996
- type: mrr_at_10
value: 35.555
- type: mrr_at_100
value: 36.687
- type: mrr_at_1000
value: 36.742999999999995
- type: mrr_at_3
value: 33.531
- type: mrr_at_5
value: 34.717
- type: ndcg_at_1
value: 28.260999999999996
- type: ndcg_at_10
value: 36.036
- type: ndcg_at_100
value: 42.675000000000004
- type: ndcg_at_1000
value: 45.303
- type: ndcg_at_3
value: 32.449
- type: ndcg_at_5
value: 34.293
- type: precision_at_1
value: 28.260999999999996
- type: precision_at_10
value: 6.837999999999999
- type: precision_at_100
value: 1.4569999999999999
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.028
- type: recall_at_1
value: 23.156
- type: recall_at_10
value: 45.251999999999995
- type: recall_at_100
value: 75.339
- type: recall_at_1000
value: 91.56
- type: recall_at_3
value: 34.701
- type: recall_at_5
value: 39.922999999999995
- type: map_at_1
value: 19.846
- type: map_at_10
value: 26.367
- type: map_at_100
value: 27.439999999999998
- type: map_at_1000
value: 27.552
- type: map_at_3
value: 24.006
- type: map_at_5
value: 25.230999999999998
- type: mrr_at_1
value: 21.257
- type: mrr_at_10
value: 28.071
- type: mrr_at_100
value: 29.037000000000003
- type: mrr_at_1000
value: 29.119
- type: mrr_at_3
value: 25.692999999999998
- type: mrr_at_5
value: 27.006000000000004
- type: ndcg_at_1
value: 21.257
- type: ndcg_at_10
value: 30.586000000000002
- type: ndcg_at_100
value: 35.949
- type: ndcg_at_1000
value: 38.728
- type: ndcg_at_3
value: 25.862000000000002
- type: ndcg_at_5
value: 27.967
- type: precision_at_1
value: 21.257
- type: precision_at_10
value: 4.861
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 10.906
- type: precision_at_5
value: 7.763000000000001
- type: recall_at_1
value: 19.846
- type: recall_at_10
value: 41.805
- type: recall_at_100
value: 66.89699999999999
- type: recall_at_1000
value: 87.401
- type: recall_at_3
value: 29.261
- type: recall_at_5
value: 34.227000000000004
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.333
- type: map_at_10
value: 17.14
- type: map_at_100
value: 18.878
- type: map_at_1000
value: 19.067
- type: map_at_3
value: 14.123
- type: map_at_5
value: 15.699
- type: mrr_at_1
value: 23.192
- type: mrr_at_10
value: 33.553
- type: mrr_at_100
value: 34.553
- type: mrr_at_1000
value: 34.603
- type: mrr_at_3
value: 29.848000000000003
- type: mrr_at_5
value: 32.18
- type: ndcg_at_1
value: 23.192
- type: ndcg_at_10
value: 24.707
- type: ndcg_at_100
value: 31.701
- type: ndcg_at_1000
value: 35.260999999999996
- type: ndcg_at_3
value: 19.492
- type: ndcg_at_5
value: 21.543
- type: precision_at_1
value: 23.192
- type: precision_at_10
value: 7.824000000000001
- type: precision_at_100
value: 1.52
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 14.180000000000001
- type: precision_at_5
value: 11.530999999999999
- type: recall_at_1
value: 10.333
- type: recall_at_10
value: 30.142999999999997
- type: recall_at_100
value: 54.298
- type: recall_at_1000
value: 74.337
- type: recall_at_3
value: 17.602999999999998
- type: recall_at_5
value: 22.938
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.03
- type: map_at_10
value: 17.345
- type: map_at_100
value: 23.462
- type: map_at_1000
value: 24.77
- type: map_at_3
value: 12.714
- type: map_at_5
value: 14.722
- type: mrr_at_1
value: 61.0
- type: mrr_at_10
value: 69.245
- type: mrr_at_100
value: 69.715
- type: mrr_at_1000
value: 69.719
- type: mrr_at_3
value: 67.583
- type: mrr_at_5
value: 68.521
- type: ndcg_at_1
value: 47.625
- type: ndcg_at_10
value: 35.973
- type: ndcg_at_100
value: 39.875
- type: ndcg_at_1000
value: 46.922000000000004
- type: ndcg_at_3
value: 40.574
- type: ndcg_at_5
value: 38.18
- type: precision_at_1
value: 61.0
- type: precision_at_10
value: 29.049999999999997
- type: precision_at_100
value: 8.828
- type: precision_at_1000
value: 1.8290000000000002
- type: precision_at_3
value: 45.333
- type: precision_at_5
value: 37.9
- type: recall_at_1
value: 8.03
- type: recall_at_10
value: 22.334
- type: recall_at_100
value: 45.919
- type: recall_at_1000
value: 68.822
- type: recall_at_3
value: 14.038999999999998
- type: recall_at_5
value: 17.118
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.714999999999996
- type: f1
value: 39.83929362259356
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 52.242999999999995
- type: map_at_10
value: 64.087
- type: map_at_100
value: 64.549
- type: map_at_1000
value: 64.567
- type: map_at_3
value: 61.667
- type: map_at_5
value: 63.266
- type: mrr_at_1
value: 56.271
- type: mrr_at_10
value: 68.146
- type: mrr_at_100
value: 68.524
- type: mrr_at_1000
value: 68.53200000000001
- type: mrr_at_3
value: 65.869
- type: mrr_at_5
value: 67.37100000000001
- type: ndcg_at_1
value: 56.271
- type: ndcg_at_10
value: 70.109
- type: ndcg_at_100
value: 72.09
- type: ndcg_at_1000
value: 72.479
- type: ndcg_at_3
value: 65.559
- type: ndcg_at_5
value: 68.242
- type: precision_at_1
value: 56.271
- type: precision_at_10
value: 9.286999999999999
- type: precision_at_100
value: 1.039
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 26.308
- type: precision_at_5
value: 17.291
- type: recall_at_1
value: 52.242999999999995
- type: recall_at_10
value: 84.71
- type: recall_at_100
value: 93.309
- type: recall_at_1000
value: 96.013
- type: recall_at_3
value: 72.554
- type: recall_at_5
value: 79.069
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.346
- type: map_at_10
value: 24.552
- type: map_at_100
value: 26.161
- type: map_at_1000
value: 26.345000000000002
- type: map_at_3
value: 21.208
- type: map_at_5
value: 22.959
- type: mrr_at_1
value: 29.166999999999998
- type: mrr_at_10
value: 38.182
- type: mrr_at_100
value: 39.22
- type: mrr_at_1000
value: 39.263
- type: mrr_at_3
value: 35.983
- type: mrr_at_5
value: 37.14
- type: ndcg_at_1
value: 29.166999999999998
- type: ndcg_at_10
value: 31.421
- type: ndcg_at_100
value: 38.129999999999995
- type: ndcg_at_1000
value: 41.569
- type: ndcg_at_3
value: 28.172000000000004
- type: ndcg_at_5
value: 29.029
- type: precision_at_1
value: 29.166999999999998
- type: precision_at_10
value: 8.997
- type: precision_at_100
value: 1.5709999999999997
- type: precision_at_1000
value: 0.22
- type: precision_at_3
value: 19.187
- type: precision_at_5
value: 13.980999999999998
- type: recall_at_1
value: 14.346
- type: recall_at_10
value: 37.963
- type: recall_at_100
value: 63.43299999999999
- type: recall_at_1000
value: 84.057
- type: recall_at_3
value: 26.119999999999997
- type: recall_at_5
value: 30.988
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.059
- type: map_at_10
value: 46.421
- type: map_at_100
value: 47.323
- type: map_at_1000
value: 47.403
- type: map_at_3
value: 43.553999999999995
- type: map_at_5
value: 45.283
- type: mrr_at_1
value: 66.117
- type: mrr_at_10
value: 73.10900000000001
- type: mrr_at_100
value: 73.444
- type: mrr_at_1000
value: 73.46000000000001
- type: mrr_at_3
value: 71.70400000000001
- type: mrr_at_5
value: 72.58099999999999
- type: ndcg_at_1
value: 66.117
- type: ndcg_at_10
value: 55.696999999999996
- type: ndcg_at_100
value: 59.167
- type: ndcg_at_1000
value: 60.809000000000005
- type: ndcg_at_3
value: 51.243
- type: ndcg_at_5
value: 53.627
- type: precision_at_1
value: 66.117
- type: precision_at_10
value: 11.538
- type: precision_at_100
value: 1.429
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 31.861
- type: precision_at_5
value: 20.997
- type: recall_at_1
value: 33.059
- type: recall_at_10
value: 57.691
- type: recall_at_100
value: 71.458
- type: recall_at_1000
value: 82.35
- type: recall_at_3
value: 47.792
- type: recall_at_5
value: 52.492000000000004
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 80.544
- type: ap
value: 74.69592367984956
- type: f1
value: 80.51138138449883
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 17.095
- type: map_at_10
value: 28.038999999999998
- type: map_at_100
value: 29.246
- type: map_at_1000
value: 29.311
- type: map_at_3
value: 24.253
- type: map_at_5
value: 26.442
- type: mrr_at_1
value: 17.535999999999998
- type: mrr_at_10
value: 28.53
- type: mrr_at_100
value: 29.697000000000003
- type: mrr_at_1000
value: 29.755
- type: mrr_at_3
value: 24.779999999999998
- type: mrr_at_5
value: 26.942
- type: ndcg_at_1
value: 17.549999999999997
- type: ndcg_at_10
value: 34.514
- type: ndcg_at_100
value: 40.497
- type: ndcg_at_1000
value: 42.17
- type: ndcg_at_3
value: 26.764
- type: ndcg_at_5
value: 30.678
- type: precision_at_1
value: 17.549999999999997
- type: precision_at_10
value: 5.692
- type: precision_at_100
value: 0.8699999999999999
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 11.562
- type: precision_at_5
value: 8.917
- type: recall_at_1
value: 17.095
- type: recall_at_10
value: 54.642
- type: recall_at_100
value: 82.652
- type: recall_at_1000
value: 95.555
- type: recall_at_3
value: 33.504
- type: recall_at_5
value: 42.925000000000004
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.75558595531236
- type: f1
value: 91.25979279648296
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.90424076607387
- type: f1
value: 52.067408707562244
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.13449899125757
- type: f1
value: 67.62456762910598
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 74.862138533961
- type: f1
value: 74.66457222091381
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.10761942610792
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.673172170578408
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.058704977250315
- type: mrr
value: 33.24327760839221
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.163
- type: map_at_10
value: 11.652999999999999
- type: map_at_100
value: 14.849
- type: map_at_1000
value: 16.253999999999998
- type: map_at_3
value: 8.616999999999999
- type: map_at_5
value: 10.100000000000001
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 52.25
- type: mrr_at_100
value: 52.761
- type: mrr_at_1000
value: 52.811
- type: mrr_at_3
value: 50.31
- type: mrr_at_5
value: 51.347
- type: ndcg_at_1
value: 42.105
- type: ndcg_at_10
value: 32.044
- type: ndcg_at_100
value: 29.763
- type: ndcg_at_1000
value: 38.585
- type: ndcg_at_3
value: 36.868
- type: ndcg_at_5
value: 35.154999999999994
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 23.622
- type: precision_at_100
value: 7.7490000000000006
- type: precision_at_1000
value: 2.054
- type: precision_at_3
value: 34.262
- type: precision_at_5
value: 30.154999999999998
- type: recall_at_1
value: 5.163
- type: recall_at_10
value: 15.478
- type: recall_at_100
value: 30.424
- type: recall_at_1000
value: 62.67
- type: recall_at_3
value: 9.615
- type: recall_at_5
value: 12.369
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.618000000000002
- type: map_at_10
value: 35.465
- type: map_at_100
value: 36.712
- type: map_at_1000
value: 36.757
- type: map_at_3
value: 31.189
- type: map_at_5
value: 33.537
- type: mrr_at_1
value: 24.305
- type: mrr_at_10
value: 37.653
- type: mrr_at_100
value: 38.662
- type: mrr_at_1000
value: 38.694
- type: mrr_at_3
value: 33.889
- type: mrr_at_5
value: 35.979
- type: ndcg_at_1
value: 24.305
- type: ndcg_at_10
value: 43.028
- type: ndcg_at_100
value: 48.653999999999996
- type: ndcg_at_1000
value: 49.733
- type: ndcg_at_3
value: 34.768
- type: ndcg_at_5
value: 38.753
- type: precision_at_1
value: 24.305
- type: precision_at_10
value: 7.59
- type: precision_at_100
value: 1.076
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 16.271
- type: precision_at_5
value: 12.068
- type: recall_at_1
value: 21.618000000000002
- type: recall_at_10
value: 63.977
- type: recall_at_100
value: 89.03999999999999
- type: recall_at_1000
value: 97.10600000000001
- type: recall_at_3
value: 42.422
- type: recall_at_5
value: 51.629000000000005
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.405
- type: map_at_10
value: 83.05
- type: map_at_100
value: 83.684
- type: map_at_1000
value: 83.70400000000001
- type: map_at_3
value: 80.08800000000001
- type: map_at_5
value: 81.937
- type: mrr_at_1
value: 79.85
- type: mrr_at_10
value: 86.369
- type: mrr_at_100
value: 86.48599999999999
- type: mrr_at_1000
value: 86.48700000000001
- type: mrr_at_3
value: 85.315
- type: mrr_at_5
value: 86.044
- type: ndcg_at_1
value: 79.86999999999999
- type: ndcg_at_10
value: 87.04499999999999
- type: ndcg_at_100
value: 88.373
- type: ndcg_at_1000
value: 88.531
- type: ndcg_at_3
value: 84.04
- type: ndcg_at_5
value: 85.684
- type: precision_at_1
value: 79.86999999999999
- type: precision_at_10
value: 13.183
- type: precision_at_100
value: 1.51
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.67
- type: precision_at_5
value: 24.12
- type: recall_at_1
value: 69.405
- type: recall_at_10
value: 94.634
- type: recall_at_100
value: 99.214
- type: recall_at_1000
value: 99.958
- type: recall_at_3
value: 85.992
- type: recall_at_5
value: 90.656
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.191676323145465
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.4874020363744
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.228
- type: map_at_10
value: 11.245
- type: map_at_100
value: 13.353000000000002
- type: map_at_1000
value: 13.665
- type: map_at_3
value: 7.779999999999999
- type: map_at_5
value: 9.405
- type: mrr_at_1
value: 20.9
- type: mrr_at_10
value: 31.657999999999998
- type: mrr_at_100
value: 32.769999999999996
- type: mrr_at_1000
value: 32.833
- type: mrr_at_3
value: 28.333000000000002
- type: mrr_at_5
value: 30.043
- type: ndcg_at_1
value: 20.9
- type: ndcg_at_10
value: 19.073
- type: ndcg_at_100
value: 27.055
- type: ndcg_at_1000
value: 32.641
- type: ndcg_at_3
value: 17.483999999999998
- type: ndcg_at_5
value: 15.42
- type: precision_at_1
value: 20.9
- type: precision_at_10
value: 10.17
- type: precision_at_100
value: 2.162
- type: precision_at_1000
value: 0.35100000000000003
- type: precision_at_3
value: 16.467000000000002
- type: precision_at_5
value: 13.68
- type: recall_at_1
value: 4.228
- type: recall_at_10
value: 20.573
- type: recall_at_100
value: 43.887
- type: recall_at_1000
value: 71.22
- type: recall_at_3
value: 10.023
- type: recall_at_5
value: 13.873
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 82.77965135067481
- type: cos_sim_spearman
value: 75.85121335808076
- type: euclidean_pearson
value: 80.09115175262697
- type: euclidean_spearman
value: 75.72249155647123
- type: manhattan_pearson
value: 79.89723577351782
- type: manhattan_spearman
value: 75.49855259442387
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 80.46084116030949
- type: cos_sim_spearman
value: 72.57579204392951
- type: euclidean_pearson
value: 76.39020830763684
- type: euclidean_spearman
value: 72.3718627025895
- type: manhattan_pearson
value: 76.6148833027359
- type: manhattan_spearman
value: 72.57570008442319
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.43678068337017
- type: cos_sim_spearman
value: 82.38941154076062
- type: euclidean_pearson
value: 81.59260573633661
- type: euclidean_spearman
value: 82.31144262574114
- type: manhattan_pearson
value: 81.43266909137056
- type: manhattan_spearman
value: 82.14704293004861
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.73713431763163
- type: cos_sim_spearman
value: 77.97860512809388
- type: euclidean_pearson
value: 80.35755041527027
- type: euclidean_spearman
value: 78.021703511412
- type: manhattan_pearson
value: 80.24440317109162
- type: manhattan_spearman
value: 77.93165415697575
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.15111852351204
- type: cos_sim_spearman
value: 86.54032447238258
- type: euclidean_pearson
value: 86.14157021537433
- type: euclidean_spearman
value: 86.67537291929713
- type: manhattan_pearson
value: 86.081041854808
- type: manhattan_spearman
value: 86.61561701560558
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.34532445104026
- type: cos_sim_spearman
value: 83.31325001474116
- type: euclidean_pearson
value: 82.81892375201032
- type: euclidean_spearman
value: 83.4521695148055
- type: manhattan_pearson
value: 82.72503790526163
- type: manhattan_spearman
value: 83.37833652941349
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.25463453839801
- type: cos_sim_spearman
value: 88.27655263515948
- type: euclidean_pearson
value: 88.0248334411439
- type: euclidean_spearman
value: 88.18141448876868
- type: manhattan_pearson
value: 87.8080451127279
- type: manhattan_spearman
value: 88.01028114423058
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.57551045355218
- type: cos_sim_spearman
value: 66.67614095126629
- type: euclidean_pearson
value: 66.0787243112528
- type: euclidean_spearman
value: 66.83660560636939
- type: manhattan_pearson
value: 66.74684019662031
- type: manhattan_spearman
value: 67.11761598074368
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.70881496766829
- type: cos_sim_spearman
value: 84.37803542941634
- type: euclidean_pearson
value: 84.84501245857096
- type: euclidean_spearman
value: 84.47088079741476
- type: manhattan_pearson
value: 84.77244090794765
- type: manhattan_spearman
value: 84.43307343706205
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.53946254759089
- type: mrr
value: 94.68259953554072
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.817
- type: map_at_10
value: 62.339999999999996
- type: map_at_100
value: 62.88
- type: map_at_1000
value: 62.909000000000006
- type: map_at_3
value: 59.004
- type: map_at_5
value: 60.906000000000006
- type: mrr_at_1
value: 54.333
- type: mrr_at_10
value: 63.649
- type: mrr_at_100
value: 64.01
- type: mrr_at_1000
value: 64.039
- type: mrr_at_3
value: 61.056
- type: mrr_at_5
value: 62.639
- type: ndcg_at_1
value: 54.333
- type: ndcg_at_10
value: 67.509
- type: ndcg_at_100
value: 69.69999999999999
- type: ndcg_at_1000
value: 70.613
- type: ndcg_at_3
value: 61.729
- type: ndcg_at_5
value: 64.696
- type: precision_at_1
value: 54.333
- type: precision_at_10
value: 9.2
- type: precision_at_100
value: 1.043
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 24.0
- type: precision_at_5
value: 16.2
- type: recall_at_1
value: 51.817
- type: recall_at_10
value: 82.056
- type: recall_at_100
value: 91.667
- type: recall_at_1000
value: 99.0
- type: recall_at_3
value: 66.717
- type: recall_at_5
value: 74.17200000000001
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82475247524752
- type: cos_sim_ap
value: 95.4781199603258
- type: cos_sim_f1
value: 91.16186693147964
- type: cos_sim_precision
value: 90.53254437869822
- type: cos_sim_recall
value: 91.8
- type: dot_accuracy
value: 99.75049504950495
- type: dot_ap
value: 93.05183539809457
- type: dot_f1
value: 87.31117824773412
- type: dot_precision
value: 87.93103448275862
- type: dot_recall
value: 86.7
- type: euclidean_accuracy
value: 99.82475247524752
- type: euclidean_ap
value: 95.38547978154382
- type: euclidean_f1
value: 91.16325511732403
- type: euclidean_precision
value: 91.02691924227318
- type: euclidean_recall
value: 91.3
- type: manhattan_accuracy
value: 99.82574257425742
- type: manhattan_ap
value: 95.47237521890308
- type: manhattan_f1
value: 91.27849355797821
- type: manhattan_precision
value: 90.47151277013754
- type: manhattan_recall
value: 92.10000000000001
- type: max_accuracy
value: 99.82574257425742
- type: max_ap
value: 95.4781199603258
- type: max_f1
value: 91.27849355797821
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.542169376331245
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.74399302634387
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.65076347632749
- type: mrr
value: 50.418099057804945
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.73997756592847
- type: cos_sim_spearman
value: 29.465208011593308
- type: dot_pearson
value: 24.83735342474541
- type: dot_spearman
value: 26.005180528584855
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.208
- type: map_at_10
value: 1.434
- type: map_at_100
value: 7.829
- type: map_at_1000
value: 19.807
- type: map_at_3
value: 0.549
- type: map_at_5
value: 0.8330000000000001
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 85.35199999999999
- type: mrr_at_100
value: 85.673
- type: mrr_at_1000
value: 85.673
- type: mrr_at_3
value: 84.667
- type: mrr_at_5
value: 85.06700000000001
- type: ndcg_at_1
value: 72.0
- type: ndcg_at_10
value: 59.214999999999996
- type: ndcg_at_100
value: 44.681
- type: ndcg_at_1000
value: 43.035000000000004
- type: ndcg_at_3
value: 66.53099999999999
- type: ndcg_at_5
value: 63.23
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 62.4
- type: precision_at_100
value: 45.76
- type: precision_at_1000
value: 19.05
- type: precision_at_3
value: 71.333
- type: precision_at_5
value: 67.2
- type: recall_at_1
value: 0.208
- type: recall_at_10
value: 1.6580000000000001
- type: recall_at_100
value: 11.324
- type: recall_at_1000
value: 41.537
- type: recall_at_3
value: 0.579
- type: recall_at_5
value: 0.8959999999999999
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.442
- type: map_at_10
value: 8.863
- type: map_at_100
value: 14.606
- type: map_at_1000
value: 16.258
- type: map_at_3
value: 4.396
- type: map_at_5
value: 6.199000000000001
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 43.492
- type: mrr_at_100
value: 44.557
- type: mrr_at_1000
value: 44.557
- type: mrr_at_3
value: 40.816
- type: mrr_at_5
value: 42.143
- type: ndcg_at_1
value: 25.509999999999998
- type: ndcg_at_10
value: 22.076
- type: ndcg_at_100
value: 34.098
- type: ndcg_at_1000
value: 46.265
- type: ndcg_at_3
value: 24.19
- type: ndcg_at_5
value: 23.474
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.286
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_3
value: 25.85
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.442
- type: recall_at_10
value: 15.012
- type: recall_at_100
value: 45.865
- type: recall_at_1000
value: 82.958
- type: recall_at_3
value: 5.731
- type: recall_at_5
value: 9.301
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.974
- type: ap
value: 14.534996211286682
- type: f1
value: 54.785946183399005
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.56819468024901
- type: f1
value: 58.92391487111204
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 43.273202335218194
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.37742146986946
- type: cos_sim_ap
value: 68.1684129575579
- type: cos_sim_f1
value: 64.93475108748189
- type: cos_sim_precision
value: 59.89745876058849
- type: cos_sim_recall
value: 70.89709762532982
- type: dot_accuracy
value: 80.49710913750968
- type: dot_ap
value: 54.699790073944186
- type: dot_f1
value: 54.45130013221684
- type: dot_precision
value: 46.74612183125236
- type: dot_recall
value: 65.19788918205805
- type: euclidean_accuracy
value: 84.5085533766466
- type: euclidean_ap
value: 68.38835695236224
- type: euclidean_f1
value: 65.3391121002694
- type: euclidean_precision
value: 58.75289656625237
- type: euclidean_recall
value: 73.58839050131925
- type: manhattan_accuracy
value: 84.40126363473803
- type: manhattan_ap
value: 68.09539181555348
- type: manhattan_f1
value: 64.99028182701653
- type: manhattan_precision
value: 60.22062134173795
- type: manhattan_recall
value: 70.58047493403694
- type: max_accuracy
value: 84.5085533766466
- type: max_ap
value: 68.38835695236224
- type: max_f1
value: 65.3391121002694
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.34167733923235
- type: cos_sim_ap
value: 84.84136381147736
- type: cos_sim_f1
value: 77.01434980904001
- type: cos_sim_precision
value: 74.27937915742794
- type: cos_sim_recall
value: 79.95842315983985
- type: dot_accuracy
value: 85.06422944075756
- type: dot_ap
value: 76.49446747522325
- type: dot_f1
value: 71.11606520830432
- type: dot_precision
value: 64.93638676844785
- type: dot_recall
value: 78.59562673236834
- type: euclidean_accuracy
value: 88.45810532852097
- type: euclidean_ap
value: 84.91526721863501
- type: euclidean_f1
value: 77.04399001750662
- type: euclidean_precision
value: 74.62298867162133
- type: euclidean_recall
value: 79.62734832152756
- type: manhattan_accuracy
value: 88.46004579500912
- type: manhattan_ap
value: 84.81590026238194
- type: manhattan_f1
value: 76.97804626491822
- type: manhattan_precision
value: 73.79237288135593
- type: manhattan_recall
value: 80.45118570988605
- type: max_accuracy
value: 88.46004579500912
- type: max_ap
value: 84.91526721863501
- type: max_f1
value: 77.04399001750662
---
# {gte-tiny}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the size.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5 | HIT-TMG | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2501.01028",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-12-26T08:53:26 | 2025-03-13T06:45:59 | 59,100 | 57 | ---
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: KaLM-Embedding
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 94.68515742128936
- type: ap
value: 61.63091951041035
- type: ap_weighted
value: 61.63091951041035
- type: f1
value: 87.0707144798333
- type: f1_weighted
value: 94.92507672044346
- type: main_score
value: 94.68515742128936
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 91.73134328358209
- type: ap
value: 68.35026743834399
- type: ap_weighted
value: 68.35026743834399
- type: f1
value: 87.90511074439425
- type: f1_weighted
value: 92.04737679749206
- type: main_score
value: 91.73134328358209
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.55665
- type: ap
value: 95.07549068622335
- type: ap_weighted
value: 95.07549068622335
- type: f1
value: 96.55609959481485
- type: f1_weighted
value: 96.55609959481488
- type: main_score
value: 96.55665
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.42000000000001
- type: f1
value: 59.435009609195
- type: f1_weighted
value: 59.435009609195
- type: main_score
value: 61.42000000000001
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 58.626999999999995
- type: map_at_1
value: 33.073
- type: map_at_10
value: 49.632
- type: map_at_100
value: 50.33800000000001
- type: map_at_1000
value: 50.341
- type: map_at_20
value: 50.224000000000004
- type: map_at_3
value: 44.832
- type: map_at_5
value: 47.620000000000005
- type: mrr_at_1
value: 33.57041251778094
- type: mrr_at_10
value: 49.811380929802155
- type: mrr_at_100
value: 50.51757318808999
- type: mrr_at_1000
value: 50.52099783252713
- type: mrr_at_20
value: 50.40338101381789
- type: mrr_at_3
value: 45.00948316737797
- type: mrr_at_5
value: 47.77619724988161
- type: nauc_map_at_1000_diff1
value: 13.48102412913682
- type: nauc_map_at_1000_max
value: -8.628057456116096
- type: nauc_map_at_1000_std
value: -11.093713781480051
- type: nauc_map_at_100_diff1
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- type: nauc_map_at_100_max
value: -8.618385685478875
- type: nauc_map_at_100_std
value: -11.08784320605541
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value: 13.356168065508726
- type: nauc_map_at_10_max
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- type: nauc_map_at_10_std
value: -11.179349250075992
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- type: nauc_mrr_at_1000_diff1
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- type: nauc_mrr_at_1000_max
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- type: nauc_mrr_at_1000_std
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- type: nauc_mrr_at_100_diff1
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- type: nauc_mrr_at_100_max
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- type: nauc_mrr_at_10_diff1
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- type: nauc_mrr_at_10_max
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- type: nauc_recall_at_1000_std
value: 86.59852334082566
- type: nauc_recall_at_100_diff1
value: 35.05590224798152
- type: nauc_recall_at_100_max
value: 60.87922050664376
- type: nauc_recall_at_100_std
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type: mteb/arxiv-clustering-p2p
config: default
split: test
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type: Clustering
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split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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type: Reranking
dataset:
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type: mteb/askubuntudupquestions-reranking
config: default
split: test
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type: mteb/cqadupstack-android
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dataset:
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type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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type: mteb/mtop_intent
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split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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dataset:
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type: mteb/amazon_massive_intent
config: en
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revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
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value: -0.8298370121173152
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value: -47.59134911021335
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value: 71.54324183557964
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value: -66.27316488339173
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value: -41.53738015617803
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value: 70.15017942540359
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value: 11.699321903394438
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value: -61.79366072599887
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value: -56.82607742333548
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value: 72.07603033579613
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value: 13.121563460576949
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value: -63.3551506977008
- type: ndcg_at_1
value: 79.66
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value: 87.469
- type: ndcg_at_100
value: 88.742
- type: ndcg_at_1000
value: 88.835
- type: ndcg_at_20
value: 88.19
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value: 84.301
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value: 86.122
- type: precision_at_1
value: 79.66
- type: precision_at_10
value: 13.366
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value: 1.53
- type: precision_at_1000
value: 0.157
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value: 7.114
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value: 36.953
- type: precision_at_5
value: 24.436
- type: recall_at_1
value: 69.226
- type: recall_at_10
value: 95.31500000000001
- type: recall_at_100
value: 99.586
- type: recall_at_1000
value: 99.978
- type: recall_at_20
value: 97.626
- type: recall_at_3
value: 86.39
- type: recall_at_5
value: 91.429
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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value: 55.46831401047426
- type: v_measure
value: 55.46831401047426
- type: v_measure_std
value: 4.874007949262904
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 65.95763514051492
- type: v_measure
value: 65.95763514051492
- type: v_measure_std
value: 12.11633090692919
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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value: 19.967
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value: 4.555
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value: 13.828
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value: 8.368
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- type: mrr_at_1000
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value: 31.966666666666622
- type: nauc_map_at_1000_diff1
value: 15.246786102454319
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value: 16.942548728665557
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value: 16.7166806855191
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value: 13.598940269616955
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value: 5.690909359575662
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value: 15.057405804364974
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value: 7.752631479331336
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value: 27.923553597579676
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value: 8.860227830088574
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value: 19.34882283787698
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value: 34.88770887741456
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value: 10.3005989655898
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value: 13.626386741802321
- type: ndcg_at_1
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value: 19.967
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value: 32.675
- type: ndcg_at_20
value: 22.508
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value: 18.593
- type: ndcg_at_5
value: 16.395
- type: precision_at_1
value: 22.5
- type: precision_at_10
value: 10.48
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value: 2.139
- type: precision_at_1000
value: 0.338
- type: precision_at_20
value: 6.710000000000001
- type: precision_at_3
value: 17.5
- type: precision_at_5
value: 14.540000000000001
- type: recall_at_1
value: 4.555
- type: recall_at_10
value: 21.292
- type: recall_at_100
value: 43.438
- type: recall_at_1000
value: 68.693
- type: recall_at_20
value: 27.235
- type: recall_at_3
value: 10.635
- type: recall_at_5
value: 14.773
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 83.80746802573121
- type: cosine_spearman
value: 79.72822038569593
- type: euclidean_pearson
value: 81.45002980681224
- type: euclidean_spearman
value: 79.7282231441618
- type: main_score
value: 79.72822038569593
- type: manhattan_pearson
value: 81.45611398621358
- type: manhattan_spearman
value: 79.59811193829754
- type: pearson
value: 83.80746802573121
- type: spearman
value: 79.72822038569593
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 87.3063084337013
- type: cosine_spearman
value: 80.16653334500153
- type: euclidean_pearson
value: 84.24369437820418
- type: euclidean_spearman
value: 80.16592228280822
- type: main_score
value: 80.16653334500153
- type: manhattan_pearson
value: 84.41598625996673
- type: manhattan_spearman
value: 80.37993757607657
- type: pearson
value: 87.3063084337013
- type: spearman
value: 80.16653334500153
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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value: 82.56917286956067
- type: cosine_spearman
value: 83.86019247474584
- type: euclidean_pearson
value: 83.50577094515269
- type: euclidean_spearman
value: 83.86019240020342
- type: main_score
value: 83.86019247474584
- type: manhattan_pearson
value: 83.7035831723258
- type: manhattan_spearman
value: 84.06951592208223
- type: pearson
value: 82.56917286956067
- type: spearman
value: 83.86019247474584
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 82.57349080713945
- type: cosine_spearman
value: 80.5656376543151
- type: euclidean_pearson
value: 82.20020711843652
- type: euclidean_spearman
value: 80.56562435929797
- type: main_score
value: 80.5656376543151
- type: manhattan_pearson
value: 82.02137444105155
- type: manhattan_spearman
value: 80.49473867409459
- type: pearson
value: 82.57349080713945
- type: spearman
value: 80.5656376543151
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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value: 85.89932616809199
- type: cosine_spearman
value: 87.34265089376507
- type: euclidean_pearson
value: 86.70361638349567
- type: euclidean_spearman
value: 87.34265218193244
- type: main_score
value: 87.34265089376507
- type: manhattan_pearson
value: 86.51826134790748
- type: manhattan_spearman
value: 87.18812519859725
- type: pearson
value: 85.89932616809199
- type: spearman
value: 87.34265089376507
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 83.57539157713117
- type: cosine_spearman
value: 84.8327592322146
- type: euclidean_pearson
value: 84.04928896411587
- type: euclidean_spearman
value: 84.83275862198806
- type: main_score
value: 84.8327592322146
- type: manhattan_pearson
value: 83.88919473293718
- type: manhattan_spearman
value: 84.64175696279177
- type: pearson
value: 83.57539157713117
- type: spearman
value: 84.8327592322146
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 56.77939191972398
- type: cosine_spearman
value: 56.44421451336106
- type: euclidean_pearson
value: 57.84746999725719
- type: euclidean_spearman
value: 56.44421451336106
- type: main_score
value: 56.44421451336106
- type: manhattan_pearson
value: 56.368375736935185
- type: manhattan_spearman
value: 54.443033747967476
- type: pearson
value: 56.77939191972398
- type: spearman
value: 56.44421451336106
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 72.7123285460032
- type: cosine_spearman
value: 73.0413412902408
- type: euclidean_pearson
value: 73.26576109213129
- type: euclidean_spearman
value: 73.0413412902408
- type: main_score
value: 73.0413412902408
- type: manhattan_pearson
value: 73.18845328161191
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value: 73.01804685924571
- type: pearson
value: 72.7123285460032
- type: spearman
value: 73.0413412902408
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 85.76937213938014
- type: cosine_spearman
value: 86.42806650035523
- type: euclidean_pearson
value: 86.6429129637805
- type: euclidean_spearman
value: 86.42806650035523
- type: main_score
value: 86.42806650035523
- type: manhattan_pearson
value: 86.57962956067654
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value: 86.18220646628836
- type: pearson
value: 85.76937213938014
- type: spearman
value: 86.42806650035523
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 53.63838562350928
- type: cosine_spearman
value: 51.425433307585536
- type: euclidean_pearson
value: 53.89159715974961
- type: euclidean_spearman
value: 51.425433307585536
- type: main_score
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- type: manhattan_pearson
value: 53.072978089852526
- type: manhattan_spearman
value: 50.42207979849882
- type: pearson
value: 53.63838562350928
- type: spearman
value: 51.425433307585536
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 74.86829926679785
- type: cosine_spearman
value: 75.97371995137269
- type: euclidean_pearson
value: 75.31029737838625
- type: euclidean_spearman
value: 75.97371995137269
- type: main_score
value: 75.97371995137269
- type: manhattan_pearson
value: 75.65839894637686
- type: manhattan_spearman
value: 76.1355272163419
- type: pearson
value: 74.86829926679785
- type: spearman
value: 75.97371995137269
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 74.71447286299487
- type: cosine_spearman
value: 74.06702660747132
- type: euclidean_pearson
value: 75.38807553614491
- type: euclidean_spearman
value: 74.06702660747132
- type: main_score
value: 74.06702660747132
- type: manhattan_pearson
value: 75.12369492510138
- type: manhattan_spearman
value: 73.91369082324793
- type: pearson
value: 74.71447286299487
- type: spearman
value: 74.06702660747132
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 72.38044871568002
- type: cosine_spearman
value: 72.67462698051628
- type: euclidean_pearson
value: 72.93088753362804
- type: euclidean_spearman
value: 72.67462698051628
- type: main_score
value: 72.67462698051628
- type: manhattan_pearson
value: 72.36507526375641
- type: manhattan_spearman
value: 72.05743987811199
- type: pearson
value: 72.38044871568002
- type: spearman
value: 72.67462698051628
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 70.21041932205698
- type: cosine_spearman
value: 69.70028178727163
- type: euclidean_pearson
value: 70.90563853073118
- type: euclidean_spearman
value: 69.70028178727163
- type: main_score
value: 69.70028178727163
- type: manhattan_pearson
value: 70.57814054683753
- type: manhattan_spearman
value: 69.09462658139957
- type: pearson
value: 70.21041932205698
- type: spearman
value: 69.70028178727163
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 69.13900601047764
- type: cosine_spearman
value: 69.2134140940147
- type: euclidean_pearson
value: 70.74438767160798
- type: euclidean_spearman
value: 69.2134140940147
- type: main_score
value: 69.2134140940147
- type: manhattan_pearson
value: 71.02398307661159
- type: manhattan_spearman
value: 69.37936110586129
- type: pearson
value: 69.13900601047764
- type: spearman
value: 69.2134140940147
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 64.8805006605935
- type: cosine_spearman
value: 63.81067904946749
- type: euclidean_pearson
value: 67.42954643546759
- type: euclidean_spearman
value: 63.81067904946749
- type: main_score
value: 63.81067904946749
- type: manhattan_pearson
value: 68.5718639745086
- type: manhattan_spearman
value: 64.77027566921338
- type: pearson
value: 64.8805006605935
- type: spearman
value: 63.81067904946749
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 75.50011820404733
- type: cosine_spearman
value: 78.22942242197527
- type: euclidean_pearson
value: 76.7001402354704
- type: euclidean_spearman
value: 78.22942242197527
- type: main_score
value: 78.22942242197527
- type: manhattan_pearson
value: 78.77879852950737
- type: manhattan_spearman
value: 79.58485702006229
- type: pearson
value: 75.50011820404733
- type: spearman
value: 78.22942242197527
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 78.98435260035146
- type: cosine_spearman
value: 79.0083835281799
- type: euclidean_pearson
value: 79.05051294275746
- type: euclidean_spearman
value: 79.0083835281799
- type: main_score
value: 79.0083835281799
- type: manhattan_pearson
value: 79.28546319449809
- type: manhattan_spearman
value: 78.79790129500051
- type: pearson
value: 78.98435260035146
- type: spearman
value: 79.0083835281799
- type: cosine_pearson
value: 78.98435260035146
- type: cosine_spearman
value: 79.0083835281799
- type: euclidean_pearson
value: 79.05051294275746
- type: euclidean_spearman
value: 79.0083835281799
- type: main_score
value: 79.0083835281799
- type: manhattan_pearson
value: 79.28546319449809
- type: manhattan_spearman
value: 78.79790129500051
- type: pearson
value: 78.98435260035146
- type: spearman
value: 79.0083835281799
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 76.5491270627292
- type: cosine_spearman
value: 76.34747339191613
- type: euclidean_pearson
value: 77.52703884917828
- type: euclidean_spearman
value: 76.34747339191613
- type: main_score
value: 76.34747339191613
- type: manhattan_pearson
value: 77.94417346436772
- type: manhattan_spearman
value: 76.15552728253843
- type: pearson
value: 76.5491270627292
- type: spearman
value: 76.34747339191613
- type: cosine_pearson
value: 76.5491270627292
- type: cosine_spearman
value: 76.34747339191613
- type: euclidean_pearson
value: 77.52703884917828
- type: euclidean_spearman
value: 76.34747339191613
- type: main_score
value: 76.34747339191613
- type: manhattan_pearson
value: 77.94417346436772
- type: manhattan_spearman
value: 76.15552728253843
- type: pearson
value: 76.5491270627292
- type: spearman
value: 76.34747339191613
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cosine_pearson
value: 81.44263012519023
- type: cosine_spearman
value: 84.1219543406234
- type: euclidean_pearson
value: 83.61379006230743
- type: euclidean_spearman
value: 84.1219543406234
- type: main_score
value: 84.1219543406234
- type: manhattan_pearson
value: 83.40336227949633
- type: manhattan_spearman
value: 83.94639826019986
- type: pearson
value: 81.44263012519023
- type: spearman
value: 84.1219543406234
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 80.99314184170828
- type: map
value: 80.99314184170828
- type: mrr
value: 94.80081918807409
- type: nAUC_map_diff1
value: 2.519316498883737
- type: nAUC_map_max
value: 53.83115362914741
- type: nAUC_map_std
value: 71.18884208143534
- type: nAUC_mrr_diff1
value: 49.13813585315955
- type: nAUC_mrr_max
value: 82.97821200827201
- type: nAUC_mrr_std
value: 80.52851378034694
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 72.887
- type: map_at_1
value: 56.983
- type: map_at_10
value: 68.02
- type: map_at_100
value: 68.47800000000001
- type: map_at_1000
value: 68.49300000000001
- type: map_at_20
value: 68.319
- type: map_at_3
value: 64.735
- type: map_at_5
value: 66.797
- type: mrr_at_1
value: 60.0
- type: mrr_at_10
value: 68.80674603174603
- type: mrr_at_100
value: 69.17683357934396
- type: mrr_at_1000
value: 69.19182270659053
- type: mrr_at_20
value: 69.04443591467584
- type: mrr_at_3
value: 66.22222222222221
- type: mrr_at_5
value: 67.88888888888889
- type: nauc_map_at_1000_diff1
value: 65.74751339600985
- type: nauc_map_at_1000_max
value: 51.01631394192724
- type: nauc_map_at_1000_std
value: -4.749077682272638
- type: nauc_map_at_100_diff1
value: 65.7511025839809
- type: nauc_map_at_100_max
value: 51.02179965776254
- type: nauc_map_at_100_std
value: -4.750648691834171
- type: nauc_map_at_10_diff1
value: 65.8078854827133
- type: nauc_map_at_10_max
value: 51.17250624028733
- type: nauc_map_at_10_std
value: -5.145740047184135
- type: nauc_map_at_1_diff1
value: 69.1712101533685
- type: nauc_map_at_1_max
value: 46.030719621812224
- type: nauc_map_at_1_std
value: -8.492699133614764
- type: nauc_map_at_20_diff1
value: 65.72860985168403
- type: nauc_map_at_20_max
value: 51.02243609996885
- type: nauc_map_at_20_std
value: -4.897680708725447
- type: nauc_map_at_3_diff1
value: 66.04795347641515
- type: nauc_map_at_3_max
value: 49.28417356777598
- type: nauc_map_at_3_std
value: -8.144355960324793
- type: nauc_map_at_5_diff1
value: 65.39564471101812
- type: nauc_map_at_5_max
value: 49.8786868247306
- type: nauc_map_at_5_std
value: -5.361632284686927
- type: nauc_mrr_at_1000_diff1
value: 65.35483966038701
- type: nauc_mrr_at_1000_max
value: 52.46038918168655
- type: nauc_mrr_at_1000_std
value: -2.9488525087661865
- type: nauc_mrr_at_100_diff1
value: 65.35869633533838
- type: nauc_mrr_at_100_max
value: 52.465078488348915
- type: nauc_mrr_at_100_std
value: -2.9515006369786185
- type: nauc_mrr_at_10_diff1
value: 65.32937085681456
- type: nauc_mrr_at_10_max
value: 52.69576591011457
- type: nauc_mrr_at_10_std
value: -3.211748975877107
- type: nauc_mrr_at_1_diff1
value: 68.74146631047786
- type: nauc_mrr_at_1_max
value: 50.13505491243696
- type: nauc_mrr_at_1_std
value: -1.8066686454932306
- type: nauc_mrr_at_20_diff1
value: 65.30292147401966
- type: nauc_mrr_at_20_max
value: 52.506885523141
- type: nauc_mrr_at_20_std
value: -3.030133033199617
- type: nauc_mrr_at_3_diff1
value: 64.76693508063609
- type: nauc_mrr_at_3_max
value: 52.16422755733336
- type: nauc_mrr_at_3_std
value: -4.375097949954107
- type: nauc_mrr_at_5_diff1
value: 64.72226560669719
- type: nauc_mrr_at_5_max
value: 52.402248270260976
- type: nauc_mrr_at_5_std
value: -2.3470762334639543
- type: nauc_ndcg_at_1000_diff1
value: 65.05291476181804
- type: nauc_ndcg_at_1000_max
value: 52.29203656356969
- type: nauc_ndcg_at_1000_std
value: -3.2044176855608937
- type: nauc_ndcg_at_100_diff1
value: 65.14234495976609
- type: nauc_ndcg_at_100_max
value: 52.648998314377046
- type: nauc_ndcg_at_100_std
value: -2.836497638004637
- type: nauc_ndcg_at_10_diff1
value: 65.07401371760211
- type: nauc_ndcg_at_10_max
value: 53.576715684408214
- type: nauc_ndcg_at_10_std
value: -4.308181380375265
- type: nauc_ndcg_at_1_diff1
value: 68.74146631047786
- type: nauc_ndcg_at_1_max
value: 50.13505491243696
- type: nauc_ndcg_at_1_std
value: -1.8066686454932306
- type: nauc_ndcg_at_20_diff1
value: 64.84825741925087
- type: nauc_ndcg_at_20_max
value: 52.92789159833541
- type: nauc_ndcg_at_20_std
value: -3.3072860677499856
- type: nauc_ndcg_at_3_diff1
value: 64.37308313406504
- type: nauc_ndcg_at_3_max
value: 51.32442115826017
- type: nauc_ndcg_at_3_std
value: -7.029903565507216
- type: nauc_ndcg_at_5_diff1
value: 63.86750880299841
- type: nauc_ndcg_at_5_max
value: 51.37420252304249
- type: nauc_ndcg_at_5_std
value: -3.6483750498877447
- type: nauc_precision_at_1000_diff1
value: -26.510729177260178
- type: nauc_precision_at_1000_max
value: 14.42625173734718
- type: nauc_precision_at_1000_std
value: 45.496457507034286
- type: nauc_precision_at_100_diff1
value: -14.374685492703476
- type: nauc_precision_at_100_max
value: 21.681911020335644
- type: nauc_precision_at_100_std
value: 42.16203415919248
- type: nauc_precision_at_10_diff1
value: 7.815716722545709
- type: nauc_precision_at_10_max
value: 36.03562535537121
- type: nauc_precision_at_10_std
value: 28.24000548987315
- type: nauc_precision_at_1_diff1
value: 68.74146631047786
- type: nauc_precision_at_1_max
value: 50.13505491243696
- type: nauc_precision_at_1_std
value: -1.8066686454932306
- type: nauc_precision_at_20_diff1
value: -1.0860405342771857
- type: nauc_precision_at_20_max
value: 29.773416761471406
- type: nauc_precision_at_20_std
value: 34.781738507563304
- type: nauc_precision_at_3_diff1
value: 36.2217738770724
- type: nauc_precision_at_3_max
value: 43.356155845594834
- type: nauc_precision_at_3_std
value: 9.506045734283152
- type: nauc_precision_at_5_diff1
value: 20.749541232335027
- type: nauc_precision_at_5_max
value: 38.57715809985047
- type: nauc_precision_at_5_std
value: 22.581774153881096
- type: nauc_recall_at_1000_diff1
value: 12.278244631182748
- type: nauc_recall_at_1000_max
value: -56.34920634920787
- type: nauc_recall_at_1000_std
value: 12.278244631182748
- type: nauc_recall_at_100_diff1
value: 64.01833460657008
- type: nauc_recall_at_100_max
value: 60.00763941940402
- type: nauc_recall_at_100_std
value: 18.572277395806154
- type: nauc_recall_at_10_diff1
value: 62.32242861171786
- type: nauc_recall_at_10_max
value: 62.75965074851927
- type: nauc_recall_at_10_std
value: -6.085630582444918
- type: nauc_recall_at_1_diff1
value: 69.1712101533685
- type: nauc_recall_at_1_max
value: 46.030719621812224
- type: nauc_recall_at_1_std
value: -8.492699133614764
- type: nauc_recall_at_20_diff1
value: 59.109050124114724
- type: nauc_recall_at_20_max
value: 59.41051217234864
- type: nauc_recall_at_20_std
value: 3.246339186081203
- type: nauc_recall_at_3_diff1
value: 60.981933198798856
- type: nauc_recall_at_3_max
value: 51.04347606806292
- type: nauc_recall_at_3_std
value: -12.852440576759369
- type: nauc_recall_at_5_diff1
value: 58.155380815418766
- type: nauc_recall_at_5_max
value: 52.3232345936131
- type: nauc_recall_at_5_std
value: -1.264784301127294
- type: ndcg_at_1
value: 60.0
- type: ndcg_at_10
value: 72.887
- type: ndcg_at_100
value: 74.92699999999999
- type: ndcg_at_1000
value: 75.351
- type: ndcg_at_20
value: 73.882
- type: ndcg_at_3
value: 67.289
- type: ndcg_at_5
value: 70.348
- type: precision_at_1
value: 60.0
- type: precision_at_10
value: 9.9
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.167
- type: precision_at_3
value: 26.556
- type: precision_at_5
value: 17.933
- type: recall_at_1
value: 56.983
- type: recall_at_10
value: 86.989
- type: recall_at_100
value: 96.333
- type: recall_at_1000
value: 99.667
- type: recall_at_20
value: 90.889
- type: recall_at_3
value: 71.989
- type: recall_at_5
value: 79.578
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cosine_accuracy
value: 99.73861386138614
- type: cosine_accuracy_threshold
value: 90.32489657402039
- type: cosine_ap
value: 92.64599869853095
- type: cosine_f1
value: 86.43371017471738
- type: cosine_f1_threshold
value: 90.2580976486206
- type: cosine_precision
value: 88.90063424947145
- type: cosine_recall
value: 84.1
- type: dot_accuracy
value: 99.73861386138614
- type: dot_accuracy_threshold
value: 90.32490253448486
- type: dot_ap
value: 92.64595539427177
- type: dot_f1
value: 86.43371017471738
- type: dot_f1_threshold
value: 90.25810956954956
- type: dot_precision
value: 88.90063424947145
- type: dot_recall
value: 84.1
- type: euclidean_accuracy
value: 99.73861386138614
- type: euclidean_accuracy_threshold
value: 43.988871574401855
- type: euclidean_ap
value: 92.64599869853096
- type: euclidean_f1
value: 86.43371017471738
- type: euclidean_f1_threshold
value: 44.14045214653015
- type: euclidean_precision
value: 88.90063424947145
- type: euclidean_recall
value: 84.1
- type: main_score
value: 93.05685558073557
- type: manhattan_accuracy
value: 99.74752475247524
- type: manhattan_accuracy_threshold
value: 1051.1520385742188
- type: manhattan_ap
value: 93.05685558073557
- type: manhattan_f1
value: 87.06240487062405
- type: manhattan_f1_threshold
value: 1051.5135765075684
- type: manhattan_precision
value: 88.36251287332647
- type: manhattan_recall
value: 85.8
- type: max_ap
value: 93.05685558073557
- type: max_f1
value: 87.06240487062405
- type: max_precision
value: 88.90063424947145
- type: max_recall
value: 85.8
- type: similarity_accuracy
value: 99.73861386138614
- type: similarity_accuracy_threshold
value: 90.32489657402039
- type: similarity_ap
value: 92.64599869853095
- type: similarity_f1
value: 86.43371017471738
- type: similarity_f1_threshold
value: 90.2580976486206
- type: similarity_precision
value: 88.90063424947145
- type: similarity_recall
value: 84.1
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 66.37519604053632
- type: v_measure
value: 66.37519604053632
- type: v_measure_std
value: 3.410007122108013
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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dataset:
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revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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dataset:
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revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 89.27734375
- type: ap
value: 35.194960239779064
- type: ap_weighted
value: 35.194960239779064
- type: f1
value: 74.3697271257718
- type: f1_weighted
value: 90.81303890497676
- type: main_score
value: 89.27734375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 76.3469156762875
- type: f1
value: 76.53667544013393
- type: f1_weighted
value: 75.88734027230643
- type: main_score
value: 76.3469156762875
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 49.32687063841142
- type: v_measure
value: 49.32687063841142
- type: v_measure_std
value: 1.4415471512453628
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 85.24169994635513
- type: cosine_accuracy_threshold
value: 91.52344465255737
- type: cosine_ap
value: 71.43912939014582
- type: cosine_f1
value: 66.87586293460525
- type: cosine_f1_threshold
value: 90.44243097305298
- type: cosine_precision
value: 63.777830979171654
- type: cosine_recall
value: 70.29023746701847
- type: dot_accuracy
value: 85.24169994635513
- type: dot_accuracy_threshold
value: 91.52344465255737
- type: dot_ap
value: 71.43910347543935
- type: dot_f1
value: 66.87586293460525
- type: dot_f1_threshold
value: 90.44243693351746
- type: dot_precision
value: 63.777830979171654
- type: dot_recall
value: 70.29023746701847
- type: euclidean_accuracy
value: 85.24169994635513
- type: euclidean_accuracy_threshold
value: 41.17414355278015
- type: euclidean_ap
value: 71.43911775197303
- type: euclidean_f1
value: 66.87586293460525
- type: euclidean_f1_threshold
value: 43.72085928916931
- type: euclidean_precision
value: 63.777830979171654
- type: euclidean_recall
value: 70.29023746701847
- type: main_score
value: 71.43912939014582
- type: manhattan_accuracy
value: 85.02115992132086
- type: manhattan_accuracy_threshold
value: 970.2783584594727
- type: manhattan_ap
value: 71.11811615315935
- type: manhattan_f1
value: 66.46334024643161
- type: manhattan_f1_threshold
value: 1044.2398071289062
- type: manhattan_precision
value: 61.81075561606535
- type: manhattan_recall
value: 71.87335092348285
- type: max_ap
value: 71.43912939014582
- type: max_f1
value: 66.87586293460525
- type: max_precision
value: 63.777830979171654
- type: max_recall
value: 71.87335092348285
- type: similarity_accuracy
value: 85.24169994635513
- type: similarity_accuracy_threshold
value: 91.52344465255737
- type: similarity_ap
value: 71.43912939014582
- type: similarity_f1
value: 66.87586293460525
- type: similarity_f1_threshold
value: 90.44243097305298
- type: similarity_precision
value: 63.777830979171654
- type: similarity_recall
value: 70.29023746701847
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 88.85590095859045
- type: cosine_accuracy_threshold
value: 88.8430118560791
- type: cosine_ap
value: 85.68756441554032
- type: cosine_f1
value: 77.73691444704485
- type: cosine_f1_threshold
value: 87.81020641326904
- type: cosine_precision
value: 75.10587897494796
- type: cosine_recall
value: 80.55897751770866
- type: dot_accuracy
value: 88.85590095859045
- type: dot_accuracy_threshold
value: 88.84301781654358
- type: dot_ap
value: 85.6875209900991
- type: dot_f1
value: 77.73691444704485
- type: dot_f1_threshold
value: 87.810218334198
- type: dot_precision
value: 75.10587897494796
- type: dot_recall
value: 80.55897751770866
- type: euclidean_accuracy
value: 88.85590095859045
- type: euclidean_accuracy_threshold
value: 47.23767042160034
- type: euclidean_ap
value: 85.68752572543856
- type: euclidean_f1
value: 77.73691444704485
- type: euclidean_f1_threshold
value: 49.375683069229126
- type: euclidean_precision
value: 75.10587897494796
- type: euclidean_recall
value: 80.55897751770866
- type: main_score
value: 85.68756441554032
- type: manhattan_accuracy
value: 88.86366282454303
- type: manhattan_accuracy_threshold
value: 1111.7809295654297
- type: manhattan_ap
value: 85.65387985046206
- type: manhattan_f1
value: 77.74802915365164
- type: manhattan_f1_threshold
value: 1163.0821228027344
- type: manhattan_precision
value: 75.18699654775605
- type: manhattan_recall
value: 80.48968278410841
- type: max_ap
value: 85.68756441554032
- type: max_f1
value: 77.74802915365164
- type: max_precision
value: 75.18699654775605
- type: max_recall
value: 80.55897751770866
- type: similarity_accuracy
value: 88.85590095859045
- type: similarity_accuracy_threshold
value: 88.8430118560791
- type: similarity_ap
value: 85.68756441554032
- type: similarity_f1
value: 77.73691444704485
- type: similarity_f1_threshold
value: 87.81020641326904
- type: similarity_precision
value: 75.10587897494796
- type: similarity_recall
value: 80.55897751770866
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cosine_pearson
value: 37.12876239452581
- type: cosine_spearman
value: 38.067257174211036
- type: euclidean_pearson
value: 36.735658383168044
- type: euclidean_spearman
value: 38.06725717137013
- type: main_score
value: 38.067257174211036
- type: manhattan_pearson
value: 36.43829602976309
- type: manhattan_spearman
value: 37.73959740816105
- type: pearson
value: 37.12876239452581
- type: spearman
value: 38.067257174211036
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 45.55151167790002
- type: cosine_spearman
value: 46.22151486832593
- type: euclidean_pearson
value: 48.31921364975583
- type: euclidean_spearman
value: 46.22151824485588
- type: main_score
value: 46.22151486832593
- type: manhattan_pearson
value: 48.12062382773407
- type: manhattan_spearman
value: 46.00780031287469
- type: pearson
value: 45.55151167790002
- type: spearman
value: 46.22151486832593
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.978
- type: f1
value: 47.04754599162115
- type: f1_weighted
value: 47.04754599162114
- type: main_score
value: 50.978
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cosine_pearson
value: 53.84488943094217
- type: cosine_spearman
value: 54.49605523234321
- type: euclidean_pearson
value: 53.50943690170382
- type: euclidean_spearman
value: 54.49605869546392
- type: main_score
value: 54.49605523234321
- type: manhattan_pearson
value: 53.24052011197216
- type: manhattan_spearman
value: 54.215799016988996
- type: pearson
value: 53.84488943094217
- type: spearman
value: 54.49605523234321
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 46.915444917880286
- type: v_measure
value: 46.915444917880286
- type: v_measure_std
value: 1.5837973591025165
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 44.89461372862163
- type: v_measure
value: 44.89461372862163
- type: v_measure_std
value: 1.0128909727353739
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: main_score
value: 82.21191216422777
- type: map
value: 82.21191216422777
- type: mrr
value: 84.98567460317462
- type: nAUC_map_diff1
value: 55.27793122843884
- type: nAUC_map_max
value: 61.78021845841622
- type: nAUC_map_std
value: 24.32874017213633
- type: nAUC_mrr_diff1
value: 62.56855683834338
- type: nAUC_mrr_max
value: 69.83242448232605
- type: nAUC_mrr_std
value: 30.753945346898497
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: main_score
value: 83.08227189741763
- type: map
value: 83.08227189741763
- type: mrr
value: 86.19051587301587
- type: nAUC_map_diff1
value: 58.735936259553654
- type: nAUC_map_max
value: 60.14841462146858
- type: nAUC_map_std
value: 17.761882943403222
- type: nAUC_mrr_diff1
value: 67.43320028439246
- type: nAUC_mrr_max
value: 70.96971853942028
- type: nAUC_mrr_std
value: 27.963212156051533
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
- type: main_score
value: 42.114000000000004
- type: map_at_1
value: 24.712999999999997
- type: map_at_10
value: 35.977
- type: map_at_100
value: 37.626
- type: map_at_1000
value: 37.768
- type: map_at_20
value: 36.855
- type: map_at_3
value: 32.263
- type: map_at_5
value: 34.458
- type: mrr_at_1
value: 37.80945236309077
- type: mrr_at_10
value: 45.2176833891012
- type: mrr_at_100
value: 46.120647602744036
- type: mrr_at_1000
value: 46.18072259198463
- type: mrr_at_20
value: 45.733062196151344
- type: mrr_at_3
value: 42.95657247645238
- type: mrr_at_5
value: 44.2944069350671
- type: nauc_map_at_1000_diff1
value: 47.73425387510818
- type: nauc_map_at_1000_max
value: 35.955688760781
- type: nauc_map_at_1000_std
value: -5.337048907024208
- type: nauc_map_at_100_diff1
value: 47.691721439486415
- type: nauc_map_at_100_max
value: 35.9120286982508
- type: nauc_map_at_100_std
value: -5.388715572820414
- type: nauc_map_at_10_diff1
value: 47.59595769097994
- type: nauc_map_at_10_max
value: 34.868575436637464
- type: nauc_map_at_10_std
value: -6.38635344513967
- type: nauc_map_at_1_diff1
value: 51.697402901732794
- type: nauc_map_at_1_max
value: 27.169028835962006
- type: nauc_map_at_1_std
value: -8.36280973549372
- type: nauc_map_at_20_diff1
value: 47.6021479339286
- type: nauc_map_at_20_max
value: 35.42358169640706
- type: nauc_map_at_20_std
value: -5.995707059550167
- type: nauc_map_at_3_diff1
value: 48.12995337761301
- type: nauc_map_at_3_max
value: 32.29594600680629
- type: nauc_map_at_3_std
value: -7.995165343021071
- type: nauc_map_at_5_diff1
value: 47.97666743341936
- type: nauc_map_at_5_max
value: 33.98865164184045
- type: nauc_map_at_5_std
value: -7.202624274535322
- type: nauc_mrr_at_1000_diff1
value: 53.91400893930025
- type: nauc_mrr_at_1000_max
value: 42.756637412490015
- type: nauc_mrr_at_1000_std
value: -1.7033054319306657
- type: nauc_mrr_at_100_diff1
value: 53.88165315274917
- type: nauc_mrr_at_100_max
value: 42.751083094268274
- type: nauc_mrr_at_100_std
value: -1.6918871915944649
- type: nauc_mrr_at_10_diff1
value: 53.91581744122965
- type: nauc_mrr_at_10_max
value: 42.58709846134356
- type: nauc_mrr_at_10_std
value: -1.9103231104709821
- type: nauc_mrr_at_1_diff1
value: 59.37942921389244
- type: nauc_mrr_at_1_max
value: 43.889994340792285
- type: nauc_mrr_at_1_std
value: -2.9334150073957
- type: nauc_mrr_at_20_diff1
value: 53.82364054543749
- type: nauc_mrr_at_20_max
value: 42.657132121329866
- type: nauc_mrr_at_20_std
value: -1.814080881374272
- type: nauc_mrr_at_3_diff1
value: 54.82194831261354
- type: nauc_mrr_at_3_max
value: 42.867175235515795
- type: nauc_mrr_at_3_std
value: -2.3672343827332343
- type: nauc_mrr_at_5_diff1
value: 54.28110236719863
- type: nauc_mrr_at_5_max
value: 42.773659727997604
- type: nauc_mrr_at_5_std
value: -1.9843150717670213
- type: nauc_ndcg_at_1000_diff1
value: 48.08750394739328
- type: nauc_ndcg_at_1000_max
value: 39.354002076766356
- type: nauc_ndcg_at_1000_std
value: -1.642209870956442
- type: nauc_ndcg_at_100_diff1
value: 47.02373205823187
- type: nauc_ndcg_at_100_max
value: 39.071767544616016
- type: nauc_ndcg_at_100_std
value: -1.4318887896374275
- type: nauc_ndcg_at_10_diff1
value: 46.87201101025155
- type: nauc_ndcg_at_10_max
value: 36.596107252610715
- type: nauc_ndcg_at_10_std
value: -4.491855380124351
- type: nauc_ndcg_at_1_diff1
value: 59.37942921389244
- type: nauc_ndcg_at_1_max
value: 43.889994340792285
- type: nauc_ndcg_at_1_std
value: -2.9334150073957
- type: nauc_ndcg_at_20_diff1
value: 46.69383079853486
- type: nauc_ndcg_at_20_max
value: 37.42651190472187
- type: nauc_ndcg_at_20_std
value: -3.8283649672503555
- type: nauc_ndcg_at_3_diff1
value: 48.767394875248215
- type: nauc_ndcg_at_3_max
value: 37.896558640830364
- type: nauc_ndcg_at_3_std
value: -4.549323419648498
- type: nauc_ndcg_at_5_diff1
value: 47.86427501476368
- type: nauc_ndcg_at_5_max
value: 36.911634117836165
- type: nauc_ndcg_at_5_std
value: -4.724216711512233
- type: nauc_precision_at_1000_diff1
value: 5.215840982527869
- type: nauc_precision_at_1000_max
value: 29.159568859241432
- type: nauc_precision_at_1000_std
value: 21.082099239516065
- type: nauc_precision_at_100_diff1
value: 11.484383878054516
- type: nauc_precision_at_100_max
value: 36.979612782453096
- type: nauc_precision_at_100_std
value: 21.37719349152994
- type: nauc_precision_at_10_diff1
value: 25.43246921524832
- type: nauc_precision_at_10_max
value: 42.19035665238435
- type: nauc_precision_at_10_std
value: 9.100059364841798
- type: nauc_precision_at_1_diff1
value: 59.37942921389244
- type: nauc_precision_at_1_max
value: 43.889994340792285
- type: nauc_precision_at_1_std
value: -2.9334150073957
- type: nauc_precision_at_20_diff1
value: 20.71145394651256
- type: nauc_precision_at_20_max
value: 40.864109895424676
- type: nauc_precision_at_20_std
value: 12.37767593665739
- type: nauc_precision_at_3_diff1
value: 36.796679107157864
- type: nauc_precision_at_3_max
value: 43.86084233138964
- type: nauc_precision_at_3_std
value: 2.284927565289776
- type: nauc_precision_at_5_diff1
value: 31.736303229558853
- type: nauc_precision_at_5_max
value: 43.979769552220695
- type: nauc_precision_at_5_std
value: 4.953476938204816
- type: nauc_recall_at_1000_diff1
value: 33.99656164674807
- type: nauc_recall_at_1000_max
value: 51.409315146611604
- type: nauc_recall_at_1000_std
value: 46.40969047509407
- type: nauc_recall_at_100_diff1
value: 28.008400601965988
- type: nauc_recall_at_100_max
value: 35.45902334586444
- type: nauc_recall_at_100_std
value: 12.00354716277477
- type: nauc_recall_at_10_diff1
value: 35.11874174482894
- type: nauc_recall_at_10_max
value: 29.383597829116265
- type: nauc_recall_at_10_std
value: -3.990278366414269
- type: nauc_recall_at_1_diff1
value: 51.697402901732794
- type: nauc_recall_at_1_max
value: 27.169028835962006
- type: nauc_recall_at_1_std
value: -8.36280973549372
- type: nauc_recall_at_20_diff1
value: 32.85706970308098
- type: nauc_recall_at_20_max
value: 30.609762001249297
- type: nauc_recall_at_20_std
value: -2.0802705155631305
- type: nauc_recall_at_3_diff1
value: 41.293789470043485
- type: nauc_recall_at_3_max
value: 29.091549134985883
- type: nauc_recall_at_3_std
value: -7.863274012056401
- type: nauc_recall_at_5_diff1
value: 38.87911660795755
- type: nauc_recall_at_5_max
value: 29.81974055210633
- type: nauc_recall_at_5_std
value: -5.940913211156581
- type: ndcg_at_1
value: 37.809
- type: ndcg_at_10
value: 42.114000000000004
- type: ndcg_at_100
value: 48.76
- type: ndcg_at_1000
value: 51.41
- type: ndcg_at_20
value: 44.528
- type: ndcg_at_3
value: 37.621
- type: ndcg_at_5
value: 39.627
- type: precision_at_1
value: 37.809
- type: precision_at_10
value: 9.215
- type: precision_at_100
value: 1.468
- type: precision_at_1000
value: 0.18
- type: precision_at_20
value: 5.423
- type: precision_at_3
value: 21.214
- type: precision_at_5
value: 15.323999999999998
- type: recall_at_1
value: 24.712999999999997
- type: recall_at_10
value: 51.08200000000001
- type: recall_at_100
value: 78.855
- type: recall_at_1000
value: 96.897
- type: recall_at_20
value: 59.232
- type: recall_at_3
value: 37.147000000000006
- type: recall_at_5
value: 43.579
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
metrics:
- type: cosine_accuracy
value: 69.70535177390259
- type: cosine_accuracy_threshold
value: 85.75683832168579
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dataset:
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type: C-MTEB/EcomRetrieval
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type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
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dataset:
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type: C-MTEB/JDReview-classification
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split: test
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
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dataset:
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type: C-MTEB/LCQMC
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revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
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- type: ndcg_at_10
value: 78.975
- type: ndcg_at_100
value: 80.415
- type: ndcg_at_1000
value: 80.683
- type: ndcg_at_20
value: 79.752
- type: ndcg_at_3
value: 75.315
- type: ndcg_at_5
value: 77.336
- type: precision_at_1
value: 68.052
- type: precision_at_10
value: 9.602
- type: precision_at_100
value: 1.031
- type: precision_at_1000
value: 0.105
- type: precision_at_20
value: 4.968
- type: precision_at_3
value: 28.405
- type: precision_at_5
value: 18.129
- type: recall_at_1
value: 65.864
- type: recall_at_10
value: 90.29599999999999
- type: recall_at_100
value: 96.712
- type: recall_at_1000
value: 98.817
- type: recall_at_20
value: 93.28999999999999
- type: recall_at_3
value: 80.599
- type: recall_at_5
value: 85.384
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 68.52723604572965
- type: f1
value: 65.11440259918176
- type: f1_weighted
value: 67.36553063592758
- type: main_score
value: 68.52723604572965
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-TW)
type: mteb/amazon_massive_intent
config: zh-TW
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 60.00672494956289
- type: f1
value: 57.630108926504654
- type: f1_weighted
value: 58.013112273357294
- type: main_score
value: 60.00672494956289
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 75.48755884330868
- type: f1
value: 74.96341150854326
- type: f1_weighted
value: 75.11344709750414
- type: main_score
value: 75.48755884330868
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 69.20645595158035
- type: f1
value: 69.57805291990489
- type: f1_weighted
value: 68.41325928547694
- type: main_score
value: 69.20645595158035
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
- type: main_score
value: 56.842999999999996
- type: map_at_1
value: 48.4
- type: map_at_10
value: 54.127
- type: map_at_100
value: 54.74099999999999
- type: map_at_1000
value: 54.791999999999994
- type: map_at_20
value: 54.510999999999996
- type: map_at_3
value: 52.900000000000006
- type: map_at_5
value: 53.555
- type: mrr_at_1
value: 48.4
- type: mrr_at_10
value: 54.12742063492064
- type: mrr_at_100
value: 54.74007856972957
- type: mrr_at_1000
value: 54.79120377168234
- type: mrr_at_20
value: 54.50988389233748
- type: mrr_at_3
value: 52.89999999999999
- type: mrr_at_5
value: 53.554999999999986
- type: nauc_map_at_1000_diff1
value: 78.4025670906739
- type: nauc_map_at_1000_max
value: 54.375261014986506
- type: nauc_map_at_1000_std
value: 20.01404778643528
- type: nauc_map_at_100_diff1
value: 78.38648222989812
- type: nauc_map_at_100_max
value: 54.36062306870368
- type: nauc_map_at_100_std
value: 20.027286514844604
- type: nauc_map_at_10_diff1
value: 78.66966790097088
- type: nauc_map_at_10_max
value: 54.49489942528706
- type: nauc_map_at_10_std
value: 19.706732968916686
- type: nauc_map_at_1_diff1
value: 81.3863109971009
- type: nauc_map_at_1_max
value: 54.45112546839308
- type: nauc_map_at_1_std
value: 16.647233102073926
- type: nauc_map_at_20_diff1
value: 78.35531215313732
- type: nauc_map_at_20_max
value: 54.38122778164954
- type: nauc_map_at_20_std
value: 19.919068985031778
- type: nauc_map_at_3_diff1
value: 78.76127354528398
- type: nauc_map_at_3_max
value: 54.73984834363167
- type: nauc_map_at_3_std
value: 18.978057003463505
- type: nauc_map_at_5_diff1
value: 78.86532352860041
- type: nauc_map_at_5_max
value: 54.89472733309794
- type: nauc_map_at_5_std
value: 19.905059369622226
- type: nauc_mrr_at_1000_diff1
value: 78.40339084857348
- type: nauc_mrr_at_1000_max
value: 54.3765713570027
- type: nauc_mrr_at_1000_std
value: 20.01339841032316
- type: nauc_mrr_at_100_diff1
value: 78.38730500946644
- type: nauc_mrr_at_100_max
value: 54.361931749382975
- type: nauc_mrr_at_100_std
value: 20.026637904594022
- type: nauc_mrr_at_10_diff1
value: 78.66966790097088
- type: nauc_mrr_at_10_max
value: 54.49489942528706
- type: nauc_mrr_at_10_std
value: 19.706732968916686
- type: nauc_mrr_at_1_diff1
value: 81.3863109971009
- type: nauc_mrr_at_1_max
value: 54.45112546839308
- type: nauc_mrr_at_1_std
value: 16.647233102073926
- type: nauc_mrr_at_20_diff1
value: 78.3561572811205
- type: nauc_mrr_at_20_max
value: 54.38258490064744
- type: nauc_mrr_at_20_std
value: 19.918521746919023
- type: nauc_mrr_at_3_diff1
value: 78.76127354528398
- type: nauc_mrr_at_3_max
value: 54.73984834363167
- type: nauc_mrr_at_3_std
value: 18.978057003463505
- type: nauc_mrr_at_5_diff1
value: 78.86532352860041
- type: nauc_mrr_at_5_max
value: 54.89472733309794
- type: nauc_mrr_at_5_std
value: 19.905059369622226
- type: nauc_ndcg_at_1000_diff1
value: 76.97351511935506
- type: nauc_ndcg_at_1000_max
value: 53.77087107676227
- type: nauc_ndcg_at_1000_std
value: 22.05965205980897
- type: nauc_ndcg_at_100_diff1
value: 76.53100615219961
- type: nauc_ndcg_at_100_max
value: 53.310394382128834
- type: nauc_ndcg_at_100_std
value: 22.50789785109818
- type: nauc_ndcg_at_10_diff1
value: 77.54846078594903
- type: nauc_ndcg_at_10_max
value: 53.933288762789324
- type: nauc_ndcg_at_10_std
value: 20.831152333725047
- type: nauc_ndcg_at_1_diff1
value: 81.3863109971009
- type: nauc_ndcg_at_1_max
value: 54.45112546839308
- type: nauc_ndcg_at_1_std
value: 16.647233102073926
- type: nauc_ndcg_at_20_diff1
value: 76.34535899505026
- type: nauc_ndcg_at_20_max
value: 53.51900448424713
- type: nauc_ndcg_at_20_std
value: 21.786462318073514
- type: nauc_ndcg_at_3_diff1
value: 77.9282109219262
- type: nauc_ndcg_at_3_max
value: 54.68499432962286
- type: nauc_ndcg_at_3_std
value: 19.565421430997855
- type: nauc_ndcg_at_5_diff1
value: 78.08531364224505
- type: nauc_ndcg_at_5_max
value: 54.990763159275524
- type: nauc_ndcg_at_5_std
value: 21.24642472562554
- type: nauc_precision_at_1000_diff1
value: 60.947712418300526
- type: nauc_precision_at_1000_max
value: 48.95614495798286
- type: nauc_precision_at_1000_std
value: 52.283934407096034
- type: nauc_precision_at_100_diff1
value: 65.20401832958854
- type: nauc_precision_at_100_max
value: 45.622481375866016
- type: nauc_precision_at_100_std
value: 39.317645096209475
- type: nauc_precision_at_10_diff1
value: 73.52050673935791
- type: nauc_precision_at_10_max
value: 51.501061174147324
- type: nauc_precision_at_10_std
value: 24.660571885937692
- type: nauc_precision_at_1_diff1
value: 81.3863109971009
- type: nauc_precision_at_1_max
value: 54.45112546839308
- type: nauc_precision_at_1_std
value: 16.647233102073926
- type: nauc_precision_at_20_diff1
value: 67.23012065064371
- type: nauc_precision_at_20_max
value: 49.276980564706854
- type: nauc_precision_at_20_std
value: 30.08840589323484
- type: nauc_precision_at_3_diff1
value: 75.41179105476107
- type: nauc_precision_at_3_max
value: 54.46883825911932
- type: nauc_precision_at_3_std
value: 21.287190629517518
- type: nauc_precision_at_5_diff1
value: 75.66706449322432
- type: nauc_precision_at_5_max
value: 55.30373302312931
- type: nauc_precision_at_5_std
value: 25.617663499161512
- type: nauc_recall_at_1000_diff1
value: 60.94771241830054
- type: nauc_recall_at_1000_max
value: 48.956144957983234
- type: nauc_recall_at_1000_std
value: 52.283934407096
- type: nauc_recall_at_100_diff1
value: 65.20401832958845
- type: nauc_recall_at_100_max
value: 45.622481375866066
- type: nauc_recall_at_100_std
value: 39.31764509620953
- type: nauc_recall_at_10_diff1
value: 73.52050673935793
- type: nauc_recall_at_10_max
value: 51.5010611741474
- type: nauc_recall_at_10_std
value: 24.660571885937728
- type: nauc_recall_at_1_diff1
value: 81.3863109971009
- type: nauc_recall_at_1_max
value: 54.45112546839308
- type: nauc_recall_at_1_std
value: 16.647233102073926
- type: nauc_recall_at_20_diff1
value: 67.2301206506438
- type: nauc_recall_at_20_max
value: 49.276980564706946
- type: nauc_recall_at_20_std
value: 30.08840589323487
- type: nauc_recall_at_3_diff1
value: 75.411791054761
- type: nauc_recall_at_3_max
value: 54.468838259119266
- type: nauc_recall_at_3_std
value: 21.287190629517486
- type: nauc_recall_at_5_diff1
value: 75.66706449322434
- type: nauc_recall_at_5_max
value: 55.30373302312929
- type: nauc_recall_at_5_std
value: 25.61766349916154
- type: ndcg_at_1
value: 48.4
- type: ndcg_at_10
value: 56.842999999999996
- type: ndcg_at_100
value: 60.035000000000004
- type: ndcg_at_1000
value: 61.592999999999996
- type: ndcg_at_20
value: 58.219
- type: ndcg_at_3
value: 54.269
- type: ndcg_at_5
value: 55.435
- type: precision_at_1
value: 48.4
- type: precision_at_10
value: 6.54
- type: precision_at_100
value: 0.8089999999999999
- type: precision_at_1000
value: 0.094
- type: precision_at_20
value: 3.54
- type: precision_at_3
value: 19.400000000000002
- type: precision_at_5
value: 12.2
- type: recall_at_1
value: 48.4
- type: recall_at_10
value: 65.4
- type: recall_at_100
value: 80.9
- type: recall_at_1000
value: 93.60000000000001
- type: recall_at_20
value: 70.8
- type: recall_at_3
value: 58.199999999999996
- type: recall_at_5
value: 61.0
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: test
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 78.22
- type: f1
value: 77.72387105131016
- type: f1_weighted
value: 77.72387105131016
- type: main_score
value: 78.22
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cosine_accuracy
value: 66.21548456957228
- type: cosine_accuracy_threshold
value: 85.5343759059906
- type: cosine_ap
value: 69.21438509764522
- type: cosine_f1
value: 70.44534412955467
- type: cosine_f1_threshold
value: 83.85478258132935
- type: cosine_precision
value: 61.36363636363637
- type: cosine_recall
value: 82.68215417106653
- type: dot_accuracy
value: 66.21548456957228
- type: dot_accuracy_threshold
value: 85.5343759059906
- type: dot_ap
value: 69.21438509764522
- type: dot_f1
value: 70.44534412955467
- type: dot_f1_threshold
value: 83.85477662086487
- type: dot_precision
value: 61.36363636363637
- type: dot_recall
value: 82.68215417106653
- type: euclidean_accuracy
value: 66.21548456957228
- type: euclidean_accuracy_threshold
value: 53.787779808044434
- type: euclidean_ap
value: 69.21438509764522
- type: euclidean_f1
value: 70.44534412955467
- type: euclidean_f1_threshold
value: 56.82467818260193
- type: euclidean_precision
value: 61.36363636363637
- type: euclidean_recall
value: 82.68215417106653
- type: main_score
value: 69.21438509764522
- type: manhattan_accuracy
value: 65.89063345966431
- type: manhattan_accuracy_threshold
value: 1257.1067810058594
- type: manhattan_ap
value: 68.93169452602294
- type: manhattan_f1
value: 70.018281535649
- type: manhattan_f1_threshold
value: 1321.6852188110352
- type: manhattan_precision
value: 61.72441579371475
- type: manhattan_recall
value: 80.8870116156283
- type: max_ap
value: 69.21438509764522
- type: max_f1
value: 70.44534412955467
- type: max_precision
value: 61.72441579371475
- type: max_recall
value: 82.68215417106653
- type: similarity_accuracy
value: 66.21548456957228
- type: similarity_accuracy_threshold
value: 85.5343759059906
- type: similarity_ap
value: 69.21438509764522
- type: similarity_f1
value: 70.44534412955467
- type: similarity_f1_threshold
value: 83.85478258132935
- type: similarity_precision
value: 61.36363636363637
- type: similarity_recall
value: 82.68215417106653
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 93.09
- type: ap
value: 92.02754366798146
- type: ap_weighted
value: 92.02754366798146
- type: f1
value: 93.08751542879655
- type: f1_weighted
value: 93.092429173112
- type: main_score
value: 93.09
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cosine_pearson
value: 14.700393205389778
- type: cosine_spearman
value: 16.3328315656736
- type: euclidean_pearson
value: 18.37941425496121
- type: euclidean_spearman
value: 16.35419723095118
- type: main_score
value: 16.3328315656736
- type: manhattan_pearson
value: 18.461692452314196
- type: manhattan_spearman
value: 16.49877203419426
- type: pearson
value: 14.700393205389778
- type: spearman
value: 16.3328315656736
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cosine_pearson
value: 31.771341848950048
- type: cosine_spearman
value: 35.21248641540282
- type: euclidean_pearson
value: 32.86721005963099
- type: euclidean_spearman
value: 35.21281584988107
- type: main_score
value: 35.21248641540282
- type: manhattan_pearson
value: 32.994593737560464
- type: manhattan_spearman
value: 35.29332061037625
- type: pearson
value: 31.771341848950048
- type: spearman
value: 35.21248641540282
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 74.05009462610907
- type: cosine_spearman
value: 73.57662463047618
- type: euclidean_pearson
value: 71.97192342862905
- type: euclidean_spearman
value: 73.57662463047618
- type: main_score
value: 73.57662463047618
- type: manhattan_pearson
value: 71.66171363912555
- type: manhattan_spearman
value: 73.33995365752388
- type: pearson
value: 74.05009462610907
- type: spearman
value: 73.57662463047618
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
metrics:
- type: cosine_pearson
value: 76.44164798054604
- type: cosine_spearman
value: 77.80818016295537
- type: euclidean_pearson
value: 77.23235480541429
- type: euclidean_spearman
value: 77.80820115269609
- type: main_score
value: 77.80818016295537
- type: manhattan_pearson
value: 77.1383518573764
- type: manhattan_spearman
value: 77.74335331319868
- type: pearson
value: 76.44164798054604
- type: spearman
value: 77.80818016295537
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: main_score
value: 66.73229864475817
- type: map
value: 66.73229864475817
- type: mrr
value: 76.93718364122901
- type: nAUC_map_diff1
value: -10.073077377159336
- type: nAUC_map_max
value: 36.06382407917089
- type: nAUC_map_std
value: -2.3465936800383975
- type: nAUC_mrr_diff1
value: -7.614514359731517
- type: nAUC_mrr_max
value: 29.970126517689895
- type: nAUC_mrr_std
value: -4.000393719743965
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
metrics:
- type: main_score
value: 84.066
- type: map_at_1
value: 27.311999999999998
- type: map_at_10
value: 76.45100000000001
- type: map_at_100
value: 80.081
- type: map_at_1000
value: 80.148
- type: map_at_20
value: 79.19500000000001
- type: map_at_3
value: 53.785000000000004
- type: map_at_5
value: 66.08800000000001
- type: mrr_at_1
value: 89.5493599859723
- type: mrr_at_10
value: 92.04882420558401
- type: mrr_at_100
value: 92.13851397485055
- type: mrr_at_1000
value: 92.142380377799
- type: mrr_at_20
value: 92.10835206390658
- type: mrr_at_3
value: 91.55634461394584
- type: mrr_at_5
value: 91.86626921503318
- type: nauc_map_at_1000_diff1
value: 13.881593411464955
- type: nauc_map_at_1000_max
value: 45.98958134604669
- type: nauc_map_at_1000_std
value: 20.749937417202162
- type: nauc_map_at_100_diff1
value: 13.897201544868256
- type: nauc_map_at_100_max
value: 45.8975771537659
- type: nauc_map_at_100_std
value: 20.702471991537816
- type: nauc_map_at_10_diff1
value: 18.574187481021404
- type: nauc_map_at_10_max
value: 33.03798476409084
- type: nauc_map_at_10_std
value: 4.923864341255681
- type: nauc_map_at_1_diff1
value: 53.60647320762889
- type: nauc_map_at_1_max
value: -25.482104681284262
- type: nauc_map_at_1_std
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type: C-MTEB/waimai-classification
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revision: 339287def212450dcaa9df8c22bf93e9980c7023
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revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
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config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 83.3649289099526
- type: f1
value: 79.77180137779536
- type: f1_weighted
value: 83.01264683214308
- type: main_score
value: 83.3649289099526
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: main_score
value: 56.59401317251221
- type: v_measure
value: 56.59401317251221
- type: v_measure_std
value: 36.0760983348482
- type: main_score
value: 71.82457224763512
- type: v_measure
value: 71.82457224763512
- type: v_measure_std
value: 33.00235264955351
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 66.61398789509077
- type: f1
value: 60.956092562089324
- type: f1_weighted
value: 64.68321586762302
- type: main_score
value: 66.61398789509077
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 75.68594485541358
- type: f1
value: 74.71713629733614
- type: f1_weighted
value: 75.49907502505853
- type: main_score
value: 75.68594485541358
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: main_score
value: 28.939999999999998
- type: map_at_1
value: 16.585
- type: map_at_10
value: 24.496000000000002
- type: map_at_100
value: 25.769
- type: map_at_1000
value: 25.857999999999997
- type: map_at_20
value: 25.224000000000004
- type: map_at_3
value: 21.908
- type: map_at_5
value: 23.464
- type: mrr_at_1
value: 16.584766584766587
- type: mrr_at_10
value: 24.49603824603824
- type: mrr_at_100
value: 25.76910538724444
- type: mrr_at_1000
value: 25.857970951595167
- type: mrr_at_20
value: 25.223803880220952
- type: mrr_at_3
value: 21.908271908271885
- type: mrr_at_5
value: 23.46437346437344
- type: nauc_map_at_1000_diff1
value: 24.449324509741782
- type: nauc_map_at_1000_max
value: 16.857551392045668
- type: nauc_map_at_1000_std
value: 9.029392522714542
- type: nauc_map_at_100_diff1
value: 24.416377398368994
- type: nauc_map_at_100_max
value: 16.874285303124278
- type: nauc_map_at_100_std
value: 9.042769202417494
- type: nauc_map_at_10_diff1
value: 24.30792102146879
- type: nauc_map_at_10_max
value: 16.618278881262178
- type: nauc_map_at_10_std
value: 8.925464095934705
- type: nauc_map_at_1_diff1
value: 29.76904844307235
- type: nauc_map_at_1_max
value: 14.721060890557535
- type: nauc_map_at_1_std
value: 4.1356111912072935
- type: nauc_map_at_20_diff1
value: 24.42346651590614
- type: nauc_map_at_20_max
value: 16.780464496035194
- type: nauc_map_at_20_std
value: 8.981541510161698
- type: nauc_map_at_3_diff1
value: 25.463420671464554
- type: nauc_map_at_3_max
value: 16.4183897697643
- type: nauc_map_at_3_std
value: 7.625690829397712
- type: nauc_map_at_5_diff1
value: 24.93868444905917
- type: nauc_map_at_5_max
value: 16.091635346098528
- type: nauc_map_at_5_std
value: 8.39683861182176
- type: nauc_mrr_at_1000_diff1
value: 24.449324509741782
- type: nauc_mrr_at_1000_max
value: 16.857551392045668
- type: nauc_mrr_at_1000_std
value: 9.029392522714542
- type: nauc_mrr_at_100_diff1
value: 24.416377398368994
- type: nauc_mrr_at_100_max
value: 16.874285303124278
- type: nauc_mrr_at_100_std
value: 9.042769202417494
- type: nauc_mrr_at_10_diff1
value: 24.30792102146879
- type: nauc_mrr_at_10_max
value: 16.618278881262178
- type: nauc_mrr_at_10_std
value: 8.925464095934705
- type: nauc_mrr_at_1_diff1
value: 29.76904844307235
- type: nauc_mrr_at_1_max
value: 14.721060890557535
- type: nauc_mrr_at_1_std
value: 4.1356111912072935
- type: nauc_mrr_at_20_diff1
value: 24.42346651590614
- type: nauc_mrr_at_20_max
value: 16.780464496035194
- type: nauc_mrr_at_20_std
value: 8.981541510161698
- type: nauc_mrr_at_3_diff1
value: 25.463420671464554
- type: nauc_mrr_at_3_max
value: 16.4183897697643
- type: nauc_mrr_at_3_std
value: 7.625690829397712
- type: nauc_mrr_at_5_diff1
value: 24.93868444905917
- type: nauc_mrr_at_5_max
value: 16.091635346098528
- type: nauc_mrr_at_5_std
value: 8.39683861182176
- type: nauc_ndcg_at_1000_diff1
value: 23.235835294659104
- type: nauc_ndcg_at_1000_max
value: 18.130069784749416
- type: nauc_ndcg_at_1000_std
value: 11.373761760081479
- type: nauc_ndcg_at_100_diff1
value: 22.573685299807515
- type: nauc_ndcg_at_100_max
value: 18.932477112977686
- type: nauc_ndcg_at_100_std
value: 12.149429764085687
- type: nauc_ndcg_at_10_diff1
value: 22.056289907300375
- type: nauc_ndcg_at_10_max
value: 17.44090382465399
- type: nauc_ndcg_at_10_std
value: 11.078636741064129
- type: nauc_ndcg_at_1_diff1
value: 29.76904844307235
- type: nauc_ndcg_at_1_max
value: 14.721060890557535
- type: nauc_ndcg_at_1_std
value: 4.1356111912072935
- type: nauc_ndcg_at_20_diff1
value: 22.35349136882196
- type: nauc_ndcg_at_20_max
value: 18.085452594784922
- type: nauc_ndcg_at_20_std
value: 11.380430365055334
- type: nauc_ndcg_at_3_diff1
value: 24.25988476966241
- type: nauc_ndcg_at_3_max
value: 16.80378720501912
- type: nauc_ndcg_at_3_std
value: 8.666118913493746
- type: nauc_ndcg_at_5_diff1
value: 23.43312793787156
- type: nauc_ndcg_at_5_max
value: 16.291356652340085
- type: nauc_ndcg_at_5_std
value: 9.985808307664348
- type: nauc_precision_at_1000_diff1
value: 20.59167291436066
- type: nauc_precision_at_1000_max
value: 37.11669863650303
- type: nauc_precision_at_1000_std
value: 53.79402863337551
- type: nauc_precision_at_100_diff1
value: 16.48046435474148
- type: nauc_precision_at_100_max
value: 30.49436900744379
- type: nauc_precision_at_100_std
value: 27.593241126791774
- type: nauc_precision_at_10_diff1
value: 16.21264848839721
- type: nauc_precision_at_10_max
value: 19.68782276242417
- type: nauc_precision_at_10_std
value: 16.66213297771153
- type: nauc_precision_at_1_diff1
value: 29.76904844307235
- type: nauc_precision_at_1_max
value: 14.721060890557535
- type: nauc_precision_at_1_std
value: 4.1356111912072935
- type: nauc_precision_at_20_diff1
value: 16.782602338251536
- type: nauc_precision_at_20_max
value: 22.266838160036038
- type: nauc_precision_at_20_std
value: 18.225681654722052
- type: nauc_precision_at_3_diff1
value: 21.258021189556455
- type: nauc_precision_at_3_max
value: 17.741568150869735
- type: nauc_precision_at_3_std
value: 11.278258942650373
- type: nauc_precision_at_5_diff1
value: 19.74165284616189
- type: nauc_precision_at_5_max
value: 16.72366359140884
- type: nauc_precision_at_5_std
value: 13.991493395533237
- type: nauc_recall_at_1000_diff1
value: 20.591672914360146
- type: nauc_recall_at_1000_max
value: 37.11669863650274
- type: nauc_recall_at_1000_std
value: 53.79402863337549
- type: nauc_recall_at_100_diff1
value: 16.480464354741507
- type: nauc_recall_at_100_max
value: 30.494369007443765
- type: nauc_recall_at_100_std
value: 27.59324112679175
- type: nauc_recall_at_10_diff1
value: 16.2126484883972
- type: nauc_recall_at_10_max
value: 19.6878227624242
- type: nauc_recall_at_10_std
value: 16.66213297771157
- type: nauc_recall_at_1_diff1
value: 29.76904844307235
- type: nauc_recall_at_1_max
value: 14.721060890557535
- type: nauc_recall_at_1_std
value: 4.1356111912072935
- type: nauc_recall_at_20_diff1
value: 16.782602338251547
- type: nauc_recall_at_20_max
value: 22.266838160036027
- type: nauc_recall_at_20_std
value: 18.22568165472206
- type: nauc_recall_at_3_diff1
value: 21.258021189556477
- type: nauc_recall_at_3_max
value: 17.741568150869757
- type: nauc_recall_at_3_std
value: 11.278258942650384
- type: nauc_recall_at_5_diff1
value: 19.741652846161877
- type: nauc_recall_at_5_max
value: 16.723663591408847
- type: nauc_recall_at_5_std
value: 13.991493395533256
- type: ndcg_at_1
value: 16.585
- type: ndcg_at_10
value: 28.939999999999998
- type: ndcg_at_100
value: 35.568
- type: ndcg_at_1000
value: 38.201
- type: ndcg_at_20
value: 31.576999999999998
- type: ndcg_at_3
value: 23.634
- type: ndcg_at_5
value: 26.462000000000003
- type: precision_at_1
value: 16.585
- type: precision_at_10
value: 4.316
- type: precision_at_100
value: 0.752
- type: precision_at_1000
value: 0.096
- type: precision_at_20
value: 2.6780000000000004
- type: precision_at_3
value: 9.541
- type: precision_at_5
value: 7.109
- type: recall_at_1
value: 16.585
- type: recall_at_10
value: 43.161
- type: recall_at_100
value: 75.184
- type: recall_at_1000
value: 96.478
- type: recall_at_20
value: 53.563
- type: recall_at_3
value: 28.624
- type: recall_at_5
value: 35.545
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 82.90190735694823
- type: cosine_accuracy_threshold
value: 86.7036521434784
- type: cosine_ap
value: 93.15630587159856
- type: cosine_f1
value: 88.28744750349978
- type: cosine_f1_threshold
value: 86.63686513900757
- type: cosine_precision
value: 83.27464788732394
- type: cosine_recall
value: 93.94240317775571
- type: dot_accuracy
value: 82.90190735694823
- type: dot_accuracy_threshold
value: 86.70365810394287
- type: dot_ap
value: 93.15630587159856
- type: dot_f1
value: 88.28744750349978
- type: dot_f1_threshold
value: 86.63686513900757
- type: dot_precision
value: 83.27464788732394
- type: dot_recall
value: 93.94240317775571
- type: euclidean_accuracy
value: 82.90190735694823
- type: euclidean_accuracy_threshold
value: 51.56809687614441
- type: euclidean_ap
value: 93.15630587159856
- type: euclidean_f1
value: 88.28744750349978
- type: euclidean_f1_threshold
value: 51.69745683670044
- type: euclidean_precision
value: 83.27464788732394
- type: euclidean_recall
value: 93.94240317775571
- type: main_score
value: 93.15630587159856
- type: manhattan_accuracy
value: 82.83378746594006
- type: manhattan_accuracy_threshold
value: 1203.4871101379395
- type: manhattan_ap
value: 93.13986431895378
- type: manhattan_f1
value: 88.20224719101122
- type: manhattan_f1_threshold
value: 1204.0313720703125
- type: manhattan_precision
value: 83.43666961913198
- type: manhattan_recall
value: 93.545183714002
- type: max_ap
value: 93.15630587159856
- type: max_f1
value: 88.28744750349978
- type: max_precision
value: 83.43666961913198
- type: max_recall
value: 93.94240317775571
- type: similarity_accuracy
value: 82.90190735694823
- type: similarity_accuracy_threshold
value: 86.7036521434784
- type: similarity_ap
value: 93.15630587159856
- type: similarity_f1
value: 88.28744750349978
- type: similarity_f1_threshold
value: 86.63686513900757
- type: similarity_precision
value: 83.27464788732394
- type: similarity_recall
value: 93.94240317775571
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 61.0
- type: cosine_accuracy_threshold
value: 99.4693398475647
- type: cosine_ap
value: 60.596634996072794
- type: cosine_f1
value: 62.77477477477477
- type: cosine_f1_threshold
value: 95.54682970046997
- type: cosine_precision
value: 46.52777777777778
- type: cosine_recall
value: 96.4562569213732
- type: dot_accuracy
value: 61.0
- type: dot_accuracy_threshold
value: 99.4693398475647
- type: dot_ap
value: 60.60423446409202
- type: dot_f1
value: 62.77477477477477
- type: dot_f1_threshold
value: 95.54683566093445
- type: dot_precision
value: 46.52777777777778
- type: dot_recall
value: 96.4562569213732
- type: euclidean_accuracy
value: 61.0
- type: euclidean_accuracy_threshold
value: 10.301996022462845
- type: euclidean_ap
value: 60.596589949359995
- type: euclidean_f1
value: 62.77477477477477
- type: euclidean_f1_threshold
value: 29.843494296073914
- type: euclidean_precision
value: 46.52777777777778
- type: euclidean_recall
value: 96.4562569213732
- type: main_score
value: 60.81315897987771
- type: manhattan_accuracy
value: 61.1
- type: manhattan_accuracy_threshold
value: 241.11316204071045
- type: manhattan_ap
value: 60.81315897987771
- type: manhattan_f1
value: 62.80575539568345
- type: manhattan_f1_threshold
value: 701.3707160949707
- type: manhattan_precision
value: 46.510388918486946
- type: manhattan_recall
value: 96.67774086378738
- type: max_ap
value: 60.81315897987771
- type: max_f1
value: 62.80575539568345
- type: max_precision
value: 46.52777777777778
- type: max_recall
value: 96.67774086378738
- type: similarity_accuracy
value: 61.0
- type: similarity_accuracy_threshold
value: 99.4693398475647
- type: similarity_ap
value: 60.596634996072794
- type: similarity_f1
value: 62.77477477477477
- type: similarity_f1_threshold
value: 95.54682970046997
- type: similarity_precision
value: 46.52777777777778
- type: similarity_recall
value: 96.4562569213732
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cosine_pearson
value: 80.2458052019468
- type: cosine_spearman
value: 76.90282547383322
- type: euclidean_pearson
value: 76.96025494858225
- type: euclidean_spearman
value: 76.90369875962587
- type: main_score
value: 76.90282547383322
- type: manhattan_pearson
value: 76.96191769993092
- type: manhattan_spearman
value: 76.78368027265795
- type: pearson
value: 80.2458052019468
- type: spearman
value: 76.90282547383322
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 71.8166682422511
- type: cosine_spearman
value: 80.753981314684
- type: euclidean_pearson
value: 77.26991759068049
- type: euclidean_spearman
value: 80.753981314684
- type: main_score
value: 80.753981314684
- type: manhattan_pearson
value: 79.8934147126028
- type: manhattan_spearman
value: 81.7842698600436
- type: pearson
value: 71.8166682422511
- type: spearman
value: 80.753981314684
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 59.72069019022611
- type: cosine_spearman
value: 66.19562370923092
- type: euclidean_pearson
value: 60.38869296832584
- type: euclidean_spearman
value: 66.19562370923092
- type: main_score
value: 66.19562370923092
- type: manhattan_pearson
value: 60.735818878018044
- type: manhattan_spearman
value: 66.3310023111313
- type: pearson
value: 59.72069019022611
- type: spearman
value: 66.19562370923092
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 90.73440998579521
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 90.27154868530998
- type: euclidean_spearman
value: 84.51542547285167
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 89.71184089391063
- type: manhattan_spearman
value: 84.51542547285167
- type: pearson
value: 90.73440998579521
- type: spearman
value: 84.51542547285167
- type: cosine_pearson
value: 90.73440998579521
- type: cosine_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 90.27154868530998
- type: euclidean_spearman
value: 84.51542547285167
- type: main_score
value: 84.51542547285167
- type: manhattan_pearson
value: 89.71184089391063
- type: manhattan_spearman
value: 84.51542547285167
- type: pearson
value: 90.73440998579521
- type: spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cosine_pearson
value: 79.50928730728984
- type: cosine_spearman
value: 80.0932172585886
- type: euclidean_pearson
value: 78.52408843163009
- type: euclidean_spearman
value: 80.09288222088635
- type: main_score
value: 80.0932172585886
- type: manhattan_pearson
value: 78.48826046695687
- type: manhattan_spearman
value: 80.02496924199357
- type: pearson
value: 79.50928730728984
- type: spearman
value: 80.0932172585886
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cosine_pearson
value: 31.635895833264797
- type: cosine_spearman
value: 29.316197375472907
- type: dot_pearson
value: 31.635903578046314
- type: dot_spearman
value: 29.320397860238174
- type: main_score
value: 29.316197375472907
- type: pearson
value: 31.635895833264797
- type: spearman
value: 29.316197375472907
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
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type: PL-MTEB/8tags-clustering
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split: test
revision: 78b962b130c6690659c65abf67bf1c2f030606b6
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type: laugustyniak/abusive-clauses-pl
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type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
metrics:
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- task:
type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
metrics:
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- type: cosine_ap
value: 62.95127012761914
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- type: dot_accuracy
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value: 48.82096069868996
- type: dot_recall
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- type: euclidean_accuracy
value: 77.82307378719935
- type: euclidean_accuracy_threshold
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- type: euclidean_ap
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value: 60.530590146183
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- type: euclidean_recall
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- type: main_score
value: 62.95127012761915
- type: manhattan_accuracy
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- type: manhattan_ap
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- type: manhattan_recall
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- type: max_ap
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- type: max_precision
value: 49.090102086107414
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- type: similarity_accuracy
value: 77.82307378719935
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value: 48.82096069868996
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value: 79.62962962962963
- task:
type: STS
dataset:
name: MTEB SICK-R-PL
type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
metrics:
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value: 66.17464577596678
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dataset:
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type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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dataset:
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type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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dataset:
name: MTEB SciFact-PL
type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
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type: clarin-knext/trec-covid-pl
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type: MultilabelClassification
dataset:
name: MTEB CEDRClassification
type: ai-forever/cedr-classification
config: default
split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
metrics:
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dataset:
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type: ai-forever/georeview-classification
config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
metrics:
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dataset:
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type: ai-forever/georeview-clustering-p2p
config: default
split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
metrics:
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dataset:
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type: ai-forever/headline-classification
config: default
split: test
revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb
metrics:
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dataset:
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type: ai-forever/inappropriateness-classification
config: default
split: test
revision: 601651fdc45ef243751676e62dd7a19f491c0285
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dataset:
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type: ai-forever/kinopoisk-sentiment-classification
config: default
split: test
revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
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dataset:
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type: miracl/mmteb-miracl-reranking
config: ru
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revision: 6d1962c527217f8927fca80f890f14f36b2802af
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value: 20.600178046207702
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- type: nAUC_Recall@1_std(MIRACL)
value: 3.2097645527643595
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value: -18.18036800889588
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value: 48.61058795989584
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value: 26.280248121862066
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value: 28.457322956745617
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value: 13.335687765558976
- task:
type: Retrieval
dataset:
name: MTEB MIRACLRetrieval (ru)
type: miracl/mmteb-miracl
config: ru
split: dev
revision: main
metrics:
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- type: precision_at_1
value: 48.482
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- type: recall_at_1
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- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
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- type: recall_at_20
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- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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value: 66.0659045057162
- type: f1
value: 61.35064382748825
- type: f1_weighted
value: 64.6551910369696
- type: main_score
value: 66.0659045057162
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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value: 74.80833893745798
- type: f1
value: 73.75893026985123
- type: f1_weighted
value: 74.48216382388233
- type: main_score
value: 74.80833893745798
- task:
type: STS
dataset:
name: MTEB RUParaPhraserSTS
type: merionum/ru_paraphraser
config: default
split: test
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
metrics:
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value: 60.71240323001735
- type: cosine_spearman
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- type: euclidean_pearson
value: 65.04715258693518
- type: euclidean_spearman
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- type: main_score
value: 68.14010632490151
- type: manhattan_pearson
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- type: manhattan_spearman
value: 67.99309271156126
- type: pearson
value: 60.71240323001735
- type: spearman
value: 68.14010632490151
- task:
type: Retrieval
dataset:
name: MTEB RiaNewsRetrieval
type: ai-forever/ria-news-retrieval
config: default
split: test
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
metrics:
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- type: map_at_1000
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value: 81.74
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value: 85.68
- task:
type: Reranking
dataset:
name: MTEB RuBQReranking
type: ai-forever/rubq-reranking
config: default
split: test
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
metrics:
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value: 70.23399911521713
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- task:
type: Retrieval
dataset:
name: MTEB RuBQRetrieval
type: ai-forever/rubq-retrieval
config: default
split: test
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
metrics:
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value: 13.694
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- type: precision_at_20
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- type: precision_at_3
value: 33.353
- type: precision_at_5
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- type: recall_at_1
value: 38.682
- type: recall_at_10
value: 83.624
- type: recall_at_100
value: 95.964
- type: recall_at_1000
value: 99.072
- type: recall_at_20
value: 89.334
- type: recall_at_3
value: 64.039
- type: recall_at_5
value: 73.92999999999999
- task:
type: Classification
dataset:
name: MTEB RuReviewsClassification
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
- type: accuracy
value: 67.6953125
- type: f1
value: 65.90453756121588
- type: f1_weighted
value: 65.90276417438837
- type: main_score
value: 67.6953125
- task:
type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
- type: cosine_pearson
value: 76.29471433169448
- type: cosine_spearman
value: 76.87365514666403
- type: euclidean_pearson
value: 75.61167023011886
- type: euclidean_spearman
value: 76.87303787773313
- type: main_score
value: 76.87365514666403
- type: manhattan_pearson
value: 75.66392608968061
- type: manhattan_spearman
value: 76.92734684196252
- type: pearson
value: 76.29471433169448
- type: spearman
value: 76.87365514666403
- task:
type: Classification
dataset:
name: MTEB RuSciBenchGRNTIClassification
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: accuracy
value: 62.76367187500001
- type: f1
value: 61.038547511485696
- type: f1_weighted
value: 61.05100853971954
- type: main_score
value: 62.76367187500001
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
- type: main_score
value: 58.12317505878799
- type: v_measure
value: 58.12317505878799
- type: v_measure_std
value: 0.8074216341773242
- task:
type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
- type: accuracy
value: 49.765625
- type: f1
value: 47.21576484201538
- type: f1_weighted
value: 47.216631424004845
- type: main_score
value: 49.765625
- task:
type: Clustering
dataset:
name: MTEB RuSciBenchOECDClusteringP2P
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
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value: 48.94279918311604
- type: v_measure
value: 48.94279918311604
- type: v_measure_std
value: 0.6861637451888378
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 64.72567885537859
- type: cosine_spearman
value: 67.02766486173321
- type: euclidean_pearson
value: 65.20776364711082
- type: euclidean_spearman
value: 67.02766486173321
- type: main_score
value: 67.02766486173321
- type: manhattan_pearson
value: 65.01652728805082
- type: manhattan_spearman
value: 67.01526065421112
- type: pearson
value: 64.72567885537859
- type: spearman
value: 67.02766486173321
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
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value: 31.5966796875
- type: f1
value: 37.51973703475147
- type: lrap
value: 47.855767144096504
- type: main_score
value: 31.5966796875
- task:
type: PairClassification
dataset:
name: MTEB TERRa
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
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value: 57.65472312703584
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value: 76.06719732284546
- type: cosine_precision
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- type: cosine_recall
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- type: dot_accuracy
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- type: dot_recall
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- type: euclidean_accuracy
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- type: euclidean_recall
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- type: main_score
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- type: manhattan_recall
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- type: max_ap
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- type: max_precision
value: 51.01351351351351
- type: max_recall
value: 99.34640522875817
- type: similarity_accuracy
value: 57.65472312703584
- type: similarity_accuracy_threshold
value: 83.7156891822815
- type: similarity_ap
value: 55.055463204979915
- type: similarity_f1
value: 67.26057906458797
- type: similarity_f1_threshold
value: 76.06719732284546
- type: similarity_precision
value: 51.01351351351351
- type: similarity_recall
value: 98.69281045751634
---
-
<h1 align="center">KaLM-Embedding</h1>
**KaLM-Embedding** is a series of embedding models adapted from auto-regressive LLMs with superior training data.
KaLM-embedding-multilingual-mini is trained from [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) with massive weakly-supervised pre-training and supervised fine-tuning data.
## 📑 Open-source Plan
- [x] Model Checkpoint
- [x] [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1)
- [x] [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1)
- [x] [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5)
- [ ] KaLM-embedding-multilingual-max-v1
- [x] Training and Evaluation Code: [HITsz-TMG/KaLM-Embedding](https://github.com/HITsz-TMG/KaLM-Embedding)
- [x] Technical Report: [KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model](https://arxiv.org/abs/2501.01028)
- [ ] Training Data
## Evaluation
| Model Name | Model Size | C-MTEB(35) | MTEB(56) | avg
|:----:|:---:|:---:|:---:|:---:|
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 58.81 | 61.5 | 60.16
| [bge-m3 (dense)](https://huggingface.co/BAAI/bge-m3) | 560M | 60.80 | 59.84 | 60.32
| [gte-multilingual-base (dense)](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | **305M** | 62.72 | 61.40 | 62.06
| [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1) | 494M | 62.31 | 61.87 | 62.09
| [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1) | 494M | 63.57 | 64.74 | 64.16
| [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5) | 494M | **64.13** | **64.94** | **64.53**
## Requirements
Since we have used the Qwen2 model, we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Usage
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code
model.max_seq_length = 512
embeddings = model.encode(
sentences,
normalize_embeddings=True,
batch_size=256,
show_progress_bar=True
)
print(embeddings)
```
We add instruction for asymmetric tasks: retrieval, reranking, classification and clustering.
<!-- We add instruction for classification and clustering. -->
If you want to add instruction to the query (no instruction for the corpus), you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code
model.max_seq_length = 512
prompt = "Instruct: Classifying the category of french news. \n Query: "
embeddings = model.encode(
sentences,
prompt=prompt,
normalize_embeddings=True,
batch_size=256,
show_progress_bar=True
)
print(embeddings)
```
## Citation
Please cite the repo if you use the model or code in this repo.
```
@article{hu2025kalm,
title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},
journal={arXiv preprint arXiv:2501.01028},
year={2025}
}
```
## Contact
If you encounter any issue, feel free to contact us via the email: [email protected]
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
deepvk/USER-base | deepvk | sentence-similarity | [
"sentence-transformers",
"safetensors",
"deberta",
"feature-extraction",
"sentence-similarity",
"ru",
"dataset:deepvk/ru-HNP",
"dataset:deepvk/ru-WANLI",
"dataset:Shitao/bge-m3-data",
"dataset:RussianNLP/russian_super_glue",
"dataset:reciTAL/mlsum",
"dataset:Helsinki-NLP/opus-100",
"dataset:Helsinki-NLP/bible_para",
"dataset:d0rj/rudetoxifier_data_detox",
"dataset:s-nlp/ru_paradetox",
"dataset:Milana/russian_keywords",
"dataset:IlyaGusev/gazeta",
"dataset:d0rj/gsm8k-ru",
"dataset:bragovo/dsum_ru",
"dataset:CarlBrendt/Summ_Dialog_News",
"arxiv:2311.13534",
"arxiv:2309.12871",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-10T14:09:06 | 2024-11-25T14:28:55 | 51,079 | 17 | ---
datasets:
- deepvk/ru-HNP
- deepvk/ru-WANLI
- Shitao/bge-m3-data
- RussianNLP/russian_super_glue
- reciTAL/mlsum
- Helsinki-NLP/opus-100
- Helsinki-NLP/bible_para
- d0rj/rudetoxifier_data_detox
- s-nlp/ru_paradetox
- Milana/russian_keywords
- IlyaGusev/gazeta
- d0rj/gsm8k-ru
- bragovo/dsum_ru
- CarlBrendt/Summ_Dialog_News
language:
- ru
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# USER-base
**U**niversal **S**entence **E**ncoder for **R**ussian (USER) is a [sentence-transformer](https://www.SBERT.net) model for extracting embeddings exclusively for Russian language.
It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is initialized from [`deepvk/deberta-v1-base`](https://huggingface.co/deepvk/deberta-v1-base) and trained to work exclusively with the Russian language. Its quality on other languages was not evaluated.
## Usage
Using this model becomes easy when you have [`sentence-transformers`](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
queries = [
"Когда был спущен на воду первый миноносец «Спокойный»?",
"Есть ли нефть в Удмуртии?"
]
passages = [
"Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года.",
"Нефтепоисковые работы в Удмуртии были начаты сразу после Второй мировой войны в 1945 году и продолжаются по сей день. Добыча нефти началась в 1967 году."
]
model = SentenceTransformer("deepvk/USER-base")
# Prompt should be specified according to the task (either 'query' or 'passage').
passage_embeddings = model.encode(passages, normalize_embeddings=True, prompt_name='passage')
# For tasks other than retrieval, you can simply use the `query` prompt, which is set by default.
query_embeddings = model.encode(queries, normalize_embeddings=True)
```
However, you can use model directly with [`transformers`](https://huggingface.co/docs/transformers/en/index)
```python
import torch.nn.functional as F
from torch import Tensor, inference_mode
from transformers import AutoTokenizer, AutoModel
def average_pool(
last_hidden_states: Tensor,
attention_mask: Tensor
) -> Tensor:
last_hidden = last_hidden_states.masked_fill(
~attention_mask[..., None].bool(), 0.0
)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
# You should manually add prompts when using the model directly. Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = [
"query: Когда был спущен на воду первый миноносец «Спокойный»?",
"query: Есть ли нефть в Удмуртии?",
"passage: Спокойный (эсминец)\nЗачислен в списки ВМФ СССР 19 августа 1952 года.",
"passage: Нефтепоисковые работы в Удмуртии были начаты сразу после Второй мировой войны в 1945 году и продолжаются по сей день. Добыча нефти началась в 1967 году."
]
tokenizer = AutoTokenizer.from_pretrained("deepvk/USER-base")
model = AutoModel.from_pretrained("deepvk/USER-base")
batch_dict = tokenizer(
input_texts, padding=True, truncation=True, return_tensors="pt"
)
with inference_mode():
outputs = model(**batch_dict)
embeddings = average_pool(
outputs.last_hidden_state, batch_dict["attention_mask"]
)
embeddings = F.normalize(embeddings, p=2, dim=1)
# Scores for query-passage
scores = (embeddings[:2] @ embeddings[2:].T) * 100
# [[55.86, 30.95],
# [22.82, 59.46]]
print(scores.round(decimals=2))
```
⚠️ **Attention** ⚠️
Each input text should start with "query: " or "passage: ".
For tasks other than retrieval, you can simply use the "query: " prefix.
## Training Details
We aimed to follow the [`bge-base-en`](https://huggingface.co/BAAI/bge-base-en) model training algorithm, but we made several improvements along the way.
**Initialization:** [`deepvk/deberta-v1-base`](https://huggingface.co/deepvk/deberta-v1-base)
**First-stage:** Contrastive pre-training with weak supervision on the Russian part of [mMarco corpus](https://github.com/unicamp-dl/mMARCO).
**Second-stage:** Supervised fine-tuning two different models based on data symmetry and then merging via [`LM-Cocktail`](https://arxiv.org/abs/2311.13534):
1. We modified the instruction design by simplifying the multilingual approach to facilitate easier inference.
For symmetric data `(S1, S2)`, we used the instructions: `"query: S1"` and `"query: S2"`, and for asymmetric data, we used `"query: S1"` with `"passage: S2"`.
2. Since we split the data, we could additionally apply the [AnglE loss](https://arxiv.org/abs/2309.12871) to the symmetric model, which enhances performance on symmetric tasks.
3. Finally, we combined the two models, tuning the weights for the merger using `LM-Cocktail` to produce the final model, **USER**.
### Dataset
During model development, we additional collect 2 datasets:
[`deepvk/ru-HNP`](https://huggingface.co/datasets/deepvk/ru-HNP) and
[`deepvk/ru-WANLI`](https://huggingface.co/datasets/deepvk/ru-WANLI).
| Symmetric Dataset | Size | Asymmetric Dataset | Size |
|-------------------|-------|--------------------|------|
| **AllNLI** | 282 644 | [**MIRACL**](https://huggingface.co/datasets/Shitao/bge-m3-data/tree/main) | 10 000 |
| [MedNLI](https://github.com/jgc128/mednli) | 3 699 | [MLDR](https://huggingface.co/datasets/Shitao/bge-m3-data/tree/main) | 1 864 |
| [RCB](https://huggingface.co/datasets/RussianNLP/russian_super_glue) | 392 | [Lenta](https://github.com/yutkin/Lenta.Ru-News-Dataset) | 185 972 |
| [Terra](https://huggingface.co/datasets/RussianNLP/russian_super_glue) | 1 359 | [Mlsum](https://huggingface.co/datasets/reciTAL/mlsum) | 51 112 |
| [Tapaco](https://huggingface.co/datasets/tapaco) | 91 240 | [Mr-TyDi](https://huggingface.co/datasets/Shitao/bge-m3-data/tree/main) | 536 600 |
| [Opus100](https://huggingface.co/datasets/Helsinki-NLP/opus-100) | 1 000 000 | [Panorama](https://huggingface.co/datasets/its5Q/panorama) | 11 024 |
| [BiblePar](https://huggingface.co/datasets/Helsinki-NLP/bible_para) | 62 195 | [PravoIsrael](https://huggingface.co/datasets/TarasHu/pravoIsrael) | 26 364 |
| [RudetoxifierDataDetox](https://huggingface.co/datasets/d0rj/rudetoxifier_data_detox) | 31 407 | [Xlsum](https://huggingface.co/datasets/csebuetnlp/xlsum) | 124 486 |
| [RuParadetox](https://huggingface.co/datasets/s-nlp/ru_paradetox) | 11 090 | [Fialka-v1](https://huggingface.co/datasets/0x7o/fialka-v1) | 130 000 |
| [**deepvk/ru-WANLI**](https://huggingface.co/datasets/deepvk/ru-WANLI) | 35 455 | [RussianKeywords](https://huggingface.co/datasets/Milana/russian_keywords) | 16 461 |
| [**deepvk/ru-HNP**](https://huggingface.co/datasets/deepvk/ru-HNP) | 500 000 | [Gazeta](https://huggingface.co/datasets/IlyaGusev/gazeta) | 121 928 |
| | | [Gsm8k-ru](https://huggingface.co/datasets/d0rj/gsm8k-ru) | 7 470 |
| | | [DSumRu](https://huggingface.co/datasets/bragovo/dsum_ru) | 27 191 |
| | | [SummDialogNews](https://huggingface.co/datasets/CarlBrendt/Summ_Dialog_News) | 75 700 |
**Total positive pairs:** 3,352,653
**Total negative pairs:** 792,644 (negative pairs from AIINLI, MIRACL, deepvk/ru-WANLI, deepvk/ru-HNP)
For all labeled datasets, we only use its training set for fine-tuning.
For datasets Gazeta, Mlsum, Xlsum: pairs (title/text) and (title/summary) are combined and used as asymmetric data.
`AllNLI` is an translated to Russian combination of SNLI, MNLI, and ANLI.
## Experiments
As a baseline, we chose the current top models from the [`encodechka`](https://github.com/avidale/encodechka) leaderboard table. In addition, we evaluate model on the russian subset of [`MTEB`](https://github.com/embeddings-benchmark/mteb), which include 10 tasks. Unfortunately, we could not validate the bge-m3 on some MTEB tasks, specifically clustering, due to excessive computational resources. Besides these two benchmarks, we also evaluated the models on the [`MIRACL`](https://github.com/project-miracl/miracl). All experiments were conducted using NVIDIA TESLA A100 40 GB GPU. We use validation scripts from the official repositories for each of the tasks.
| Model | Size (w/o Embeddings) | [**Encodechka**](https://github.com/avidale/encodechka) (*Mean S*) | [**MTEB**](https://github.com/embeddings-benchmark/mteb) (*Mean Ru*) | [**Miracl**](http://miracl.ai/) (*Recall@100*) |
|-----------------------------------------|-------|-----------------------------|------------------------|--------------------------------|
| [`bge-m3`](https://huggingface.co/BAAI/bge-m3) | 303 | **0.786** | **0.694** | **0.959** |
| [`multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) | 303 | 0.78 | 0.665 | 0.927 |
| `USER` (this model) | 85 | <u>0.772</u> | <u>0.666</u> | 0.763 |
[`paraphrase-multilingual-mpnet-base-v2`](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 85 | 0.76 | 0.625 | 0.149 |
| [`multilingual-e5-base`](https://huggingface.co/intfloat/multilingual-e5-base) | 85 | 0.756 | 0.645 | <u>0.915</u> |
| [`LaBSE-en-ru`](https://huggingface.co/cointegrated/LaBSE-en-ru) | 85 | 0.74 | 0.599 | 0.327 |
| [`sn-xlm-roberta-base-snli-mnli-anli-xnli`](https://huggingface.co/symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli) | 85 | 0.74 | 0.593 | 0.08 |
Model sizes are shown, with larger models visually distinct from the others.
Absolute leaders in the metrics are highlighted in bold, and the leaders among models of our size is underlined.
In this way, our solution outperforms all other models of the same size on both Encodechka and MTEB. Given that the model is slightly underperforming in retrieval tasks relative to existing solutions, we aim to address this in our future research.
## FAQ
**Do I need to add the prefix "query: " and "passage: " to input texts?**
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
- Use `"query: "` and `"passage: "` correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
- Use `"query: "` prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
- Use `"query: "` prefix if you want to use embeddings as features, such as linear probing classification, clustering.
## Citations
```
@misc{deepvk2024user,
title={USER: Universal Sentence Encoder for Russian},
author={Malashenko, Boris and Zemerov, Anton and Spirin, Egor},
url={https://huggingface.co/datasets/deepvk/USER-base},
publisher={Hugging Face}
year={2024},
}
``` | [
"SEMANTIC_SIMILARITY"
] | [
"MEDNLI"
] |
allenai/biomed_roberta_base | allenai | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"en",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2022-10-03T22:05:08 | 49,847 | 26 | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/allenai.png
---
# BioMed-RoBERTa-base
BioMed-RoBERTa-base is a language model based on the RoBERTa-base (Liu et. al, 2019) architecture. We adapt RoBERTa-base to 2.68 million scientific papers from the [Semantic Scholar](https://www.semanticscholar.org) corpus via continued pretraining. This amounts to 7.55B tokens and 47GB of data. We use the full text of the papers in training, not just abstracts.
Specific details of the adaptive pretraining procedure can be found in Gururangan et. al, 2020.
## Evaluation
BioMed-RoBERTa achieves competitive performance to state of the art models on a number of NLP tasks in the biomedical domain (numbers are mean (standard deviation) over 3+ random seeds)
| Task | Task Type | RoBERTa-base | BioMed-RoBERTa-base |
|--------------|---------------------|--------------|---------------------|
| RCT-180K | Text Classification | 86.4 (0.3) | 86.9 (0.2) |
| ChemProt | Relation Extraction | 81.1 (1.1) | 83.0 (0.7) |
| JNLPBA | NER | 74.3 (0.2) | 75.2 (0.1) |
| BC5CDR | NER | 85.6 (0.1) | 87.8 (0.1) |
| NCBI-Disease | NER | 86.6 (0.3) | 87.1 (0.8) |
More evaluations TBD.
## Citation
If using this model, please cite the following paper:
```bibtex
@inproceedings{domains,
author = {Suchin Gururangan and Ana Marasović and Swabha Swayamdipta and Kyle Lo and Iz Beltagy and Doug Downey and Noah A. Smith},
title = {Don't Stop Pretraining: Adapt Language Models to Domains and Tasks},
year = {2020},
booktitle = {Proceedings of ACL},
}
```
| [
"RELATION_EXTRACTION",
"TEXT_CLASSIFICATION"
] | [
"BC5CDR",
"CHEMPROT",
"JNLPBA",
"NCBI DISEASE"
] |
jinaai/jina-embeddings-v2-base-de | jinaai | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"de",
"en",
"arxiv:2108.12409",
"arxiv:2402.17016",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"region:eu"
] | 2024-01-12T14:04:50 | 2025-01-06T16:26:47 | 49,768 | 72 | ---
language:
- de
- en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
inference: false
model-index:
- name: jina-embeddings-v2-base-de
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.76119402985076
- type: ap
value: 35.99577188521176
- type: f1
value: 67.50397431543269
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 68.9186295503212
- type: ap
value: 79.73307115840507
- type: f1
value: 66.66245744831339
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 77.52215
- type: ap
value: 71.85051037177416
- type: f1
value: 77.4171096157774
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.498
- type: f1
value: 38.058193386555956
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 37.717999999999996
- type: f1
value: 37.22674371574757
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.319999999999997
- type: map_at_10
value: 40.351
- type: map_at_100
value: 41.435
- type: map_at_1000
value: 41.443000000000005
- type: map_at_3
value: 35.266
- type: map_at_5
value: 37.99
- type: mrr_at_1
value: 25.746999999999996
- type: mrr_at_10
value: 40.515
- type: mrr_at_100
value: 41.606
- type: mrr_at_1000
value: 41.614000000000004
- type: mrr_at_3
value: 35.42
- type: mrr_at_5
value: 38.112
- type: ndcg_at_1
value: 25.319999999999997
- type: ndcg_at_10
value: 49.332
- type: ndcg_at_100
value: 53.909
- type: ndcg_at_1000
value: 54.089
- type: ndcg_at_3
value: 38.705
- type: ndcg_at_5
value: 43.606
- type: precision_at_1
value: 25.319999999999997
- type: precision_at_10
value: 7.831
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.24
- type: precision_at_5
value: 12.119
- type: recall_at_1
value: 25.319999999999997
- type: recall_at_10
value: 78.307
- type: recall_at_100
value: 98.222
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 48.72
- type: recall_at_5
value: 60.597
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.43100588255654
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.08988904593667
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.55514765595906
- type: mrr
value: 73.51393835465858
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 79.6723823121172
- type: cos_sim_spearman
value: 76.90596922214986
- type: euclidean_pearson
value: 77.87910737957918
- type: euclidean_spearman
value: 76.66319260598262
- type: manhattan_pearson
value: 77.37039493457965
- type: manhattan_spearman
value: 76.09872191280964
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.97703549060543
- type: f1
value: 98.86569241475296
- type: precision
value: 98.81002087682673
- type: recall
value: 98.97703549060543
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 83.93506493506493
- type: f1
value: 83.91014949949302
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 34.970675877585144
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.779230269190954
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringP2P
type: slvnwhrl/blurbs-clustering-p2p
config: default
split: test
revision: a2dd5b02a77de3466a3eaa98ae586b5610314496
metrics:
- type: v_measure
value: 35.490175601567216
- task:
type: Clustering
dataset:
name: MTEB BlurbsClusteringS2S
type: slvnwhrl/blurbs-clustering-s2s
config: default
split: test
revision: 9bfff9a7f8f6dc6ffc9da71c48dd48b68696471d
metrics:
- type: v_measure
value: 16.16638280560168
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.830999999999996
- type: map_at_10
value: 41.355
- type: map_at_100
value: 42.791000000000004
- type: map_at_1000
value: 42.918
- type: map_at_3
value: 38.237
- type: map_at_5
value: 40.066
- type: mrr_at_1
value: 38.484
- type: mrr_at_10
value: 47.593
- type: mrr_at_100
value: 48.388
- type: mrr_at_1000
value: 48.439
- type: mrr_at_3
value: 45.279
- type: mrr_at_5
value: 46.724
- type: ndcg_at_1
value: 38.484
- type: ndcg_at_10
value: 47.27
- type: ndcg_at_100
value: 52.568000000000005
- type: ndcg_at_1000
value: 54.729000000000006
- type: ndcg_at_3
value: 43.061
- type: ndcg_at_5
value: 45.083
- type: precision_at_1
value: 38.484
- type: precision_at_10
value: 8.927
- type: precision_at_100
value: 1.425
- type: precision_at_1000
value: 0.19
- type: precision_at_3
value: 20.791999999999998
- type: precision_at_5
value: 14.85
- type: recall_at_1
value: 30.830999999999996
- type: recall_at_10
value: 57.87799999999999
- type: recall_at_100
value: 80.124
- type: recall_at_1000
value: 94.208
- type: recall_at_3
value: 45.083
- type: recall_at_5
value: 51.154999999999994
- type: map_at_1
value: 25.782
- type: map_at_10
value: 34.492
- type: map_at_100
value: 35.521
- type: map_at_1000
value: 35.638
- type: map_at_3
value: 31.735999999999997
- type: map_at_5
value: 33.339
- type: mrr_at_1
value: 32.357
- type: mrr_at_10
value: 39.965
- type: mrr_at_100
value: 40.644000000000005
- type: mrr_at_1000
value: 40.695
- type: mrr_at_3
value: 37.739
- type: mrr_at_5
value: 39.061
- type: ndcg_at_1
value: 32.357
- type: ndcg_at_10
value: 39.644
- type: ndcg_at_100
value: 43.851
- type: ndcg_at_1000
value: 46.211999999999996
- type: ndcg_at_3
value: 35.675000000000004
- type: ndcg_at_5
value: 37.564
- type: precision_at_1
value: 32.357
- type: precision_at_10
value: 7.344
- type: precision_at_100
value: 1.201
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 17.155
- type: precision_at_5
value: 12.166
- type: recall_at_1
value: 25.782
- type: recall_at_10
value: 49.132999999999996
- type: recall_at_100
value: 67.24
- type: recall_at_1000
value: 83.045
- type: recall_at_3
value: 37.021
- type: recall_at_5
value: 42.548
- type: map_at_1
value: 35.778999999999996
- type: map_at_10
value: 47.038000000000004
- type: map_at_100
value: 48.064
- type: map_at_1000
value: 48.128
- type: map_at_3
value: 44.186
- type: map_at_5
value: 45.788000000000004
- type: mrr_at_1
value: 41.254000000000005
- type: mrr_at_10
value: 50.556999999999995
- type: mrr_at_100
value: 51.296
- type: mrr_at_1000
value: 51.331
- type: mrr_at_3
value: 48.318
- type: mrr_at_5
value: 49.619
- type: ndcg_at_1
value: 41.254000000000005
- type: ndcg_at_10
value: 52.454
- type: ndcg_at_100
value: 56.776
- type: ndcg_at_1000
value: 58.181000000000004
- type: ndcg_at_3
value: 47.713
- type: ndcg_at_5
value: 49.997
- type: precision_at_1
value: 41.254000000000005
- type: precision_at_10
value: 8.464
- type: precision_at_100
value: 1.157
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 21.526
- type: precision_at_5
value: 14.696000000000002
- type: recall_at_1
value: 35.778999999999996
- type: recall_at_10
value: 64.85300000000001
- type: recall_at_100
value: 83.98400000000001
- type: recall_at_1000
value: 94.18299999999999
- type: recall_at_3
value: 51.929
- type: recall_at_5
value: 57.666
- type: map_at_1
value: 21.719
- type: map_at_10
value: 29.326999999999998
- type: map_at_100
value: 30.314000000000004
- type: map_at_1000
value: 30.397000000000002
- type: map_at_3
value: 27.101
- type: map_at_5
value: 28.141
- type: mrr_at_1
value: 23.503
- type: mrr_at_10
value: 31.225
- type: mrr_at_100
value: 32.096000000000004
- type: mrr_at_1000
value: 32.159
- type: mrr_at_3
value: 29.076999999999998
- type: mrr_at_5
value: 30.083
- type: ndcg_at_1
value: 23.503
- type: ndcg_at_10
value: 33.842
- type: ndcg_at_100
value: 39.038000000000004
- type: ndcg_at_1000
value: 41.214
- type: ndcg_at_3
value: 29.347
- type: ndcg_at_5
value: 31.121
- type: precision_at_1
value: 23.503
- type: precision_at_10
value: 5.266
- type: precision_at_100
value: 0.831
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 12.504999999999999
- type: precision_at_5
value: 8.565000000000001
- type: recall_at_1
value: 21.719
- type: recall_at_10
value: 46.024
- type: recall_at_100
value: 70.78999999999999
- type: recall_at_1000
value: 87.022
- type: recall_at_3
value: 33.64
- type: recall_at_5
value: 37.992
- type: map_at_1
value: 15.601
- type: map_at_10
value: 22.054000000000002
- type: map_at_100
value: 23.177
- type: map_at_1000
value: 23.308
- type: map_at_3
value: 19.772000000000002
- type: map_at_5
value: 21.055
- type: mrr_at_1
value: 19.403000000000002
- type: mrr_at_10
value: 26.409
- type: mrr_at_100
value: 27.356
- type: mrr_at_1000
value: 27.441
- type: mrr_at_3
value: 24.108999999999998
- type: mrr_at_5
value: 25.427
- type: ndcg_at_1
value: 19.403000000000002
- type: ndcg_at_10
value: 26.474999999999998
- type: ndcg_at_100
value: 32.086
- type: ndcg_at_1000
value: 35.231
- type: ndcg_at_3
value: 22.289
- type: ndcg_at_5
value: 24.271
- type: precision_at_1
value: 19.403000000000002
- type: precision_at_10
value: 4.813
- type: precision_at_100
value: 0.8869999999999999
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 10.531
- type: precision_at_5
value: 7.710999999999999
- type: recall_at_1
value: 15.601
- type: recall_at_10
value: 35.916
- type: recall_at_100
value: 60.8
- type: recall_at_1000
value: 83.245
- type: recall_at_3
value: 24.321
- type: recall_at_5
value: 29.372999999999998
- type: map_at_1
value: 25.522
- type: map_at_10
value: 34.854
- type: map_at_100
value: 36.269
- type: map_at_1000
value: 36.387
- type: map_at_3
value: 32.187
- type: map_at_5
value: 33.692
- type: mrr_at_1
value: 31.375999999999998
- type: mrr_at_10
value: 40.471000000000004
- type: mrr_at_100
value: 41.481
- type: mrr_at_1000
value: 41.533
- type: mrr_at_3
value: 38.274
- type: mrr_at_5
value: 39.612
- type: ndcg_at_1
value: 31.375999999999998
- type: ndcg_at_10
value: 40.298
- type: ndcg_at_100
value: 46.255
- type: ndcg_at_1000
value: 48.522
- type: ndcg_at_3
value: 36.049
- type: ndcg_at_5
value: 38.095
- type: precision_at_1
value: 31.375999999999998
- type: precision_at_10
value: 7.305000000000001
- type: precision_at_100
value: 1.201
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 17.132
- type: precision_at_5
value: 12.107999999999999
- type: recall_at_1
value: 25.522
- type: recall_at_10
value: 50.988
- type: recall_at_100
value: 76.005
- type: recall_at_1000
value: 91.11200000000001
- type: recall_at_3
value: 38.808
- type: recall_at_5
value: 44.279
- type: map_at_1
value: 24.615000000000002
- type: map_at_10
value: 32.843
- type: map_at_100
value: 34.172999999999995
- type: map_at_1000
value: 34.286
- type: map_at_3
value: 30.125
- type: map_at_5
value: 31.495
- type: mrr_at_1
value: 30.023
- type: mrr_at_10
value: 38.106
- type: mrr_at_100
value: 39.01
- type: mrr_at_1000
value: 39.071
- type: mrr_at_3
value: 35.674
- type: mrr_at_5
value: 36.924
- type: ndcg_at_1
value: 30.023
- type: ndcg_at_10
value: 38.091
- type: ndcg_at_100
value: 43.771
- type: ndcg_at_1000
value: 46.315
- type: ndcg_at_3
value: 33.507
- type: ndcg_at_5
value: 35.304
- type: precision_at_1
value: 30.023
- type: precision_at_10
value: 6.837999999999999
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 15.562999999999999
- type: precision_at_5
value: 10.936
- type: recall_at_1
value: 24.615000000000002
- type: recall_at_10
value: 48.691
- type: recall_at_100
value: 72.884
- type: recall_at_1000
value: 90.387
- type: recall_at_3
value: 35.659
- type: recall_at_5
value: 40.602
- type: map_at_1
value: 23.223666666666666
- type: map_at_10
value: 31.338166666666673
- type: map_at_100
value: 32.47358333333333
- type: map_at_1000
value: 32.5955
- type: map_at_3
value: 28.84133333333333
- type: map_at_5
value: 30.20808333333333
- type: mrr_at_1
value: 27.62483333333333
- type: mrr_at_10
value: 35.385916666666674
- type: mrr_at_100
value: 36.23325
- type: mrr_at_1000
value: 36.29966666666667
- type: mrr_at_3
value: 33.16583333333333
- type: mrr_at_5
value: 34.41983333333334
- type: ndcg_at_1
value: 27.62483333333333
- type: ndcg_at_10
value: 36.222
- type: ndcg_at_100
value: 41.29491666666666
- type: ndcg_at_1000
value: 43.85508333333333
- type: ndcg_at_3
value: 31.95116666666667
- type: ndcg_at_5
value: 33.88541666666667
- type: precision_at_1
value: 27.62483333333333
- type: precision_at_10
value: 6.339916666666667
- type: precision_at_100
value: 1.0483333333333333
- type: precision_at_1000
value: 0.14608333333333334
- type: precision_at_3
value: 14.726500000000003
- type: precision_at_5
value: 10.395
- type: recall_at_1
value: 23.223666666666666
- type: recall_at_10
value: 46.778999999999996
- type: recall_at_100
value: 69.27141666666667
- type: recall_at_1000
value: 87.27383333333334
- type: recall_at_3
value: 34.678749999999994
- type: recall_at_5
value: 39.79900000000001
- type: map_at_1
value: 21.677
- type: map_at_10
value: 27.828000000000003
- type: map_at_100
value: 28.538999999999998
- type: map_at_1000
value: 28.64
- type: map_at_3
value: 26.105
- type: map_at_5
value: 27.009
- type: mrr_at_1
value: 24.387
- type: mrr_at_10
value: 30.209999999999997
- type: mrr_at_100
value: 30.953000000000003
- type: mrr_at_1000
value: 31.029
- type: mrr_at_3
value: 28.707
- type: mrr_at_5
value: 29.610999999999997
- type: ndcg_at_1
value: 24.387
- type: ndcg_at_10
value: 31.378
- type: ndcg_at_100
value: 35.249
- type: ndcg_at_1000
value: 37.923
- type: ndcg_at_3
value: 28.213
- type: ndcg_at_5
value: 29.658
- type: precision_at_1
value: 24.387
- type: precision_at_10
value: 4.8309999999999995
- type: precision_at_100
value: 0.73
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 12.168
- type: precision_at_5
value: 8.251999999999999
- type: recall_at_1
value: 21.677
- type: recall_at_10
value: 40.069
- type: recall_at_100
value: 58.077
- type: recall_at_1000
value: 77.97
- type: recall_at_3
value: 31.03
- type: recall_at_5
value: 34.838
- type: map_at_1
value: 14.484
- type: map_at_10
value: 20.355
- type: map_at_100
value: 21.382
- type: map_at_1000
value: 21.511
- type: map_at_3
value: 18.448
- type: map_at_5
value: 19.451999999999998
- type: mrr_at_1
value: 17.584
- type: mrr_at_10
value: 23.825
- type: mrr_at_100
value: 24.704
- type: mrr_at_1000
value: 24.793000000000003
- type: mrr_at_3
value: 21.92
- type: mrr_at_5
value: 22.97
- type: ndcg_at_1
value: 17.584
- type: ndcg_at_10
value: 24.315
- type: ndcg_at_100
value: 29.354999999999997
- type: ndcg_at_1000
value: 32.641999999999996
- type: ndcg_at_3
value: 20.802
- type: ndcg_at_5
value: 22.335
- type: precision_at_1
value: 17.584
- type: precision_at_10
value: 4.443
- type: precision_at_100
value: 0.8160000000000001
- type: precision_at_1000
value: 0.128
- type: precision_at_3
value: 9.807
- type: precision_at_5
value: 7.0889999999999995
- type: recall_at_1
value: 14.484
- type: recall_at_10
value: 32.804
- type: recall_at_100
value: 55.679
- type: recall_at_1000
value: 79.63
- type: recall_at_3
value: 22.976
- type: recall_at_5
value: 26.939
- type: map_at_1
value: 22.983999999999998
- type: map_at_10
value: 30.812
- type: map_at_100
value: 31.938
- type: map_at_1000
value: 32.056000000000004
- type: map_at_3
value: 28.449999999999996
- type: map_at_5
value: 29.542
- type: mrr_at_1
value: 27.145999999999997
- type: mrr_at_10
value: 34.782999999999994
- type: mrr_at_100
value: 35.699
- type: mrr_at_1000
value: 35.768
- type: mrr_at_3
value: 32.572
- type: mrr_at_5
value: 33.607
- type: ndcg_at_1
value: 27.145999999999997
- type: ndcg_at_10
value: 35.722
- type: ndcg_at_100
value: 40.964
- type: ndcg_at_1000
value: 43.598
- type: ndcg_at_3
value: 31.379
- type: ndcg_at_5
value: 32.924
- type: precision_at_1
value: 27.145999999999997
- type: precision_at_10
value: 6.063000000000001
- type: precision_at_100
value: 0.9730000000000001
- type: precision_at_1000
value: 0.13
- type: precision_at_3
value: 14.366000000000001
- type: precision_at_5
value: 9.776
- type: recall_at_1
value: 22.983999999999998
- type: recall_at_10
value: 46.876
- type: recall_at_100
value: 69.646
- type: recall_at_1000
value: 88.305
- type: recall_at_3
value: 34.471000000000004
- type: recall_at_5
value: 38.76
- type: map_at_1
value: 23.017000000000003
- type: map_at_10
value: 31.049
- type: map_at_100
value: 32.582
- type: map_at_1000
value: 32.817
- type: map_at_3
value: 28.303
- type: map_at_5
value: 29.854000000000003
- type: mrr_at_1
value: 27.866000000000003
- type: mrr_at_10
value: 35.56
- type: mrr_at_100
value: 36.453
- type: mrr_at_1000
value: 36.519
- type: mrr_at_3
value: 32.938
- type: mrr_at_5
value: 34.391
- type: ndcg_at_1
value: 27.866000000000003
- type: ndcg_at_10
value: 36.506
- type: ndcg_at_100
value: 42.344
- type: ndcg_at_1000
value: 45.213
- type: ndcg_at_3
value: 31.805
- type: ndcg_at_5
value: 33.933
- type: precision_at_1
value: 27.866000000000003
- type: precision_at_10
value: 7.016
- type: precision_at_100
value: 1.468
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 14.822
- type: precision_at_5
value: 10.791
- type: recall_at_1
value: 23.017000000000003
- type: recall_at_10
value: 47.053
- type: recall_at_100
value: 73.177
- type: recall_at_1000
value: 91.47800000000001
- type: recall_at_3
value: 33.675
- type: recall_at_5
value: 39.36
- type: map_at_1
value: 16.673
- type: map_at_10
value: 24.051000000000002
- type: map_at_100
value: 24.933
- type: map_at_1000
value: 25.06
- type: map_at_3
value: 21.446
- type: map_at_5
value: 23.064
- type: mrr_at_1
value: 18.115000000000002
- type: mrr_at_10
value: 25.927
- type: mrr_at_100
value: 26.718999999999998
- type: mrr_at_1000
value: 26.817999999999998
- type: mrr_at_3
value: 23.383000000000003
- type: mrr_at_5
value: 25.008999999999997
- type: ndcg_at_1
value: 18.115000000000002
- type: ndcg_at_10
value: 28.669
- type: ndcg_at_100
value: 33.282000000000004
- type: ndcg_at_1000
value: 36.481
- type: ndcg_at_3
value: 23.574
- type: ndcg_at_5
value: 26.340000000000003
- type: precision_at_1
value: 18.115000000000002
- type: precision_at_10
value: 4.769
- type: precision_at_100
value: 0.767
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 10.351
- type: precision_at_5
value: 7.8
- type: recall_at_1
value: 16.673
- type: recall_at_10
value: 41.063
- type: recall_at_100
value: 62.851
- type: recall_at_1000
value: 86.701
- type: recall_at_3
value: 27.532
- type: recall_at_5
value: 34.076
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.752
- type: map_at_10
value: 15.120000000000001
- type: map_at_100
value: 16.678
- type: map_at_1000
value: 16.854
- type: map_at_3
value: 12.603
- type: map_at_5
value: 13.918
- type: mrr_at_1
value: 19.283
- type: mrr_at_10
value: 29.145
- type: mrr_at_100
value: 30.281000000000002
- type: mrr_at_1000
value: 30.339
- type: mrr_at_3
value: 26.069
- type: mrr_at_5
value: 27.864
- type: ndcg_at_1
value: 19.283
- type: ndcg_at_10
value: 21.804000000000002
- type: ndcg_at_100
value: 28.576
- type: ndcg_at_1000
value: 32.063
- type: ndcg_at_3
value: 17.511
- type: ndcg_at_5
value: 19.112000000000002
- type: precision_at_1
value: 19.283
- type: precision_at_10
value: 6.873
- type: precision_at_100
value: 1.405
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 13.16
- type: precision_at_5
value: 10.189
- type: recall_at_1
value: 8.752
- type: recall_at_10
value: 27.004
- type: recall_at_100
value: 50.648
- type: recall_at_1000
value: 70.458
- type: recall_at_3
value: 16.461000000000002
- type: recall_at_5
value: 20.973
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.81
- type: map_at_10
value: 14.056
- type: map_at_100
value: 18.961
- type: map_at_1000
value: 20.169
- type: map_at_3
value: 10.496
- type: map_at_5
value: 11.952
- type: mrr_at_1
value: 53.5
- type: mrr_at_10
value: 63.479
- type: mrr_at_100
value: 63.971999999999994
- type: mrr_at_1000
value: 63.993
- type: mrr_at_3
value: 61.541999999999994
- type: mrr_at_5
value: 62.778999999999996
- type: ndcg_at_1
value: 42.25
- type: ndcg_at_10
value: 31.471
- type: ndcg_at_100
value: 35.115
- type: ndcg_at_1000
value: 42.408
- type: ndcg_at_3
value: 35.458
- type: ndcg_at_5
value: 32.973
- type: precision_at_1
value: 53.5
- type: precision_at_10
value: 24.85
- type: precision_at_100
value: 7.79
- type: precision_at_1000
value: 1.599
- type: precision_at_3
value: 38.667
- type: precision_at_5
value: 31.55
- type: recall_at_1
value: 6.81
- type: recall_at_10
value: 19.344
- type: recall_at_100
value: 40.837
- type: recall_at_1000
value: 64.661
- type: recall_at_3
value: 11.942
- type: recall_at_5
value: 14.646
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.64499999999999
- type: f1
value: 39.39106911352714
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 48.196
- type: map_at_10
value: 61.404
- type: map_at_100
value: 61.846000000000004
- type: map_at_1000
value: 61.866
- type: map_at_3
value: 58.975
- type: map_at_5
value: 60.525
- type: mrr_at_1
value: 52.025
- type: mrr_at_10
value: 65.43299999999999
- type: mrr_at_100
value: 65.80799999999999
- type: mrr_at_1000
value: 65.818
- type: mrr_at_3
value: 63.146
- type: mrr_at_5
value: 64.64
- type: ndcg_at_1
value: 52.025
- type: ndcg_at_10
value: 67.889
- type: ndcg_at_100
value: 69.864
- type: ndcg_at_1000
value: 70.337
- type: ndcg_at_3
value: 63.315
- type: ndcg_at_5
value: 65.91799999999999
- type: precision_at_1
value: 52.025
- type: precision_at_10
value: 9.182
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.108
- type: precision_at_3
value: 25.968000000000004
- type: precision_at_5
value: 17.006
- type: recall_at_1
value: 48.196
- type: recall_at_10
value: 83.885
- type: recall_at_100
value: 92.671
- type: recall_at_1000
value: 96.018
- type: recall_at_3
value: 71.59
- type: recall_at_5
value: 77.946
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.193000000000001
- type: map_at_10
value: 25.168000000000003
- type: map_at_100
value: 27.017000000000003
- type: map_at_1000
value: 27.205000000000002
- type: map_at_3
value: 21.746
- type: map_at_5
value: 23.579
- type: mrr_at_1
value: 31.635999999999996
- type: mrr_at_10
value: 40.077
- type: mrr_at_100
value: 41.112
- type: mrr_at_1000
value: 41.160999999999994
- type: mrr_at_3
value: 37.937
- type: mrr_at_5
value: 39.18
- type: ndcg_at_1
value: 31.635999999999996
- type: ndcg_at_10
value: 32.298
- type: ndcg_at_100
value: 39.546
- type: ndcg_at_1000
value: 42.88
- type: ndcg_at_3
value: 29.221999999999998
- type: ndcg_at_5
value: 30.069000000000003
- type: precision_at_1
value: 31.635999999999996
- type: precision_at_10
value: 9.367
- type: precision_at_100
value: 1.645
- type: precision_at_1000
value: 0.22399999999999998
- type: precision_at_3
value: 20.01
- type: precision_at_5
value: 14.753
- type: recall_at_1
value: 15.193000000000001
- type: recall_at_10
value: 38.214999999999996
- type: recall_at_100
value: 65.95
- type: recall_at_1000
value: 85.85300000000001
- type: recall_at_3
value: 26.357000000000003
- type: recall_at_5
value: 31.319999999999997
- task:
type: Retrieval
dataset:
name: MTEB GerDaLIR
type: jinaai/ger_da_lir
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.363
- type: map_at_10
value: 16.222
- type: map_at_100
value: 17.28
- type: map_at_1000
value: 17.380000000000003
- type: map_at_3
value: 14.054
- type: map_at_5
value: 15.203
- type: mrr_at_1
value: 11.644
- type: mrr_at_10
value: 17.625
- type: mrr_at_100
value: 18.608
- type: mrr_at_1000
value: 18.695999999999998
- type: mrr_at_3
value: 15.481
- type: mrr_at_5
value: 16.659
- type: ndcg_at_1
value: 11.628
- type: ndcg_at_10
value: 20.028000000000002
- type: ndcg_at_100
value: 25.505
- type: ndcg_at_1000
value: 28.288000000000004
- type: ndcg_at_3
value: 15.603
- type: ndcg_at_5
value: 17.642
- type: precision_at_1
value: 11.628
- type: precision_at_10
value: 3.5589999999999997
- type: precision_at_100
value: 0.664
- type: precision_at_1000
value: 0.092
- type: precision_at_3
value: 7.109999999999999
- type: precision_at_5
value: 5.401
- type: recall_at_1
value: 10.363
- type: recall_at_10
value: 30.586000000000002
- type: recall_at_100
value: 56.43
- type: recall_at_1000
value: 78.142
- type: recall_at_3
value: 18.651
- type: recall_at_5
value: 23.493
- task:
type: Retrieval
dataset:
name: MTEB GermanDPR
type: deepset/germandpr
config: default
split: test
revision: 5129d02422a66be600ac89cd3e8531b4f97d347d
metrics:
- type: map_at_1
value: 60.78
- type: map_at_10
value: 73.91499999999999
- type: map_at_100
value: 74.089
- type: map_at_1000
value: 74.09400000000001
- type: map_at_3
value: 71.87
- type: map_at_5
value: 73.37700000000001
- type: mrr_at_1
value: 60.78
- type: mrr_at_10
value: 73.91499999999999
- type: mrr_at_100
value: 74.089
- type: mrr_at_1000
value: 74.09400000000001
- type: mrr_at_3
value: 71.87
- type: mrr_at_5
value: 73.37700000000001
- type: ndcg_at_1
value: 60.78
- type: ndcg_at_10
value: 79.35600000000001
- type: ndcg_at_100
value: 80.077
- type: ndcg_at_1000
value: 80.203
- type: ndcg_at_3
value: 75.393
- type: ndcg_at_5
value: 78.077
- type: precision_at_1
value: 60.78
- type: precision_at_10
value: 9.59
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.52
- type: precision_at_5
value: 18.4
- type: recall_at_1
value: 60.78
- type: recall_at_10
value: 95.902
- type: recall_at_100
value: 99.024
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 85.56099999999999
- type: recall_at_5
value: 92.0
- task:
type: STS
dataset:
name: MTEB GermanSTSBenchmark
type: jinaai/german-STSbenchmark
config: default
split: test
revision: 49d9b423b996fea62b483f9ee6dfb5ec233515ca
metrics:
- type: cos_sim_pearson
value: 88.49524420894356
- type: cos_sim_spearman
value: 88.32407839427714
- type: euclidean_pearson
value: 87.25098779877104
- type: euclidean_spearman
value: 88.22738098593608
- type: manhattan_pearson
value: 87.23872691839607
- type: manhattan_spearman
value: 88.2002968380165
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.81
- type: map_at_10
value: 46.238
- type: map_at_100
value: 47.141
- type: map_at_1000
value: 47.213
- type: map_at_3
value: 43.248999999999995
- type: map_at_5
value: 45.078
- type: mrr_at_1
value: 63.619
- type: mrr_at_10
value: 71.279
- type: mrr_at_100
value: 71.648
- type: mrr_at_1000
value: 71.665
- type: mrr_at_3
value: 69.76599999999999
- type: mrr_at_5
value: 70.743
- type: ndcg_at_1
value: 63.619
- type: ndcg_at_10
value: 55.38999999999999
- type: ndcg_at_100
value: 58.80800000000001
- type: ndcg_at_1000
value: 60.331999999999994
- type: ndcg_at_3
value: 50.727
- type: ndcg_at_5
value: 53.284
- type: precision_at_1
value: 63.619
- type: precision_at_10
value: 11.668000000000001
- type: precision_at_100
value: 1.434
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 32.001000000000005
- type: precision_at_5
value: 21.223
- type: recall_at_1
value: 31.81
- type: recall_at_10
value: 58.339
- type: recall_at_100
value: 71.708
- type: recall_at_1000
value: 81.85
- type: recall_at_3
value: 48.001
- type: recall_at_5
value: 53.059
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 68.60640000000001
- type: ap
value: 62.84296904042086
- type: f1
value: 68.50643633327537
- task:
type: Reranking
dataset:
name: MTEB MIRACL
type: jinaai/miracl
config: default
split: test
revision: 8741c3b61cd36ed9ca1b3d4203543a41793239e2
metrics:
- type: map
value: 64.29704335389768
- type: mrr
value: 72.11962197159565
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.3844049247606
- type: f1
value: 89.2124328528015
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.36855452240067
- type: f1
value: 87.35458822097442
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.48654810761514
- type: f1
value: 50.07229882504409
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 63.832065370526905
- type: f1
value: 46.283579383385806
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.89038332212509
- type: f1
value: 61.86279849685129
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 69.11230665770006
- type: f1
value: 67.44780095350535
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (de)
type: mteb/amazon_massive_scenario
config: de
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.25084061869536
- type: f1
value: 71.43965023016408
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.73907195696032
- type: f1
value: 73.69920814839061
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.32577306498249
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.759349326367783
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.401342674703425
- type: mrr
value: 31.384379585660987
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.855
- type: map_at_10
value: 10.01
- type: map_at_100
value: 12.461
- type: map_at_1000
value: 13.776
- type: map_at_3
value: 7.252
- type: map_at_5
value: 8.679
- type: mrr_at_1
value: 41.176
- type: mrr_at_10
value: 49.323
- type: mrr_at_100
value: 49.954
- type: mrr_at_1000
value: 49.997
- type: mrr_at_3
value: 46.904
- type: mrr_at_5
value: 48.375
- type: ndcg_at_1
value: 39.318999999999996
- type: ndcg_at_10
value: 28.607
- type: ndcg_at_100
value: 26.554
- type: ndcg_at_1000
value: 35.731
- type: ndcg_at_3
value: 32.897999999999996
- type: ndcg_at_5
value: 31.53
- type: precision_at_1
value: 41.176
- type: precision_at_10
value: 20.867
- type: precision_at_100
value: 6.796
- type: precision_at_1000
value: 1.983
- type: precision_at_3
value: 30.547
- type: precision_at_5
value: 27.245
- type: recall_at_1
value: 4.855
- type: recall_at_10
value: 14.08
- type: recall_at_100
value: 28.188000000000002
- type: recall_at_1000
value: 60.07900000000001
- type: recall_at_3
value: 7.947
- type: recall_at_5
value: 10.786
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.906999999999996
- type: map_at_10
value: 41.147
- type: map_at_100
value: 42.269
- type: map_at_1000
value: 42.308
- type: map_at_3
value: 36.638999999999996
- type: map_at_5
value: 39.285
- type: mrr_at_1
value: 30.359
- type: mrr_at_10
value: 43.607
- type: mrr_at_100
value: 44.454
- type: mrr_at_1000
value: 44.481
- type: mrr_at_3
value: 39.644
- type: mrr_at_5
value: 42.061
- type: ndcg_at_1
value: 30.330000000000002
- type: ndcg_at_10
value: 48.899
- type: ndcg_at_100
value: 53.612
- type: ndcg_at_1000
value: 54.51200000000001
- type: ndcg_at_3
value: 40.262
- type: ndcg_at_5
value: 44.787
- type: precision_at_1
value: 30.330000000000002
- type: precision_at_10
value: 8.323
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 18.395
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 26.906999999999996
- type: recall_at_10
value: 70.215
- type: recall_at_100
value: 90.61200000000001
- type: recall_at_1000
value: 97.294
- type: recall_at_3
value: 47.784
- type: recall_at_5
value: 58.251
- task:
type: PairClassification
dataset:
name: MTEB PawsX
type: paws-x
config: default
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 60.5
- type: cos_sim_ap
value: 57.606096528877494
- type: cos_sim_f1
value: 62.24240307369892
- type: cos_sim_precision
value: 45.27439024390244
- type: cos_sim_recall
value: 99.55307262569832
- type: dot_accuracy
value: 57.699999999999996
- type: dot_ap
value: 51.289351057160616
- type: dot_f1
value: 62.25953130465197
- type: dot_precision
value: 45.31568228105906
- type: dot_recall
value: 99.4413407821229
- type: euclidean_accuracy
value: 60.45
- type: euclidean_ap
value: 57.616461421424034
- type: euclidean_f1
value: 62.313697657913416
- type: euclidean_precision
value: 45.657826313052524
- type: euclidean_recall
value: 98.10055865921787
- type: manhattan_accuracy
value: 60.3
- type: manhattan_ap
value: 57.580565271667325
- type: manhattan_f1
value: 62.24240307369892
- type: manhattan_precision
value: 45.27439024390244
- type: manhattan_recall
value: 99.55307262569832
- type: max_accuracy
value: 60.5
- type: max_ap
value: 57.616461421424034
- type: max_f1
value: 62.313697657913416
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.21300000000001
- type: map_at_10
value: 84.136
- type: map_at_100
value: 84.796
- type: map_at_1000
value: 84.812
- type: map_at_3
value: 81.182
- type: map_at_5
value: 83.027
- type: mrr_at_1
value: 80.91000000000001
- type: mrr_at_10
value: 87.155
- type: mrr_at_100
value: 87.27000000000001
- type: mrr_at_1000
value: 87.271
- type: mrr_at_3
value: 86.158
- type: mrr_at_5
value: 86.828
- type: ndcg_at_1
value: 80.88
- type: ndcg_at_10
value: 87.926
- type: ndcg_at_100
value: 89.223
- type: ndcg_at_1000
value: 89.321
- type: ndcg_at_3
value: 85.036
- type: ndcg_at_5
value: 86.614
- type: precision_at_1
value: 80.88
- type: precision_at_10
value: 13.350000000000001
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.173
- type: precision_at_5
value: 24.476
- type: recall_at_1
value: 70.21300000000001
- type: recall_at_10
value: 95.12
- type: recall_at_100
value: 99.535
- type: recall_at_1000
value: 99.977
- type: recall_at_3
value: 86.833
- type: recall_at_5
value: 91.26100000000001
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 47.754688783184875
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 54.875736374329364
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.773
- type: map_at_10
value: 9.447
- type: map_at_100
value: 11.1
- type: map_at_1000
value: 11.37
- type: map_at_3
value: 6.787
- type: map_at_5
value: 8.077
- type: mrr_at_1
value: 18.5
- type: mrr_at_10
value: 28.227000000000004
- type: mrr_at_100
value: 29.445
- type: mrr_at_1000
value: 29.515
- type: mrr_at_3
value: 25.2
- type: mrr_at_5
value: 27.055
- type: ndcg_at_1
value: 18.5
- type: ndcg_at_10
value: 16.29
- type: ndcg_at_100
value: 23.250999999999998
- type: ndcg_at_1000
value: 28.445999999999998
- type: ndcg_at_3
value: 15.376000000000001
- type: ndcg_at_5
value: 13.528
- type: precision_at_1
value: 18.5
- type: precision_at_10
value: 8.51
- type: precision_at_100
value: 1.855
- type: precision_at_1000
value: 0.311
- type: precision_at_3
value: 14.533
- type: precision_at_5
value: 12.0
- type: recall_at_1
value: 3.773
- type: recall_at_10
value: 17.282
- type: recall_at_100
value: 37.645
- type: recall_at_1000
value: 63.138000000000005
- type: recall_at_3
value: 8.853
- type: recall_at_5
value: 12.168
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.32789517976525
- type: cos_sim_spearman
value: 80.32750384145629
- type: euclidean_pearson
value: 81.5025131452508
- type: euclidean_spearman
value: 80.24797115147175
- type: manhattan_pearson
value: 81.51634463412002
- type: manhattan_spearman
value: 80.24614721495055
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 88.47050448992432
- type: cos_sim_spearman
value: 80.58919997743621
- type: euclidean_pearson
value: 85.83258918113664
- type: euclidean_spearman
value: 80.97441389240902
- type: manhattan_pearson
value: 85.7798262013878
- type: manhattan_spearman
value: 80.97208703064196
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.95341439711532
- type: cos_sim_spearman
value: 86.59127484634989
- type: euclidean_pearson
value: 85.57850603454227
- type: euclidean_spearman
value: 86.47130477363419
- type: manhattan_pearson
value: 85.59387925447652
- type: manhattan_spearman
value: 86.50665427391583
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.39810909161844
- type: cos_sim_spearman
value: 82.98595295546008
- type: euclidean_pearson
value: 84.04681129969951
- type: euclidean_spearman
value: 82.98197460689866
- type: manhattan_pearson
value: 83.9918798171185
- type: manhattan_spearman
value: 82.91148131768082
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.02072712147692
- type: cos_sim_spearman
value: 88.78821332623012
- type: euclidean_pearson
value: 88.12132045572747
- type: euclidean_spearman
value: 88.74273451067364
- type: manhattan_pearson
value: 88.05431550059166
- type: manhattan_spearman
value: 88.67610233020723
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 82.96134704624787
- type: cos_sim_spearman
value: 84.44062976314666
- type: euclidean_pearson
value: 84.03642536310323
- type: euclidean_spearman
value: 84.4535014579785
- type: manhattan_pearson
value: 83.92874228901483
- type: manhattan_spearman
value: 84.33634314951631
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.3154168064887
- type: cos_sim_spearman
value: 86.72393652571682
- type: euclidean_pearson
value: 86.04193246174164
- type: euclidean_spearman
value: 86.30482896608093
- type: manhattan_pearson
value: 85.95524084651859
- type: manhattan_spearman
value: 86.06031431994282
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.91079682750804
- type: cos_sim_spearman
value: 89.30961836617064
- type: euclidean_pearson
value: 88.86249564158628
- type: euclidean_spearman
value: 89.04772899592396
- type: manhattan_pearson
value: 88.85579791315043
- type: manhattan_spearman
value: 88.94190462541333
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.00558145551088
- type: cos_sim_spearman
value: 67.96601170393878
- type: euclidean_pearson
value: 67.87627043214336
- type: euclidean_spearman
value: 66.76402572303859
- type: manhattan_pearson
value: 67.88306560555452
- type: manhattan_spearman
value: 66.6273862035506
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 50.83759332748726
- type: cos_sim_spearman
value: 59.066344562858006
- type: euclidean_pearson
value: 50.08955848154131
- type: euclidean_spearman
value: 58.36517305855221
- type: manhattan_pearson
value: 50.05257267223111
- type: manhattan_spearman
value: 58.37570252804986
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.22749007956492
- type: cos_sim_spearman
value: 55.97282077657827
- type: euclidean_pearson
value: 62.10661533695752
- type: euclidean_spearman
value: 53.62780854854067
- type: manhattan_pearson
value: 62.37138085709719
- type: manhattan_spearman
value: 54.17556356828155
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.91145397065878
- type: cos_sim_spearman
value: 88.13960018389005
- type: euclidean_pearson
value: 87.67618876224006
- type: euclidean_spearman
value: 87.99119480810556
- type: manhattan_pearson
value: 87.67920297334753
- type: manhattan_spearman
value: 87.99113250064492
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 78.09133563707582
- type: mrr
value: 93.2415288052543
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 47.760999999999996
- type: map_at_10
value: 56.424
- type: map_at_100
value: 57.24399999999999
- type: map_at_1000
value: 57.278
- type: map_at_3
value: 53.68000000000001
- type: map_at_5
value: 55.442
- type: mrr_at_1
value: 50.666999999999994
- type: mrr_at_10
value: 58.012
- type: mrr_at_100
value: 58.736
- type: mrr_at_1000
value: 58.769000000000005
- type: mrr_at_3
value: 56.056
- type: mrr_at_5
value: 57.321999999999996
- type: ndcg_at_1
value: 50.666999999999994
- type: ndcg_at_10
value: 60.67700000000001
- type: ndcg_at_100
value: 64.513
- type: ndcg_at_1000
value: 65.62400000000001
- type: ndcg_at_3
value: 56.186
- type: ndcg_at_5
value: 58.692
- type: precision_at_1
value: 50.666999999999994
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 1.023
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 21.889
- type: precision_at_5
value: 14.866999999999999
- type: recall_at_1
value: 47.760999999999996
- type: recall_at_10
value: 72.006
- type: recall_at_100
value: 89.767
- type: recall_at_1000
value: 98.833
- type: recall_at_3
value: 60.211000000000006
- type: recall_at_5
value: 66.3
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 94.86690691995835
- type: cos_sim_f1
value: 89.37875751503007
- type: cos_sim_precision
value: 89.5582329317269
- type: cos_sim_recall
value: 89.2
- type: dot_accuracy
value: 99.76336633663367
- type: dot_ap
value: 94.26453740761586
- type: dot_f1
value: 88.00783162016641
- type: dot_precision
value: 86.19367209971237
- type: dot_recall
value: 89.9
- type: euclidean_accuracy
value: 99.7940594059406
- type: euclidean_ap
value: 94.85459757524379
- type: euclidean_f1
value: 89.62779156327544
- type: euclidean_precision
value: 88.96551724137932
- type: euclidean_recall
value: 90.3
- type: manhattan_accuracy
value: 99.79009900990098
- type: manhattan_ap
value: 94.76971336654465
- type: manhattan_f1
value: 89.35323383084577
- type: manhattan_precision
value: 88.91089108910892
- type: manhattan_recall
value: 89.8
- type: max_accuracy
value: 99.7940594059406
- type: max_ap
value: 94.86690691995835
- type: max_f1
value: 89.62779156327544
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.38197670064987
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.08330158937971
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 49.50367079063226
- type: mrr
value: 50.30444943128768
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.37739520909561
- type: cos_sim_spearman
value: 31.548500943973913
- type: dot_pearson
value: 29.983610104303
- type: dot_spearman
value: 29.90185869098618
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.5810000000000002
- type: map_at_100
value: 9.064
- type: map_at_1000
value: 22.161
- type: map_at_3
value: 0.536
- type: map_at_5
value: 0.8370000000000001
- type: mrr_at_1
value: 80.0
- type: mrr_at_10
value: 86.75
- type: mrr_at_100
value: 86.799
- type: mrr_at_1000
value: 86.799
- type: mrr_at_3
value: 85.0
- type: mrr_at_5
value: 86.5
- type: ndcg_at_1
value: 73.0
- type: ndcg_at_10
value: 65.122
- type: ndcg_at_100
value: 51.853
- type: ndcg_at_1000
value: 47.275
- type: ndcg_at_3
value: 66.274
- type: ndcg_at_5
value: 64.826
- type: precision_at_1
value: 80.0
- type: precision_at_10
value: 70.19999999999999
- type: precision_at_100
value: 53.480000000000004
- type: precision_at_1000
value: 20.946
- type: precision_at_3
value: 71.333
- type: precision_at_5
value: 70.0
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.884
- type: recall_at_100
value: 12.57
- type: recall_at_1000
value: 44.208999999999996
- type: recall_at_3
value: 0.5890000000000001
- type: recall_at_5
value: 0.95
- task:
type: Clustering
dataset:
name: MTEB TenKGnadClusteringP2P
type: slvnwhrl/tenkgnad-clustering-p2p
config: default
split: test
revision: 5c59e41555244b7e45c9a6be2d720ab4bafae558
metrics:
- type: v_measure
value: 42.84199261133083
- task:
type: Clustering
dataset:
name: MTEB TenKGnadClusteringS2S
type: slvnwhrl/tenkgnad-clustering-s2s
config: default
split: test
revision: 6cddbe003f12b9b140aec477b583ac4191f01786
metrics:
- type: v_measure
value: 23.689557114798838
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.941
- type: map_at_10
value: 8.222
- type: map_at_100
value: 14.277999999999999
- type: map_at_1000
value: 15.790000000000001
- type: map_at_3
value: 4.4670000000000005
- type: map_at_5
value: 5.762
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 38.784
- type: mrr_at_100
value: 39.724
- type: mrr_at_1000
value: 39.724
- type: mrr_at_3
value: 33.333
- type: mrr_at_5
value: 37.415
- type: ndcg_at_1
value: 22.448999999999998
- type: ndcg_at_10
value: 21.026
- type: ndcg_at_100
value: 33.721000000000004
- type: ndcg_at_1000
value: 45.045
- type: ndcg_at_3
value: 20.053
- type: ndcg_at_5
value: 20.09
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.469
- type: precision_at_1000
value: 1.48
- type: precision_at_3
value: 21.769
- type: precision_at_5
value: 21.224
- type: recall_at_1
value: 1.941
- type: recall_at_10
value: 14.915999999999999
- type: recall_at_100
value: 46.155
- type: recall_at_1000
value: 80.664
- type: recall_at_3
value: 5.629
- type: recall_at_5
value: 8.437
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.64800000000001
- type: ap
value: 12.914826731261094
- type: f1
value: 53.05213503422915
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.427277872099594
- type: f1
value: 60.78292007556828
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 40.48134168406559
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.79465935506944
- type: cos_sim_ap
value: 70.24589055290592
- type: cos_sim_f1
value: 65.0994575045208
- type: cos_sim_precision
value: 63.76518218623482
- type: cos_sim_recall
value: 66.49076517150397
- type: dot_accuracy
value: 84.63968528342374
- type: dot_ap
value: 69.84683095084355
- type: dot_f1
value: 64.50606169727523
- type: dot_precision
value: 59.1719885487778
- type: dot_recall
value: 70.89709762532982
- type: euclidean_accuracy
value: 84.76485664898374
- type: euclidean_ap
value: 70.20556438685551
- type: euclidean_f1
value: 65.06796614516543
- type: euclidean_precision
value: 63.29840319361277
- type: euclidean_recall
value: 66.93931398416886
- type: manhattan_accuracy
value: 84.72313286046374
- type: manhattan_ap
value: 70.17151475534308
- type: manhattan_f1
value: 65.31379180759113
- type: manhattan_precision
value: 62.17505366086334
- type: manhattan_recall
value: 68.7862796833773
- type: max_accuracy
value: 84.79465935506944
- type: max_ap
value: 70.24589055290592
- type: max_f1
value: 65.31379180759113
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.95874568246207
- type: cos_sim_ap
value: 85.82517548264127
- type: cos_sim_f1
value: 78.22288041466125
- type: cos_sim_precision
value: 75.33875338753387
- type: cos_sim_recall
value: 81.33661841700031
- type: dot_accuracy
value: 88.836496293709
- type: dot_ap
value: 85.53430720252186
- type: dot_f1
value: 78.10616085869725
- type: dot_precision
value: 74.73269555430501
- type: dot_recall
value: 81.79858330766862
- type: euclidean_accuracy
value: 88.92769821865176
- type: euclidean_ap
value: 85.65904346964223
- type: euclidean_f1
value: 77.98774074208407
- type: euclidean_precision
value: 73.72282795035315
- type: euclidean_recall
value: 82.77640899291654
- type: manhattan_accuracy
value: 88.86366282454303
- type: manhattan_ap
value: 85.61599642231819
- type: manhattan_f1
value: 78.01480509061737
- type: manhattan_precision
value: 74.10460685833044
- type: manhattan_recall
value: 82.36064059131506
- type: max_accuracy
value: 88.95874568246207
- type: max_ap
value: 85.82517548264127
- type: max_f1
value: 78.22288041466125
- task:
type: Retrieval
dataset:
name: MTEB WikiCLIR
type: None
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.9539999999999997
- type: map_at_10
value: 7.407
- type: map_at_100
value: 8.677999999999999
- type: map_at_1000
value: 9.077
- type: map_at_3
value: 5.987
- type: map_at_5
value: 6.6979999999999995
- type: mrr_at_1
value: 35.65
- type: mrr_at_10
value: 45.097
- type: mrr_at_100
value: 45.83
- type: mrr_at_1000
value: 45.871
- type: mrr_at_3
value: 42.63
- type: mrr_at_5
value: 44.104
- type: ndcg_at_1
value: 29.215000000000003
- type: ndcg_at_10
value: 22.694
- type: ndcg_at_100
value: 22.242
- type: ndcg_at_1000
value: 27.069
- type: ndcg_at_3
value: 27.641
- type: ndcg_at_5
value: 25.503999999999998
- type: precision_at_1
value: 35.65
- type: precision_at_10
value: 12.795000000000002
- type: precision_at_100
value: 3.354
- type: precision_at_1000
value: 0.743
- type: precision_at_3
value: 23.403
- type: precision_at_5
value: 18.474
- type: recall_at_1
value: 3.9539999999999997
- type: recall_at_10
value: 11.301
- type: recall_at_100
value: 22.919999999999998
- type: recall_at_1000
value: 40.146
- type: recall_at_3
value: 7.146
- type: recall_at_5
value: 8.844000000000001
- task:
type: Retrieval
dataset:
name: MTEB XMarket
type: jinaai/xmarket_de
config: default
split: test
revision: 2336818db4c06570fcdf263e1bcb9993b786f67a
metrics:
- type: map_at_1
value: 4.872
- type: map_at_10
value: 10.658
- type: map_at_100
value: 13.422999999999998
- type: map_at_1000
value: 14.245
- type: map_at_3
value: 7.857
- type: map_at_5
value: 9.142999999999999
- type: mrr_at_1
value: 16.744999999999997
- type: mrr_at_10
value: 24.416
- type: mrr_at_100
value: 25.432
- type: mrr_at_1000
value: 25.502999999999997
- type: mrr_at_3
value: 22.096
- type: mrr_at_5
value: 23.421
- type: ndcg_at_1
value: 16.695999999999998
- type: ndcg_at_10
value: 18.66
- type: ndcg_at_100
value: 24.314
- type: ndcg_at_1000
value: 29.846
- type: ndcg_at_3
value: 17.041999999999998
- type: ndcg_at_5
value: 17.585
- type: precision_at_1
value: 16.695999999999998
- type: precision_at_10
value: 10.374
- type: precision_at_100
value: 3.988
- type: precision_at_1000
value: 1.1860000000000002
- type: precision_at_3
value: 14.21
- type: precision_at_5
value: 12.623000000000001
- type: recall_at_1
value: 4.872
- type: recall_at_10
value: 18.624
- type: recall_at_100
value: 40.988
- type: recall_at_1000
value: 65.33
- type: recall_at_3
value: 10.162
- type: recall_at_5
value: 13.517999999999999
---
<!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
## Quick Start
The easiest way to starting using `jina-embeddings-v2-base-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
## Intended Usage & Model Info
`jina-embeddings-v2-base-de` is a German/English bilingual text **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed German-English input without bias.
Additionally, we provide the following embedding models:
`jina-embeddings-v2-base-de` ist ein zweisprachiges **Text Embedding Modell** für Deutsch und Englisch,
welches Texteingaben mit einer Länge von bis zu **8192 Token unterstützt**.
Es basiert auf der adaptierten Bert-Modell-Architektur JinaBERT,
welche mithilfe einer symmetrische Variante von [ALiBi](https://arxiv.org/abs/2108.12409) längere Eingabetexte erlaubt.
Wir haben, das Model für hohe Performance in einsprachigen und cross-lingual Anwendungen entwickelt und speziell darauf trainiert,
gemischte deutsch-englische Eingaben ohne einen Bias zu kodieren.
Des Weiteren stellen wir folgende Embedding-Modelle bereit:
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings **(you are here)**.
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.
## Data & Parameters
The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016).
## Usage
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>
### Why mean pooling?
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.
However, if you would like to do it without using the default `encode` function:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['How is the weather today?', 'What is the current weather like today?']
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-de')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
You can use Jina Embedding models directly from transformers package.
```python
!pip install transformers
import torch
from transformers import AutoModel
from numpy.linalg import norm
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True, torch_dtype=torch.bfloat16)
embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?'])
print(cos_sim(embeddings[0], embeddings[1]))
```
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(
['Very long ... document'],
max_length=2048
)
```
Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"jinaai/jina-embeddings-v2-base-de", # switch to en/zh for English or Chinese
trust_remote_code=True
)
# control your input sequence length up to 8192
model.max_seq_length = 1024
embeddings = model.encode([
'How is the weather today?',
'Wie ist das Wetter heute?'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
## Alternatives to Using Transformers Package
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
## Benchmark Results
We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks:
<img src="de_evaluation_results.png" width="780px">
## Use Jina Embeddings for RAG
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
```
@article{mohr2024multi,
title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},
author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others},
journal={arXiv preprint arXiv:2402.17016},
year={2024}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
aaditya/Llama3-OpenBioLLM-8B | aaditya | text-generation | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-3",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"distillation",
"en",
"arxiv:2305.18290",
"arxiv:2303.13375",
"arxiv:2212.13138",
"arxiv:2305.09617",
"arxiv:2402.07023",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-20T00:53:57 | 2025-01-18T07:40:39 | 49,409 | 182 | ---
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
license: llama3
tags:
- llama-3
- llama
- Mixtral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- distillation
widget:
- example_title: OpenBioLLM-8B
messages:
- role: system
content: You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: How long does it take for newborn jaundice to go away?
output:
text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition
in newborns where the yellowing of the skin and eyes occurs due to an elevated
level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when
red blood cells break down. In most cases, newborn jaundice resolves on its
own without any specific treatment.
The duration of newborn jaundice can vary depending on several factors such
as the underlying cause, gestational age at birth, and individual variations
in bilirubin metabolism. Here are some general guidelines:
1. Physiological jaundice: This is the most common type of newborn jaundice
and usually appears within 24-72 hours after birth. It tends to peak between
the second and fifth day of life and gradually improves over the next week or
two. By the time the baby is one week old, the jaundice should have mostly resolved.
2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and
may appear later than physiological jaundice, typically between the fifth and
fourteenth day of life. It tends to persist for a longer duration but usually
resolves within six weeks after birth. 3. Pathological jaundice: This type of
jaundice is less common and occurs due to an underlying medical condition that
affects bilirubin metabolism or liver function. The duration of pathological
jaundice depends on the specific cause and may require treatment.
It''s important for parents to monitor their newborn''s jaundice closely and
seek medical advice if the jaundice progresses rapidly, becomes severe, or is
accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness.
In these cases, further evaluation and management may be necessary. Remember
that each baby is unique, and the timing of jaundice resolution can vary. If
you have concerns about your newborn''s jaundice, it''s always best to consult
with a healthcare professional for personalized advice and guidance.'
model-index:
- name: OpenBioLLM-8B
results: []
---
<div align="center">
<img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div>
<div align="center">
<h1>Advancing Open-source Large Language Models in Medical Domain</h1>
</div>
<p align="center" style="margin-top: 0px;">
<a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">Online Demo</span>
</a> |
<a href="https://github.com/openlifescience-ai">
<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">GitHub</span>
</a> |
<a href="#">
<img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style="margin-right: 5px;">Paper</span>
</a> |
<a href="https://discord.gg/A5Fjf5zC69">
<img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text">Discord</span>
</a>
</p>
Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model
OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks.
🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency.
🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks.
🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include:
<div align="center">
<img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png">
</div>
- **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290)
- **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar)
- **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)
This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications.
⚙️ **Release Details**:
- **Model Size**: 8 billion parameters
- **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B-GGUF)
- **Language(s) (NLP):** en
- **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs
- **License:** Meta-Llama License
- **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B)
- **Resources for more information:**
- Paper: Coming soon
The model can be fine-tuned for more specialized tasks and datasets as needed.
OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.
We are excited to share OpenBioLLM-8B with researchers and developers around the world.
### Community & Resources
#### 🔥 Your Daily Dose of Medical AI Breakthroughs 🚀
We turn hours of the latest research papers into minutes. Get daily tweets and news on the latest medical AI breakthroughs, dataset releases, and benchmark results – all carefully curated to save you time while keeping you informed.
<div align="center">
<table>
<tr>
<td align="center">
<a href="https://twitter.com/OpenLifeSciAI">
<img src="https://img.shields.io/badge/X-Follow%20%40OpenLifeSciAI-black?style=flat&logo=x" alt="Twitter Follow"/>
<br>
Daily updates on Medical LLMs,<br>datasets & benchmarks
</a>
</td>
<td align="center">
<a href="https://www.linkedin.com/company/openlifesciai/">
<img src="https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn"/>
<br>
Daily news on Medical LLMs,<br>datasets & benchmarks
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://www.youtube.com/@OpenlifesciAI">
<img src="https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube" alt="YouTube"/>
<br>
Video & audio summaries of<br>latest research
</a>
</td>
<td align="center">
<a href="https://t.co/l5z6y6C4cM">
<img src="https://img.shields.io/badge/Discord-Join-7289DA?style=for-the-badge&logo=discord" alt="Discord"/>
<br>
Connect with researchers &<br>discuss latest developments
</a>
</td>
</tr>
</table>
</div>
### Use with transformers
**Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.**
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "aaditya/OpenBioLLM-Llama3-8B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."},
{"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.0,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## **Training procedure**
### **Training hyperparameters**
<details>
<summary>Click to see details</summary>
- learning_rate: 0.0002
- lr_scheduler: cosine
- train_batch_size: 12
- eval_batch_size: 8
- GPU: H100 80GB SXM5
- num_devices: 1
- optimizer: adamw_bnb_8bit
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
</details>
### **Peft hyperparameters**
<details>
<summary>Click to see details</summary>
- adapter: qlora
- lora_r: 128
- lora_alpha: 256
- lora_dropout: 0.05
- lora_target_linear: true
-lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
</details>
### **Training results**
### **Framework versions**
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Axolotl
- Lm harness for evaluation
# Benchmark Results
🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.
🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.
| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg |
|--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|
| **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** |
| Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 |
| **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 |
| Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 |
| **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 |
| Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 |
| GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 |
| Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 |
| gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 |
| Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 |
| Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 |
| MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 |
| BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 |
| AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 |
| ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 |
<div align="center">
<img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png">
</div>
## Detailed Medical Subjectwise accuracy
# Use Cases & Examples
🚨 **Below results are from the quantized version of OpenBioLLM-70B**
# Summarize Clinical Notes
OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries
# Answer Medical Questions
OpenBioLLM-70B can provide answers to a wide range of medical questions.
<details>
<summary>Click to see details</summary>
</details>
# Clinical Entity Recognition
OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.
# Biomarkers Extraction
# Classification
OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization
# De-Identification
OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.
**Advisory Notice!**
While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.
Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations.
OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.
Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including:
- Thorough testing and evaluation in relevant clinical scenarios
- Alignment with evidence-based guidelines and best practices
- Mitigation of potential biases and failure modes
- Integration with human oversight and interpretation
- Compliance with regulatory and ethical standards
Always consult a qualified healthcare provider for personal medical needs.
# Citation
If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:
```
@misc{OpenBioLLMs,
author = {Ankit Pal, Malaikannan Sankarasubbu},
title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}
}
```
The accompanying paper is currently in progress and will be released soon.
<div align="center">
<h2> 💌 Contact </h2>
</div>
We look forward to hearing you and collaborating on this exciting project!
**Contributors:**
- [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com]
- Saama AI Labs
- Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.
# References
We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models!
Result sources
- [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375)
- [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138)
- [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617)
- [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023) | [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] |
microsoft/biogpt | microsoft | text-generation | [
"transformers",
"pytorch",
"biogpt",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-11-20T13:20:45 | 2023-02-03T09:28:24 | 47,178 | 245 | ---
language: en
license: mit
widget:
- text: COVID-19 is
---
## BioGPT
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> from transformers import BioGptTokenizer, BioGptForCausalLM
>>> model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
>>> tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
>>> set_seed(42)
>>> generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True)
[{'generated_text': 'COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population'},
{'generated_text': 'COVID-19 is one of the largest viral epidemics in the world.'},
{'generated_text': 'COVID-19 is a common condition affecting an estimated 1.1 million people in the United States alone.'},
{'generated_text': 'COVID-19 is a pandemic, the incidence has been increased in a manner similar to that in other'},
{'generated_text': 'COVID-19 is transmitted via droplets, air-borne, or airborne transmission.'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BioGptTokenizer, BioGptForCausalLM
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
Beam-search decoding:
```python
import torch
from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed
tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt")
model = BioGptForCausalLM.from_pretrained("microsoft/biogpt")
sentence = "COVID-19 is"
inputs = tokenizer(sentence, return_tensors="pt")
set_seed(42)
with torch.no_grad():
beam_output = model.generate(**inputs,
min_length=100,
max_length=1024,
num_beams=5,
early_stopping=True
)
tokenizer.decode(beam_output[0], skip_special_tokens=True)
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK), and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and more than 800,000 deaths.'
```
## Citation
If you find BioGPT useful in your research, please cite the following paper:
```latex
@article{10.1093/bib/bbac409,
author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan},
title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}",
journal = {Briefings in Bioinformatics},
volume = {23},
number = {6},
year = {2022},
month = {09},
abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}",
issn = {1477-4054},
doi = {10.1093/bib/bbac409},
url = {https://doi.org/10.1093/bib/bbac409},
note = {bbac409},
eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf},
}
```
| [
"RELATION_EXTRACTION"
] | [
"BC5CDR",
"PUBMEDQA"
] |
EleutherAI/pythia-410m | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-13T18:45:00 | 2023-07-09T16:01:42 | 44,953 | 23 | ---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-410M
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-410M for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-410M as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-410M has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-410M will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-410M to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-410M may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-410M.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-410M.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
avsolatorio/GIST-all-MiniLM-L6-v2 | avsolatorio | sentence-similarity | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"en",
"arxiv:2402.16829",
"arxiv:2212.09741",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-02-03T05:28:49 | 2024-04-24T23:15:05 | 44,157 | 8 | ---
language:
- en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
model-index:
- name: GIST-all-MiniLM-L6-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 72.8955223880597
- type: ap
value: 35.447605103320775
- type: f1
value: 66.82951715365854
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 87.19474999999998
- type: ap
value: 83.09577890808514
- type: f1
value: 87.13833121762009
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.556000000000004
- type: f1
value: 42.236256693772276
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.884999999999998
- type: map_at_10
value: 42.364000000000004
- type: map_at_100
value: 43.382
- type: map_at_1000
value: 43.391000000000005
- type: map_at_3
value: 37.162
- type: map_at_5
value: 40.139
- type: mrr_at_1
value: 26.884999999999998
- type: mrr_at_10
value: 42.193999999999996
- type: mrr_at_100
value: 43.211
- type: mrr_at_1000
value: 43.221
- type: mrr_at_3
value: 36.949
- type: mrr_at_5
value: 40.004
- type: ndcg_at_1
value: 26.884999999999998
- type: ndcg_at_10
value: 51.254999999999995
- type: ndcg_at_100
value: 55.481
- type: ndcg_at_1000
value: 55.68300000000001
- type: ndcg_at_3
value: 40.565
- type: ndcg_at_5
value: 45.882
- type: precision_at_1
value: 26.884999999999998
- type: precision_at_10
value: 7.9799999999999995
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.808999999999997
- type: precision_at_5
value: 12.645999999999999
- type: recall_at_1
value: 26.884999999999998
- type: recall_at_10
value: 79.801
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 50.427
- type: recall_at_5
value: 63.229
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 45.31044837358167
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.44751738734691
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.96517580629869
- type: mrr
value: 76.30051004704744
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 83.97262600499639
- type: cos_sim_spearman
value: 81.25787561220484
- type: euclidean_pearson
value: 64.96260261677082
- type: euclidean_spearman
value: 64.17616109254686
- type: manhattan_pearson
value: 65.05620628102835
- type: manhattan_spearman
value: 64.71171546419122
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.2435064935065
- type: f1
value: 84.2334859253828
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.38358435972693
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.093619653843124
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.016999999999996
- type: map_at_10
value: 47.019
- type: map_at_100
value: 48.634
- type: map_at_1000
value: 48.757
- type: map_at_3
value: 43.372
- type: map_at_5
value: 45.314
- type: mrr_at_1
value: 43.491
- type: mrr_at_10
value: 53.284
- type: mrr_at_100
value: 54.038
- type: mrr_at_1000
value: 54.071000000000005
- type: mrr_at_3
value: 51.001
- type: mrr_at_5
value: 52.282
- type: ndcg_at_1
value: 43.491
- type: ndcg_at_10
value: 53.498999999999995
- type: ndcg_at_100
value: 58.733999999999995
- type: ndcg_at_1000
value: 60.307
- type: ndcg_at_3
value: 48.841
- type: ndcg_at_5
value: 50.76199999999999
- type: precision_at_1
value: 43.491
- type: precision_at_10
value: 10.315000000000001
- type: precision_at_100
value: 1.6209999999999998
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 23.462
- type: precision_at_5
value: 16.652
- type: recall_at_1
value: 35.016999999999996
- type: recall_at_10
value: 64.92
- type: recall_at_100
value: 86.605
- type: recall_at_1000
value: 96.174
- type: recall_at_3
value: 50.99
- type: recall_at_5
value: 56.93
- type: map_at_1
value: 29.866
- type: map_at_10
value: 40.438
- type: map_at_100
value: 41.77
- type: map_at_1000
value: 41.913
- type: map_at_3
value: 37.634
- type: map_at_5
value: 39.226
- type: mrr_at_1
value: 37.834
- type: mrr_at_10
value: 46.765
- type: mrr_at_100
value: 47.410000000000004
- type: mrr_at_1000
value: 47.461
- type: mrr_at_3
value: 44.735
- type: mrr_at_5
value: 46.028000000000006
- type: ndcg_at_1
value: 37.834
- type: ndcg_at_10
value: 46.303
- type: ndcg_at_100
value: 50.879
- type: ndcg_at_1000
value: 53.112
- type: ndcg_at_3
value: 42.601
- type: ndcg_at_5
value: 44.384
- type: precision_at_1
value: 37.834
- type: precision_at_10
value: 8.898
- type: precision_at_100
value: 1.4409999999999998
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 20.977
- type: precision_at_5
value: 14.841
- type: recall_at_1
value: 29.866
- type: recall_at_10
value: 56.06100000000001
- type: recall_at_100
value: 75.809
- type: recall_at_1000
value: 89.875
- type: recall_at_3
value: 44.707
- type: recall_at_5
value: 49.846000000000004
- type: map_at_1
value: 38.985
- type: map_at_10
value: 51.165000000000006
- type: map_at_100
value: 52.17
- type: map_at_1000
value: 52.229000000000006
- type: map_at_3
value: 48.089999999999996
- type: map_at_5
value: 49.762
- type: mrr_at_1
value: 44.577
- type: mrr_at_10
value: 54.493
- type: mrr_at_100
value: 55.137
- type: mrr_at_1000
value: 55.167
- type: mrr_at_3
value: 52.079
- type: mrr_at_5
value: 53.518
- type: ndcg_at_1
value: 44.577
- type: ndcg_at_10
value: 56.825
- type: ndcg_at_100
value: 60.842
- type: ndcg_at_1000
value: 62.015
- type: ndcg_at_3
value: 51.699
- type: ndcg_at_5
value: 54.11
- type: precision_at_1
value: 44.577
- type: precision_at_10
value: 9.11
- type: precision_at_100
value: 1.206
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 23.156
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 38.985
- type: recall_at_10
value: 70.164
- type: recall_at_100
value: 87.708
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 56.285
- type: recall_at_5
value: 62.303
- type: map_at_1
value: 28.137
- type: map_at_10
value: 36.729
- type: map_at_100
value: 37.851
- type: map_at_1000
value: 37.932
- type: map_at_3
value: 34.074
- type: map_at_5
value: 35.398
- type: mrr_at_1
value: 30.621
- type: mrr_at_10
value: 39.007
- type: mrr_at_100
value: 39.961
- type: mrr_at_1000
value: 40.02
- type: mrr_at_3
value: 36.591
- type: mrr_at_5
value: 37.806
- type: ndcg_at_1
value: 30.621
- type: ndcg_at_10
value: 41.772
- type: ndcg_at_100
value: 47.181
- type: ndcg_at_1000
value: 49.053999999999995
- type: ndcg_at_3
value: 36.577
- type: ndcg_at_5
value: 38.777
- type: precision_at_1
value: 30.621
- type: precision_at_10
value: 6.372999999999999
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.367
- type: precision_at_5
value: 10.531
- type: recall_at_1
value: 28.137
- type: recall_at_10
value: 55.162
- type: recall_at_100
value: 79.931
- type: recall_at_1000
value: 93.67
- type: recall_at_3
value: 41.057
- type: recall_at_5
value: 46.327
- type: map_at_1
value: 16.798
- type: map_at_10
value: 25.267
- type: map_at_100
value: 26.579000000000004
- type: map_at_1000
value: 26.697
- type: map_at_3
value: 22.456
- type: map_at_5
value: 23.912
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 29.843999999999998
- type: mrr_at_100
value: 30.849
- type: mrr_at_1000
value: 30.916
- type: mrr_at_3
value: 27.156000000000002
- type: mrr_at_5
value: 28.518
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 30.792
- type: ndcg_at_100
value: 36.945
- type: ndcg_at_1000
value: 39.619
- type: ndcg_at_3
value: 25.52
- type: ndcg_at_5
value: 27.776
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.734
- type: precision_at_100
value: 1.031
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.148
- type: precision_at_5
value: 9.055
- type: recall_at_1
value: 16.798
- type: recall_at_10
value: 43.332
- type: recall_at_100
value: 70.016
- type: recall_at_1000
value: 88.90400000000001
- type: recall_at_3
value: 28.842000000000002
- type: recall_at_5
value: 34.37
- type: map_at_1
value: 31.180000000000003
- type: map_at_10
value: 41.78
- type: map_at_100
value: 43.102000000000004
- type: map_at_1000
value: 43.222
- type: map_at_3
value: 38.505
- type: map_at_5
value: 40.443
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.481
- type: mrr_at_100
value: 48.268
- type: mrr_at_1000
value: 48.313
- type: mrr_at_3
value: 44.946999999999996
- type: mrr_at_5
value: 46.492
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.827
- type: ndcg_at_100
value: 53.407000000000004
- type: ndcg_at_1000
value: 55.321
- type: ndcg_at_3
value: 42.815
- type: ndcg_at_5
value: 45.363
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.652999999999999
- type: precision_at_100
value: 1.354
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 20.372
- type: precision_at_5
value: 14.591000000000001
- type: recall_at_1
value: 31.180000000000003
- type: recall_at_10
value: 59.894000000000005
- type: recall_at_100
value: 83.722
- type: recall_at_1000
value: 95.705
- type: recall_at_3
value: 45.824
- type: recall_at_5
value: 52.349999999999994
- type: map_at_1
value: 24.66
- type: map_at_10
value: 34.141
- type: map_at_100
value: 35.478
- type: map_at_1000
value: 35.594
- type: map_at_3
value: 30.446
- type: map_at_5
value: 32.583
- type: mrr_at_1
value: 29.909000000000002
- type: mrr_at_10
value: 38.949
- type: mrr_at_100
value: 39.803
- type: mrr_at_1000
value: 39.867999999999995
- type: mrr_at_3
value: 35.921
- type: mrr_at_5
value: 37.753
- type: ndcg_at_1
value: 29.909000000000002
- type: ndcg_at_10
value: 40.012
- type: ndcg_at_100
value: 45.707
- type: ndcg_at_1000
value: 48.15
- type: ndcg_at_3
value: 34.015
- type: ndcg_at_5
value: 37.002
- type: precision_at_1
value: 29.909000000000002
- type: precision_at_10
value: 7.693999999999999
- type: precision_at_100
value: 1.2229999999999999
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 16.323999999999998
- type: precision_at_5
value: 12.306000000000001
- type: recall_at_1
value: 24.66
- type: recall_at_10
value: 52.478
- type: recall_at_100
value: 77.051
- type: recall_at_1000
value: 93.872
- type: recall_at_3
value: 36.382999999999996
- type: recall_at_5
value: 43.903999999999996
- type: map_at_1
value: 26.768416666666667
- type: map_at_10
value: 36.2485
- type: map_at_100
value: 37.520833333333336
- type: map_at_1000
value: 37.64033333333334
- type: map_at_3
value: 33.25791666666667
- type: map_at_5
value: 34.877250000000004
- type: mrr_at_1
value: 31.65408333333334
- type: mrr_at_10
value: 40.43866666666667
- type: mrr_at_100
value: 41.301249999999996
- type: mrr_at_1000
value: 41.357499999999995
- type: mrr_at_3
value: 37.938916666666664
- type: mrr_at_5
value: 39.35183333333334
- type: ndcg_at_1
value: 31.65408333333334
- type: ndcg_at_10
value: 41.76983333333334
- type: ndcg_at_100
value: 47.138
- type: ndcg_at_1000
value: 49.33816666666667
- type: ndcg_at_3
value: 36.76683333333333
- type: ndcg_at_5
value: 39.04441666666666
- type: precision_at_1
value: 31.65408333333334
- type: precision_at_10
value: 7.396249999999998
- type: precision_at_100
value: 1.1974166666666666
- type: precision_at_1000
value: 0.15791666666666668
- type: precision_at_3
value: 16.955583333333333
- type: precision_at_5
value: 12.09925
- type: recall_at_1
value: 26.768416666666667
- type: recall_at_10
value: 53.82366666666667
- type: recall_at_100
value: 77.39600000000002
- type: recall_at_1000
value: 92.46300000000001
- type: recall_at_3
value: 39.90166666666667
- type: recall_at_5
value: 45.754000000000005
- type: map_at_1
value: 24.369
- type: map_at_10
value: 32.025
- type: map_at_100
value: 33.08
- type: map_at_1000
value: 33.169
- type: map_at_3
value: 29.589
- type: map_at_5
value: 30.894
- type: mrr_at_1
value: 27.301
- type: mrr_at_10
value: 34.64
- type: mrr_at_100
value: 35.556
- type: mrr_at_1000
value: 35.616
- type: mrr_at_3
value: 32.515
- type: mrr_at_5
value: 33.666000000000004
- type: ndcg_at_1
value: 27.301
- type: ndcg_at_10
value: 36.386
- type: ndcg_at_100
value: 41.598
- type: ndcg_at_1000
value: 43.864999999999995
- type: ndcg_at_3
value: 32.07
- type: ndcg_at_5
value: 34.028999999999996
- type: precision_at_1
value: 27.301
- type: precision_at_10
value: 5.782
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 13.804
- type: precision_at_5
value: 9.693
- type: recall_at_1
value: 24.369
- type: recall_at_10
value: 47.026
- type: recall_at_100
value: 70.76400000000001
- type: recall_at_1000
value: 87.705
- type: recall_at_3
value: 35.366
- type: recall_at_5
value: 40.077
- type: map_at_1
value: 17.878
- type: map_at_10
value: 25.582
- type: map_at_100
value: 26.848
- type: map_at_1000
value: 26.985
- type: map_at_3
value: 22.997
- type: map_at_5
value: 24.487000000000002
- type: mrr_at_1
value: 22.023
- type: mrr_at_10
value: 29.615000000000002
- type: mrr_at_100
value: 30.656
- type: mrr_at_1000
value: 30.737
- type: mrr_at_3
value: 27.322999999999997
- type: mrr_at_5
value: 28.665000000000003
- type: ndcg_at_1
value: 22.023
- type: ndcg_at_10
value: 30.476999999999997
- type: ndcg_at_100
value: 36.258
- type: ndcg_at_1000
value: 39.287
- type: ndcg_at_3
value: 25.995
- type: ndcg_at_5
value: 28.174
- type: precision_at_1
value: 22.023
- type: precision_at_10
value: 5.657
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 12.491
- type: precision_at_5
value: 9.112
- type: recall_at_1
value: 17.878
- type: recall_at_10
value: 41.155
- type: recall_at_100
value: 66.62599999999999
- type: recall_at_1000
value: 88.08200000000001
- type: recall_at_3
value: 28.505000000000003
- type: recall_at_5
value: 34.284
- type: map_at_1
value: 26.369999999999997
- type: map_at_10
value: 36.115
- type: map_at_100
value: 37.346000000000004
- type: map_at_1000
value: 37.449
- type: map_at_3
value: 32.976
- type: map_at_5
value: 34.782000000000004
- type: mrr_at_1
value: 30.784
- type: mrr_at_10
value: 40.014
- type: mrr_at_100
value: 40.913
- type: mrr_at_1000
value: 40.967999999999996
- type: mrr_at_3
value: 37.205
- type: mrr_at_5
value: 38.995999999999995
- type: ndcg_at_1
value: 30.784
- type: ndcg_at_10
value: 41.797000000000004
- type: ndcg_at_100
value: 47.355000000000004
- type: ndcg_at_1000
value: 49.535000000000004
- type: ndcg_at_3
value: 36.29
- type: ndcg_at_5
value: 39.051
- type: precision_at_1
value: 30.784
- type: precision_at_10
value: 7.164
- type: precision_at_100
value: 1.122
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 16.636
- type: precision_at_5
value: 11.996
- type: recall_at_1
value: 26.369999999999997
- type: recall_at_10
value: 55.010000000000005
- type: recall_at_100
value: 79.105
- type: recall_at_1000
value: 94.053
- type: recall_at_3
value: 40.139
- type: recall_at_5
value: 47.089
- type: map_at_1
value: 26.421
- type: map_at_10
value: 35.253
- type: map_at_100
value: 36.97
- type: map_at_1000
value: 37.195
- type: map_at_3
value: 32.068000000000005
- type: map_at_5
value: 33.763
- type: mrr_at_1
value: 31.423000000000002
- type: mrr_at_10
value: 39.995999999999995
- type: mrr_at_100
value: 40.977999999999994
- type: mrr_at_1000
value: 41.024
- type: mrr_at_3
value: 36.989
- type: mrr_at_5
value: 38.629999999999995
- type: ndcg_at_1
value: 31.423000000000002
- type: ndcg_at_10
value: 41.382000000000005
- type: ndcg_at_100
value: 47.532000000000004
- type: ndcg_at_1000
value: 49.829
- type: ndcg_at_3
value: 35.809000000000005
- type: ndcg_at_5
value: 38.308
- type: precision_at_1
value: 31.423000000000002
- type: precision_at_10
value: 7.885000000000001
- type: precision_at_100
value: 1.609
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 16.469
- type: precision_at_5
value: 12.174
- type: recall_at_1
value: 26.421
- type: recall_at_10
value: 53.618
- type: recall_at_100
value: 80.456
- type: recall_at_1000
value: 94.505
- type: recall_at_3
value: 37.894
- type: recall_at_5
value: 44.352999999999994
- type: map_at_1
value: 21.54
- type: map_at_10
value: 29.468
- type: map_at_100
value: 30.422
- type: map_at_1000
value: 30.542
- type: map_at_3
value: 26.888
- type: map_at_5
value: 27.962999999999997
- type: mrr_at_1
value: 23.29
- type: mrr_at_10
value: 31.176
- type: mrr_at_100
value: 32.046
- type: mrr_at_1000
value: 32.129000000000005
- type: mrr_at_3
value: 28.804999999999996
- type: mrr_at_5
value: 29.868
- type: ndcg_at_1
value: 23.29
- type: ndcg_at_10
value: 34.166000000000004
- type: ndcg_at_100
value: 39.217999999999996
- type: ndcg_at_1000
value: 41.964
- type: ndcg_at_3
value: 28.970000000000002
- type: ndcg_at_5
value: 30.797
- type: precision_at_1
value: 23.29
- type: precision_at_10
value: 5.489999999999999
- type: precision_at_100
value: 0.874
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 12.261
- type: precision_at_5
value: 8.503
- type: recall_at_1
value: 21.54
- type: recall_at_10
value: 47.064
- type: recall_at_100
value: 70.959
- type: recall_at_1000
value: 91.032
- type: recall_at_3
value: 32.828
- type: recall_at_5
value: 37.214999999999996
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.102
- type: map_at_10
value: 17.469
- type: map_at_100
value: 19.244
- type: map_at_1000
value: 19.435
- type: map_at_3
value: 14.257
- type: map_at_5
value: 16.028000000000002
- type: mrr_at_1
value: 22.866
- type: mrr_at_10
value: 33.535
- type: mrr_at_100
value: 34.583999999999996
- type: mrr_at_1000
value: 34.622
- type: mrr_at_3
value: 29.946
- type: mrr_at_5
value: 32.157000000000004
- type: ndcg_at_1
value: 22.866
- type: ndcg_at_10
value: 25.16
- type: ndcg_at_100
value: 32.347
- type: ndcg_at_1000
value: 35.821
- type: ndcg_at_3
value: 19.816
- type: ndcg_at_5
value: 22.026
- type: precision_at_1
value: 22.866
- type: precision_at_10
value: 8.072
- type: precision_at_100
value: 1.5709999999999997
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 14.701
- type: precision_at_5
value: 11.960999999999999
- type: recall_at_1
value: 10.102
- type: recall_at_10
value: 31.086000000000002
- type: recall_at_100
value: 55.896
- type: recall_at_1000
value: 75.375
- type: recall_at_3
value: 18.343999999999998
- type: recall_at_5
value: 24.102
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.961
- type: map_at_10
value: 16.058
- type: map_at_100
value: 21.878
- type: map_at_1000
value: 23.156
- type: map_at_3
value: 12.206999999999999
- type: map_at_5
value: 13.747000000000002
- type: mrr_at_1
value: 60.5
- type: mrr_at_10
value: 68.488
- type: mrr_at_100
value: 69.02199999999999
- type: mrr_at_1000
value: 69.03200000000001
- type: mrr_at_3
value: 66.792
- type: mrr_at_5
value: 67.62899999999999
- type: ndcg_at_1
value: 49.125
- type: ndcg_at_10
value: 34.827999999999996
- type: ndcg_at_100
value: 38.723
- type: ndcg_at_1000
value: 45.988
- type: ndcg_at_3
value: 40.302
- type: ndcg_at_5
value: 36.781000000000006
- type: precision_at_1
value: 60.5
- type: precision_at_10
value: 26.825
- type: precision_at_100
value: 8.445
- type: precision_at_1000
value: 1.7000000000000002
- type: precision_at_3
value: 43.25
- type: precision_at_5
value: 34.5
- type: recall_at_1
value: 7.961
- type: recall_at_10
value: 20.843
- type: recall_at_100
value: 43.839
- type: recall_at_1000
value: 67.33
- type: recall_at_3
value: 13.516
- type: recall_at_5
value: 15.956000000000001
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.06000000000001
- type: f1
value: 47.21494728335567
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 56.798
- type: map_at_10
value: 67.644
- type: map_at_100
value: 68.01700000000001
- type: map_at_1000
value: 68.038
- type: map_at_3
value: 65.539
- type: map_at_5
value: 66.912
- type: mrr_at_1
value: 61.221000000000004
- type: mrr_at_10
value: 71.97099999999999
- type: mrr_at_100
value: 72.262
- type: mrr_at_1000
value: 72.27
- type: mrr_at_3
value: 70.052
- type: mrr_at_5
value: 71.324
- type: ndcg_at_1
value: 61.221000000000004
- type: ndcg_at_10
value: 73.173
- type: ndcg_at_100
value: 74.779
- type: ndcg_at_1000
value: 75.229
- type: ndcg_at_3
value: 69.291
- type: ndcg_at_5
value: 71.552
- type: precision_at_1
value: 61.221000000000004
- type: precision_at_10
value: 9.449
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 27.467999999999996
- type: precision_at_5
value: 17.744
- type: recall_at_1
value: 56.798
- type: recall_at_10
value: 85.991
- type: recall_at_100
value: 92.973
- type: recall_at_1000
value: 96.089
- type: recall_at_3
value: 75.576
- type: recall_at_5
value: 81.12
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.323
- type: map_at_10
value: 30.279
- type: map_at_100
value: 32.153999999999996
- type: map_at_1000
value: 32.339
- type: map_at_3
value: 26.336
- type: map_at_5
value: 28.311999999999998
- type: mrr_at_1
value: 35.339999999999996
- type: mrr_at_10
value: 44.931
- type: mrr_at_100
value: 45.818999999999996
- type: mrr_at_1000
value: 45.864
- type: mrr_at_3
value: 42.618
- type: mrr_at_5
value: 43.736999999999995
- type: ndcg_at_1
value: 35.339999999999996
- type: ndcg_at_10
value: 37.852999999999994
- type: ndcg_at_100
value: 44.888
- type: ndcg_at_1000
value: 48.069
- type: ndcg_at_3
value: 34.127
- type: ndcg_at_5
value: 35.026
- type: precision_at_1
value: 35.339999999999996
- type: precision_at_10
value: 10.617
- type: precision_at_100
value: 1.7930000000000001
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 22.582
- type: precision_at_5
value: 16.605
- type: recall_at_1
value: 18.323
- type: recall_at_10
value: 44.948
- type: recall_at_100
value: 71.11800000000001
- type: recall_at_1000
value: 90.104
- type: recall_at_3
value: 31.661
- type: recall_at_5
value: 36.498000000000005
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.668
- type: map_at_10
value: 43.669999999999995
- type: map_at_100
value: 44.646
- type: map_at_1000
value: 44.731
- type: map_at_3
value: 40.897
- type: map_at_5
value: 42.559999999999995
- type: mrr_at_1
value: 61.336999999999996
- type: mrr_at_10
value: 68.496
- type: mrr_at_100
value: 68.916
- type: mrr_at_1000
value: 68.938
- type: mrr_at_3
value: 66.90700000000001
- type: mrr_at_5
value: 67.91199999999999
- type: ndcg_at_1
value: 61.336999999999996
- type: ndcg_at_10
value: 52.588
- type: ndcg_at_100
value: 56.389
- type: ndcg_at_1000
value: 58.187999999999995
- type: ndcg_at_3
value: 48.109
- type: ndcg_at_5
value: 50.498
- type: precision_at_1
value: 61.336999999999996
- type: precision_at_10
value: 11.033
- type: precision_at_100
value: 1.403
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 30.105999999999998
- type: precision_at_5
value: 19.954
- type: recall_at_1
value: 30.668
- type: recall_at_10
value: 55.165
- type: recall_at_100
value: 70.169
- type: recall_at_1000
value: 82.12
- type: recall_at_3
value: 45.159
- type: recall_at_5
value: 49.885000000000005
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 78.542
- type: ap
value: 72.50692137216646
- type: f1
value: 78.40630687221642
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 18.613
- type: map_at_10
value: 29.98
- type: map_at_100
value: 31.136999999999997
- type: map_at_1000
value: 31.196
- type: map_at_3
value: 26.339000000000002
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 19.054
- type: mrr_at_10
value: 30.476
- type: mrr_at_100
value: 31.588
- type: mrr_at_1000
value: 31.641000000000002
- type: mrr_at_3
value: 26.834000000000003
- type: mrr_at_5
value: 28.849000000000004
- type: ndcg_at_1
value: 19.083
- type: ndcg_at_10
value: 36.541000000000004
- type: ndcg_at_100
value: 42.35
- type: ndcg_at_1000
value: 43.9
- type: ndcg_at_3
value: 29.015
- type: ndcg_at_5
value: 32.622
- type: precision_at_1
value: 19.083
- type: precision_at_10
value: 5.914
- type: precision_at_100
value: 0.889
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 12.483
- type: precision_at_5
value: 9.315
- type: recall_at_1
value: 18.613
- type: recall_at_10
value: 56.88999999999999
- type: recall_at_100
value: 84.207
- type: recall_at_1000
value: 96.20100000000001
- type: recall_at_3
value: 36.262
- type: recall_at_5
value: 44.925
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.77656178750571
- type: f1
value: 94.37966073742972
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.72457820337438
- type: f1
value: 59.11327646329634
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.17753866846
- type: f1
value: 71.22604635414544
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.67787491593813
- type: f1
value: 76.87653151298177
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.3485843514749
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 29.792796913883617
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.310305659169963
- type: mrr
value: 32.38286775798406
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.968
- type: map_at_10
value: 11.379
- type: map_at_100
value: 14.618999999999998
- type: map_at_1000
value: 16.055
- type: map_at_3
value: 8.34
- type: map_at_5
value: 9.690999999999999
- type: mrr_at_1
value: 43.034
- type: mrr_at_10
value: 51.019999999999996
- type: mrr_at_100
value: 51.63100000000001
- type: mrr_at_1000
value: 51.681
- type: mrr_at_3
value: 49.174
- type: mrr_at_5
value: 50.181
- type: ndcg_at_1
value: 41.176
- type: ndcg_at_10
value: 31.341
- type: ndcg_at_100
value: 29.451
- type: ndcg_at_1000
value: 38.007000000000005
- type: ndcg_at_3
value: 36.494
- type: ndcg_at_5
value: 34.499
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 23.375
- type: precision_at_100
value: 7.799
- type: precision_at_1000
value: 2.059
- type: precision_at_3
value: 34.675
- type: precision_at_5
value: 30.154999999999998
- type: recall_at_1
value: 4.968
- type: recall_at_10
value: 15.104999999999999
- type: recall_at_100
value: 30.741000000000003
- type: recall_at_1000
value: 61.182
- type: recall_at_3
value: 9.338000000000001
- type: recall_at_5
value: 11.484
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.716
- type: map_at_10
value: 38.32
- type: map_at_100
value: 39.565
- type: map_at_1000
value: 39.602
- type: map_at_3
value: 33.848
- type: map_at_5
value: 36.471
- type: mrr_at_1
value: 26.912000000000003
- type: mrr_at_10
value: 40.607
- type: mrr_at_100
value: 41.589
- type: mrr_at_1000
value: 41.614000000000004
- type: mrr_at_3
value: 36.684
- type: mrr_at_5
value: 39.036
- type: ndcg_at_1
value: 26.883000000000003
- type: ndcg_at_10
value: 46.096
- type: ndcg_at_100
value: 51.513
- type: ndcg_at_1000
value: 52.366
- type: ndcg_at_3
value: 37.549
- type: ndcg_at_5
value: 41.971000000000004
- type: precision_at_1
value: 26.883000000000003
- type: precision_at_10
value: 8.004
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.516000000000002
- type: precision_at_5
value: 13.019
- type: recall_at_1
value: 23.716
- type: recall_at_10
value: 67.656
- type: recall_at_100
value: 91.413
- type: recall_at_1000
value: 97.714
- type: recall_at_3
value: 45.449
- type: recall_at_5
value: 55.598000000000006
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.486
- type: map_at_10
value: 84.292
- type: map_at_100
value: 84.954
- type: map_at_1000
value: 84.969
- type: map_at_3
value: 81.295
- type: map_at_5
value: 83.165
- type: mrr_at_1
value: 81.16
- type: mrr_at_10
value: 87.31
- type: mrr_at_100
value: 87.423
- type: mrr_at_1000
value: 87.423
- type: mrr_at_3
value: 86.348
- type: mrr_at_5
value: 86.991
- type: ndcg_at_1
value: 81.17
- type: ndcg_at_10
value: 88.067
- type: ndcg_at_100
value: 89.34
- type: ndcg_at_1000
value: 89.43900000000001
- type: ndcg_at_3
value: 85.162
- type: ndcg_at_5
value: 86.752
- type: precision_at_1
value: 81.17
- type: precision_at_10
value: 13.394
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.193
- type: precision_at_5
value: 24.482
- type: recall_at_1
value: 70.486
- type: recall_at_10
value: 95.184
- type: recall_at_100
value: 99.53999999999999
- type: recall_at_1000
value: 99.98700000000001
- type: recall_at_3
value: 86.89
- type: recall_at_5
value: 91.365
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 44.118229475102154
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 48.68049097629063
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.888
- type: map_at_10
value: 12.770999999999999
- type: map_at_100
value: 15.238
- type: map_at_1000
value: 15.616
- type: map_at_3
value: 8.952
- type: map_at_5
value: 10.639999999999999
- type: mrr_at_1
value: 24.099999999999998
- type: mrr_at_10
value: 35.375
- type: mrr_at_100
value: 36.442
- type: mrr_at_1000
value: 36.488
- type: mrr_at_3
value: 31.717000000000002
- type: mrr_at_5
value: 33.722
- type: ndcg_at_1
value: 24.099999999999998
- type: ndcg_at_10
value: 21.438
- type: ndcg_at_100
value: 30.601
- type: ndcg_at_1000
value: 36.678
- type: ndcg_at_3
value: 19.861
- type: ndcg_at_5
value: 17.263
- type: precision_at_1
value: 24.099999999999998
- type: precision_at_10
value: 11.4
- type: precision_at_100
value: 2.465
- type: precision_at_1000
value: 0.392
- type: precision_at_3
value: 18.733
- type: precision_at_5
value: 15.22
- type: recall_at_1
value: 4.888
- type: recall_at_10
value: 23.118
- type: recall_at_100
value: 49.995
- type: recall_at_1000
value: 79.577
- type: recall_at_3
value: 11.398
- type: recall_at_5
value: 15.428
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.33198632617024
- type: cos_sim_spearman
value: 79.09232997136625
- type: euclidean_pearson
value: 81.49986011523868
- type: euclidean_spearman
value: 77.03530620283338
- type: manhattan_pearson
value: 81.4741227286667
- type: manhattan_spearman
value: 76.98641133116311
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.60103674582464
- type: cos_sim_spearman
value: 75.03945035801914
- type: euclidean_pearson
value: 80.82455267481467
- type: euclidean_spearman
value: 70.3317366248871
- type: manhattan_pearson
value: 80.8928091531445
- type: manhattan_spearman
value: 70.43207370945672
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.52453177109315
- type: cos_sim_spearman
value: 83.26431569305103
- type: euclidean_pearson
value: 82.10494657997404
- type: euclidean_spearman
value: 83.41028425949024
- type: manhattan_pearson
value: 82.08669822983934
- type: manhattan_spearman
value: 83.39959776442115
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.67472020277681
- type: cos_sim_spearman
value: 78.61877889763109
- type: euclidean_pearson
value: 80.07878012437722
- type: euclidean_spearman
value: 77.44374494215397
- type: manhattan_pearson
value: 79.95988483102258
- type: manhattan_spearman
value: 77.36018101061366
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.55450610494437
- type: cos_sim_spearman
value: 87.03494331841401
- type: euclidean_pearson
value: 81.4319784394287
- type: euclidean_spearman
value: 82.47893040599372
- type: manhattan_pearson
value: 81.32627203699644
- type: manhattan_spearman
value: 82.40660565070675
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.51576965454805
- type: cos_sim_spearman
value: 83.0062959588245
- type: euclidean_pearson
value: 79.98888882568556
- type: euclidean_spearman
value: 81.08948911791873
- type: manhattan_pearson
value: 79.77952719568583
- type: manhattan_spearman
value: 80.79471040445408
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.28313046682885
- type: cos_sim_spearman
value: 87.35865211085007
- type: euclidean_pearson
value: 84.11501613667811
- type: euclidean_spearman
value: 82.82038954956121
- type: manhattan_pearson
value: 83.891278147302
- type: manhattan_spearman
value: 82.59947685165902
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.80653738006102
- type: cos_sim_spearman
value: 68.11259151179601
- type: euclidean_pearson
value: 43.16707985094242
- type: euclidean_spearman
value: 58.96200382968696
- type: manhattan_pearson
value: 43.84146858566507
- type: manhattan_spearman
value: 59.05193977207514
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.62068205073571
- type: cos_sim_spearman
value: 84.40071593577095
- type: euclidean_pearson
value: 80.90824726252514
- type: euclidean_spearman
value: 80.54974812534094
- type: manhattan_pearson
value: 80.6759008187939
- type: manhattan_spearman
value: 80.31149103896973
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.13774787530915
- type: mrr
value: 96.22233793802422
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.167
- type: map_at_10
value: 59.852000000000004
- type: map_at_100
value: 60.544
- type: map_at_1000
value: 60.577000000000005
- type: map_at_3
value: 57.242000000000004
- type: map_at_5
value: 58.704
- type: mrr_at_1
value: 51.0
- type: mrr_at_10
value: 60.575
- type: mrr_at_100
value: 61.144
- type: mrr_at_1000
value: 61.175000000000004
- type: mrr_at_3
value: 58.667
- type: mrr_at_5
value: 59.599999999999994
- type: ndcg_at_1
value: 51.0
- type: ndcg_at_10
value: 64.398
- type: ndcg_at_100
value: 67.581
- type: ndcg_at_1000
value: 68.551
- type: ndcg_at_3
value: 59.928000000000004
- type: ndcg_at_5
value: 61.986
- type: precision_at_1
value: 51.0
- type: precision_at_10
value: 8.7
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 23.666999999999998
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 49.167
- type: recall_at_10
value: 77.333
- type: recall_at_100
value: 91.833
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 65.594
- type: recall_at_5
value: 70.52199999999999
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.77227722772277
- type: cos_sim_ap
value: 94.14261011689366
- type: cos_sim_f1
value: 88.37209302325581
- type: cos_sim_precision
value: 89.36605316973414
- type: cos_sim_recall
value: 87.4
- type: dot_accuracy
value: 99.07128712871287
- type: dot_ap
value: 27.325649239129486
- type: dot_f1
value: 33.295838020247466
- type: dot_precision
value: 38.04627249357326
- type: dot_recall
value: 29.599999999999998
- type: euclidean_accuracy
value: 99.74158415841585
- type: euclidean_ap
value: 92.32695359979576
- type: euclidean_f1
value: 86.90534575772439
- type: euclidean_precision
value: 85.27430221366699
- type: euclidean_recall
value: 88.6
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 92.40335687760499
- type: manhattan_f1
value: 86.96507624200687
- type: manhattan_precision
value: 85.57599225556632
- type: manhattan_recall
value: 88.4
- type: max_accuracy
value: 99.77227722772277
- type: max_ap
value: 94.14261011689366
- type: max_f1
value: 88.37209302325581
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 53.113809982945035
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.90915908471812
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.36481271702464
- type: mrr
value: 51.05628236142942
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.311305530381826
- type: cos_sim_spearman
value: 31.22029657606254
- type: dot_pearson
value: 12.157032445910177
- type: dot_spearman
value: 13.275185888551805
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.167
- type: map_at_10
value: 1.113
- type: map_at_100
value: 5.926
- type: map_at_1000
value: 15.25
- type: map_at_3
value: 0.414
- type: map_at_5
value: 0.633
- type: mrr_at_1
value: 64.0
- type: mrr_at_10
value: 74.444
- type: mrr_at_100
value: 74.667
- type: mrr_at_1000
value: 74.679
- type: mrr_at_3
value: 72.0
- type: mrr_at_5
value: 74.0
- type: ndcg_at_1
value: 59.0
- type: ndcg_at_10
value: 51.468
- type: ndcg_at_100
value: 38.135000000000005
- type: ndcg_at_1000
value: 36.946
- type: ndcg_at_3
value: 55.827000000000005
- type: ndcg_at_5
value: 53.555
- type: precision_at_1
value: 64.0
- type: precision_at_10
value: 54.400000000000006
- type: precision_at_100
value: 39.08
- type: precision_at_1000
value: 16.618
- type: precision_at_3
value: 58.667
- type: precision_at_5
value: 56.8
- type: recall_at_1
value: 0.167
- type: recall_at_10
value: 1.38
- type: recall_at_100
value: 9.189
- type: recall_at_1000
value: 35.737
- type: recall_at_3
value: 0.455
- type: recall_at_5
value: 0.73
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.4299999999999997
- type: map_at_10
value: 8.539
- type: map_at_100
value: 14.155999999999999
- type: map_at_1000
value: 15.684999999999999
- type: map_at_3
value: 3.857
- type: map_at_5
value: 5.583
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 40.489999999999995
- type: mrr_at_100
value: 41.772999999999996
- type: mrr_at_1000
value: 41.772999999999996
- type: mrr_at_3
value: 35.034
- type: mrr_at_5
value: 38.81
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 20.787
- type: ndcg_at_100
value: 33.202
- type: ndcg_at_1000
value: 45.167
- type: ndcg_at_3
value: 18.233
- type: ndcg_at_5
value: 19.887
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.4079999999999995
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_3
value: 19.728
- type: precision_at_5
value: 21.633
- type: recall_at_1
value: 2.4299999999999997
- type: recall_at_10
value: 14.901
- type: recall_at_100
value: 46.422000000000004
- type: recall_at_1000
value: 82.83500000000001
- type: recall_at_3
value: 4.655
- type: recall_at_5
value: 8.092
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.90140000000001
- type: ap
value: 15.138716624430662
- type: f1
value: 56.08803013269606
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.85285795132994
- type: f1
value: 60.17575819903709
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 41.125150148437065
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.96751505036657
- type: cos_sim_ap
value: 70.45642872444971
- type: cos_sim_f1
value: 65.75274793133259
- type: cos_sim_precision
value: 61.806361736707686
- type: cos_sim_recall
value: 70.23746701846966
- type: dot_accuracy
value: 77.84466829588126
- type: dot_ap
value: 32.49904328313596
- type: dot_f1
value: 37.903122189387126
- type: dot_precision
value: 25.050951086956523
- type: dot_recall
value: 77.83641160949868
- type: euclidean_accuracy
value: 84.5920009536866
- type: euclidean_ap
value: 68.83700633574043
- type: euclidean_f1
value: 64.92803542871202
- type: euclidean_precision
value: 60.820465545056464
- type: euclidean_recall
value: 69.63060686015831
- type: manhattan_accuracy
value: 84.52643500029802
- type: manhattan_ap
value: 68.63286046599892
- type: manhattan_f1
value: 64.7476540705047
- type: manhattan_precision
value: 62.3291015625
- type: manhattan_recall
value: 67.36147757255937
- type: max_accuracy
value: 84.96751505036657
- type: max_ap
value: 70.45642872444971
- type: max_f1
value: 65.75274793133259
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.65603291031164
- type: cos_sim_ap
value: 85.58148320880878
- type: cos_sim_f1
value: 77.63202920041064
- type: cos_sim_precision
value: 76.68444377675957
- type: cos_sim_recall
value: 78.60332614721281
- type: dot_accuracy
value: 79.71048239996895
- type: dot_ap
value: 59.31114839296281
- type: dot_f1
value: 57.13895527483783
- type: dot_precision
value: 51.331125015335545
- type: dot_recall
value: 64.4287034185402
- type: euclidean_accuracy
value: 86.99305312997244
- type: euclidean_ap
value: 81.87075965254876
- type: euclidean_f1
value: 73.53543008715421
- type: euclidean_precision
value: 72.39964184450082
- type: euclidean_recall
value: 74.70742223591007
- type: manhattan_accuracy
value: 87.04156479217605
- type: manhattan_ap
value: 81.7850497283247
- type: manhattan_f1
value: 73.52951955143475
- type: manhattan_precision
value: 70.15875236030492
- type: manhattan_recall
value: 77.2405297197413
- type: max_accuracy
value: 88.65603291031164
- type: max_ap
value: 85.58148320880878
- type: max_f1
value: 77.63202920041064
---
<h1 align="center">GIST Embedding v0 - all-MiniLM-L6-v2</h1>
*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
The model is fine-tuned on top of the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
# Data
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
# Usage
The model can be easily loaded using the Sentence Transformers library.
```Python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
```
# Training Parameters
Below are the training parameters used to fine-tune the model:
```
Epochs = 40
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 16
Checkpoint step = 102000
Contrastive loss temperature = 0.01
```
# Evaluation
The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
# Citation
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
```
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
# Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
infgrad/stella-base-en-v2 | infgrad | feature-extraction | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"arxiv:1612.00796",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-19T06:14:31 | 2024-04-06T02:49:06 | 42,410 | 15 | ---
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: stella-base-en-v2
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.19402985074628
- type: ap
value: 40.43267503017359
- type: f1
value: 71.15585210518594
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.256675
- type: ap
value: 90.00824833079179
- type: f1
value: 93.2473146151734
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.612
- type: f1
value: 48.530785631574304
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 37.411
- type: map_at_10
value: 52.673
- type: map_at_100
value: 53.410999999999994
- type: map_at_1000
value: 53.415
- type: map_at_3
value: 48.495
- type: map_at_5
value: 51.183
- type: mrr_at_1
value: 37.838
- type: mrr_at_10
value: 52.844
- type: mrr_at_100
value: 53.581999999999994
- type: mrr_at_1000
value: 53.586
- type: mrr_at_3
value: 48.672
- type: mrr_at_5
value: 51.272
- type: ndcg_at_1
value: 37.411
- type: ndcg_at_10
value: 60.626999999999995
- type: ndcg_at_100
value: 63.675000000000004
- type: ndcg_at_1000
value: 63.776999999999994
- type: ndcg_at_3
value: 52.148
- type: ndcg_at_5
value: 57.001999999999995
- type: precision_at_1
value: 37.411
- type: precision_at_10
value: 8.578
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.91
- type: precision_at_5
value: 14.908
- type: recall_at_1
value: 37.411
- type: recall_at_10
value: 85.775
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 62.731
- type: recall_at_5
value: 74.53800000000001
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.24219029437865
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.474604844291726
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.720542706366054
- type: mrr
value: 75.59633733456448
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.31345008397868
- type: cos_sim_spearman
value: 85.94292212320399
- type: euclidean_pearson
value: 85.03974302774525
- type: euclidean_spearman
value: 85.88087251659051
- type: manhattan_pearson
value: 84.91900996712951
- type: manhattan_spearman
value: 85.96701905781116
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.72727272727273
- type: f1
value: 84.29572512364581
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.55532460397536
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.91195973591251
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.822
- type: map_at_10
value: 44.139
- type: map_at_100
value: 45.786
- type: map_at_1000
value: 45.906000000000006
- type: map_at_3
value: 40.637
- type: map_at_5
value: 42.575
- type: mrr_at_1
value: 41.059
- type: mrr_at_10
value: 50.751000000000005
- type: mrr_at_100
value: 51.548
- type: mrr_at_1000
value: 51.583999999999996
- type: mrr_at_3
value: 48.236000000000004
- type: mrr_at_5
value: 49.838
- type: ndcg_at_1
value: 41.059
- type: ndcg_at_10
value: 50.573
- type: ndcg_at_100
value: 56.25
- type: ndcg_at_1000
value: 58.004
- type: ndcg_at_3
value: 45.995000000000005
- type: ndcg_at_5
value: 48.18
- type: precision_at_1
value: 41.059
- type: precision_at_10
value: 9.757
- type: precision_at_100
value: 1.609
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 22.222
- type: precision_at_5
value: 16.023
- type: recall_at_1
value: 32.822
- type: recall_at_10
value: 61.794000000000004
- type: recall_at_100
value: 85.64699999999999
- type: recall_at_1000
value: 96.836
- type: recall_at_3
value: 47.999
- type: recall_at_5
value: 54.376999999999995
- type: map_at_1
value: 29.579
- type: map_at_10
value: 39.787
- type: map_at_100
value: 40.976
- type: map_at_1000
value: 41.108
- type: map_at_3
value: 36.819
- type: map_at_5
value: 38.437
- type: mrr_at_1
value: 37.516
- type: mrr_at_10
value: 45.822
- type: mrr_at_100
value: 46.454
- type: mrr_at_1000
value: 46.495999999999995
- type: mrr_at_3
value: 43.556
- type: mrr_at_5
value: 44.814
- type: ndcg_at_1
value: 37.516
- type: ndcg_at_10
value: 45.5
- type: ndcg_at_100
value: 49.707
- type: ndcg_at_1000
value: 51.842
- type: ndcg_at_3
value: 41.369
- type: ndcg_at_5
value: 43.161
- type: precision_at_1
value: 37.516
- type: precision_at_10
value: 8.713
- type: precision_at_100
value: 1.38
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 20.233999999999998
- type: precision_at_5
value: 14.280000000000001
- type: recall_at_1
value: 29.579
- type: recall_at_10
value: 55.458
- type: recall_at_100
value: 73.49799999999999
- type: recall_at_1000
value: 87.08200000000001
- type: recall_at_3
value: 42.858000000000004
- type: recall_at_5
value: 48.215
- type: map_at_1
value: 40.489999999999995
- type: map_at_10
value: 53.313
- type: map_at_100
value: 54.290000000000006
- type: map_at_1000
value: 54.346000000000004
- type: map_at_3
value: 49.983
- type: map_at_5
value: 51.867
- type: mrr_at_1
value: 46.27
- type: mrr_at_10
value: 56.660999999999994
- type: mrr_at_100
value: 57.274
- type: mrr_at_1000
value: 57.301
- type: mrr_at_3
value: 54.138
- type: mrr_at_5
value: 55.623999999999995
- type: ndcg_at_1
value: 46.27
- type: ndcg_at_10
value: 59.192
- type: ndcg_at_100
value: 63.026
- type: ndcg_at_1000
value: 64.079
- type: ndcg_at_3
value: 53.656000000000006
- type: ndcg_at_5
value: 56.387
- type: precision_at_1
value: 46.27
- type: precision_at_10
value: 9.511
- type: precision_at_100
value: 1.23
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 24.096
- type: precision_at_5
value: 16.476
- type: recall_at_1
value: 40.489999999999995
- type: recall_at_10
value: 73.148
- type: recall_at_100
value: 89.723
- type: recall_at_1000
value: 97.073
- type: recall_at_3
value: 58.363
- type: recall_at_5
value: 65.083
- type: map_at_1
value: 26.197
- type: map_at_10
value: 35.135
- type: map_at_100
value: 36.14
- type: map_at_1000
value: 36.216
- type: map_at_3
value: 32.358
- type: map_at_5
value: 33.814
- type: mrr_at_1
value: 28.475
- type: mrr_at_10
value: 37.096000000000004
- type: mrr_at_100
value: 38.006
- type: mrr_at_1000
value: 38.06
- type: mrr_at_3
value: 34.52
- type: mrr_at_5
value: 35.994
- type: ndcg_at_1
value: 28.475
- type: ndcg_at_10
value: 40.263
- type: ndcg_at_100
value: 45.327
- type: ndcg_at_1000
value: 47.225
- type: ndcg_at_3
value: 34.882000000000005
- type: ndcg_at_5
value: 37.347
- type: precision_at_1
value: 28.475
- type: precision_at_10
value: 6.249
- type: precision_at_100
value: 0.919
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.689
- type: precision_at_5
value: 10.237
- type: recall_at_1
value: 26.197
- type: recall_at_10
value: 54.17999999999999
- type: recall_at_100
value: 77.768
- type: recall_at_1000
value: 91.932
- type: recall_at_3
value: 39.804
- type: recall_at_5
value: 45.660000000000004
- type: map_at_1
value: 16.683
- type: map_at_10
value: 25.013999999999996
- type: map_at_100
value: 26.411
- type: map_at_1000
value: 26.531
- type: map_at_3
value: 22.357
- type: map_at_5
value: 23.982999999999997
- type: mrr_at_1
value: 20.896
- type: mrr_at_10
value: 29.758000000000003
- type: mrr_at_100
value: 30.895
- type: mrr_at_1000
value: 30.964999999999996
- type: mrr_at_3
value: 27.177
- type: mrr_at_5
value: 28.799999999999997
- type: ndcg_at_1
value: 20.896
- type: ndcg_at_10
value: 30.294999999999998
- type: ndcg_at_100
value: 36.68
- type: ndcg_at_1000
value: 39.519
- type: ndcg_at_3
value: 25.480999999999998
- type: ndcg_at_5
value: 28.027
- type: precision_at_1
value: 20.896
- type: precision_at_10
value: 5.56
- type: precision_at_100
value: 1.006
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.231
- type: precision_at_5
value: 9.104
- type: recall_at_1
value: 16.683
- type: recall_at_10
value: 41.807
- type: recall_at_100
value: 69.219
- type: recall_at_1000
value: 89.178
- type: recall_at_3
value: 28.772
- type: recall_at_5
value: 35.167
- type: map_at_1
value: 30.653000000000002
- type: map_at_10
value: 41.21
- type: map_at_100
value: 42.543
- type: map_at_1000
value: 42.657000000000004
- type: map_at_3
value: 38.094
- type: map_at_5
value: 39.966
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.087
- type: mrr_at_100
value: 47.959
- type: mrr_at_1000
value: 48.003
- type: mrr_at_3
value: 45.043
- type: mrr_at_5
value: 46.352
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.158
- type: ndcg_at_100
value: 52.65
- type: ndcg_at_1000
value: 54.644999999999996
- type: ndcg_at_3
value: 42.632999999999996
- type: ndcg_at_5
value: 44.994
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.498999999999999
- type: precision_at_100
value: 1.308
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 20.308
- type: precision_at_5
value: 14.283000000000001
- type: recall_at_1
value: 30.653000000000002
- type: recall_at_10
value: 58.826
- type: recall_at_100
value: 81.94
- type: recall_at_1000
value: 94.71000000000001
- type: recall_at_3
value: 45.965
- type: recall_at_5
value: 52.294
- type: map_at_1
value: 26.71
- type: map_at_10
value: 36.001
- type: map_at_100
value: 37.416
- type: map_at_1000
value: 37.522
- type: map_at_3
value: 32.841
- type: map_at_5
value: 34.515
- type: mrr_at_1
value: 32.647999999999996
- type: mrr_at_10
value: 41.43
- type: mrr_at_100
value: 42.433
- type: mrr_at_1000
value: 42.482
- type: mrr_at_3
value: 39.117000000000004
- type: mrr_at_5
value: 40.35
- type: ndcg_at_1
value: 32.647999999999996
- type: ndcg_at_10
value: 41.629
- type: ndcg_at_100
value: 47.707
- type: ndcg_at_1000
value: 49.913000000000004
- type: ndcg_at_3
value: 36.598000000000006
- type: ndcg_at_5
value: 38.696000000000005
- type: precision_at_1
value: 32.647999999999996
- type: precision_at_10
value: 7.704999999999999
- type: precision_at_100
value: 1.242
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 17.314
- type: precision_at_5
value: 12.374
- type: recall_at_1
value: 26.71
- type: recall_at_10
value: 52.898
- type: recall_at_100
value: 79.08
- type: recall_at_1000
value: 93.94
- type: recall_at_3
value: 38.731
- type: recall_at_5
value: 44.433
- type: map_at_1
value: 26.510999999999996
- type: map_at_10
value: 35.755333333333326
- type: map_at_100
value: 36.97525
- type: map_at_1000
value: 37.08741666666667
- type: map_at_3
value: 32.921
- type: map_at_5
value: 34.45041666666667
- type: mrr_at_1
value: 31.578416666666666
- type: mrr_at_10
value: 40.06066666666667
- type: mrr_at_100
value: 40.93350000000001
- type: mrr_at_1000
value: 40.98716666666667
- type: mrr_at_3
value: 37.710499999999996
- type: mrr_at_5
value: 39.033249999999995
- type: ndcg_at_1
value: 31.578416666666666
- type: ndcg_at_10
value: 41.138666666666666
- type: ndcg_at_100
value: 46.37291666666666
- type: ndcg_at_1000
value: 48.587500000000006
- type: ndcg_at_3
value: 36.397083333333335
- type: ndcg_at_5
value: 38.539
- type: precision_at_1
value: 31.578416666666666
- type: precision_at_10
value: 7.221583333333332
- type: precision_at_100
value: 1.1581666666666668
- type: precision_at_1000
value: 0.15416666666666667
- type: precision_at_3
value: 16.758
- type: precision_at_5
value: 11.830916666666665
- type: recall_at_1
value: 26.510999999999996
- type: recall_at_10
value: 52.7825
- type: recall_at_100
value: 75.79675
- type: recall_at_1000
value: 91.10483333333335
- type: recall_at_3
value: 39.48233333333334
- type: recall_at_5
value: 45.07116666666667
- type: map_at_1
value: 24.564
- type: map_at_10
value: 31.235000000000003
- type: map_at_100
value: 32.124
- type: map_at_1000
value: 32.216
- type: map_at_3
value: 29.330000000000002
- type: map_at_5
value: 30.379
- type: mrr_at_1
value: 27.761000000000003
- type: mrr_at_10
value: 34.093
- type: mrr_at_100
value: 34.885
- type: mrr_at_1000
value: 34.957
- type: mrr_at_3
value: 32.388
- type: mrr_at_5
value: 33.269
- type: ndcg_at_1
value: 27.761000000000003
- type: ndcg_at_10
value: 35.146
- type: ndcg_at_100
value: 39.597
- type: ndcg_at_1000
value: 42.163000000000004
- type: ndcg_at_3
value: 31.674000000000003
- type: ndcg_at_5
value: 33.224
- type: precision_at_1
value: 27.761000000000003
- type: precision_at_10
value: 5.383
- type: precision_at_100
value: 0.836
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 13.599
- type: precision_at_5
value: 9.202
- type: recall_at_1
value: 24.564
- type: recall_at_10
value: 44.36
- type: recall_at_100
value: 64.408
- type: recall_at_1000
value: 83.892
- type: recall_at_3
value: 34.653
- type: recall_at_5
value: 38.589
- type: map_at_1
value: 17.01
- type: map_at_10
value: 24.485
- type: map_at_100
value: 25.573
- type: map_at_1000
value: 25.703
- type: map_at_3
value: 21.953
- type: map_at_5
value: 23.294999999999998
- type: mrr_at_1
value: 20.544
- type: mrr_at_10
value: 28.238000000000003
- type: mrr_at_100
value: 29.142000000000003
- type: mrr_at_1000
value: 29.219
- type: mrr_at_3
value: 25.802999999999997
- type: mrr_at_5
value: 27.105
- type: ndcg_at_1
value: 20.544
- type: ndcg_at_10
value: 29.387999999999998
- type: ndcg_at_100
value: 34.603
- type: ndcg_at_1000
value: 37.564
- type: ndcg_at_3
value: 24.731
- type: ndcg_at_5
value: 26.773000000000003
- type: precision_at_1
value: 20.544
- type: precision_at_10
value: 5.509
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 11.757
- type: precision_at_5
value: 8.596
- type: recall_at_1
value: 17.01
- type: recall_at_10
value: 40.392
- type: recall_at_100
value: 64.043
- type: recall_at_1000
value: 85.031
- type: recall_at_3
value: 27.293
- type: recall_at_5
value: 32.586999999999996
- type: map_at_1
value: 27.155
- type: map_at_10
value: 35.92
- type: map_at_100
value: 37.034
- type: map_at_1000
value: 37.139
- type: map_at_3
value: 33.263999999999996
- type: map_at_5
value: 34.61
- type: mrr_at_1
value: 32.183
- type: mrr_at_10
value: 40.099000000000004
- type: mrr_at_100
value: 41.001
- type: mrr_at_1000
value: 41.059
- type: mrr_at_3
value: 37.889
- type: mrr_at_5
value: 39.007999999999996
- type: ndcg_at_1
value: 32.183
- type: ndcg_at_10
value: 41.127
- type: ndcg_at_100
value: 46.464
- type: ndcg_at_1000
value: 48.67
- type: ndcg_at_3
value: 36.396
- type: ndcg_at_5
value: 38.313
- type: precision_at_1
value: 32.183
- type: precision_at_10
value: 6.847
- type: precision_at_100
value: 1.0739999999999998
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 16.356
- type: precision_at_5
value: 11.362
- type: recall_at_1
value: 27.155
- type: recall_at_10
value: 52.922000000000004
- type: recall_at_100
value: 76.39
- type: recall_at_1000
value: 91.553
- type: recall_at_3
value: 39.745999999999995
- type: recall_at_5
value: 44.637
- type: map_at_1
value: 25.523
- type: map_at_10
value: 34.268
- type: map_at_100
value: 35.835
- type: map_at_1000
value: 36.046
- type: map_at_3
value: 31.662000000000003
- type: map_at_5
value: 32.71
- type: mrr_at_1
value: 31.028
- type: mrr_at_10
value: 38.924
- type: mrr_at_100
value: 39.95
- type: mrr_at_1000
value: 40.003
- type: mrr_at_3
value: 36.594
- type: mrr_at_5
value: 37.701
- type: ndcg_at_1
value: 31.028
- type: ndcg_at_10
value: 39.848
- type: ndcg_at_100
value: 45.721000000000004
- type: ndcg_at_1000
value: 48.424
- type: ndcg_at_3
value: 35.329
- type: ndcg_at_5
value: 36.779
- type: precision_at_1
value: 31.028
- type: precision_at_10
value: 7.51
- type: precision_at_100
value: 1.478
- type: precision_at_1000
value: 0.24
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 11.383000000000001
- type: recall_at_1
value: 25.523
- type: recall_at_10
value: 50.735
- type: recall_at_100
value: 76.593
- type: recall_at_1000
value: 93.771
- type: recall_at_3
value: 37.574000000000005
- type: recall_at_5
value: 41.602
- type: map_at_1
value: 20.746000000000002
- type: map_at_10
value: 28.557
- type: map_at_100
value: 29.575000000000003
- type: map_at_1000
value: 29.659000000000002
- type: map_at_3
value: 25.753999999999998
- type: map_at_5
value: 27.254
- type: mrr_at_1
value: 22.736
- type: mrr_at_10
value: 30.769000000000002
- type: mrr_at_100
value: 31.655
- type: mrr_at_1000
value: 31.717000000000002
- type: mrr_at_3
value: 28.065
- type: mrr_at_5
value: 29.543999999999997
- type: ndcg_at_1
value: 22.736
- type: ndcg_at_10
value: 33.545
- type: ndcg_at_100
value: 38.743
- type: ndcg_at_1000
value: 41.002
- type: ndcg_at_3
value: 28.021
- type: ndcg_at_5
value: 30.586999999999996
- type: precision_at_1
value: 22.736
- type: precision_at_10
value: 5.416
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.953
- type: precision_at_5
value: 8.651
- type: recall_at_1
value: 20.746000000000002
- type: recall_at_10
value: 46.87
- type: recall_at_100
value: 71.25200000000001
- type: recall_at_1000
value: 88.26
- type: recall_at_3
value: 32.029999999999994
- type: recall_at_5
value: 38.21
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 12.105
- type: map_at_10
value: 20.577
- type: map_at_100
value: 22.686999999999998
- type: map_at_1000
value: 22.889
- type: map_at_3
value: 17.174
- type: map_at_5
value: 18.807
- type: mrr_at_1
value: 27.101
- type: mrr_at_10
value: 38.475
- type: mrr_at_100
value: 39.491
- type: mrr_at_1000
value: 39.525
- type: mrr_at_3
value: 34.886
- type: mrr_at_5
value: 36.922
- type: ndcg_at_1
value: 27.101
- type: ndcg_at_10
value: 29.002
- type: ndcg_at_100
value: 37.218
- type: ndcg_at_1000
value: 40.644000000000005
- type: ndcg_at_3
value: 23.464
- type: ndcg_at_5
value: 25.262
- type: precision_at_1
value: 27.101
- type: precision_at_10
value: 9.179
- type: precision_at_100
value: 1.806
- type: precision_at_1000
value: 0.244
- type: precision_at_3
value: 17.394000000000002
- type: precision_at_5
value: 13.342
- type: recall_at_1
value: 12.105
- type: recall_at_10
value: 35.143
- type: recall_at_100
value: 63.44499999999999
- type: recall_at_1000
value: 82.49499999999999
- type: recall_at_3
value: 21.489
- type: recall_at_5
value: 26.82
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.769
- type: map_at_10
value: 18.619
- type: map_at_100
value: 26.3
- type: map_at_1000
value: 28.063
- type: map_at_3
value: 13.746
- type: map_at_5
value: 16.035
- type: mrr_at_1
value: 65.25
- type: mrr_at_10
value: 73.678
- type: mrr_at_100
value: 73.993
- type: mrr_at_1000
value: 74.003
- type: mrr_at_3
value: 72.042
- type: mrr_at_5
value: 72.992
- type: ndcg_at_1
value: 53.625
- type: ndcg_at_10
value: 39.638
- type: ndcg_at_100
value: 44.601
- type: ndcg_at_1000
value: 52.80200000000001
- type: ndcg_at_3
value: 44.727
- type: ndcg_at_5
value: 42.199
- type: precision_at_1
value: 65.25
- type: precision_at_10
value: 31.025000000000002
- type: precision_at_100
value: 10.174999999999999
- type: precision_at_1000
value: 2.0740000000000003
- type: precision_at_3
value: 48.083
- type: precision_at_5
value: 40.6
- type: recall_at_1
value: 8.769
- type: recall_at_10
value: 23.910999999999998
- type: recall_at_100
value: 51.202999999999996
- type: recall_at_1000
value: 77.031
- type: recall_at_3
value: 15.387999999999998
- type: recall_at_5
value: 18.919
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 54.47
- type: f1
value: 48.21839043361556
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 63.564
- type: map_at_10
value: 74.236
- type: map_at_100
value: 74.53699999999999
- type: map_at_1000
value: 74.557
- type: map_at_3
value: 72.556
- type: map_at_5
value: 73.656
- type: mrr_at_1
value: 68.497
- type: mrr_at_10
value: 78.373
- type: mrr_at_100
value: 78.54299999999999
- type: mrr_at_1000
value: 78.549
- type: mrr_at_3
value: 77.03
- type: mrr_at_5
value: 77.938
- type: ndcg_at_1
value: 68.497
- type: ndcg_at_10
value: 79.12599999999999
- type: ndcg_at_100
value: 80.319
- type: ndcg_at_1000
value: 80.71199999999999
- type: ndcg_at_3
value: 76.209
- type: ndcg_at_5
value: 77.90700000000001
- type: precision_at_1
value: 68.497
- type: precision_at_10
value: 9.958
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 29.908
- type: precision_at_5
value: 18.971
- type: recall_at_1
value: 63.564
- type: recall_at_10
value: 90.05199999999999
- type: recall_at_100
value: 95.028
- type: recall_at_1000
value: 97.667
- type: recall_at_3
value: 82.17999999999999
- type: recall_at_5
value: 86.388
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.042
- type: map_at_10
value: 30.764999999999997
- type: map_at_100
value: 32.678000000000004
- type: map_at_1000
value: 32.881
- type: map_at_3
value: 26.525
- type: map_at_5
value: 28.932000000000002
- type: mrr_at_1
value: 37.653999999999996
- type: mrr_at_10
value: 46.597
- type: mrr_at_100
value: 47.413
- type: mrr_at_1000
value: 47.453
- type: mrr_at_3
value: 43.775999999999996
- type: mrr_at_5
value: 45.489000000000004
- type: ndcg_at_1
value: 37.653999999999996
- type: ndcg_at_10
value: 38.615
- type: ndcg_at_100
value: 45.513999999999996
- type: ndcg_at_1000
value: 48.815999999999995
- type: ndcg_at_3
value: 34.427
- type: ndcg_at_5
value: 35.954
- type: precision_at_1
value: 37.653999999999996
- type: precision_at_10
value: 10.864
- type: precision_at_100
value: 1.7850000000000001
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_3
value: 22.788
- type: precision_at_5
value: 17.346
- type: recall_at_1
value: 19.042
- type: recall_at_10
value: 45.707
- type: recall_at_100
value: 71.152
- type: recall_at_1000
value: 90.7
- type: recall_at_3
value: 30.814000000000004
- type: recall_at_5
value: 37.478
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.001000000000005
- type: map_at_10
value: 59.611000000000004
- type: map_at_100
value: 60.582
- type: map_at_1000
value: 60.646
- type: map_at_3
value: 56.031
- type: map_at_5
value: 58.243
- type: mrr_at_1
value: 76.003
- type: mrr_at_10
value: 82.15400000000001
- type: mrr_at_100
value: 82.377
- type: mrr_at_1000
value: 82.383
- type: mrr_at_3
value: 81.092
- type: mrr_at_5
value: 81.742
- type: ndcg_at_1
value: 76.003
- type: ndcg_at_10
value: 68.216
- type: ndcg_at_100
value: 71.601
- type: ndcg_at_1000
value: 72.821
- type: ndcg_at_3
value: 63.109
- type: ndcg_at_5
value: 65.902
- type: precision_at_1
value: 76.003
- type: precision_at_10
value: 14.379
- type: precision_at_100
value: 1.702
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 40.396
- type: precision_at_5
value: 26.442
- type: recall_at_1
value: 38.001000000000005
- type: recall_at_10
value: 71.897
- type: recall_at_100
value: 85.105
- type: recall_at_1000
value: 93.133
- type: recall_at_3
value: 60.594
- type: recall_at_5
value: 66.104
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.31280000000001
- type: ap
value: 87.53723467501632
- type: f1
value: 91.30282906596291
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.917
- type: map_at_10
value: 34.117999999999995
- type: map_at_100
value: 35.283
- type: map_at_1000
value: 35.333999999999996
- type: map_at_3
value: 30.330000000000002
- type: map_at_5
value: 32.461
- type: mrr_at_1
value: 22.579
- type: mrr_at_10
value: 34.794000000000004
- type: mrr_at_100
value: 35.893
- type: mrr_at_1000
value: 35.937000000000005
- type: mrr_at_3
value: 31.091
- type: mrr_at_5
value: 33.173
- type: ndcg_at_1
value: 22.579
- type: ndcg_at_10
value: 40.951
- type: ndcg_at_100
value: 46.558
- type: ndcg_at_1000
value: 47.803000000000004
- type: ndcg_at_3
value: 33.262
- type: ndcg_at_5
value: 37.036
- type: precision_at_1
value: 22.579
- type: precision_at_10
value: 6.463000000000001
- type: precision_at_100
value: 0.928
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.174000000000001
- type: precision_at_5
value: 10.421
- type: recall_at_1
value: 21.917
- type: recall_at_10
value: 61.885
- type: recall_at_100
value: 87.847
- type: recall_at_1000
value: 97.322
- type: recall_at_3
value: 41.010000000000005
- type: recall_at_5
value: 50.031000000000006
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.49521203830369
- type: f1
value: 93.30882341740241
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.0579115367077
- type: f1
value: 51.2368258319339
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 73.88029589778077
- type: f1
value: 72.34422048584663
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.2817753866846
- type: f1
value: 77.87746050004304
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.247341454119216
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.9647477166234
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.90698374676892
- type: mrr
value: 33.07523683771251
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.717
- type: map_at_10
value: 14.566
- type: map_at_100
value: 18.465999999999998
- type: map_at_1000
value: 20.033
- type: map_at_3
value: 10.863
- type: map_at_5
value: 12.589
- type: mrr_at_1
value: 49.845
- type: mrr_at_10
value: 58.385
- type: mrr_at_100
value: 58.989999999999995
- type: mrr_at_1000
value: 59.028999999999996
- type: mrr_at_3
value: 56.76
- type: mrr_at_5
value: 57.766
- type: ndcg_at_1
value: 47.678
- type: ndcg_at_10
value: 37.511
- type: ndcg_at_100
value: 34.537
- type: ndcg_at_1000
value: 43.612
- type: ndcg_at_3
value: 43.713
- type: ndcg_at_5
value: 41.303
- type: precision_at_1
value: 49.845
- type: precision_at_10
value: 27.307
- type: precision_at_100
value: 8.746
- type: precision_at_1000
value: 2.182
- type: precision_at_3
value: 40.764
- type: precision_at_5
value: 35.232
- type: recall_at_1
value: 6.717
- type: recall_at_10
value: 18.107
- type: recall_at_100
value: 33.759
- type: recall_at_1000
value: 67.31
- type: recall_at_3
value: 11.68
- type: recall_at_5
value: 14.557999999999998
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.633999999999997
- type: map_at_10
value: 42.400999999999996
- type: map_at_100
value: 43.561
- type: map_at_1000
value: 43.592
- type: map_at_3
value: 37.865
- type: map_at_5
value: 40.650999999999996
- type: mrr_at_1
value: 31.286
- type: mrr_at_10
value: 44.996
- type: mrr_at_100
value: 45.889
- type: mrr_at_1000
value: 45.911
- type: mrr_at_3
value: 41.126000000000005
- type: mrr_at_5
value: 43.536
- type: ndcg_at_1
value: 31.257
- type: ndcg_at_10
value: 50.197
- type: ndcg_at_100
value: 55.062
- type: ndcg_at_1000
value: 55.81700000000001
- type: ndcg_at_3
value: 41.650999999999996
- type: ndcg_at_5
value: 46.324
- type: precision_at_1
value: 31.257
- type: precision_at_10
value: 8.508000000000001
- type: precision_at_100
value: 1.121
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.1
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.633999999999997
- type: recall_at_10
value: 71.40100000000001
- type: recall_at_100
value: 92.463
- type: recall_at_1000
value: 98.13199999999999
- type: recall_at_3
value: 49.382
- type: recall_at_5
value: 60.144
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.17099999999999
- type: map_at_10
value: 85.036
- type: map_at_100
value: 85.67099999999999
- type: map_at_1000
value: 85.68599999999999
- type: map_at_3
value: 82.086
- type: map_at_5
value: 83.956
- type: mrr_at_1
value: 82.04
- type: mrr_at_10
value: 88.018
- type: mrr_at_100
value: 88.114
- type: mrr_at_1000
value: 88.115
- type: mrr_at_3
value: 87.047
- type: mrr_at_5
value: 87.73100000000001
- type: ndcg_at_1
value: 82.03
- type: ndcg_at_10
value: 88.717
- type: ndcg_at_100
value: 89.904
- type: ndcg_at_1000
value: 89.991
- type: ndcg_at_3
value: 85.89099999999999
- type: ndcg_at_5
value: 87.485
- type: precision_at_1
value: 82.03
- type: precision_at_10
value: 13.444999999999999
- type: precision_at_100
value: 1.533
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.537
- type: precision_at_5
value: 24.692
- type: recall_at_1
value: 71.17099999999999
- type: recall_at_10
value: 95.634
- type: recall_at_100
value: 99.614
- type: recall_at_1000
value: 99.99
- type: recall_at_3
value: 87.48
- type: recall_at_5
value: 91.996
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.067219624685315
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 62.121822992300444
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.153
- type: map_at_10
value: 11.024000000000001
- type: map_at_100
value: 13.233
- type: map_at_1000
value: 13.62
- type: map_at_3
value: 7.779999999999999
- type: map_at_5
value: 9.529
- type: mrr_at_1
value: 20.599999999999998
- type: mrr_at_10
value: 31.361
- type: mrr_at_100
value: 32.738
- type: mrr_at_1000
value: 32.792
- type: mrr_at_3
value: 28.15
- type: mrr_at_5
value: 30.085
- type: ndcg_at_1
value: 20.599999999999998
- type: ndcg_at_10
value: 18.583
- type: ndcg_at_100
value: 27.590999999999998
- type: ndcg_at_1000
value: 34.001
- type: ndcg_at_3
value: 17.455000000000002
- type: ndcg_at_5
value: 15.588
- type: precision_at_1
value: 20.599999999999998
- type: precision_at_10
value: 9.74
- type: precision_at_100
value: 2.284
- type: precision_at_1000
value: 0.381
- type: precision_at_3
value: 16.533
- type: precision_at_5
value: 14.02
- type: recall_at_1
value: 4.153
- type: recall_at_10
value: 19.738
- type: recall_at_100
value: 46.322
- type: recall_at_1000
value: 77.378
- type: recall_at_3
value: 10.048
- type: recall_at_5
value: 14.233
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.07097501003639
- type: cos_sim_spearman
value: 81.05827848407056
- type: euclidean_pearson
value: 82.6279003372546
- type: euclidean_spearman
value: 81.00031515279802
- type: manhattan_pearson
value: 82.59338284959495
- type: manhattan_spearman
value: 80.97432711064945
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.28991993621685
- type: cos_sim_spearman
value: 78.71828082424351
- type: euclidean_pearson
value: 83.4881331520832
- type: euclidean_spearman
value: 78.51746826842316
- type: manhattan_pearson
value: 83.4109223774324
- type: manhattan_spearman
value: 78.431544382179
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 83.16651661072123
- type: cos_sim_spearman
value: 84.88094386637867
- type: euclidean_pearson
value: 84.3547603585416
- type: euclidean_spearman
value: 84.85148665860193
- type: manhattan_pearson
value: 84.29648369879266
- type: manhattan_spearman
value: 84.76074870571124
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 83.40596254292149
- type: cos_sim_spearman
value: 83.10699573133829
- type: euclidean_pearson
value: 83.22794776876958
- type: euclidean_spearman
value: 83.22583316084712
- type: manhattan_pearson
value: 83.15899233935681
- type: manhattan_spearman
value: 83.17668293648019
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.27977121352563
- type: cos_sim_spearman
value: 88.73903130248591
- type: euclidean_pearson
value: 88.30685958438735
- type: euclidean_spearman
value: 88.79755484280406
- type: manhattan_pearson
value: 88.30305607758652
- type: manhattan_spearman
value: 88.80096577072784
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.08819031430218
- type: cos_sim_spearman
value: 86.35414445951125
- type: euclidean_pearson
value: 85.4683192388315
- type: euclidean_spearman
value: 86.2079674669473
- type: manhattan_pearson
value: 85.35835702257341
- type: manhattan_spearman
value: 86.08483380002187
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.36149449801478
- type: cos_sim_spearman
value: 87.7102980757725
- type: euclidean_pearson
value: 88.16457177837161
- type: euclidean_spearman
value: 87.6598652482716
- type: manhattan_pearson
value: 88.23894728971618
- type: manhattan_spearman
value: 87.74470156709361
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.54023758394433
- type: cos_sim_spearman
value: 66.28491960187773
- type: euclidean_pearson
value: 67.0853128483472
- type: euclidean_spearman
value: 66.10307543766307
- type: manhattan_pearson
value: 66.7635365592556
- type: manhattan_spearman
value: 65.76408004780167
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 85.15858398195317
- type: cos_sim_spearman
value: 87.44850004752102
- type: euclidean_pearson
value: 86.60737082550408
- type: euclidean_spearman
value: 87.31591549824242
- type: manhattan_pearson
value: 86.56187011429977
- type: manhattan_spearman
value: 87.23854795795319
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.66210488769109
- type: mrr
value: 96.23100664767331
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 56.094
- type: map_at_10
value: 67.486
- type: map_at_100
value: 67.925
- type: map_at_1000
value: 67.949
- type: map_at_3
value: 64.857
- type: map_at_5
value: 66.31
- type: mrr_at_1
value: 58.667
- type: mrr_at_10
value: 68.438
- type: mrr_at_100
value: 68.733
- type: mrr_at_1000
value: 68.757
- type: mrr_at_3
value: 66.389
- type: mrr_at_5
value: 67.456
- type: ndcg_at_1
value: 58.667
- type: ndcg_at_10
value: 72.506
- type: ndcg_at_100
value: 74.27
- type: ndcg_at_1000
value: 74.94800000000001
- type: ndcg_at_3
value: 67.977
- type: ndcg_at_5
value: 70.028
- type: precision_at_1
value: 58.667
- type: precision_at_10
value: 9.767000000000001
- type: precision_at_100
value: 1.073
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.0
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 56.094
- type: recall_at_10
value: 86.68900000000001
- type: recall_at_100
value: 94.333
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 74.522
- type: recall_at_5
value: 79.611
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83069306930693
- type: cos_sim_ap
value: 95.69184662911199
- type: cos_sim_f1
value: 91.4027149321267
- type: cos_sim_precision
value: 91.91102123356926
- type: cos_sim_recall
value: 90.9
- type: dot_accuracy
value: 99.69405940594059
- type: dot_ap
value: 90.21674151456216
- type: dot_f1
value: 84.4489179667841
- type: dot_precision
value: 85.00506585612969
- type: dot_recall
value: 83.89999999999999
- type: euclidean_accuracy
value: 99.83069306930693
- type: euclidean_ap
value: 95.67760109671087
- type: euclidean_f1
value: 91.19754350051177
- type: euclidean_precision
value: 93.39622641509435
- type: euclidean_recall
value: 89.1
- type: manhattan_accuracy
value: 99.83267326732673
- type: manhattan_ap
value: 95.69771347732625
- type: manhattan_f1
value: 91.32420091324201
- type: manhattan_precision
value: 92.68795056642637
- type: manhattan_recall
value: 90.0
- type: max_accuracy
value: 99.83267326732673
- type: max_ap
value: 95.69771347732625
- type: max_f1
value: 91.4027149321267
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 64.47378332953092
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.79602531604151
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.80707639107175
- type: mrr
value: 54.64886522790935
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.852448373051395
- type: cos_sim_spearman
value: 32.51821499493775
- type: dot_pearson
value: 30.390650062190456
- type: dot_spearman
value: 30.588836159667636
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.51
- type: map_at_100
value: 8.882
- type: map_at_1000
value: 22.181
- type: map_at_3
value: 0.553
- type: map_at_5
value: 0.843
- type: mrr_at_1
value: 74.0
- type: mrr_at_10
value: 84.89999999999999
- type: mrr_at_100
value: 84.89999999999999
- type: mrr_at_1000
value: 84.89999999999999
- type: mrr_at_3
value: 84.0
- type: mrr_at_5
value: 84.89999999999999
- type: ndcg_at_1
value: 68.0
- type: ndcg_at_10
value: 64.792
- type: ndcg_at_100
value: 51.37199999999999
- type: ndcg_at_1000
value: 47.392
- type: ndcg_at_3
value: 68.46900000000001
- type: ndcg_at_5
value: 67.084
- type: precision_at_1
value: 74.0
- type: precision_at_10
value: 69.39999999999999
- type: precision_at_100
value: 53.080000000000005
- type: precision_at_1000
value: 21.258
- type: precision_at_3
value: 76.0
- type: precision_at_5
value: 73.2
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.7950000000000002
- type: recall_at_100
value: 12.626999999999999
- type: recall_at_1000
value: 44.84
- type: recall_at_3
value: 0.611
- type: recall_at_5
value: 0.959
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.4949999999999999
- type: map_at_10
value: 8.797
- type: map_at_100
value: 14.889
- type: map_at_1000
value: 16.309
- type: map_at_3
value: 4.389
- type: map_at_5
value: 6.776
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 35.844
- type: mrr_at_100
value: 37.119
- type: mrr_at_1000
value: 37.119
- type: mrr_at_3
value: 30.612000000000002
- type: mrr_at_5
value: 33.163
- type: ndcg_at_1
value: 16.326999999999998
- type: ndcg_at_10
value: 21.9
- type: ndcg_at_100
value: 34.705000000000005
- type: ndcg_at_1000
value: 45.709
- type: ndcg_at_3
value: 22.7
- type: ndcg_at_5
value: 23.197000000000003
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 21.02
- type: precision_at_100
value: 7.714
- type: precision_at_1000
value: 1.504
- type: precision_at_3
value: 26.531
- type: precision_at_5
value: 26.122
- type: recall_at_1
value: 1.4949999999999999
- type: recall_at_10
value: 15.504000000000001
- type: recall_at_100
value: 47.978
- type: recall_at_1000
value: 81.56
- type: recall_at_3
value: 5.569
- type: recall_at_5
value: 9.821
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.99279999999999
- type: ap
value: 15.459189680101492
- type: f1
value: 56.33023271441895
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.070175438596486
- type: f1
value: 63.28070758709465
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.076231309703054
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.21463908922931
- type: cos_sim_ap
value: 77.67287017966282
- type: cos_sim_f1
value: 70.34412955465588
- type: cos_sim_precision
value: 67.57413709285368
- type: cos_sim_recall
value: 73.35092348284961
- type: dot_accuracy
value: 85.04500208618943
- type: dot_ap
value: 70.4075203869744
- type: dot_f1
value: 66.18172537008678
- type: dot_precision
value: 64.08798813643104
- type: dot_recall
value: 68.41688654353561
- type: euclidean_accuracy
value: 87.17887584192646
- type: euclidean_ap
value: 77.5774128274464
- type: euclidean_f1
value: 70.09307972480777
- type: euclidean_precision
value: 71.70852884349986
- type: euclidean_recall
value: 68.54881266490766
- type: manhattan_accuracy
value: 87.28020504261787
- type: manhattan_ap
value: 77.57835820297892
- type: manhattan_f1
value: 70.23063591521131
- type: manhattan_precision
value: 70.97817299919159
- type: manhattan_recall
value: 69.49868073878628
- type: max_accuracy
value: 87.28020504261787
- type: max_ap
value: 77.67287017966282
- type: max_f1
value: 70.34412955465588
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.96650754841464
- type: cos_sim_ap
value: 86.00185968965064
- type: cos_sim_f1
value: 77.95861256351718
- type: cos_sim_precision
value: 74.70712773465067
- type: cos_sim_recall
value: 81.50600554357868
- type: dot_accuracy
value: 87.36950362867233
- type: dot_ap
value: 82.22071181147555
- type: dot_f1
value: 74.85680716698488
- type: dot_precision
value: 71.54688377316114
- type: dot_recall
value: 78.48783492454572
- type: euclidean_accuracy
value: 88.99561454573679
- type: euclidean_ap
value: 86.15882097229648
- type: euclidean_f1
value: 78.18463125322332
- type: euclidean_precision
value: 74.95408956067241
- type: euclidean_recall
value: 81.70619032953496
- type: manhattan_accuracy
value: 88.96650754841464
- type: manhattan_ap
value: 86.13133111232099
- type: manhattan_f1
value: 78.10771470160115
- type: manhattan_precision
value: 74.05465084184377
- type: manhattan_recall
value: 82.63012011087157
- type: max_accuracy
value: 88.99561454573679
- type: max_ap
value: 86.15882097229648
- type: max_f1
value: 78.18463125322332
---
**新闻 | News**
**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。
**[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。
**[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。
**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。
**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。
**[2023-09-11]** 开源stella-base-zh和stella-large-zh
欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！
## stella model
stella是一个通用的文本编码模型，主要有以下模型：
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No |
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes |
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes |
完整的训练思路和训练过程已记录在[博客1](https://zhuanlan.zhihu.com/p/655322183)和[博客2](https://zhuanlan.zhihu.com/p/662209559)，欢迎阅读讨论。
**训练数据：**
1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本
2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据
**训练方法：**
1. 对比学习损失函数
2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例)
3. EWC(Elastic Weights Consolidation)[4]
4. cosent loss[5]
5. 每一种类型的数据一个迭代器，分别计算loss进行更新
stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction(
比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。
**初始权重：**\
stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position
embedding使用层次分解位置编码[7]进行初始化。\
感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。
stella is a general-purpose text encoder, which mainly includes the following models:
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No |
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes |
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes |
The training data mainly includes:
1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater
than 512.
2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM.
The loss functions mainly include:
1. Contrastive learning loss function
2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives)
3. EWC (Elastic Weights Consolidation)
4. cosent loss
Model weight initialization:\
stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the
512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding.
Training strategy:\
One iterator for each type of data, separately calculating the loss.
Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation.
## Metric
#### C-MTEB leaderboard (Chinese)
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) |
|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.95 | 66.1 | 70.08 | 56.92 |
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 |
| stella-base-zh | 0.2 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 |
#### MTEB leaderboard (English)
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
|:-----------------:|:---------------:|:---------:|:---------------:|:------------:|:-------------------:|:---------------:|:-----------------------:|:-------------:|:--------------:|:--------:|:------------------:|
| stella-base-en-v2 | 0.2 | 768 | 512 | 62.61 | 75.28 | 44.9 | 86.45 | 58.77 | 50.1 | 83.02 | 32.52 |
#### Reproduce our results
**C-MTEB:**
```python
import torch
import numpy as np
from typing import List
from mteb import MTEB
from sentence_transformers import SentenceTransformer
class FastTextEncoder():
def __init__(self, model_name):
self.model = SentenceTransformer(model_name).cuda().half().eval()
self.model.max_seq_length = 512
def encode(
self,
input_texts: List[str],
*args,
**kwargs
):
new_sens = list(set(input_texts))
new_sens.sort(key=lambda x: len(x), reverse=True)
vecs = self.model.encode(
new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256
).astype(np.float32)
sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)}
vecs = vecs[[sen2arrid[sen] for sen in input_texts]]
torch.cuda.empty_cache()
return vecs
if __name__ == '__main__':
model_name = "infgrad/stella-base-zh-v2"
output_folder = "zh_mteb_results/stella-base-zh-v2"
task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks]
model = FastTextEncoder(model_name)
for task in task_names:
MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder)
```
**MTEB:**
You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py)
#### Evaluation for long text
经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的，
更致命的是那些长度大于512的文本，其重点都在前半部分
这里以CMRC2018的数据为例说明这个问题：
```
question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？
passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推......
```
passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\
简言之，现有数据集的2个问题：\
1）长度大于512的过少\
2）即便大于512，对于检索而言也只需要前512的文本内容\
导致**无法准确评估模型的长文本编码能力。**
为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是：
- CMRC2018，通用百科
- CAIL，法律阅读理解
- DRCD，繁体百科，已转简体
- Military，军工问答
- Squad，英文阅读理解，已转中文
- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9]
处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。
除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing
评测指标为Recall@5, 结果如下：
| Dataset | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh |
|:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:|
| CMRC2018 | 94.34 | 93.82 | 91.56 | 93.12 | 96.08 | 95.56 |
| CAIL | 28.04 | 33.64 | 31.22 | 33.94 | 34.62 | 37.18 |
| DRCD | 78.25 | 77.9 | 78.34 | 80.26 | 86.14 | 84.58 |
| Military | 76.61 | 73.06 | 75.65 | 75.81 | 83.71 | 80.48 |
| Squad | 91.21 | 86.61 | 87.87 | 90.38 | 93.31 | 91.21 |
| Multifieldqa_zh | 81.41 | 83.92 | 83.92 | 83.42 | 79.9 | 80.4 |
| **Average** | 74.98 | 74.83 | 74.76 | 76.15 | **78.96** | **78.24** |
**注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。
## Usage
#### stella 中文系列模型
stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致**
，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。
stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。
stella中文系列模型均使用mean pooling做为文本向量。
在sentence-transformer库中的使用方法：
```python
from sentence_transformers import SentenceTransformer
sentences = ["数据1", "数据2"]
model = SentenceTransformer('infgrad/stella-base-zh-v2')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
直接使用transformers库：
```python
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2')
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2')
sentences = ["数据1", "数据ABCDEFGH"]
batch_data = tokenizer(
batch_text_or_text_pairs=sentences,
padding="longest",
return_tensors="pt",
max_length=1024,
truncation=True,
)
attention_mask = batch_data["attention_mask"]
model_output = model(**batch_data)
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
vectors = normalize(vectors, norm="l2", axis=1, )
print(vectors.shape) # 2,768
```
#### stella models for English
**Using Sentence-Transformers:**
```python
from sentence_transformers import SentenceTransformer
sentences = ["one car come", "one car go"]
model = SentenceTransformer('infgrad/stella-base-en-v2')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
**Using HuggingFace Transformers:**
```python
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
model = AutoModel.from_pretrained('infgrad/stella-base-en-v2')
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2')
sentences = ["one car come", "one car go"]
batch_data = tokenizer(
batch_text_or_text_pairs=sentences,
padding="longest",
return_tensors="pt",
max_length=512,
truncation=True,
)
attention_mask = batch_data["attention_mask"]
model_output = model(**batch_data)
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
vectors = normalize(vectors, norm="l2", axis=1, )
print(vectors.shape) # 2,768
```
## Training Detail
**硬件：** 单卡A100-80GB
**环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing
**学习率：** 1e-6
**batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例
**数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。
## ToDoList
**评测的稳定性：**
评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。
**更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。
测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。
**OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere,
它们的效果均比不上BM25。
## Reference
1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab
2. https://github.com/wangyuxinwhy/uniem
3. https://github.com/CLUEbenchmark/SimCLUE
4. https://arxiv.org/abs/1612.00796
5. https://kexue.fm/archives/8847
6. https://huggingface.co/sensenova/piccolo-base-zh
7. https://kexue.fm/archives/7947
8. https://github.com/FlagOpen/FlagEmbedding
9. https://github.com/THUDM/LongBench
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-2.8b | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-13T14:37:12 | 2023-06-09T00:35:37 | 41,027 | 29 | ---
datasets:
- EleutherAI/pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-2.8B
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-2.8B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-2.8B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-2.8B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-2.8B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-2.8B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-2.8B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-2.8B.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
openai-community/openai-gpt | openai-community | text-generation | [
"transformers",
"pytorch",
"tf",
"rust",
"safetensors",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2024-02-19T12:39:20 | 36,981 | 251 | ---
language: en
license: mit
---
# OpenAI GPT 1
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** `openai-gpt` (a.k.a. "GPT-1") is the first transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies.
- **Developed by:** Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever. See [associated research paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) and [GitHub repo](https://github.com/openai/finetune-transformer-lm) for model developers and contributors.
- **Model Type:** Transformer-based language model
- **Language(s):** English
- **License:** [MIT License](https://github.com/openai/finetune-transformer-lm/blob/master/LICENSE)
- **Related Models:** [GPT2](https://huggingface.co/gpt2), [GPT2-Medium](https://huggingface.co/gpt2-medium), [GPT2-Large](https://huggingface.co/gpt2-large) and [GPT2-XL](https://huggingface.co/gpt2-xl)
- **Resources for more information:**
- [Research Paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
- [OpenAI Blog Post](https://openai.com/blog/language-unsupervised/)
- [GitHub Repo](https://github.com/openai/finetune-transformer-lm)
- Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt
## How to Get Started with the Model
Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='openai-gpt')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model,'he said, when i was finished.'ah well,'said the man,'that's"},
{'generated_text': 'Hello, I\'m a language model, " she said. \n she reached the bottom of the shaft and leaned a little further out. it was'},
{'generated_text': 'Hello, I\'m a language model, " she laughed. " we call that a\'white girl.\'or as we are called by the'},
{'generated_text': 'Hello, I\'m a language model, " said mr pin. " an\'the ones with the funny hats don\'t. " the rest of'},
{'generated_text': 'Hello, I\'m a language model, was\'ere \'bout to do some more dancin \', " he said, then his voice lowered to'}]
```
Here is how to use this model in PyTorch:
```python
from transformers import OpenAIGPTTokenizer, OpenAIGPTModel
import torch
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
model = OpenAIGPTModel.from_pretrained("openai-gpt")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
and in TensorFlow:
```python
from transformers import OpenAIGPTTokenizer, TFOpenAIGPTModel
tokenizer = OpenAIGPTTokenizer.from_pretrained("openai-gpt")
model = TFOpenAIGPTModel.from_pretrained("openai-gpt")
inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
outputs = model(inputs)
last_hidden_states = outputs.last_hidden_state
```
## Uses
#### Direct Use
This model can be used for language modeling tasks.
#### Downstream Use
Potential downstream uses of this model include tasks that leverage language models. In the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), the model developers discuss evaluations of the model for tasks including natural language inference (NLI), question answering, semantic similarity, and text classification.
#### Misuse and Out-of-scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
#### Biases
**CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
Predictions generated by this model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. For example:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='openai-gpt')
>>> set_seed(42)
>>> generator("The man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The man worked as a teacher for the college he'},
{'generated_text': 'The man worked as a janitor at the club.'},
{'generated_text': 'The man worked as a bodyguard in america. the'},
{'generated_text': 'The man worked as a clerk for one of the'},
{'generated_text': 'The man worked as a nurse, but there was'}]
>>> set_seed(42)
>>> generator("The woman worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The woman worked as a medical intern but is a'},
{'generated_text': 'The woman worked as a midwife, i know that'},
{'generated_text': 'The woman worked as a prostitute in a sex club'},
{'generated_text': 'The woman worked as a secretary for one of the'},
{'generated_text': 'The woman worked as a nurse, but she had'}]
```
This bias may also affect fine-tuned versions of this model. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
#### Risks and Limitations
The model developers also wrote in a [blog post](https://openai.com/blog/language-unsupervised/) about risks and limitations of the model, including:
> - **Compute Requirements:** Many previous approaches to NLP tasks train relatively small models on a single GPU from scratch. Our approach requires an expensive pre-training step - 1 month on 8 GPUs. Luckily, this only has to be done once and we’re releasing our model so others can avoid it. It is also a large model (in comparison to prior work) and consequently uses more compute and memory — we used a 37-layer (12 block) Transformer architecture, and we train on sequences of up to 512 tokens. Most experiments were conducted on 4 and 8 GPU systems. The model does fine-tune to new tasks very quickly which helps mitigate the additional resource requirements.
> - **The limits and bias of learning about the world through text:** Books and text readily available on the internet do not contain complete or even accurate information about the world. Recent work ([Lucy and Gauthier, 2017](https://arxiv.org/abs/1705.11168)) has shown that certain kinds of information are difficult to learn via just text and other work ([Gururangan et al., 2018](https://arxiv.org/abs/1803.02324)) has shown that models learn and exploit biases in data distributions.
> - **Still brittle generalization:** Although our approach improves performance across a broad range of tasks, current deep learning NLP models still exhibit surprising and counterintuitive behavior - especially when evaluated in a systematic, adversarial, or out-of-distribution way. Our approach is not immune to these issues, though we have observed some indications of progress. Our approach shows improved lexical robustness over previous purely neural approaches to textual entailment. On the dataset introduced in Glockner et al. (2018) our model achieves 83.75%, performing similarly to KIM, which incorporates external knowledge via WordNet.
## Training
#### Training Data
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> We use the BooksCorpus dataset ([Zhu et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf)) for training the language model. It contains over 7,000 unique unpublished books from a variety of genres including Adventure, Fantasy, and Romance. Crucially, it contains long stretches of contiguous text, which allows the generative model to learn to condition on long-range information.
#### Training Procedure
The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf):
> Our model largely follows the original transformer work [62]. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states. We used the Adam optimization scheme [27] with a max learning rate of 2.5e-4. The learning rate was increased linearly from zero over the first 2000 updates and annealed to 0 using a cosine schedule. We train for 100 epochs on minibatches of 64 randomly sampled, contiguous sequences of 512 tokens. Since layernorm [2] is used extensively throughout the model, a simple weight initialization of N (0, 0.02) was sufficient. We used a bytepair encoding (BPE) vocabulary with 40,000 merges [53] and residual, embedding, and attention dropouts with a rate of 0.1 for regularization. We also employed a modified version of L2 regularization proposed in [37], with w = 0.01 on all non bias or gain weights. For the activation function, we used the Gaussian Error Linear Unit (GELU) [18]. We used learned position embeddings instead of the sinusoidal version proposed in the original work. We use the ftfy library2 to clean the raw text in BooksCorpus, standardize some punctuation and whitespace, and use the spaCy tokenizer.
See the paper for further details and links to citations.
## Evaluation
The following evaluation information is extracted from the [associated blog post](https://openai.com/blog/language-unsupervised/). See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for further details.
#### Testing Data, Factors and Metrics
The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics:
- **Task:** Textual Entailment
- **Datasets:** [SNLI](https://huggingface.co/datasets/snli), [MNLI Matched](https://huggingface.co/datasets/glue), [MNLI Mismatched](https://huggingface.co/datasets/glue), [SciTail](https://huggingface.co/datasets/scitail), [QNLI](https://huggingface.co/datasets/glue), [RTE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Semantic Similarity
- **Datasets:** [STS-B](https://huggingface.co/datasets/glue), [QQP](https://huggingface.co/datasets/glue), [MRPC](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Reading Comprehension
- **Datasets:** [RACE](https://huggingface.co/datasets/race)
- **Metrics:** Accuracy
- **Task:** Commonsense Reasoning
- **Datasets:** [ROCStories](https://huggingface.co/datasets/story_cloze), [COPA](https://huggingface.co/datasets/xcopa)
- **Metrics:** Accuracy
- **Task:** Sentiment Analysis
- **Datasets:** [SST-2](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Linguistic Acceptability
- **Datasets:** [CoLA](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
- **Task:** Multi Task Benchmark
- **Datasets:** [GLUE](https://huggingface.co/datasets/glue)
- **Metrics:** Accuracy
#### Results
The model achieves the following results without any fine-tuning (zero-shot):
| Task | TE | TE | TE |TE | TE | TE | SS | SS | SS | RC | CR | CR | SA | LA | MTB |
|:--------:|:--:|:----------:|:-------------:|:-----:|:----:|:---:|:---:|:---:|:--:|:----:|:--------:|:----:|:----:|:----:|:----:|
| Dataset |SNLI|MNLI Matched|MNLI Mismatched|SciTail| QNLI | RTE |STS-B| QQP |MPRC|RACE |ROCStories|COPA | SST-2| CoLA | GLUE |
| |89.9| 82.1 | 81.4 |88.3 | 88.1 | 56.0|82.0 | 70.3|82.3|59.0 | 86.5 | 78.6 | 91.3 | 45.4 | 72.8 |
## Environmental Impact
The model developers [report that](https://openai.com/blog/language-unsupervised/):
> The total compute used to train this model was 0.96 petaflop days (pfs-days).
> 8 P600 GPU's * 30 days * 12 TFLOPS/GPU * 0.33 utilization = .96 pfs-days
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** 8 P600 GPUs
- **Hours used:** 720 hours (30 days)
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@article{radford2018improving,
title={Improving language understanding by generative pre-training},
author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others},
year={2018},
publisher={OpenAI}
}
```
APA:
*Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.*
## Model Card Authors
This model card was written by the Hugging Face team. | [
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING",
"SEMANTIC_SIMILARITY",
"TEXTUAL_ENTAILMENT"
] | [
"SCITAIL"
] |
HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1 | HIT-TMG | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2",
"feature-extraction",
"sentence-similarity",
"mteb",
"arxiv:2501.01028",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-10-23T04:21:56 | 2025-03-13T06:44:13 | 36,178 | 32 | ---
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: KaLM-Embedding
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 94.35532233883059
- type: ap
value: 60.40219300665376
- type: ap_weighted
value: 60.40219300665376
- type: f1
value: 86.52001470357649
- type: f1_weighted
value: 94.65531755022661
- type: main_score
value: 94.35532233883059
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 91.71641791044776
- type: ap
value: 68.4050364584575
- type: ap_weighted
value: 68.4050364584575
- type: f1
value: 87.91854774634491
- type: f1_weighted
value: 92.0430596057422
- type: main_score
value: 91.71641791044776
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.49945000000001
- type: ap
value: 94.97348227456295
- type: ap_weighted
value: 94.97348227456295
- type: f1
value: 96.49855824500423
- type: f1_weighted
value: 96.49855824500422
- type: main_score
value: 96.49945000000001
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.242
- type: f1
value: 59.353696237560094
- type: f1_weighted
value: 59.35369623756011
- type: main_score
value: 61.242
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 56.569
- type: map_at_1
value: 31.080999999999996
- type: map_at_10
value: 47.432
- type: map_at_100
value: 48.247
- type: map_at_1000
value: 48.251
- type: map_at_20
value: 48.114000000000004
- type: map_at_3
value: 42.425000000000004
- type: map_at_5
value: 45.128
- type: mrr_at_1
value: 31.57894736842105
- type: mrr_at_10
value: 47.6253132832081
- type: mrr_at_100
value: 48.440395388879296
- type: mrr_at_1000
value: 48.44416076630039
- type: mrr_at_20
value: 48.30706364782469
- type: mrr_at_3
value: 42.59127548601235
- type: mrr_at_5
value: 45.347321005215804
- type: nauc_map_at_1000_diff1
value: 7.110790588301176
- type: nauc_map_at_1000_max
value: -12.892696039828866
- type: nauc_map_at_1000_std
value: -15.5709273320573
- type: nauc_map_at_100_diff1
value: 7.117551663882657
- type: nauc_map_at_100_max
value: -12.882680977142957
- type: nauc_map_at_100_std
value: -15.56350483617667
- type: nauc_map_at_10_diff1
value: 6.903272993199564
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value: -13.012877497725961
- type: nauc_map_at_10_std
value: -15.947400478856006
- type: nauc_map_at_1_diff1
value: 10.03503740028087
- type: nauc_map_at_1_max
value: -13.351553937797
- type: nauc_map_at_1_std
value: -14.137614923859612
- type: nauc_map_at_20_diff1
value: 7.01754882034529
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value: -12.864438636302197
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value: -15.541510619190976
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value: 7.018587254951812
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value: -13.38420244471981
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value: -16.127099270987785
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value: 6.920961668066123
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value: -13.169892625713931
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value: -16.21272880801226
- type: nauc_mrr_at_1000_diff1
value: 5.5525831294754004
- type: nauc_mrr_at_1000_max
value: -12.98089269414052
- type: nauc_mrr_at_1000_std
value: -15.396489593627944
- type: nauc_mrr_at_100_diff1
value: 5.559525360367539
- type: nauc_mrr_at_100_max
value: -12.970885236428334
- type: nauc_mrr_at_100_std
value: -15.389102542398783
- type: nauc_mrr_at_10_diff1
value: 5.38828048977972
- type: nauc_mrr_at_10_max
value: -13.096637253890634
- type: nauc_mrr_at_10_std
value: -15.775810422484374
- type: nauc_mrr_at_1_diff1
value: 8.58091801149426
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value: -12.352949021555306
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value: -13.545487974417847
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value: 5.4666282281067735
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value: -12.952039027828944
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value: -15.367907454271231
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value: 5.1862331302405735
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value: -13.816401285559108
- type: nauc_mrr_at_3_std
value: -15.872101319770382
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value: 5.471097057115419
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value: -13.269134531334442
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value: -15.95735511276538
- type: nauc_ndcg_at_1000_diff1
value: 6.8032235432235275
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value: -12.52617810408163
- type: nauc_ndcg_at_1000_std
value: -15.38677998208727
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value: 6.971743190062509
- type: nauc_ndcg_at_100_max
value: -12.284060222136334
- type: nauc_ndcg_at_100_std
value: -15.203583619739097
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dataset:
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type: mteb/askubuntudupquestions-reranking
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type: mteb/cqadupstack-android
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type: mteb/mtop_domain
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type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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dataset:
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type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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dataset:
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type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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dataset:
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type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 60.90431312863765
- type: euclidean_spearman
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- type: main_score
value: 60.12821686053337
- type: manhattan_pearson
value: 59.05369093717122
- type: manhattan_spearman
value: 57.65837693471568
- type: pearson
value: 59.98924365555529
- type: spearman
value: 60.12821686053337
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 74.95271349225828
- type: cosine_spearman
value: 75.43839974308261
- type: euclidean_pearson
value: 75.68179466828151
- type: euclidean_spearman
value: 75.43839974308261
- type: main_score
value: 75.43839974308261
- type: manhattan_pearson
value: 75.4848070012919
- type: manhattan_spearman
value: 74.92507658877852
- type: pearson
value: 74.95271349225828
- type: spearman
value: 75.43839974308261
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: cosine_pearson
value: 86.18555151297676
- type: cosine_spearman
value: 86.40304228488033
- type: euclidean_pearson
value: 86.8788548303146
- type: euclidean_spearman
value: 86.40304228488033
- type: main_score
value: 86.40304228488033
- type: manhattan_pearson
value: 86.79312171236047
- type: manhattan_spearman
value: 86.26008520753594
- type: pearson
value: 86.18555151297676
- type: spearman
value: 86.40304228488033
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type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 54.99479996647493
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value: 53.67766339389046
- type: euclidean_pearson
value: 55.32473081178422
- type: euclidean_spearman
value: 53.67766339389046
- type: main_score
value: 53.67766339389046
- type: manhattan_pearson
value: 54.66604584985125
- type: manhattan_spearman
value: 52.48322788533404
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value: 54.99479996647493
- type: spearman
value: 53.67766339389046
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 76.65184590191937
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value: 78.04569100389011
- type: euclidean_pearson
value: 77.11425698246029
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value: 78.04569100389011
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value: 78.04569100389011
- type: manhattan_pearson
value: 77.34799982307821
- type: manhattan_spearman
value: 78.22975685912238
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value: 76.65184590191937
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value: 78.04569100389011
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type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 76.30743924244035
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- type: euclidean_pearson
value: 77.10837892816058
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- type: main_score
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value: 75.2110676227775
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 75.11771819741416
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value: 75.56941540554674
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value: 74.96778304560281
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type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 72.42454093118816
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value: 71.9097547231894
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type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 69.3881685924264
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value: 69.37162939123382
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type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 64.64985744446284
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value: 63.89323074678119
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type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 78.95094282575697
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value: 80.66341954222823
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type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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- type: manhattan_pearson
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- type: pearson
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- type: spearman
value: 77.00326330683939
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value: 77.99167158750629
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- type: euclidean_pearson
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- type: euclidean_spearman
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- type: main_score
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value: 77.99167158750629
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value: 77.00326330683939
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type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 77.21035942564082
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value: 76.57212143103963
- type: euclidean_pearson
value: 78.03973868360728
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value: 76.83958214147293
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- type: spearman
value: 76.57212143103963
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value: 77.21035942564082
- type: cosine_spearman
value: 76.57212143103963
- type: euclidean_pearson
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- type: euclidean_spearman
value: 76.57212143103963
- type: main_score
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- type: manhattan_spearman
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- type: pearson
value: 77.21035942564082
- type: spearman
value: 76.57212143103963
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 81.21615375003084
- type: cosine_spearman
value: 84.2970803211202
- type: euclidean_pearson
value: 83.54765755364517
- type: euclidean_spearman
value: 84.2970803211202
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value: 84.2970803211202
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- type: manhattan_spearman
value: 84.09545601307758
- type: pearson
value: 81.21615375003084
- type: spearman
value: 84.2970803211202
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 80.72245608609909
- type: map
value: 80.72245608609909
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value: 94.86804408373035
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value: 48.33938058863373
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value: 82.0796869926262
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value: 79.20228314778093
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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dataset:
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type: mteb/stackexchange-clustering-p2p
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split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
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split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
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value: 8.77511441582519
- type: nauc_precision_at_3_max
value: -29.389312907952135
- type: nauc_precision_at_3_std
value: -6.397150206890867
- type: nauc_precision_at_5_diff1
value: 9.795445750266063
- type: nauc_precision_at_5_max
value: -38.88827845334236
- type: nauc_precision_at_5_std
value: -3.397760151003072
- type: nauc_recall_at_1000_diff1
value: -28.033327034031043
- type: nauc_recall_at_1000_max
value: -15.30930042500693
- type: nauc_recall_at_1000_std
value: 69.27496829698434
- type: nauc_recall_at_100_diff1
value: -12.558500592244782
- type: nauc_recall_at_100_max
value: -27.109814142314832
- type: nauc_recall_at_100_std
value: 40.23660136119213
- type: nauc_recall_at_10_diff1
value: 8.859020421080002
- type: nauc_recall_at_10_max
value: -26.101835112681034
- type: nauc_recall_at_10_std
value: -12.02508230851673
- type: nauc_recall_at_1_diff1
value: 16.059944358241733
- type: nauc_recall_at_1_max
value: -25.302527766801695
- type: nauc_recall_at_1_std
value: -13.565207797491604
- type: nauc_recall_at_20_diff1
value: 6.598503996413421
- type: nauc_recall_at_20_max
value: -25.661355219947264
- type: nauc_recall_at_20_std
value: -0.5270972932429998
- type: nauc_recall_at_3_diff1
value: 15.848752699477423
- type: nauc_recall_at_3_max
value: -20.67227958185249
- type: nauc_recall_at_3_std
value: -19.687883601951533
- type: nauc_recall_at_5_diff1
value: 15.210234895525055
- type: nauc_recall_at_5_max
value: -30.20253332454299
- type: nauc_recall_at_5_std
value: -19.986130369906242
- type: ndcg_at_1
value: 40.816
- type: ndcg_at_10
value: 31.186999999999998
- type: ndcg_at_100
value: 42.742000000000004
- type: ndcg_at_1000
value: 53.230999999999995
- type: ndcg_at_20
value: 31.057000000000002
- type: ndcg_at_3
value: 34.382000000000005
- type: ndcg_at_5
value: 32.038
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.735
- type: precision_at_1000
value: 1.59
- type: precision_at_20
value: 19.898
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 3.4070000000000005
- type: recall_at_10
value: 19.987
- type: recall_at_100
value: 52.888999999999996
- type: recall_at_1000
value: 85.172
- type: recall_at_20
value: 27.025
- type: recall_at_3
value: 7.774
- type: recall_at_5
value: 11.571
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 89.0380859375
- type: ap
value: 34.26536468203791
- type: ap_weighted
value: 34.26536468203791
- type: f1
value: 73.86921962038298
- type: f1_weighted
value: 90.61132302248866
- type: main_score
value: 89.0380859375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 76.49405772495754
- type: f1
value: 76.73610452546936
- type: f1_weighted
value: 76.14362047024868
- type: main_score
value: 76.49405772495754
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 49.554702818248735
- type: v_measure
value: 49.554702818248735
- type: v_measure_std
value: 0.9278298624304031
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 85.29534481730941
- type: cosine_accuracy_threshold
value: 90.6567394733429
- type: cosine_ap
value: 71.59976408272617
- type: cosine_f1
value: 66.54452180285818
- type: cosine_f1_threshold
value: 88.94971013069153
- type: cosine_precision
value: 61.95133045258131
- type: cosine_recall
value: 71.87335092348285
- type: dot_accuracy
value: 85.29534481730941
- type: dot_accuracy_threshold
value: 90.65674543380737
- type: dot_ap
value: 71.5997871796046
- type: dot_f1
value: 66.54452180285818
- type: dot_f1_threshold
value: 88.94971013069153
- type: dot_precision
value: 61.95133045258131
- type: dot_recall
value: 71.87335092348285
- type: euclidean_accuracy
value: 85.29534481730941
- type: euclidean_accuracy_threshold
value: 43.2279109954834
- type: euclidean_ap
value: 71.59977967634174
- type: euclidean_f1
value: 66.54452180285818
- type: euclidean_f1_threshold
value: 47.01125621795654
- type: euclidean_precision
value: 61.95133045258131
- type: euclidean_recall
value: 71.87335092348285
- type: main_score
value: 71.5997871796046
- type: manhattan_accuracy
value: 85.1820945341837
- type: manhattan_accuracy_threshold
value: 1019.9851989746094
- type: manhattan_ap
value: 71.22149639016482
- type: manhattan_f1
value: 66.31834750911301
- type: manhattan_f1_threshold
value: 1109.6149444580078
- type: manhattan_precision
value: 61.46396396396396
- type: manhattan_recall
value: 72.00527704485488
- type: max_ap
value: 71.5997871796046
- type: max_f1
value: 66.54452180285818
- type: max_precision
value: 61.95133045258131
- type: max_recall
value: 72.00527704485488
- type: similarity_accuracy
value: 85.29534481730941
- type: similarity_accuracy_threshold
value: 90.6567394733429
- type: similarity_ap
value: 71.59976408272617
- type: similarity_f1
value: 66.54452180285818
- type: similarity_f1_threshold
value: 88.94971013069153
- type: similarity_precision
value: 61.95133045258131
- type: similarity_recall
value: 71.87335092348285
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.19936352699189
- type: cosine_accuracy_threshold
value: 87.72701621055603
- type: cosine_ap
value: 86.32764736710979
- type: cosine_f1
value: 78.40269966254218
- type: cosine_f1_threshold
value: 86.80565357208252
- type: cosine_precision
value: 76.41426692004093
- type: cosine_recall
value: 80.49738219895288
- type: dot_accuracy
value: 89.19936352699189
- type: dot_accuracy_threshold
value: 87.72701621055603
- type: dot_ap
value: 86.32762879051161
- type: dot_f1
value: 78.40269966254218
- type: dot_f1_threshold
value: 86.80565357208252
- type: dot_precision
value: 76.41426692004093
- type: dot_recall
value: 80.49738219895288
- type: euclidean_accuracy
value: 89.19936352699189
- type: euclidean_accuracy_threshold
value: 49.54388439655304
- type: euclidean_ap
value: 86.3276630523782
- type: euclidean_f1
value: 78.40269966254218
- type: euclidean_f1_threshold
value: 51.36992931365967
- type: euclidean_precision
value: 76.41426692004093
- type: euclidean_recall
value: 80.49738219895288
- type: main_score
value: 86.3276630523782
- type: manhattan_accuracy
value: 89.16637559669344
- type: manhattan_accuracy_threshold
value: 1150.1700401306152
- type: manhattan_ap
value: 86.28674414277404
- type: manhattan_f1
value: 78.34183768482997
- type: manhattan_f1_threshold
value: 1213.088321685791
- type: manhattan_precision
value: 75.87475651107424
- type: manhattan_recall
value: 80.97474591931014
- type: max_ap
value: 86.3276630523782
- type: max_f1
value: 78.40269966254218
- type: max_precision
value: 76.41426692004093
- type: max_recall
value: 80.97474591931014
- type: similarity_accuracy
value: 89.19936352699189
- type: similarity_accuracy_threshold
value: 87.72701621055603
- type: similarity_ap
value: 86.32764736710979
- type: similarity_f1
value: 78.40269966254218
- type: similarity_f1_threshold
value: 86.80565357208252
- type: similarity_precision
value: 76.41426692004093
- type: similarity_recall
value: 80.49738219895288
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
- type: cosine_pearson
value: 38.59465613249044
- type: cosine_spearman
value: 39.876884773191065
- type: euclidean_pearson
value: 38.370163017159996
- type: euclidean_spearman
value: 39.87692498028858
- type: main_score
value: 39.876884773191065
- type: manhattan_pearson
value: 38.058013850119785
- type: manhattan_spearman
value: 39.531271872106856
- type: pearson
value: 38.59465613249044
- type: spearman
value: 39.876884773191065
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
metrics:
- type: cosine_pearson
value: 46.457799031090666
- type: cosine_spearman
value: 47.170032935367935
- type: euclidean_pearson
value: 49.399858337266004
- type: euclidean_spearman
value: 47.17003293450119
- type: main_score
value: 47.170032935367935
- type: manhattan_pearson
value: 49.19428772786887
- type: manhattan_spearman
value: 46.94649743167009
- type: pearson
value: 46.457799031090666
- type: spearman
value: 47.170032935367935
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.246
- type: f1
value: 45.84988588370862
- type: f1_weighted
value: 45.84988588370862
- type: main_score
value: 50.246
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
metrics:
- type: cosine_pearson
value: 53.67950003884396
- type: cosine_spearman
value: 54.36088598761955
- type: euclidean_pearson
value: 53.09394654913335
- type: euclidean_spearman
value: 54.36088252221325
- type: main_score
value: 54.36088598761955
- type: manhattan_pearson
value: 52.805415867146955
- type: manhattan_spearman
value: 54.06705049402532
- type: pearson
value: 53.67950003884396
- type: spearman
value: 54.36088598761955
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: main_score
value: 41.608876653105966
- type: v_measure
value: 41.608876653105966
- type: v_measure_std
value: 1.0624705258546963
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: main_score
value: 39.7110966049789
- type: v_measure
value: 39.7110966049789
- type: v_measure_std
value: 0.875231943450341
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: main_score
value: 81.7193302624052
- type: map
value: 81.7193302624052
- type: mrr
value: 84.58841269841271
- type: nAUC_map_diff1
value: 57.41916975321788
- type: nAUC_map_max
value: 61.409376634272874
- type: nAUC_map_std
value: 28.913154318201233
- type: nAUC_mrr_diff1
value: 64.85350793018186
- type: nAUC_mrr_max
value: 69.46338529223004
- type: nAUC_mrr_std
value: 35.373588518165235
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: main_score
value: 82.59163356780259
- type: map
value: 82.59163356780259
- type: mrr
value: 85.54900793650792
- type: nAUC_map_diff1
value: 61.10665055831455
- type: nAUC_map_max
value: 60.91441391850925
- type: nAUC_map_std
value: 21.471788062972436
- type: nAUC_mrr_diff1
value: 69.95883630916767
- type: nAUC_mrr_max
value: 71.06959737866757
- type: nAUC_mrr_std
value: 30.819473605657606
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
metrics:
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value: 42.631
- type: map_at_1
value: 24.834
- type: map_at_10
value: 36.447
- type: map_at_100
value: 38.04
- type: map_at_1000
value: 38.179
- type: map_at_20
value: 37.281
- type: map_at_3
value: 32.761
- type: map_at_5
value: 34.871
- type: mrr_at_1
value: 38.05951487871968
- type: mrr_at_10
value: 45.57554071057435
- type: mrr_at_100
value: 46.447190120013
- type: mrr_at_1000
value: 46.50606585607273
- type: mrr_at_20
value: 46.057122452003696
- type: mrr_at_3
value: 43.34000166708336
- type: mrr_at_5
value: 44.58531299491537
- type: nauc_map_at_1000_diff1
value: 48.47252945149055
- type: nauc_map_at_1000_max
value: 34.62100533042246
- type: nauc_map_at_1000_std
value: -2.684326419049642
- type: nauc_map_at_100_diff1
value: 48.43175156549248
- type: nauc_map_at_100_max
value: 34.58371253483366
- type: nauc_map_at_100_std
value: -2.719072576245476
- type: nauc_map_at_10_diff1
value: 48.18476956739444
- type: nauc_map_at_10_max
value: 33.52918292302435
- type: nauc_map_at_10_std
value: -3.746440821126843
- type: nauc_map_at_1_diff1
value: 52.68253139221022
- type: nauc_map_at_1_max
value: 26.033202075590157
- type: nauc_map_at_1_std
value: -5.756330655143574
- type: nauc_map_at_20_diff1
value: 48.33335064427594
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value: 34.08423189594616
- type: nauc_map_at_20_std
value: -3.2957587803371693
- type: nauc_map_at_3_diff1
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- type: nauc_map_at_3_max
value: 30.931354812941592
- type: nauc_map_at_3_std
value: -5.397714078300849
- type: nauc_map_at_5_diff1
value: 48.582852045037974
- type: nauc_map_at_5_max
value: 32.37350218464533
- type: nauc_map_at_5_std
value: -4.604286079722004
- type: nauc_mrr_at_1000_diff1
value: 55.36516647246729
- type: nauc_mrr_at_1000_max
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value: 1.2938880389263046
- type: nauc_mrr_at_100_diff1
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value: 1.3168989639934452
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- type: nauc_mrr_at_10_std
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- type: nauc_mrr_at_1_diff1
value: 60.68654871568434
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value: 0.4003726817671297
- type: nauc_mrr_at_20_diff1
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- type: nauc_mrr_at_20_std
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- type: nauc_mrr_at_3_diff1
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- type: nauc_mrr_at_3_max
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- type: nauc_mrr_at_3_std
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- type: nauc_mrr_at_5_diff1
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- type: nauc_mrr_at_5_max
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- type: nauc_mrr_at_5_std
value: 0.9345604936396126
- type: nauc_ndcg_at_1000_diff1
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- type: nauc_ndcg_at_1000_max
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- type: nauc_ndcg_at_1000_std
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- type: nauc_ndcg_at_100_diff1
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_3_diff1
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- type: nauc_ndcg_at_5_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_precision_at_100_diff1
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- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
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- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
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- type: nauc_precision_at_3_diff1
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_diff1
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value: 9.589
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value: 1.031
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value: 0.105
- type: precision_at_20
value: 4.966
- type: precision_at_3
value: 28.319
- type: precision_at_5
value: 18.129
- type: recall_at_1
value: 65.349
- type: recall_at_10
value: 90.10000000000001
- type: recall_at_100
value: 96.685
- type: recall_at_1000
value: 98.714
- type: recall_at_20
value: 93.298
- type: recall_at_3
value: 80.324
- type: recall_at_5
value: 85.37700000000001
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-CN)
type: mteb/amazon_massive_intent
config: zh-CN
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 70.94149293880295
- type: f1
value: 67.43015916458866
- type: f1_weighted
value: 70.02165762549619
- type: main_score
value: 70.94149293880295
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (zh-TW)
type: mteb/amazon_massive_intent
config: zh-TW
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 63.05312710154675
- type: f1
value: 61.11778922874984
- type: f1_weighted
value: 61.425454449692396
- type: main_score
value: 63.05312710154675
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 77.79757901815736
- type: f1
value: 76.85610655879204
- type: f1_weighted
value: 77.36623686607157
- type: main_score
value: 77.79757901815736
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
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value: 71.34498991257566
- type: f1
value: 71.42538497861686
- type: f1_weighted
value: 70.47776598531958
- type: main_score
value: 71.34498991257566
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
metrics:
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value: 57.528999999999996
- type: map_at_1
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- type: map_at_10
value: 54.674
- type: map_at_100
value: 55.187
- type: map_at_1000
value: 55.24
- type: map_at_20
value: 54.933
- type: map_at_3
value: 53.367
- type: map_at_5
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- type: mrr_at_1
value: 48.8
- type: mrr_at_10
value: 54.71369047619046
- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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- type: mrr_at_3
value: 53.41666666666666
- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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value: 75.58859746051999
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value: 46.09484444567737
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value: 26.8242013460261
- type: ndcg_at_1
value: 48.699999999999996
- type: ndcg_at_10
value: 57.528999999999996
- type: ndcg_at_100
value: 60.38
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value: 61.937
- type: ndcg_at_20
value: 58.518
- type: ndcg_at_3
value: 54.818999999999996
- type: ndcg_at_5
value: 56.101
- type: precision_at_1
value: 48.699999999999996
- type: precision_at_10
value: 6.65
- type: precision_at_100
value: 0.8059999999999999
- type: precision_at_1000
value: 0.093
- type: precision_at_20
value: 3.5249999999999995
- type: precision_at_3
value: 19.667
- type: precision_at_5
value: 12.42
- type: recall_at_1
value: 48.699999999999996
- type: recall_at_10
value: 66.5
- type: recall_at_100
value: 80.60000000000001
- type: recall_at_1000
value: 93.2
- type: recall_at_20
value: 70.5
- type: recall_at_3
value: 59.0
- type: recall_at_5
value: 62.1
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: test
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 78.08
- type: f1
value: 77.44308848942492
- type: f1_weighted
value: 77.44308848942492
- type: main_score
value: 78.08
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
metrics:
- type: cosine_accuracy
value: 66.8651867893882
- type: cosine_accuracy_threshold
value: 84.34688448905945
- type: cosine_ap
value: 69.83287846115917
- type: cosine_f1
value: 71.33520074696546
- type: cosine_f1_threshold
value: 83.85992050170898
- type: cosine_precision
value: 63.93305439330545
- type: cosine_recall
value: 80.67581837381204
- type: dot_accuracy
value: 66.8651867893882
- type: dot_accuracy_threshold
value: 84.34690237045288
- type: dot_ap
value: 69.83287846115917
- type: dot_f1
value: 71.33520074696546
- type: dot_f1_threshold
value: 83.85992050170898
- type: dot_precision
value: 63.93305439330545
- type: dot_recall
value: 80.67581837381204
- type: euclidean_accuracy
value: 66.8651867893882
- type: euclidean_accuracy_threshold
value: 55.95196485519409
- type: euclidean_ap
value: 69.83287846115917
- type: euclidean_f1
value: 71.33520074696546
- type: euclidean_f1_threshold
value: 56.81561827659607
- type: euclidean_precision
value: 63.93305439330545
- type: euclidean_recall
value: 80.67581837381204
- type: main_score
value: 69.83287846115917
- type: manhattan_accuracy
value: 66.0530590146183
- type: manhattan_accuracy_threshold
value: 1215.458583831787
- type: manhattan_ap
value: 69.51465499538298
- type: manhattan_f1
value: 70.56159420289853
- type: manhattan_f1_threshold
value: 1344.7942733764648
- type: manhattan_precision
value: 61.77636796193497
- type: manhattan_recall
value: 82.259767687434
- type: max_ap
value: 69.83287846115917
- type: max_f1
value: 71.33520074696546
- type: max_precision
value: 63.93305439330545
- type: max_recall
value: 82.259767687434
- type: similarity_accuracy
value: 66.8651867893882
- type: similarity_accuracy_threshold
value: 84.34688448905945
- type: similarity_ap
value: 69.83287846115917
- type: similarity_f1
value: 71.33520074696546
- type: similarity_f1_threshold
value: 83.85992050170898
- type: similarity_precision
value: 63.93305439330545
- type: similarity_recall
value: 80.67581837381204
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: e610f2ebd179a8fda30ae534c3878750a96db120
metrics:
- type: accuracy
value: 93.66999999999999
- type: ap
value: 92.68160375501351
- type: ap_weighted
value: 92.68160375501351
- type: f1
value: 93.6673524115384
- type: f1_weighted
value: 93.67269842799493
- type: main_score
value: 93.66999999999999
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
metrics:
- type: cosine_pearson
value: 14.427978400689973
- type: cosine_spearman
value: 15.182736434509348
- type: euclidean_pearson
value: 17.726048874983753
- type: euclidean_spearman
value: 15.201779286945575
- type: main_score
value: 15.182736434509348
- type: manhattan_pearson
value: 17.715716154164234
- type: manhattan_spearman
value: 15.250986981738777
- type: pearson
value: 14.427978400689973
- type: spearman
value: 15.182736434509348
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
metrics:
- type: cosine_pearson
value: 28.677852039385687
- type: cosine_spearman
value: 30.317414500566187
- type: euclidean_pearson
value: 28.546943523039168
- type: euclidean_spearman
value: 30.31773442605619
- type: main_score
value: 30.317414500566187
- type: manhattan_pearson
value: 29.06524931618951
- type: manhattan_spearman
value: 30.85475318983088
- type: pearson
value: 28.677852039385687
- type: spearman
value: 30.317414500566187
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 74.25169654144152
- type: cosine_spearman
value: 74.02188505990078
- type: euclidean_pearson
value: 71.78459076777199
- type: euclidean_spearman
value: 74.02188505990078
- type: main_score
value: 74.02188505990078
- type: manhattan_pearson
value: 71.38471936226554
- type: manhattan_spearman
value: 73.72453020549669
- type: pearson
value: 74.25169654144152
- type: spearman
value: 74.02188505990078
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
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dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
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dataset:
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type: C-MTEB/T2Retrieval
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split: dev
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
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type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
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type: Clustering
dataset:
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type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
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dataset:
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type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
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type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
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type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
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type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
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type: Reranking
dataset:
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type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
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dataset:
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type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
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type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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value: 27.89909851354522
- type: nauc_recall_at_5_std
value: 7.343245149942006
- type: ndcg_at_1
value: 8.559
- type: ndcg_at_10
value: 19.828000000000003
- type: ndcg_at_100
value: 25.468000000000004
- type: ndcg_at_1000
value: 28.058
- type: ndcg_at_20
value: 22.122
- type: ndcg_at_3
value: 15.524
- type: ndcg_at_5
value: 17.579
- type: precision_at_1
value: 8.559
- type: precision_at_10
value: 3.243
- type: precision_at_100
value: 0.5950000000000001
- type: precision_at_1000
value: 0.08099999999999999
- type: precision_at_20
value: 2.072
- type: precision_at_3
value: 6.907000000000001
- type: precision_at_5
value: 5.135
- type: recall_at_1
value: 8.559
- type: recall_at_10
value: 32.432
- type: recall_at_100
value: 59.458999999999996
- type: recall_at_1000
value: 80.631
- type: recall_at_20
value: 41.441
- type: recall_at_3
value: 20.721
- type: recall_at_5
value: 25.676
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: main_score
value: 26.958035381361377
- type: v_measure
value: 26.958035381361377
- type: v_measure_std
value: 2.401353383071989
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P (fr)
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: main_score
value: 46.15554988136895
- type: v_measure
value: 46.15554988136895
- type: v_measure_std
value: 2.459531525134688
- type: main_score
value: 45.73187202144909
- type: v_measure
value: 45.73187202144909
- type: v_measure_std
value: 1.6402520163270633
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.78766050735986
- type: f1
value: 95.61497706645892
- type: f1_weighted
value: 95.79887587161483
- type: main_score
value: 95.78766050735986
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 80.8800501096148
- type: f1
value: 53.9945274705194
- type: f1_weighted
value: 80.94438738414857
- type: main_score
value: 80.8800501096148
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 83.6255924170616
- type: f1
value: 79.70294641135138
- type: f1_weighted
value: 83.33457992982105
- type: main_score
value: 83.6255924170616
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: main_score
value: 77.1970570860131
- type: v_measure
value: 77.1970570860131
- type: v_measure_std
value: 22.0055550035463
- type: main_score
value: 65.92601417312947
- type: v_measure
value: 65.92601417312947
- type: v_measure_std
value: 30.421071440935687
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 69.5359784801614
- type: f1
value: 64.640488940591
- type: f1_weighted
value: 67.85916565361048
- type: main_score
value: 69.5359784801614
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 78.52723604572965
- type: f1
value: 77.1995224144067
- type: f1_weighted
value: 78.1215987283123
- type: main_score
value: 78.52723604572965
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: main_score
value: 26.448
- type: map_at_1
value: 14.947
- type: map_at_10
value: 22.303
- type: map_at_100
value: 23.477999999999998
- type: map_at_1000
value: 23.586
- type: map_at_20
value: 22.962
- type: map_at_3
value: 19.949
- type: map_at_5
value: 21.252
- type: mrr_at_1
value: 14.946764946764945
- type: mrr_at_10
value: 22.303001053001033
- type: mrr_at_100
value: 23.478040499941816
- type: mrr_at_1000
value: 23.585987565381252
- type: mrr_at_20
value: 22.96198948271138
- type: mrr_at_3
value: 19.949494949494913
- type: mrr_at_5
value: 21.251706251706192
- type: nauc_map_at_1000_diff1
value: 30.124123232611005
- type: nauc_map_at_1000_max
value: 19.329718056410893
- type: nauc_map_at_1000_std
value: 3.7304142418877606
- type: nauc_map_at_100_diff1
value: 30.06763654065989
- type: nauc_map_at_100_max
value: 19.339926348634375
- type: nauc_map_at_100_std
value: 3.7507886962889376
- type: nauc_map_at_10_diff1
value: 30.235621359267817
- type: nauc_map_at_10_max
value: 19.315231135265865
- type: nauc_map_at_10_std
value: 3.888262415552999
- type: nauc_map_at_1_diff1
value: 37.87356036243269
- type: nauc_map_at_1_max
value: 17.63892349776284
- type: nauc_map_at_1_std
value: -2.0575597858386208
- type: nauc_map_at_20_diff1
value: 30.06800385756772
- type: nauc_map_at_20_max
value: 19.172804564418264
- type: nauc_map_at_20_std
value: 3.721149536049358
- type: nauc_map_at_3_diff1
value: 32.09160567273595
- type: nauc_map_at_3_max
value: 19.055280691204825
- type: nauc_map_at_3_std
value: 1.9160849079572526
- type: nauc_map_at_5_diff1
value: 30.81034541116131
- type: nauc_map_at_5_max
value: 19.172166581396308
- type: nauc_map_at_5_std
value: 3.251197681984862
- type: nauc_mrr_at_1000_diff1
value: 30.12412337741088
- type: nauc_mrr_at_1000_max
value: 19.329717809214035
- type: nauc_mrr_at_1000_std
value: 3.730414425912248
- type: nauc_mrr_at_100_diff1
value: 30.06763654065989
- type: nauc_mrr_at_100_max
value: 19.339926348634375
- type: nauc_mrr_at_100_std
value: 3.7507886962889376
- type: nauc_mrr_at_10_diff1
value: 30.235621359267817
- type: nauc_mrr_at_10_max
value: 19.315231135265865
- type: nauc_mrr_at_10_std
value: 3.888262415552999
- type: nauc_mrr_at_1_diff1
value: 37.87356036243269
- type: nauc_mrr_at_1_max
value: 17.63892349776284
- type: nauc_mrr_at_1_std
value: -2.0575597858386208
- type: nauc_mrr_at_20_diff1
value: 30.06800385756772
- type: nauc_mrr_at_20_max
value: 19.172804564418264
- type: nauc_mrr_at_20_std
value: 3.721149536049358
- type: nauc_mrr_at_3_diff1
value: 32.09160567273595
- type: nauc_mrr_at_3_max
value: 19.055280691204825
- type: nauc_mrr_at_3_std
value: 1.9160849079572526
- type: nauc_mrr_at_5_diff1
value: 30.81034541116131
- type: nauc_mrr_at_5_max
value: 19.172166581396308
- type: nauc_mrr_at_5_std
value: 3.251197681984862
- type: nauc_ndcg_at_1000_diff1
value: 28.057639637340476
- type: nauc_ndcg_at_1000_max
value: 20.172072747981893
- type: nauc_ndcg_at_1000_std
value: 5.991944827605241
- type: nauc_ndcg_at_100_diff1
value: 26.60019642442434
- type: nauc_ndcg_at_100_max
value: 20.47271103053784
- type: nauc_ndcg_at_100_std
value: 6.489412476969333
- type: nauc_ndcg_at_10_diff1
value: 27.165894912173762
- type: nauc_ndcg_at_10_max
value: 19.79447862928707
- type: nauc_ndcg_at_10_std
value: 6.648857204092722
- type: nauc_ndcg_at_1_diff1
value: 37.87356036243269
- type: nauc_ndcg_at_1_max
value: 17.63892349776284
- type: nauc_ndcg_at_1_std
value: -2.0575597858386208
- type: nauc_ndcg_at_20_diff1
value: 26.582793970516843
- type: nauc_ndcg_at_20_max
value: 19.348538329936638
- type: nauc_ndcg_at_20_std
value: 6.138040315782395
- type: nauc_ndcg_at_3_diff1
value: 30.57338000196413
- type: nauc_ndcg_at_3_max
value: 19.37852889877986
- type: nauc_ndcg_at_3_std
value: 3.0568087546329408
- type: nauc_ndcg_at_5_diff1
value: 28.469299405769632
- type: nauc_ndcg_at_5_max
value: 19.599386892314122
- type: nauc_ndcg_at_5_std
value: 5.299940395199246
- type: nauc_precision_at_1000_diff1
value: 24.170281200655943
- type: nauc_precision_at_1000_max
value: 39.623019898347664
- type: nauc_precision_at_1000_std
value: 44.81985014306762
- type: nauc_precision_at_100_diff1
value: 14.474857644755179
- type: nauc_precision_at_100_max
value: 26.05636850160609
- type: nauc_precision_at_100_std
value: 16.53010919038197
- type: nauc_precision_at_10_diff1
value: 19.584122367964167
- type: nauc_precision_at_10_max
value: 20.86686195708289
- type: nauc_precision_at_10_std
value: 13.525636908101404
- type: nauc_precision_at_1_diff1
value: 37.87356036243269
- type: nauc_precision_at_1_max
value: 17.63892349776284
- type: nauc_precision_at_1_std
value: -2.0575597858386208
- type: nauc_precision_at_20_diff1
value: 17.420915050615722
- type: nauc_precision_at_20_max
value: 19.45722509579383
- type: nauc_precision_at_20_std
value: 12.077196513907348
- type: nauc_precision_at_3_diff1
value: 26.865120457860247
- type: nauc_precision_at_3_max
value: 20.154933241021354
- type: nauc_precision_at_3_std
value: 5.86927947299836
- type: nauc_precision_at_5_diff1
value: 22.803351569123205
- type: nauc_precision_at_5_max
value: 20.623962388912666
- type: nauc_precision_at_5_std
value: 10.348629762758872
- type: nauc_recall_at_1000_diff1
value: 24.170281200656042
- type: nauc_recall_at_1000_max
value: 39.62301989834765
- type: nauc_recall_at_1000_std
value: 44.8198501430671
- type: nauc_recall_at_100_diff1
value: 14.474857644755254
- type: nauc_recall_at_100_max
value: 26.056368501606116
- type: nauc_recall_at_100_std
value: 16.530109190381985
- type: nauc_recall_at_10_diff1
value: 19.58412236796417
- type: nauc_recall_at_10_max
value: 20.866861957082875
- type: nauc_recall_at_10_std
value: 13.5256369081014
- type: nauc_recall_at_1_diff1
value: 37.87356036243269
- type: nauc_recall_at_1_max
value: 17.63892349776284
- type: nauc_recall_at_1_std
value: -2.0575597858386208
- type: nauc_recall_at_20_diff1
value: 17.420915050615708
- type: nauc_recall_at_20_max
value: 19.45722509579385
- type: nauc_recall_at_20_std
value: 12.077196513907353
- type: nauc_recall_at_3_diff1
value: 26.865120457860243
- type: nauc_recall_at_3_max
value: 20.15493324102137
- type: nauc_recall_at_3_std
value: 5.869279472998389
- type: nauc_recall_at_5_diff1
value: 22.803351569123215
- type: nauc_recall_at_5_max
value: 20.62396238891266
- type: nauc_recall_at_5_std
value: 10.348629762758849
- type: ndcg_at_1
value: 14.947
- type: ndcg_at_10
value: 26.448
- type: ndcg_at_100
value: 32.78
- type: ndcg_at_1000
value: 35.937000000000005
- type: ndcg_at_20
value: 28.842000000000002
- type: ndcg_at_3
value: 21.587999999999997
- type: ndcg_at_5
value: 23.942
- type: precision_at_1
value: 14.947
- type: precision_at_10
value: 3.972
- type: precision_at_100
value: 0.7080000000000001
- type: precision_at_1000
value: 0.096
- type: precision_at_20
value: 2.459
- type: precision_at_3
value: 8.777
- type: precision_at_5
value: 6.413
- type: recall_at_1
value: 14.947
- type: recall_at_10
value: 39.722
- type: recall_at_100
value: 70.844
- type: recall_at_1000
value: 96.274
- type: recall_at_20
value: 49.181000000000004
- type: recall_at_3
value: 26.331
- type: recall_at_5
value: 32.064
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cosine_accuracy
value: 82.62942779291554
- type: cosine_accuracy_threshold
value: 83.4860622882843
- type: cosine_ap
value: 93.39616519364185
- type: cosine_f1
value: 88.03378695448146
- type: cosine_f1_threshold
value: 83.4860622882843
- type: cosine_precision
value: 83.45195729537367
- type: cosine_recall
value: 93.14796425024826
- type: dot_accuracy
value: 82.62942779291554
- type: dot_accuracy_threshold
value: 83.4860622882843
- type: dot_ap
value: 93.39616519364185
- type: dot_f1
value: 88.03378695448146
- type: dot_f1_threshold
value: 83.4860622882843
- type: dot_precision
value: 83.45195729537367
- type: dot_recall
value: 93.14796425024826
- type: euclidean_accuracy
value: 82.62942779291554
- type: euclidean_accuracy_threshold
value: 57.4698805809021
- type: euclidean_ap
value: 93.39616519364185
- type: euclidean_f1
value: 88.03378695448146
- type: euclidean_f1_threshold
value: 57.4698805809021
- type: euclidean_precision
value: 83.45195729537367
- type: euclidean_recall
value: 93.14796425024826
- type: main_score
value: 93.39616519364185
- type: manhattan_accuracy
value: 82.62942779291554
- type: manhattan_accuracy_threshold
value: 1306.7530632019043
- type: manhattan_ap
value: 93.34098710518775
- type: manhattan_f1
value: 87.78409090909089
- type: manhattan_f1_threshold
value: 1335.2685928344727
- type: manhattan_precision
value: 83.89140271493213
- type: manhattan_recall
value: 92.05561072492551
- type: max_ap
value: 93.39616519364185
- type: max_f1
value: 88.03378695448146
- type: max_precision
value: 83.89140271493213
- type: max_recall
value: 93.14796425024826
- type: similarity_accuracy
value: 82.62942779291554
- type: similarity_accuracy_threshold
value: 83.4860622882843
- type: similarity_ap
value: 93.39616519364185
- type: similarity_f1
value: 88.03378695448146
- type: similarity_f1_threshold
value: 83.4860622882843
- type: similarity_precision
value: 83.45195729537367
- type: similarity_recall
value: 93.14796425024826
- task:
type: PairClassification
dataset:
name: MTEB PawsXPairClassification (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cosine_accuracy
value: 60.8
- type: cosine_accuracy_threshold
value: 98.90193939208984
- type: cosine_ap
value: 60.50913122978733
- type: cosine_f1
value: 62.69411339833874
- type: cosine_f1_threshold
value: 95.17210125923157
- type: cosine_precision
value: 46.51661307609861
- type: cosine_recall
value: 96.12403100775194
- type: dot_accuracy
value: 60.8
- type: dot_accuracy_threshold
value: 98.9019513130188
- type: dot_ap
value: 60.49770725998639
- type: dot_f1
value: 62.69411339833874
- type: dot_f1_threshold
value: 95.17210721969604
- type: dot_precision
value: 46.51661307609861
- type: dot_recall
value: 96.12403100775194
- type: euclidean_accuracy
value: 60.8
- type: euclidean_accuracy_threshold
value: 14.819307625293732
- type: euclidean_ap
value: 60.50917425308617
- type: euclidean_f1
value: 62.69411339833874
- type: euclidean_f1_threshold
value: 31.07377290725708
- type: euclidean_precision
value: 46.51661307609861
- type: euclidean_recall
value: 96.12403100775194
- type: main_score
value: 60.73371250119265
- type: manhattan_accuracy
value: 60.9
- type: manhattan_accuracy_threshold
value: 354.8734188079834
- type: manhattan_ap
value: 60.73371250119265
- type: manhattan_f1
value: 62.70506744440393
- type: manhattan_f1_threshold
value: 711.578369140625
- type: manhattan_precision
value: 46.73913043478261
- type: manhattan_recall
value: 95.23809523809523
- type: max_ap
value: 60.73371250119265
- type: max_f1
value: 62.70506744440393
- type: max_precision
value: 46.73913043478261
- type: max_recall
value: 96.12403100775194
- type: similarity_accuracy
value: 60.8
- type: similarity_accuracy_threshold
value: 98.90193939208984
- type: similarity_ap
value: 60.50913122978733
- type: similarity_f1
value: 62.69411339833874
- type: similarity_f1_threshold
value: 95.17210125923157
- type: similarity_precision
value: 46.51661307609861
- type: similarity_recall
value: 96.12403100775194
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cosine_pearson
value: 81.02846310969592
- type: cosine_spearman
value: 77.47140335069184
- type: euclidean_pearson
value: 77.4818795209704
- type: euclidean_spearman
value: 77.4714043813526
- type: main_score
value: 77.47140335069184
- type: manhattan_pearson
value: 77.44622115854098
- type: manhattan_spearman
value: 77.29743297817558
- type: pearson
value: 81.02846310969592
- type: spearman
value: 77.47140335069184
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 77.1356210910051
- type: cosine_spearman
value: 81.7065039306575
- type: euclidean_pearson
value: 79.32575551712296
- type: euclidean_spearman
value: 81.75624482168821
- type: main_score
value: 81.7065039306575
- type: manhattan_pearson
value: 81.05436417153798
- type: manhattan_spearman
value: 82.13370902176736
- type: pearson
value: 77.1356210910051
- type: spearman
value: 81.7065039306575
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cosine_pearson
value: 61.40659325490285
- type: cosine_spearman
value: 64.21007088135842
- type: euclidean_pearson
value: 61.051174476106
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type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
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type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
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type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
metrics:
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dataset:
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type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
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dataset:
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type: jinaai/xpqa
config: fra-fra
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
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type: Classification
dataset:
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type: PL-MTEB/allegro-reviews
config: default
split: test
revision: b89853e6de927b0e3bfa8ecc0e56fe4e02ceafc6
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dataset:
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type: clarin-knext/arguana-pl
config: default
split: test
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
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type: Classification
dataset:
name: MTEB CBD
type: PL-MTEB/cbd
config: default
split: test
revision: 36ddb419bcffe6a5374c3891957912892916f28d
metrics:
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dataset:
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type: PL-MTEB/cdsce-pairclassification
config: default
split: test
revision: 0a3d4aa409b22f80eb22cbf59b492637637b536d
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type: Retrieval
dataset:
name: MTEB NQ-PL
type: clarin-knext/nq-pl
config: default
split: test
revision: f171245712cf85dd4700b06bef18001578d0ca8d
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- type: recall_at_1000
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value: 36.324
- task:
type: Classification
dataset:
name: MTEB PAC
type: laugustyniak/abusive-clauses-pl
config: default
split: test
revision: fc69d1c153a8ccdcf1eef52f4e2a27f88782f543
metrics:
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value: 74.10348604798286
- type: f1
value: 61.280331645723685
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value: 65.03859489177282
- type: main_score
value: 64.80162177816392
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type: PairClassification
dataset:
name: MTEB PSC
type: PL-MTEB/psc-pairclassification
config: default
split: test
revision: d05a294af9e1d3ff2bfb6b714e08a24a6cabc669
metrics:
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- type: dot_accuracy
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- type: dot_precision
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- type: euclidean_accuracy
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- type: euclidean_accuracy_threshold
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- type: euclidean_ap
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- type: euclidean_precision
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- type: euclidean_recall
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- type: main_score
value: 99.46339853695966
- type: manhattan_accuracy
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value: 98.17073170731707
- task:
type: Classification
dataset:
name: MTEB PolEmo2.0-IN
type: PL-MTEB/polemo2_in
config: default
split: test
revision: d90724373c70959f17d2331ad51fb60c71176b03
metrics:
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value: 78.52375884318697
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value: 80.54016620498614
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type: Classification
dataset:
name: MTEB PolEmo2.0-OUT
type: PL-MTEB/polemo2_out
config: default
split: test
revision: 6a21ab8716e255ab1867265f8b396105e8aa63d4
metrics:
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value: 61.37651821862349
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type: PairClassification
dataset:
name: MTEB PPC
type: PL-MTEB/ppc-pairclassification
config: default
split: test
revision: 2c7d2df57801a591f6b1e3aaf042e7a04ec7d9f2
metrics:
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dataset:
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type: clarin-knext/quora-pl
config: default
split: test
revision: 0be27e93455051e531182b85e85e425aba12e9d4
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type: Retrieval
dataset:
name: MTEB SCIDOCS-PL
type: clarin-knext/scidocs-pl
config: default
split: test
revision: 45452b03f05560207ef19149545f168e596c9337
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type: PairClassification
dataset:
name: MTEB SICK-E-PL
type: PL-MTEB/sicke-pl-pairclassification
config: default
split: test
revision: 71bba34b0ece6c56dfcf46d9758a27f7a90f17e9
metrics:
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type: STS
dataset:
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type: PL-MTEB/sickr-pl-sts
config: default
split: test
revision: fd5c2441b7eeff8676768036142af4cfa42c1339
metrics:
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dataset:
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type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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dataset:
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type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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- task:
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dataset:
name: MTEB SciFact-PL
type: clarin-knext/scifact-pl
config: default
split: test
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
metrics:
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type: ai-forever/cedr-classification
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split: test
revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4
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config: default
split: test
revision: 3765c0d1de6b7d264bc459433c45e5a75513839c
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split: test
revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec
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type: ai-forever/inappropriateness-classification
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type: ai-forever/kinopoisk-sentiment-classification
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revision: 5911f26666ac11af46cb9c6849d0dc80a378af24
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- type: Recall@10(MIRACL)
value: 66.532
- type: Recall@100(MIRACL)
value: 79.952
- type: Recall@1000(MIRACL)
value: 79.952
- type: Recall@20(MIRACL)
value: 73.75
- type: Recall@3(MIRACL)
value: 49.541000000000004
- type: Recall@5(MIRACL)
value: 57.389
- type: main_score
value: 56.111999999999995
- type: nAUC_MAP@1000_diff1(MIRACL)
value: 15.8510181843185
- type: nAUC_MAP@1000_max(MIRACL)
value: 27.452155305037095
- type: nAUC_MAP@1000_std(MIRACL)
value: 15.147015882448075
- type: nAUC_MAP@100_diff1(MIRACL)
value: 15.8510181843185
- type: nAUC_MAP@100_max(MIRACL)
value: 27.452155305037095
- type: nAUC_MAP@100_std(MIRACL)
value: 15.147015882448075
- type: nAUC_MAP@10_diff1(MIRACL)
value: 17.808742699385363
- type: nAUC_MAP@10_max(MIRACL)
value: 25.21217663908093
- type: nAUC_MAP@10_std(MIRACL)
value: 13.970995033749716
- type: nAUC_MAP@1_diff1(MIRACL)
value: 34.30066727981356
- type: nAUC_MAP@1_max(MIRACL)
value: 11.096793012814972
- type: nAUC_MAP@1_std(MIRACL)
value: 4.298644702770651
- type: nAUC_MAP@20_diff1(MIRACL)
value: 16.499957004860978
- type: nAUC_MAP@20_max(MIRACL)
value: 26.676987318433714
- type: nAUC_MAP@20_std(MIRACL)
value: 15.166175199040485
- type: nAUC_MAP@3_diff1(MIRACL)
value: 23.797870452650084
- type: nAUC_MAP@3_max(MIRACL)
value: 18.20460307122738
- type: nAUC_MAP@3_std(MIRACL)
value: 8.985118628338126
- type: nAUC_MAP@5_diff1(MIRACL)
value: 20.549029352694866
- type: nAUC_MAP@5_max(MIRACL)
value: 21.528805328834324
- type: nAUC_MAP@5_std(MIRACL)
value: 11.131951589460492
- type: nAUC_NDCG@1000_diff1(MIRACL)
value: 5.973372149854828
- type: nAUC_NDCG@1000_max(MIRACL)
value: 36.70565868748619
- type: nAUC_NDCG@1000_std(MIRACL)
value: 19.551007976769245
- type: nAUC_NDCG@100_diff1(MIRACL)
value: 5.973372149854828
- type: nAUC_NDCG@100_max(MIRACL)
value: 36.70565868748619
- type: nAUC_NDCG@100_std(MIRACL)
value: 19.551007976769245
- type: nAUC_NDCG@10_diff1(MIRACL)
value: 10.894100451667919
- type: nAUC_NDCG@10_max(MIRACL)
value: 31.735109695399416
- type: nAUC_NDCG@10_std(MIRACL)
value: 17.674556265190706
- type: nAUC_NDCG@1_diff1(MIRACL)
value: 22.04892839322977
- type: nAUC_NDCG@1_max(MIRACL)
value: 32.51034181981298
- type: nAUC_NDCG@1_std(MIRACL)
value: 14.343760356007765
- type: nAUC_NDCG@20_diff1(MIRACL)
value: 8.074119776676103
- type: nAUC_NDCG@20_max(MIRACL)
value: 34.52221220694718
- type: nAUC_NDCG@20_std(MIRACL)
value: 19.94006423667
- type: nAUC_NDCG@3_diff1(MIRACL)
value: 16.284195830367825
- type: nAUC_NDCG@3_max(MIRACL)
value: 26.521965826220352
- type: nAUC_NDCG@3_std(MIRACL)
value: 13.850033289666094
- type: nAUC_NDCG@5_diff1(MIRACL)
value: 14.362693198633952
- type: nAUC_NDCG@5_max(MIRACL)
value: 27.781809390068872
- type: nAUC_NDCG@5_std(MIRACL)
value: 14.879808284537981
- type: nAUC_P@1000_diff1(MIRACL)
value: -27.606682296231373
- type: nAUC_P@1000_max(MIRACL)
value: 33.03084251491326
- type: nAUC_P@1000_std(MIRACL)
value: 15.674013757663898
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value: -27.606682296231327
- type: nAUC_P@100_max(MIRACL)
value: 33.03084251491332
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value: 15.674013757663937
- type: nAUC_P@10_diff1(MIRACL)
value: -23.575685602922174
- type: nAUC_P@10_max(MIRACL)
value: 36.72548498655645
- type: nAUC_P@10_std(MIRACL)
value: 21.317694028285104
- type: nAUC_P@1_diff1(MIRACL)
value: 22.04892839322977
- type: nAUC_P@1_max(MIRACL)
value: 32.51034181981298
- type: nAUC_P@1_std(MIRACL)
value: 14.343760356007765
- type: nAUC_P@20_diff1(MIRACL)
value: -26.064734965649322
- type: nAUC_P@20_max(MIRACL)
value: 34.10936682680113
- type: nAUC_P@20_std(MIRACL)
value: 20.31615496254574
- type: nAUC_P@3_diff1(MIRACL)
value: -10.903444655544746
- type: nAUC_P@3_max(MIRACL)
value: 34.33585029049373
- type: nAUC_P@3_std(MIRACL)
value: 18.620142249622834
- type: nAUC_P@5_diff1(MIRACL)
value: -18.454884144221385
- type: nAUC_P@5_max(MIRACL)
value: 35.620428961110036
- type: nAUC_P@5_std(MIRACL)
value: 20.265460635926893
- type: nAUC_Recall@1000_diff1(MIRACL)
value: -28.25716669219796
- type: nAUC_Recall@1000_max(MIRACL)
value: 59.88673755432144
- type: nAUC_Recall@1000_std(MIRACL)
value: 29.916576785101622
- type: nAUC_Recall@100_diff1(MIRACL)
value: -28.25716669219796
- type: nAUC_Recall@100_max(MIRACL)
value: 59.88673755432144
- type: nAUC_Recall@100_std(MIRACL)
value: 29.916576785101622
- type: nAUC_Recall@10_diff1(MIRACL)
value: -2.5731369116803466
- type: nAUC_Recall@10_max(MIRACL)
value: 34.37108435281944
- type: nAUC_Recall@10_std(MIRACL)
value: 20.744457001608925
- type: nAUC_Recall@1_diff1(MIRACL)
value: 34.30066727981356
- type: nAUC_Recall@1_max(MIRACL)
value: 11.096793012814972
- type: nAUC_Recall@1_std(MIRACL)
value: 4.298644702770651
- type: nAUC_Recall@20_diff1(MIRACL)
value: -13.667980220614172
- type: nAUC_Recall@20_max(MIRACL)
value: 44.947659106700044
- type: nAUC_Recall@20_std(MIRACL)
value: 29.413435369376923
- type: nAUC_Recall@3_diff1(MIRACL)
value: 15.838199908854786
- type: nAUC_Recall@3_max(MIRACL)
value: 17.368565662731196
- type: nAUC_Recall@3_std(MIRACL)
value: 10.538072940876807
- type: nAUC_Recall@5_diff1(MIRACL)
value: 8.199967584892176
- type: nAUC_Recall@5_max(MIRACL)
value: 23.500985460573578
- type: nAUC_Recall@5_std(MIRACL)
value: 13.477424183539433
- task:
type: Retrieval
dataset:
name: MTEB MIRACLRetrieval (ru)
type: miracl/mmteb-miracl
config: ru
split: dev
revision: main
metrics:
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value: 52.211
- type: map_at_1
value: 23.238
- type: map_at_10
value: 41.559000000000005
- type: map_at_100
value: 44.757999999999996
- type: map_at_1000
value: 44.861000000000004
- type: map_at_20
value: 43.461
- type: map_at_3
value: 34.593
- type: map_at_5
value: 38.056
- type: mrr_at_1
value: 47.04472843450479
- type: mrr_at_10
value: 59.587485420153286
- type: mrr_at_100
value: 60.17662556783717
- type: mrr_at_1000
value: 60.1850174860852
- type: mrr_at_20
value: 60.003979383733544
- type: mrr_at_3
value: 56.62939297124608
- type: mrr_at_5
value: 58.33067092651768
- type: nauc_map_at_1000_diff1
value: 26.665139374258256
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value: 20.20801190375824
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value: 3.35434510540552
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value: 26.691816652639787
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value: 3.371679544337864
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value: 27.24904607990151
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value: 26.8513788979128
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value: 2.2475091146613564
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value: 28.911815196615866
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value: 15.474121149651292
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value: -1.0664535264565158
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value: 27.772031743222787
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value: 16.241638808384145
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value: -0.6044307972013538
- type: nauc_mrr_at_1000_diff1
value: 26.66563442138901
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value: 27.74734004586503
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value: 10.663042801330587
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value: 26.66809693875436
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value: 10.671838040923266
- type: nauc_mrr_at_10_diff1
value: 26.587658592417736
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value: 27.872712998242328
- type: nauc_mrr_at_10_std
value: 10.979716151856918
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value: 24.98212676568516
- type: nauc_mrr_at_1_std
value: 6.094206809391165
- type: nauc_mrr_at_20_diff1
value: 26.52396413399926
- type: nauc_mrr_at_20_max
value: 27.720568784204847
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value: 10.749903126459412
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value: 26.993782403961802
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- type: nauc_mrr_at_3_std
value: 10.526250026174825
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- type: nauc_mrr_at_5_max
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- type: nauc_mrr_at_5_std
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- type: nauc_ndcg_at_1000_diff1
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- type: nauc_precision_at_3_max
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- type: nauc_precision_at_3_std
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_10_std
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- type: nauc_recall_at_1_std
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- type: nauc_recall_at_20_std
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- type: nauc_recall_at_3_std
value: -0.42190904806759966
- type: nauc_recall_at_5_diff1
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- type: nauc_recall_at_5_max
value: 12.324541527710025
- type: nauc_recall_at_5_std
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- type: ndcg_at_1
value: 47.044999999999995
- type: ndcg_at_10
value: 52.211
- type: ndcg_at_100
value: 60.777
- type: ndcg_at_1000
value: 61.951
- type: ndcg_at_20
value: 56.215
- type: ndcg_at_3
value: 45.871
- type: ndcg_at_5
value: 47.643
- type: precision_at_1
value: 47.044999999999995
- type: precision_at_10
value: 16.062
- type: precision_at_100
value: 2.563
- type: precision_at_1000
value: 0.27899999999999997
- type: precision_at_20
value: 9.9
- type: precision_at_3
value: 31.575999999999997
- type: precision_at_5
value: 24.153
- type: recall_at_1
value: 23.238
- type: recall_at_10
value: 63.479
- type: recall_at_100
value: 91.51899999999999
- type: recall_at_1000
value: 97.906
- type: recall_at_20
value: 74.705
- type: recall_at_3
value: 42.082
- type: recall_at_5
value: 50.708
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ru)
type: mteb/amazon_massive_intent
config: ru
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 68.61466039004706
- type: f1
value: 63.790707574282045
- type: f1_weighted
value: 67.28456899088164
- type: main_score
value: 68.61466039004706
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 77.97579018157364
- type: f1
value: 76.31497051309336
- type: f1_weighted
value: 77.54198422119202
- type: main_score
value: 77.97579018157364
- task:
type: STS
dataset:
name: MTEB RUParaPhraserSTS
type: merionum/ru_paraphraser
config: default
split: test
revision: 43265056790b8f7c59e0139acb4be0a8dad2c8f4
metrics:
- type: cosine_pearson
value: 62.072853635744465
- type: cosine_spearman
value: 68.32627155640247
- type: euclidean_pearson
value: 65.56072460948485
- type: euclidean_spearman
value: 68.32632364995054
- type: main_score
value: 68.32627155640247
- type: manhattan_pearson
value: 65.54799770948776
- type: manhattan_spearman
value: 68.2428132570697
- type: pearson
value: 62.072853635744465
- type: spearman
value: 68.32627155640247
- task:
type: Retrieval
dataset:
name: MTEB RiaNewsRetrieval
type: ai-forever/ria-news-retrieval
config: default
split: test
revision: 82374b0bbacda6114f39ff9c5b925fa1512ca5d7
metrics:
- type: main_score
value: 79.42399999999999
- type: map_at_1
value: 67.42
- type: map_at_10
value: 75.81700000000001
- type: map_at_100
value: 76.103
- type: map_at_1000
value: 76.11099999999999
- type: map_at_20
value: 76.011
- type: map_at_3
value: 74.38
- type: map_at_5
value: 75.31400000000001
- type: mrr_at_1
value: 67.42
- type: mrr_at_10
value: 75.81702380952322
- type: mrr_at_100
value: 76.10294206257022
- type: mrr_at_1000
value: 76.11127333184083
- type: mrr_at_20
value: 76.01092756817413
- type: mrr_at_3
value: 74.37999999999947
- type: mrr_at_5
value: 75.31449999999931
- type: nauc_map_at_1000_diff1
value: 74.47312749692254
- type: nauc_map_at_1000_max
value: 24.255650636762592
- type: nauc_map_at_1000_std
value: -13.538045103707466
- type: nauc_map_at_100_diff1
value: 74.46935527123232
- type: nauc_map_at_100_max
value: 24.260637479032273
- type: nauc_map_at_100_std
value: -13.526893488105108
- type: nauc_map_at_10_diff1
value: 74.37904649319015
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- type: nauc_map_at_10_std
value: -13.673101053529166
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value: 21.561634939014
- type: nauc_map_at_1_std
value: -15.302925641163046
- type: nauc_map_at_20_diff1
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value: -13.533874833150467
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value: -14.053104049162751
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- type: nauc_map_at_5_std
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dataset:
name: MTEB RuBQReranking
type: ai-forever/rubq-reranking
config: default
split: test
revision: 2e96b8f098fa4b0950fc58eacadeb31c0d0c7fa2
metrics:
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dataset:
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type: ai-forever/rubq-retrieval
config: default
split: test
revision: e19b6ffa60b3bc248e0b41f4cc37c26a55c2a67b
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type: Classification
dataset:
name: MTEB RuReviewsClassification
type: ai-forever/ru-reviews-classification
config: default
split: test
revision: f6d2c31f4dc6b88f468552750bfec05b4b41b05a
metrics:
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type: STS
dataset:
name: MTEB RuSTSBenchmarkSTS
type: ai-forever/ru-stsbenchmark-sts
config: default
split: test
revision: 7cf24f325c6da6195df55bef3d86b5e0616f3018
metrics:
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type: Classification
dataset:
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type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
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dataset:
name: MTEB RuSciBenchGRNTIClusteringP2P
type: ai-forever/ru-scibench-grnti-classification
config: default
split: test
revision: 673a610d6d3dd91a547a0d57ae1b56f37ebbf6a1
metrics:
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type: Classification
dataset:
name: MTEB RuSciBenchOECDClassification
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
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dataset:
name: MTEB RuSciBenchOECDClusteringP2P
type: ai-forever/ru-scibench-oecd-classification
config: default
split: test
revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471
metrics:
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dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 64.40621455763392
- type: manhattan_spearman
value: 66.99408273892949
- type: pearson
value: 64.95220904597029
- type: spearman
value: 67.35282990065247
- task:
type: MultilabelClassification
dataset:
name: MTEB SensitiveTopicsClassification
type: ai-forever/sensitive-topics-classification
config: default
split: test
revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2
metrics:
- type: accuracy
value: 29.624023437500004
- type: f1
value: 33.214028020582894
- type: lrap
value: 44.53599717881868
- type: main_score
value: 29.624023437500004
- task:
type: PairClassification
dataset:
name: MTEB TERRa
type: ai-forever/terra-pairclassification
config: default
split: dev
revision: 7b58f24536063837d644aab9a023c62199b2a612
metrics:
- type: cosine_accuracy
value: 57.98045602605863
- type: cosine_accuracy_threshold
value: 83.04829597473145
- type: cosine_ap
value: 55.56580974377611
- type: cosine_f1
value: 66.9603524229075
- type: cosine_f1_threshold
value: 73.216313123703
- type: cosine_precision
value: 50.498338870431894
- type: cosine_recall
value: 99.34640522875817
- type: dot_accuracy
value: 57.98045602605863
- type: dot_accuracy_threshold
value: 83.04829597473145
- type: dot_ap
value: 55.56580974377611
- type: dot_f1
value: 66.9603524229075
- type: dot_f1_threshold
value: 73.21631908416748
- type: dot_precision
value: 50.498338870431894
- type: dot_recall
value: 99.34640522875817
- type: euclidean_accuracy
value: 57.98045602605863
- type: euclidean_accuracy_threshold
value: 58.226633071899414
- type: euclidean_ap
value: 55.56580974377611
- type: euclidean_f1
value: 66.9603524229075
- type: euclidean_f1_threshold
value: 73.18969368934631
- type: euclidean_precision
value: 50.498338870431894
- type: euclidean_recall
value: 99.34640522875817
- type: main_score
value: 55.56580974377611
- type: manhattan_accuracy
value: 57.98045602605863
- type: manhattan_accuracy_threshold
value: 1336.6012573242188
- type: manhattan_ap
value: 55.5371135438789
- type: manhattan_f1
value: 66.95842450765863
- type: manhattan_f1_threshold
value: 1720.5078125
- type: manhattan_precision
value: 50.32894736842105
- type: manhattan_recall
value: 100.0
- type: max_ap
value: 55.56580974377611
- type: max_f1
value: 66.9603524229075
- type: max_precision
value: 50.498338870431894
- type: max_recall
value: 100.0
- type: similarity_accuracy
value: 57.98045602605863
- type: similarity_accuracy_threshold
value: 83.04829597473145
- type: similarity_ap
value: 55.56580974377611
- type: similarity_f1
value: 66.9603524229075
- type: similarity_f1_threshold
value: 73.216313123703
- type: similarity_precision
value: 50.498338870431894
- type: similarity_recall
value: 99.34640522875817
---
-
<h1 align="center">KaLM-Embedding</h1>
**KaLM-Embedding** is a series of embedding models adapted from auto-regressive LLMs with superior training data.
KaLM-embedding-multilingual-mini is trained from [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) with massive weakly-supervised pre-training and supervised fine-tuning data.
## 📑 Open-source Plan
- [x] Model Checkpoint
- [x] [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1)
- [x] [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1)
- [x] [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5)
- [ ] KaLM-embedding-multilingual-max-v1
- [x] Training and Evaluation Code: [HITsz-TMG/KaLM-Embedding](https://github.com/HITsz-TMG/KaLM-Embedding)
- [x] Technical Report: [KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model](https://arxiv.org/abs/2501.01028)
- [ ] Training Data
## Evaluation
| Model Name | Model Size | C-MTEB(35) | MTEB(56) | avg
|:----:|:---:|:---:|:---:|:---:|
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 560M | 58.81 | 61.5 | 60.16
| [bge-m3 (dense)](https://huggingface.co/BAAI/bge-m3) | 560M | 60.80 | 59.84 | 60.32
| [gte-multilingual-base (dense)](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) | **305M** | 62.72 | 61.40 | 62.06
| [KaLM-embedding-multilingual-mini-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-v1) | 494M | 62.31 | 61.87 | 62.09
| [KaLM-embedding-multilingual-mini-instruct-v1](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1) | 494M | 63.57 | 64.74 | 64.16
| [KaLM-embedding-multilingual-mini-instruct-v1.5](https://huggingface.co/HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v1.5) | 494M | **64.13** | **64.94** | **64.53**
## Requirements
Since we have used the Qwen2 model, we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Usage
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code
model.max_seq_length = 512
embeddings = model.encode(
sentences,
normalize_embeddings=True,
batch_size=256,
show_progress_bar=True
)
print(embeddings)
```
<!-- We add instruction for asymmetric tasks: retrieval, reranking, classification and clustering. -->
We add instruction for classification and clustering.
If you want to add instruction to the query (no instruction for the corpus), you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME_OR_PATH}') # Do NOT set trust_remote_code
model.max_seq_length = 512
prompt = "Instruct: Classifying the category of french news. \n Query: "
embeddings = model.encode(
sentences,
prompt=prompt,
normalize_embeddings=True,
batch_size=256,
show_progress_bar=True
)
print(embeddings)
```
## Citation
Please cite the repo if you use the model or code in this repo.
```
@article{hu2025kalm,
title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},
journal={arXiv preprint arXiv:2501.01028},
year={2025}
}
```
## Contact
If you encounter any issue, feel free to contact us via the email: [email protected]
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
TheBloke/meditron-7B-AWQ | TheBloke | text-generation | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:epfl-llm/meditron-7b",
"base_model:quantized:epfl-llm/meditron-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | 2023-11-30T22:11:31 | 2023-11-30T22:26:18 | 35,850 | 2 | ---
base_model: epfl-llm/meditron-7b
datasets:
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
model_name: Meditron 7B
inference: false
model_creator: EPFL LLM Team
model_type: llama
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Meditron 7B - AWQ
- Model creator: [EPFL LLM Team](https://huggingface.co/epfl-llm)
- Original model: [Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b)
<!-- description start -->
## Description
This repo contains AWQ model files for [EPFL LLM Team's Meditron 7B](https://huggingface.co/epfl-llm/meditron-7b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/meditron-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/meditron-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/meditron-7B-GGUF)
* [EPFL LLM Team's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/epfl-llm/meditron-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/meditron-7B-AWQ/tree/main) | 4 | 128 | [Medical Medaow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/viewer/) | 4096 | 3.89 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/meditron-7B-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `meditron-7B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/meditron-7B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/meditron-7B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/meditron-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/meditron-7B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: EPFL LLM Team's Meditron 7B
<img width=50% src="meditron_LOGO.png" alt="Alt text" title="Meditron-logo">
# Model Card for Meditron-7B-v1.0
Meditron is a suite of open-source medical Large Language Models (LLMs).
Meditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a [new dataset](https://huggingface.co/datasets/epfl-llm/guidelines) of internationally-recognized medical guidelines, and general domain data from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
Meditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.
<details open>
<summary><strong>Advisory Notice</strong></summary>
<blockquote style="padding: 10px; margin: 0 0 10px; border-left: 5px solid #ddd;">
While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints.
We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings.
</blockquote>
</details>
## Model Details
- **Developed by:** [EPFL LLM Team](https://huggingface.co/epfl-llm)
- **Model type:** Causal decoder-only transformer language model
- **Language(s):** English (mainly)
- **Model License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Code License:** [APACHE 2.0 LICENSE](LICENSE)
- **Continue-pretrained from model:** [Llama-2-7B](https://huggingface.co/meta-llama/Llama-2-7b)
- **Context length:** 2K tokens
- **Input:** Text-only data
- **Output:** Model generates text only
- **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model's performance.
- **Knowledge Cutoff:** August 2023
### Model Sources
- **Repository:** [epflLLM/meditron](https://github.com/epfLLM/meditron)
- **Trainer:** [epflLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)
- **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)*
## Uses
Meditron-7B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to:
- Medical exam question answering
- Supporting differential diagnosis
- Disease information (symptoms, cause, treatment) query
- General health information query
### Direct Use
It is possible to use this model to generate text, which is useful for experimentation and understanding its capabilities.
It should not be used directly for production or work that may impact people.
### Downstream Use
Meditron-7B is a foundation model that can be finetuned, instruction-tuned, or RLHF-tuned for specific downstream tasks and applications.
The main way we have used this model is finetuning for downstream question-answering tasks, but we encourage using this model for additional applications.
Specific formatting needs to be followed to prompt our finetuned models, including the `<|im_start|>`, `<|im_end|>` tags, and `system`, `question`, `answer` identifiers.
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>question
{prompt}<|im_end|>
<|im_start|>answer
"""
**Note 1**: The above formatting is not required for running the base model (this repository)
**Note 2**: the above formatting is just an example of a finetuning template. This format is not a requirement if you use your own formatting option for the finetuning of the model.
To run proper generation with this base model, we recommend using a high-throughput and memory-efficient inference engine, such as [vLLM](https://github.com/vllm-project/vllm), with a UI that supports chat and text generation, such as [BetterChatGPT](https://github.com/ztjhz/BetterChatGPT)
To see more details about model deployment and generation, please see our [documentation](https://github.com/epfLLM/meditron/blob/main/deployment/README.md).
### Out-of-Scope Use
We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
## Truthfulness, Helpfulness, Risk, and Bias
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
We did an initial assessment of Meditron models' **Truthfulness** against baseline models and consumer-level medical models.
We use TruthfulQA (multiple choice) as the main evaluation benchmark.
We only focus on the categories that are relevant to the medical domain, including Health, Nutrition, Psychology, and Science.
For 7B models, we perform one-shot evaluations for consistent answer generation.
For 70B models, the evaluations are under the zero-shot setting.
Below, we report the detailed truthfulness performance of each category.
| | | | | | | | |
| --- | ------ |----- |----- |----- |----- |----- |----- |
|Category | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b |
|Health | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 |
|Nutrition | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 |
|Psychology| 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 |
|Science | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 |
|Avg | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 |
| | | | | | | |
For a more detailed performance analysis, please see our paper.
Significant research is still required to fully explore potential bias, fairness, and safety issues with this language model.
Please recognize that our evaluation on Meditron-7B's helpfulness, risk, and bias are highly limited.
Thus, as we noted in the safety notice, we strongly against any deployment in medical applications without further alignment process and rigorous evaluation!
### Recommendations
**IMPORTANT!**
Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.
While this model is capable of generating natural language text, we have only begun to explore this capability and its limitations.
Understanding these limitations is especially important in a domain like medicine.
Therefore, we strongly recommend against using this model in production for natural language generation or for professional purposes related to health and medicine.
## Training Details
### Training Data
Meditron’s domain-adaptive pre-training corpus GAP-Replay combines 48.1B tokens from four corpora:
- [**Clinical Guidelines**](https://huggingface.co/datasets/epfl-llm/guidelines): a new dataset of 46K internationally-recognized clinical practice guidelines from various healthcare-related sources, including hospitals and international organizations.
- **Medical Paper Abstracts**: 16.1M abstracts extracted from closed-access PubMed and PubMed Central papers.
- **Medical Papers**: full-text articles extracted from 5M publicly available PubMed and PubMed Central papers.
- **Replay Data**: 400M tokens of general domain pretraining data sampled from [RedPajama-v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
<img width=75% src="gap-replay.png" alt="Alt text" title="Meditron-logo">
#### Data Preprocessing
Please see the detailed preprocessing procedure in our paper.
### Training Procedure
We used the [Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) distributed training library, a derivative of Nvidia's Megatron LM project, to optimize training efficiency.
Hardware consists of 1 node of 8x NVIDIA A100 (80GB) SXM GPUs connected by NVLink and NVSwitch with a single Nvidia ConnectX-6 DX network card and equipped with 2 x AMD EPYC 7543 32-Core Processors and 512 GB of RAM.
Our three way parallelism scheme uses:
- Data Parallelism (DP -- different GPUs process different subsets of the batches) of 2,
- Pipeline Parallelism (PP -- different GPUs process different layers) of 4,
- Tensor Parallelism (TP -- different GPUs process different subtensors for matrix multiplication) of 1.
#### Training Hyperparameters
| | |
| --- | ------ |
| bf16 | true |
| lr | 3e-4 |
| eps | 1e-5 |
| betas | \[0.9, 0.95\] |
| clip_grad | 1 |
| weight decay | 0.1 |
| DP size | 16 |
| TP size | 4 |
| PP size | 1 |
| seq length | 2048 |
| lr scheduler | cosine|
| min lr | 1e-6 |
| warmup iteration | 2000 |
| micro batch size | 10 |
| global batch size | 1600 |
| | |
#### Sizes
The model was trained in September 2023.
The model architecture is exactly Llama 2, meaning
| | |
| --- | ------ |
| Model size | 7B |
| Hidden dimension | 4096 |
| Num. attention heads | 32 |
| Num. layers | 32 |
| | |
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data & Metrics
#### Testing Data
- [MedQA (USMLE)](https://huggingface.co/datasets/bigbio/med_qa)
- [MedMCQA](https://huggingface.co/datasets/medmcqa)
- [PubMedQA](https://huggingface.co/datasets/bigbio/pubmed_qa)
- [MMLU-Medical](https://huggingface.co/datasets/lukaemon/mmlu)
- [MedQA-4-Option](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
#### Metrics
- Accuracy: suite the evaluation of multiple-choice question-answering tasks.
### Results
We finetune meditron-7b, llama-2-7b, pmc-llama-7b on each benchmark (pubmedqa, medmcqa, medqa)'s training data individually.
We report the finetuned models' performance with top token selection as the inference mode.
For MMLU-Medical, models finetuned on MedMCQA are used for inference.
For MedQA-4-Option, models finetuned on MedQA are used for inference.
For a more detailed performance analysis, please see our paper.
| | | | | | |
| --- | ------ |----- |----- |----- |----- |
|Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* |
|MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 |
|PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 |
|MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 |
|MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 |
|MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 |
|Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 |
| | | | | | |
**Note**: models with * are already instruction-tuned, so we exclude them from further finetuning on any training data.
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 8 x NVIDIA A100 (80GB) SXM
- **Total GPU hours:** 588.8
- **Hardware Provider:** EPFL Research Computing Platform
- **Compute Region:** Switzerland
- **Carbon Emitted:** Switzerland has a carbon efficiency of 0.016 kgCO2/kWh (https://www.carbonfootprint.com/docs/2018_8_electricity_factors_august_2018_-_online_sources.pdf). 73.6 hours of 8 A100s means 588.8 hours at a TDP of 400W. Assuming a Power Usage effectiveness of 1.5, total emissions are estimated to be:
(400W / 1000W/kWh / GPU * 0.016 kgCO2/kWh * 73.6 h * 8 GPU) * 1.8 PUE = 6.8 kgCO2.
## Citation
**BibTeX:**
If you use Meditron or its training data, please cite our work:
```
@misc{chen2023meditron70b,
title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models},
author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
year={2023},
eprint={2311.16079},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@software{epfmedtrn,
author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut},
title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models},
month = November,
year = 2023,
url = {https://github.com/epfLLM/meditron}
}
```
| [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] |
hkunlp/instructor-xl | hkunlp | sentence-similarity | [
"sentence-transformers",
"pytorch",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"arxiv:2212.09741",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2022-12-20T06:07:18 | 2023-01-21T06:33:27 | 35,591 | 562 | ---
language: en
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
inference: false
model-index:
- name: final_xl_results
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 85.08955223880596
- type: ap
value: 52.66066378722476
- type: f1
value: 79.63340218960269
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 86.542
- type: ap
value: 81.92695193008987
- type: f1
value: 86.51466132573681
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 42.964
- type: f1
value: 41.43146249774862
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.872
- type: map_at_10
value: 46.342
- type: map_at_100
value: 47.152
- type: map_at_1000
value: 47.154
- type: map_at_3
value: 41.216
- type: map_at_5
value: 44.035999999999994
- type: mrr_at_1
value: 30.939
- type: mrr_at_10
value: 46.756
- type: mrr_at_100
value: 47.573
- type: mrr_at_1000
value: 47.575
- type: mrr_at_3
value: 41.548
- type: mrr_at_5
value: 44.425
- type: ndcg_at_1
value: 29.872
- type: ndcg_at_10
value: 55.65
- type: ndcg_at_100
value: 58.88099999999999
- type: ndcg_at_1000
value: 58.951
- type: ndcg_at_3
value: 45.0
- type: ndcg_at_5
value: 50.09
- type: precision_at_1
value: 29.872
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.658
- type: precision_at_5
value: 13.669999999999998
- type: recall_at_1
value: 29.872
- type: recall_at_10
value: 85.491
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 55.974000000000004
- type: recall_at_5
value: 68.35
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 42.452729850641276
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.21141846480423
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 65.34710928952622
- type: mrr
value: 77.61124301983028
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.15312230525639
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 82.66233766233766
- type: f1
value: 82.04175284777669
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.36697339826455
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.551241447593092
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.797000000000004
- type: map_at_10
value: 48.46
- type: map_at_100
value: 49.968
- type: map_at_1000
value: 50.080000000000005
- type: map_at_3
value: 44.71
- type: map_at_5
value: 46.592
- type: mrr_at_1
value: 45.494
- type: mrr_at_10
value: 54.747
- type: mrr_at_100
value: 55.43599999999999
- type: mrr_at_1000
value: 55.464999999999996
- type: mrr_at_3
value: 52.361000000000004
- type: mrr_at_5
value: 53.727000000000004
- type: ndcg_at_1
value: 45.494
- type: ndcg_at_10
value: 54.989
- type: ndcg_at_100
value: 60.096000000000004
- type: ndcg_at_1000
value: 61.58
- type: ndcg_at_3
value: 49.977
- type: ndcg_at_5
value: 51.964999999999996
- type: precision_at_1
value: 45.494
- type: precision_at_10
value: 10.558
- type: precision_at_100
value: 1.6049999999999998
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 23.796
- type: precision_at_5
value: 16.881
- type: recall_at_1
value: 36.797000000000004
- type: recall_at_10
value: 66.83
- type: recall_at_100
value: 88.34100000000001
- type: recall_at_1000
value: 97.202
- type: recall_at_3
value: 51.961999999999996
- type: recall_at_5
value: 57.940000000000005
- type: map_at_1
value: 32.597
- type: map_at_10
value: 43.424
- type: map_at_100
value: 44.78
- type: map_at_1000
value: 44.913
- type: map_at_3
value: 40.315
- type: map_at_5
value: 41.987
- type: mrr_at_1
value: 40.382
- type: mrr_at_10
value: 49.219
- type: mrr_at_100
value: 49.895
- type: mrr_at_1000
value: 49.936
- type: mrr_at_3
value: 46.996
- type: mrr_at_5
value: 48.231
- type: ndcg_at_1
value: 40.382
- type: ndcg_at_10
value: 49.318
- type: ndcg_at_100
value: 53.839999999999996
- type: ndcg_at_1000
value: 55.82899999999999
- type: ndcg_at_3
value: 44.914
- type: ndcg_at_5
value: 46.798
- type: precision_at_1
value: 40.382
- type: precision_at_10
value: 9.274000000000001
- type: precision_at_100
value: 1.497
- type: precision_at_1000
value: 0.198
- type: precision_at_3
value: 21.592
- type: precision_at_5
value: 15.159
- type: recall_at_1
value: 32.597
- type: recall_at_10
value: 59.882000000000005
- type: recall_at_100
value: 78.446
- type: recall_at_1000
value: 90.88000000000001
- type: recall_at_3
value: 46.9
- type: recall_at_5
value: 52.222
- type: map_at_1
value: 43.8
- type: map_at_10
value: 57.293000000000006
- type: map_at_100
value: 58.321
- type: map_at_1000
value: 58.361
- type: map_at_3
value: 53.839999999999996
- type: map_at_5
value: 55.838
- type: mrr_at_1
value: 49.592000000000006
- type: mrr_at_10
value: 60.643
- type: mrr_at_100
value: 61.23499999999999
- type: mrr_at_1000
value: 61.251999999999995
- type: mrr_at_3
value: 58.265
- type: mrr_at_5
value: 59.717
- type: ndcg_at_1
value: 49.592000000000006
- type: ndcg_at_10
value: 63.364
- type: ndcg_at_100
value: 67.167
- type: ndcg_at_1000
value: 67.867
- type: ndcg_at_3
value: 57.912
- type: ndcg_at_5
value: 60.697
- type: precision_at_1
value: 49.592000000000006
- type: precision_at_10
value: 10.088
- type: precision_at_100
value: 1.2930000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.789
- type: precision_at_5
value: 17.541999999999998
- type: recall_at_1
value: 43.8
- type: recall_at_10
value: 77.635
- type: recall_at_100
value: 93.748
- type: recall_at_1000
value: 98.468
- type: recall_at_3
value: 63.223
- type: recall_at_5
value: 70.122
- type: map_at_1
value: 27.721
- type: map_at_10
value: 35.626999999999995
- type: map_at_100
value: 36.719
- type: map_at_1000
value: 36.8
- type: map_at_3
value: 32.781
- type: map_at_5
value: 34.333999999999996
- type: mrr_at_1
value: 29.604999999999997
- type: mrr_at_10
value: 37.564
- type: mrr_at_100
value: 38.505
- type: mrr_at_1000
value: 38.565
- type: mrr_at_3
value: 34.727000000000004
- type: mrr_at_5
value: 36.207
- type: ndcg_at_1
value: 29.604999999999997
- type: ndcg_at_10
value: 40.575
- type: ndcg_at_100
value: 45.613
- type: ndcg_at_1000
value: 47.676
- type: ndcg_at_3
value: 34.811
- type: ndcg_at_5
value: 37.491
- type: precision_at_1
value: 29.604999999999997
- type: precision_at_10
value: 6.1690000000000005
- type: precision_at_100
value: 0.906
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.237
- type: precision_at_5
value: 10.056
- type: recall_at_1
value: 27.721
- type: recall_at_10
value: 54.041
- type: recall_at_100
value: 76.62299999999999
- type: recall_at_1000
value: 92.134
- type: recall_at_3
value: 38.582
- type: recall_at_5
value: 44.989000000000004
- type: map_at_1
value: 16.553
- type: map_at_10
value: 25.384
- type: map_at_100
value: 26.655
- type: map_at_1000
value: 26.778000000000002
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.119
- type: mrr_at_1
value: 20.149
- type: mrr_at_10
value: 29.705
- type: mrr_at_100
value: 30.672
- type: mrr_at_1000
value: 30.737
- type: mrr_at_3
value: 27.032
- type: mrr_at_5
value: 28.369
- type: ndcg_at_1
value: 20.149
- type: ndcg_at_10
value: 30.843999999999998
- type: ndcg_at_100
value: 36.716
- type: ndcg_at_1000
value: 39.495000000000005
- type: ndcg_at_3
value: 25.918999999999997
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.149
- type: precision_at_10
value: 5.858
- type: precision_at_100
value: 1.009
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 12.645000000000001
- type: precision_at_5
value: 9.179
- type: recall_at_1
value: 16.553
- type: recall_at_10
value: 43.136
- type: recall_at_100
value: 68.562
- type: recall_at_1000
value: 88.208
- type: recall_at_3
value: 29.493000000000002
- type: recall_at_5
value: 34.751
- type: map_at_1
value: 28.000999999999998
- type: map_at_10
value: 39.004
- type: map_at_100
value: 40.461999999999996
- type: map_at_1000
value: 40.566
- type: map_at_3
value: 35.805
- type: map_at_5
value: 37.672
- type: mrr_at_1
value: 33.782000000000004
- type: mrr_at_10
value: 44.702
- type: mrr_at_100
value: 45.528
- type: mrr_at_1000
value: 45.576
- type: mrr_at_3
value: 42.14
- type: mrr_at_5
value: 43.651
- type: ndcg_at_1
value: 33.782000000000004
- type: ndcg_at_10
value: 45.275999999999996
- type: ndcg_at_100
value: 50.888
- type: ndcg_at_1000
value: 52.879
- type: ndcg_at_3
value: 40.191
- type: ndcg_at_5
value: 42.731
- type: precision_at_1
value: 33.782000000000004
- type: precision_at_10
value: 8.200000000000001
- type: precision_at_100
value: 1.287
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 19.185
- type: precision_at_5
value: 13.667000000000002
- type: recall_at_1
value: 28.000999999999998
- type: recall_at_10
value: 58.131
- type: recall_at_100
value: 80.869
- type: recall_at_1000
value: 93.931
- type: recall_at_3
value: 44.161
- type: recall_at_5
value: 50.592000000000006
- type: map_at_1
value: 28.047
- type: map_at_10
value: 38.596000000000004
- type: map_at_100
value: 40.116
- type: map_at_1000
value: 40.232
- type: map_at_3
value: 35.205
- type: map_at_5
value: 37.076
- type: mrr_at_1
value: 34.932
- type: mrr_at_10
value: 44.496
- type: mrr_at_100
value: 45.47
- type: mrr_at_1000
value: 45.519999999999996
- type: mrr_at_3
value: 41.743
- type: mrr_at_5
value: 43.352000000000004
- type: ndcg_at_1
value: 34.932
- type: ndcg_at_10
value: 44.901
- type: ndcg_at_100
value: 50.788999999999994
- type: ndcg_at_1000
value: 52.867
- type: ndcg_at_3
value: 39.449
- type: ndcg_at_5
value: 41.929
- type: precision_at_1
value: 34.932
- type: precision_at_10
value: 8.311
- type: precision_at_100
value: 1.3050000000000002
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 18.836
- type: precision_at_5
value: 13.447000000000001
- type: recall_at_1
value: 28.047
- type: recall_at_10
value: 57.717
- type: recall_at_100
value: 82.182
- type: recall_at_1000
value: 95.82000000000001
- type: recall_at_3
value: 42.448
- type: recall_at_5
value: 49.071
- type: map_at_1
value: 27.861250000000005
- type: map_at_10
value: 37.529583333333335
- type: map_at_100
value: 38.7915
- type: map_at_1000
value: 38.90558333333335
- type: map_at_3
value: 34.57333333333333
- type: map_at_5
value: 36.187166666666656
- type: mrr_at_1
value: 32.88291666666666
- type: mrr_at_10
value: 41.79750000000001
- type: mrr_at_100
value: 42.63183333333333
- type: mrr_at_1000
value: 42.68483333333333
- type: mrr_at_3
value: 39.313750000000006
- type: mrr_at_5
value: 40.70483333333333
- type: ndcg_at_1
value: 32.88291666666666
- type: ndcg_at_10
value: 43.09408333333333
- type: ndcg_at_100
value: 48.22158333333333
- type: ndcg_at_1000
value: 50.358000000000004
- type: ndcg_at_3
value: 38.129583333333336
- type: ndcg_at_5
value: 40.39266666666666
- type: precision_at_1
value: 32.88291666666666
- type: precision_at_10
value: 7.5584999999999996
- type: precision_at_100
value: 1.1903333333333332
- type: precision_at_1000
value: 0.15658333333333332
- type: precision_at_3
value: 17.495916666666666
- type: precision_at_5
value: 12.373833333333332
- type: recall_at_1
value: 27.861250000000005
- type: recall_at_10
value: 55.215916666666665
- type: recall_at_100
value: 77.392
- type: recall_at_1000
value: 92.04908333333334
- type: recall_at_3
value: 41.37475
- type: recall_at_5
value: 47.22908333333333
- type: map_at_1
value: 25.064999999999998
- type: map_at_10
value: 31.635999999999996
- type: map_at_100
value: 32.596000000000004
- type: map_at_1000
value: 32.695
- type: map_at_3
value: 29.612
- type: map_at_5
value: 30.768
- type: mrr_at_1
value: 28.528
- type: mrr_at_10
value: 34.717
- type: mrr_at_100
value: 35.558
- type: mrr_at_1000
value: 35.626000000000005
- type: mrr_at_3
value: 32.745000000000005
- type: mrr_at_5
value: 33.819
- type: ndcg_at_1
value: 28.528
- type: ndcg_at_10
value: 35.647
- type: ndcg_at_100
value: 40.207
- type: ndcg_at_1000
value: 42.695
- type: ndcg_at_3
value: 31.878
- type: ndcg_at_5
value: 33.634
- type: precision_at_1
value: 28.528
- type: precision_at_10
value: 5.46
- type: precision_at_100
value: 0.84
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 13.547999999999998
- type: precision_at_5
value: 9.325
- type: recall_at_1
value: 25.064999999999998
- type: recall_at_10
value: 45.096000000000004
- type: recall_at_100
value: 65.658
- type: recall_at_1000
value: 84.128
- type: recall_at_3
value: 34.337
- type: recall_at_5
value: 38.849000000000004
- type: map_at_1
value: 17.276
- type: map_at_10
value: 24.535
- type: map_at_100
value: 25.655
- type: map_at_1000
value: 25.782
- type: map_at_3
value: 22.228
- type: map_at_5
value: 23.612
- type: mrr_at_1
value: 21.266
- type: mrr_at_10
value: 28.474
- type: mrr_at_100
value: 29.398000000000003
- type: mrr_at_1000
value: 29.482000000000003
- type: mrr_at_3
value: 26.245
- type: mrr_at_5
value: 27.624
- type: ndcg_at_1
value: 21.266
- type: ndcg_at_10
value: 29.087000000000003
- type: ndcg_at_100
value: 34.374
- type: ndcg_at_1000
value: 37.433
- type: ndcg_at_3
value: 25.040000000000003
- type: ndcg_at_5
value: 27.116
- type: precision_at_1
value: 21.266
- type: precision_at_10
value: 5.258
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.849
- type: precision_at_5
value: 8.699
- type: recall_at_1
value: 17.276
- type: recall_at_10
value: 38.928000000000004
- type: recall_at_100
value: 62.529
- type: recall_at_1000
value: 84.44800000000001
- type: recall_at_3
value: 27.554000000000002
- type: recall_at_5
value: 32.915
- type: map_at_1
value: 27.297
- type: map_at_10
value: 36.957
- type: map_at_100
value: 38.252
- type: map_at_1000
value: 38.356
- type: map_at_3
value: 34.121
- type: map_at_5
value: 35.782000000000004
- type: mrr_at_1
value: 32.275999999999996
- type: mrr_at_10
value: 41.198
- type: mrr_at_100
value: 42.131
- type: mrr_at_1000
value: 42.186
- type: mrr_at_3
value: 38.557
- type: mrr_at_5
value: 40.12
- type: ndcg_at_1
value: 32.275999999999996
- type: ndcg_at_10
value: 42.516
- type: ndcg_at_100
value: 48.15
- type: ndcg_at_1000
value: 50.344
- type: ndcg_at_3
value: 37.423
- type: ndcg_at_5
value: 39.919
- type: precision_at_1
value: 32.275999999999996
- type: precision_at_10
value: 7.155
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 17.163999999999998
- type: precision_at_5
value: 12.127
- type: recall_at_1
value: 27.297
- type: recall_at_10
value: 55.238
- type: recall_at_100
value: 79.2
- type: recall_at_1000
value: 94.258
- type: recall_at_3
value: 41.327000000000005
- type: recall_at_5
value: 47.588
- type: map_at_1
value: 29.142000000000003
- type: map_at_10
value: 38.769
- type: map_at_100
value: 40.292
- type: map_at_1000
value: 40.510000000000005
- type: map_at_3
value: 35.39
- type: map_at_5
value: 37.009
- type: mrr_at_1
value: 34.19
- type: mrr_at_10
value: 43.418
- type: mrr_at_100
value: 44.132
- type: mrr_at_1000
value: 44.175
- type: mrr_at_3
value: 40.547
- type: mrr_at_5
value: 42.088
- type: ndcg_at_1
value: 34.19
- type: ndcg_at_10
value: 45.14
- type: ndcg_at_100
value: 50.364
- type: ndcg_at_1000
value: 52.481
- type: ndcg_at_3
value: 39.466
- type: ndcg_at_5
value: 41.772
- type: precision_at_1
value: 34.19
- type: precision_at_10
value: 8.715
- type: precision_at_100
value: 1.6150000000000002
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 18.248
- type: precision_at_5
value: 13.161999999999999
- type: recall_at_1
value: 29.142000000000003
- type: recall_at_10
value: 57.577999999999996
- type: recall_at_100
value: 81.428
- type: recall_at_1000
value: 94.017
- type: recall_at_3
value: 41.402
- type: recall_at_5
value: 47.695
- type: map_at_1
value: 22.039
- type: map_at_10
value: 30.669999999999998
- type: map_at_100
value: 31.682
- type: map_at_1000
value: 31.794
- type: map_at_3
value: 28.139999999999997
- type: map_at_5
value: 29.457
- type: mrr_at_1
value: 24.399
- type: mrr_at_10
value: 32.687
- type: mrr_at_100
value: 33.622
- type: mrr_at_1000
value: 33.698
- type: mrr_at_3
value: 30.407
- type: mrr_at_5
value: 31.552999999999997
- type: ndcg_at_1
value: 24.399
- type: ndcg_at_10
value: 35.472
- type: ndcg_at_100
value: 40.455000000000005
- type: ndcg_at_1000
value: 43.15
- type: ndcg_at_3
value: 30.575000000000003
- type: ndcg_at_5
value: 32.668
- type: precision_at_1
value: 24.399
- type: precision_at_10
value: 5.656
- type: precision_at_100
value: 0.874
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 13.062000000000001
- type: precision_at_5
value: 9.242
- type: recall_at_1
value: 22.039
- type: recall_at_10
value: 48.379
- type: recall_at_100
value: 71.11800000000001
- type: recall_at_1000
value: 91.095
- type: recall_at_3
value: 35.108
- type: recall_at_5
value: 40.015
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.144
- type: map_at_10
value: 18.238
- type: map_at_100
value: 20.143
- type: map_at_1000
value: 20.346
- type: map_at_3
value: 14.809
- type: map_at_5
value: 16.567999999999998
- type: mrr_at_1
value: 22.671
- type: mrr_at_10
value: 34.906
- type: mrr_at_100
value: 35.858000000000004
- type: mrr_at_1000
value: 35.898
- type: mrr_at_3
value: 31.238
- type: mrr_at_5
value: 33.342
- type: ndcg_at_1
value: 22.671
- type: ndcg_at_10
value: 26.540000000000003
- type: ndcg_at_100
value: 34.138000000000005
- type: ndcg_at_1000
value: 37.72
- type: ndcg_at_3
value: 20.766000000000002
- type: ndcg_at_5
value: 22.927
- type: precision_at_1
value: 22.671
- type: precision_at_10
value: 8.619
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.592
- type: precision_at_5
value: 12.43
- type: recall_at_1
value: 10.144
- type: recall_at_10
value: 33.46
- type: recall_at_100
value: 59.758
- type: recall_at_1000
value: 79.704
- type: recall_at_3
value: 19.604
- type: recall_at_5
value: 25.367
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.654
- type: map_at_10
value: 18.506
- type: map_at_100
value: 26.412999999999997
- type: map_at_1000
value: 28.13
- type: map_at_3
value: 13.379
- type: map_at_5
value: 15.529000000000002
- type: mrr_at_1
value: 66.0
- type: mrr_at_10
value: 74.13
- type: mrr_at_100
value: 74.48700000000001
- type: mrr_at_1000
value: 74.49799999999999
- type: mrr_at_3
value: 72.75
- type: mrr_at_5
value: 73.762
- type: ndcg_at_1
value: 54.50000000000001
- type: ndcg_at_10
value: 40.236
- type: ndcg_at_100
value: 44.690999999999995
- type: ndcg_at_1000
value: 52.195
- type: ndcg_at_3
value: 45.632
- type: ndcg_at_5
value: 42.952
- type: precision_at_1
value: 66.0
- type: precision_at_10
value: 31.724999999999998
- type: precision_at_100
value: 10.299999999999999
- type: precision_at_1000
value: 2.194
- type: precision_at_3
value: 48.75
- type: precision_at_5
value: 41.6
- type: recall_at_1
value: 8.654
- type: recall_at_10
value: 23.74
- type: recall_at_100
value: 50.346999999999994
- type: recall_at_1000
value: 74.376
- type: recall_at_3
value: 14.636
- type: recall_at_5
value: 18.009
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 53.245
- type: f1
value: 48.74520523753552
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 51.729
- type: map_at_10
value: 63.904
- type: map_at_100
value: 64.363
- type: map_at_1000
value: 64.38199999999999
- type: map_at_3
value: 61.393
- type: map_at_5
value: 63.02100000000001
- type: mrr_at_1
value: 55.686
- type: mrr_at_10
value: 67.804
- type: mrr_at_100
value: 68.15299999999999
- type: mrr_at_1000
value: 68.161
- type: mrr_at_3
value: 65.494
- type: mrr_at_5
value: 67.01599999999999
- type: ndcg_at_1
value: 55.686
- type: ndcg_at_10
value: 70.025
- type: ndcg_at_100
value: 72.011
- type: ndcg_at_1000
value: 72.443
- type: ndcg_at_3
value: 65.32900000000001
- type: ndcg_at_5
value: 68.05600000000001
- type: precision_at_1
value: 55.686
- type: precision_at_10
value: 9.358
- type: precision_at_100
value: 1.05
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 26.318
- type: precision_at_5
value: 17.321
- type: recall_at_1
value: 51.729
- type: recall_at_10
value: 85.04
- type: recall_at_100
value: 93.777
- type: recall_at_1000
value: 96.824
- type: recall_at_3
value: 72.521
- type: recall_at_5
value: 79.148
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.765
- type: map_at_10
value: 39.114
- type: map_at_100
value: 40.987
- type: map_at_1000
value: 41.155
- type: map_at_3
value: 34.028000000000006
- type: map_at_5
value: 36.925000000000004
- type: mrr_at_1
value: 46.451
- type: mrr_at_10
value: 54.711
- type: mrr_at_100
value: 55.509
- type: mrr_at_1000
value: 55.535000000000004
- type: mrr_at_3
value: 52.649
- type: mrr_at_5
value: 53.729000000000006
- type: ndcg_at_1
value: 46.451
- type: ndcg_at_10
value: 46.955999999999996
- type: ndcg_at_100
value: 53.686
- type: ndcg_at_1000
value: 56.230000000000004
- type: ndcg_at_3
value: 43.374
- type: ndcg_at_5
value: 44.372
- type: precision_at_1
value: 46.451
- type: precision_at_10
value: 13.256
- type: precision_at_100
value: 2.019
- type: precision_at_1000
value: 0.247
- type: precision_at_3
value: 29.115000000000002
- type: precision_at_5
value: 21.389
- type: recall_at_1
value: 23.765
- type: recall_at_10
value: 53.452999999999996
- type: recall_at_100
value: 78.828
- type: recall_at_1000
value: 93.938
- type: recall_at_3
value: 39.023
- type: recall_at_5
value: 45.18
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.918000000000003
- type: map_at_10
value: 46.741
- type: map_at_100
value: 47.762
- type: map_at_1000
value: 47.849000000000004
- type: map_at_3
value: 43.578
- type: map_at_5
value: 45.395
- type: mrr_at_1
value: 63.834999999999994
- type: mrr_at_10
value: 71.312
- type: mrr_at_100
value: 71.695
- type: mrr_at_1000
value: 71.714
- type: mrr_at_3
value: 69.82000000000001
- type: mrr_at_5
value: 70.726
- type: ndcg_at_1
value: 63.834999999999994
- type: ndcg_at_10
value: 55.879999999999995
- type: ndcg_at_100
value: 59.723000000000006
- type: ndcg_at_1000
value: 61.49400000000001
- type: ndcg_at_3
value: 50.964
- type: ndcg_at_5
value: 53.47
- type: precision_at_1
value: 63.834999999999994
- type: precision_at_10
value: 11.845
- type: precision_at_100
value: 1.4869999999999999
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 32.158
- type: precision_at_5
value: 21.278
- type: recall_at_1
value: 31.918000000000003
- type: recall_at_10
value: 59.223000000000006
- type: recall_at_100
value: 74.328
- type: recall_at_1000
value: 86.05000000000001
- type: recall_at_3
value: 48.238
- type: recall_at_5
value: 53.193999999999996
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 79.7896
- type: ap
value: 73.65166029460288
- type: f1
value: 79.71794693711813
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.239
- type: map_at_10
value: 34.542
- type: map_at_100
value: 35.717999999999996
- type: map_at_1000
value: 35.764
- type: map_at_3
value: 30.432
- type: map_at_5
value: 32.81
- type: mrr_at_1
value: 22.908
- type: mrr_at_10
value: 35.127
- type: mrr_at_100
value: 36.238
- type: mrr_at_1000
value: 36.278
- type: mrr_at_3
value: 31.076999999999998
- type: mrr_at_5
value: 33.419
- type: ndcg_at_1
value: 22.908
- type: ndcg_at_10
value: 41.607
- type: ndcg_at_100
value: 47.28
- type: ndcg_at_1000
value: 48.414
- type: ndcg_at_3
value: 33.253
- type: ndcg_at_5
value: 37.486000000000004
- type: precision_at_1
value: 22.908
- type: precision_at_10
value: 6.645
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.130999999999998
- type: precision_at_5
value: 10.616
- type: recall_at_1
value: 22.239
- type: recall_at_10
value: 63.42
- type: recall_at_100
value: 89.696
- type: recall_at_1000
value: 98.351
- type: recall_at_3
value: 40.77
- type: recall_at_5
value: 50.93
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 95.06839945280439
- type: f1
value: 94.74276398224072
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.25718194254446
- type: f1
value: 53.91164489161391
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.47948890383323
- type: f1
value: 69.98520247230257
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.46603900470748
- type: f1
value: 76.44111526065399
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.19106070798198
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.78772205248094
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.811231631488507
- type: mrr
value: 32.98200485378021
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.9
- type: map_at_10
value: 13.703000000000001
- type: map_at_100
value: 17.251
- type: map_at_1000
value: 18.795
- type: map_at_3
value: 10.366999999999999
- type: map_at_5
value: 11.675
- type: mrr_at_1
value: 47.059
- type: mrr_at_10
value: 55.816
- type: mrr_at_100
value: 56.434
- type: mrr_at_1000
value: 56.467
- type: mrr_at_3
value: 53.973000000000006
- type: mrr_at_5
value: 55.257999999999996
- type: ndcg_at_1
value: 44.737
- type: ndcg_at_10
value: 35.997
- type: ndcg_at_100
value: 33.487
- type: ndcg_at_1000
value: 41.897
- type: ndcg_at_3
value: 41.18
- type: ndcg_at_5
value: 38.721
- type: precision_at_1
value: 46.129999999999995
- type: precision_at_10
value: 26.533
- type: precision_at_100
value: 8.706
- type: precision_at_1000
value: 2.16
- type: precision_at_3
value: 38.493
- type: precision_at_5
value: 33.189
- type: recall_at_1
value: 6.9
- type: recall_at_10
value: 17.488999999999997
- type: recall_at_100
value: 34.583000000000006
- type: recall_at_1000
value: 64.942
- type: recall_at_3
value: 11.494
- type: recall_at_5
value: 13.496
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.028999999999996
- type: map_at_10
value: 49.307
- type: map_at_100
value: 50.205
- type: map_at_1000
value: 50.23
- type: map_at_3
value: 44.782
- type: map_at_5
value: 47.599999999999994
- type: mrr_at_1
value: 37.108999999999995
- type: mrr_at_10
value: 51.742999999999995
- type: mrr_at_100
value: 52.405
- type: mrr_at_1000
value: 52.422000000000004
- type: mrr_at_3
value: 48.087999999999994
- type: mrr_at_5
value: 50.414
- type: ndcg_at_1
value: 37.08
- type: ndcg_at_10
value: 57.236
- type: ndcg_at_100
value: 60.931999999999995
- type: ndcg_at_1000
value: 61.522
- type: ndcg_at_3
value: 48.93
- type: ndcg_at_5
value: 53.561
- type: precision_at_1
value: 37.08
- type: precision_at_10
value: 9.386
- type: precision_at_100
value: 1.1480000000000001
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 22.258
- type: precision_at_5
value: 16.025
- type: recall_at_1
value: 33.028999999999996
- type: recall_at_10
value: 78.805
- type: recall_at_100
value: 94.643
- type: recall_at_1000
value: 99.039
- type: recall_at_3
value: 57.602
- type: recall_at_5
value: 68.253
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.122
- type: map_at_10
value: 85.237
- type: map_at_100
value: 85.872
- type: map_at_1000
value: 85.885
- type: map_at_3
value: 82.27499999999999
- type: map_at_5
value: 84.13199999999999
- type: mrr_at_1
value: 81.73
- type: mrr_at_10
value: 87.834
- type: mrr_at_100
value: 87.92
- type: mrr_at_1000
value: 87.921
- type: mrr_at_3
value: 86.878
- type: mrr_at_5
value: 87.512
- type: ndcg_at_1
value: 81.73
- type: ndcg_at_10
value: 88.85499999999999
- type: ndcg_at_100
value: 89.992
- type: ndcg_at_1000
value: 90.07
- type: ndcg_at_3
value: 85.997
- type: ndcg_at_5
value: 87.55199999999999
- type: precision_at_1
value: 81.73
- type: precision_at_10
value: 13.491
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.623
- type: precision_at_5
value: 24.742
- type: recall_at_1
value: 71.122
- type: recall_at_10
value: 95.935
- type: recall_at_100
value: 99.657
- type: recall_at_1000
value: 99.996
- type: recall_at_3
value: 87.80799999999999
- type: recall_at_5
value: 92.161
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.490029238193756
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 65.13153408508836
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.202999999999999
- type: map_at_10
value: 10.174
- type: map_at_100
value: 12.138
- type: map_at_1000
value: 12.418
- type: map_at_3
value: 7.379
- type: map_at_5
value: 8.727
- type: mrr_at_1
value: 20.7
- type: mrr_at_10
value: 30.389
- type: mrr_at_100
value: 31.566
- type: mrr_at_1000
value: 31.637999999999998
- type: mrr_at_3
value: 27.133000000000003
- type: mrr_at_5
value: 29.078
- type: ndcg_at_1
value: 20.7
- type: ndcg_at_10
value: 17.355999999999998
- type: ndcg_at_100
value: 25.151
- type: ndcg_at_1000
value: 30.37
- type: ndcg_at_3
value: 16.528000000000002
- type: ndcg_at_5
value: 14.396999999999998
- type: precision_at_1
value: 20.7
- type: precision_at_10
value: 8.98
- type: precision_at_100
value: 2.015
- type: precision_at_1000
value: 0.327
- type: precision_at_3
value: 15.367
- type: precision_at_5
value: 12.559999999999999
- type: recall_at_1
value: 4.202999999999999
- type: recall_at_10
value: 18.197
- type: recall_at_100
value: 40.903
- type: recall_at_1000
value: 66.427
- type: recall_at_3
value: 9.362
- type: recall_at_5
value: 12.747
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.69890989765257
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 75.31953790551489
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 87.44050861280759
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.86922869270393
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 88.9399170304284
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.38015314088582
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.53653527788835
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 68.64526474250209
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.56156983963042
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.48610254648003
- type: mrr
value: 94.02481505422682
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 48.983
- type: map_at_10
value: 59.077999999999996
- type: map_at_100
value: 59.536
- type: map_at_1000
value: 59.575
- type: map_at_3
value: 55.691
- type: map_at_5
value: 57.410000000000004
- type: mrr_at_1
value: 51.666999999999994
- type: mrr_at_10
value: 60.427
- type: mrr_at_100
value: 60.763
- type: mrr_at_1000
value: 60.79900000000001
- type: mrr_at_3
value: 57.556
- type: mrr_at_5
value: 59.089000000000006
- type: ndcg_at_1
value: 51.666999999999994
- type: ndcg_at_10
value: 64.559
- type: ndcg_at_100
value: 66.58
- type: ndcg_at_1000
value: 67.64
- type: ndcg_at_3
value: 58.287
- type: ndcg_at_5
value: 61.001000000000005
- type: precision_at_1
value: 51.666999999999994
- type: precision_at_10
value: 9.067
- type: precision_at_100
value: 1.0170000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.0
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 48.983
- type: recall_at_10
value: 80.289
- type: recall_at_100
value: 89.43299999999999
- type: recall_at_1000
value: 97.667
- type: recall_at_3
value: 62.978
- type: recall_at_5
value: 69.872
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.79009900990098
- type: cos_sim_ap
value: 94.94115052608419
- type: cos_sim_f1
value: 89.1260162601626
- type: cos_sim_precision
value: 90.599173553719
- type: cos_sim_recall
value: 87.7
- type: dot_accuracy
value: 99.79009900990098
- type: dot_ap
value: 94.94115052608419
- type: dot_f1
value: 89.1260162601626
- type: dot_precision
value: 90.599173553719
- type: dot_recall
value: 87.7
- type: euclidean_accuracy
value: 99.79009900990098
- type: euclidean_ap
value: 94.94115052608419
- type: euclidean_f1
value: 89.1260162601626
- type: euclidean_precision
value: 90.599173553719
- type: euclidean_recall
value: 87.7
- type: manhattan_accuracy
value: 99.7940594059406
- type: manhattan_ap
value: 94.95271414642431
- type: manhattan_f1
value: 89.24508790072387
- type: manhattan_precision
value: 92.3982869379015
- type: manhattan_recall
value: 86.3
- type: max_accuracy
value: 99.7940594059406
- type: max_ap
value: 94.95271414642431
- type: max_f1
value: 89.24508790072387
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.43866571935851
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.16579026551532
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.518952473513934
- type: mrr
value: 53.292457134368895
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.12529588316604
- type: cos_sim_spearman
value: 32.31662126895294
- type: dot_pearson
value: 31.125303796647056
- type: dot_spearman
value: 32.31662126895294
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.219
- type: map_at_10
value: 1.7469999999999999
- type: map_at_100
value: 10.177999999999999
- type: map_at_1000
value: 26.108999999999998
- type: map_at_3
value: 0.64
- type: map_at_5
value: 0.968
- type: mrr_at_1
value: 82.0
- type: mrr_at_10
value: 89.067
- type: mrr_at_100
value: 89.067
- type: mrr_at_1000
value: 89.067
- type: mrr_at_3
value: 88.333
- type: mrr_at_5
value: 88.73299999999999
- type: ndcg_at_1
value: 78.0
- type: ndcg_at_10
value: 71.398
- type: ndcg_at_100
value: 55.574999999999996
- type: ndcg_at_1000
value: 51.771
- type: ndcg_at_3
value: 77.765
- type: ndcg_at_5
value: 73.614
- type: precision_at_1
value: 82.0
- type: precision_at_10
value: 75.4
- type: precision_at_100
value: 58.040000000000006
- type: precision_at_1000
value: 23.516000000000002
- type: precision_at_3
value: 84.0
- type: precision_at_5
value: 78.4
- type: recall_at_1
value: 0.219
- type: recall_at_10
value: 1.958
- type: recall_at_100
value: 13.797999999999998
- type: recall_at_1000
value: 49.881
- type: recall_at_3
value: 0.672
- type: recall_at_5
value: 1.0370000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.8610000000000002
- type: map_at_10
value: 8.705
- type: map_at_100
value: 15.164
- type: map_at_1000
value: 16.78
- type: map_at_3
value: 4.346
- type: map_at_5
value: 6.151
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 41.556
- type: mrr_at_100
value: 42.484
- type: mrr_at_1000
value: 42.494
- type: mrr_at_3
value: 37.755
- type: mrr_at_5
value: 40.102
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 23.439
- type: ndcg_at_100
value: 36.948
- type: ndcg_at_1000
value: 48.408
- type: ndcg_at_3
value: 22.261
- type: ndcg_at_5
value: 23.085
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 21.633
- type: precision_at_100
value: 8.02
- type: precision_at_1000
value: 1.5939999999999999
- type: precision_at_3
value: 23.810000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 1.8610000000000002
- type: recall_at_10
value: 15.876000000000001
- type: recall_at_100
value: 50.300999999999995
- type: recall_at_1000
value: 86.098
- type: recall_at_3
value: 5.892
- type: recall_at_5
value: 9.443
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.3264
- type: ap
value: 13.249577616243794
- type: f1
value: 53.621518367695685
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.57611771363894
- type: f1
value: 61.79797478568639
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 53.38315344479284
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.55438993860642
- type: cos_sim_ap
value: 77.98702600017738
- type: cos_sim_f1
value: 71.94971653931476
- type: cos_sim_precision
value: 67.50693802035153
- type: cos_sim_recall
value: 77.01846965699208
- type: dot_accuracy
value: 87.55438993860642
- type: dot_ap
value: 77.98702925907986
- type: dot_f1
value: 71.94971653931476
- type: dot_precision
value: 67.50693802035153
- type: dot_recall
value: 77.01846965699208
- type: euclidean_accuracy
value: 87.55438993860642
- type: euclidean_ap
value: 77.98702951957925
- type: euclidean_f1
value: 71.94971653931476
- type: euclidean_precision
value: 67.50693802035153
- type: euclidean_recall
value: 77.01846965699208
- type: manhattan_accuracy
value: 87.54246885617214
- type: manhattan_ap
value: 77.95531413902947
- type: manhattan_f1
value: 71.93605683836589
- type: manhattan_precision
value: 69.28152492668622
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.55438993860642
- type: max_ap
value: 77.98702951957925
- type: max_f1
value: 71.94971653931476
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.47296930182016
- type: cos_sim_ap
value: 86.92853616302108
- type: cos_sim_f1
value: 79.35138351681047
- type: cos_sim_precision
value: 76.74820143884892
- type: cos_sim_recall
value: 82.13735756082538
- type: dot_accuracy
value: 89.47296930182016
- type: dot_ap
value: 86.92854339601595
- type: dot_f1
value: 79.35138351681047
- type: dot_precision
value: 76.74820143884892
- type: dot_recall
value: 82.13735756082538
- type: euclidean_accuracy
value: 89.47296930182016
- type: euclidean_ap
value: 86.92854191061649
- type: euclidean_f1
value: 79.35138351681047
- type: euclidean_precision
value: 76.74820143884892
- type: euclidean_recall
value: 82.13735756082538
- type: manhattan_accuracy
value: 89.47685023479644
- type: manhattan_ap
value: 86.90063722679578
- type: manhattan_f1
value: 79.30753865502702
- type: manhattan_precision
value: 76.32066068631639
- type: manhattan_recall
value: 82.53772713273791
- type: max_accuracy
value: 89.47685023479644
- type: max_ap
value: 86.92854339601595
- type: max_f1
value: 79.35138351681047
---
# hkunlp/instructor-xl
We introduce **Instructor**👨🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨 achieves sota on 70 diverse embedding tasks!
The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
**************************** **Updates** ****************************
* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance.
* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out!
## Quick start
<hr />
## Installation
```bash
pip install InstructorEmbedding
```
## Compute your customized embeddings
Then you can use the model like this to calculate domain-specific and task-aware embeddings:
```python
from InstructorEmbedding import INSTRUCTOR
model = INSTRUCTOR('hkunlp/instructor-xl')
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Science title:"
embeddings = model.encode([[instruction,sentence]])
print(embeddings)
```
## Use cases
<hr />
## Calculate embeddings for your customized texts
If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
Represent the `domain` `text_type` for `task_objective`:
* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
## Calculate Sentence similarities
You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
```python
from sklearn.metrics.pairwise import cosine_similarity
sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
embeddings_a = model.encode(sentences_a)
embeddings_b = model.encode(sentences_b)
similarities = cosine_similarity(embeddings_a,embeddings_b)
print(similarities)
```
## Information Retrieval
You can also use **customized embeddings** for information retrieval.
```python
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
query = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
query_embeddings = model.encode(query)
corpus_embeddings = model.encode(corpus)
similarities = cosine_similarity(query_embeddings,corpus_embeddings)
retrieved_doc_id = np.argmax(similarities)
print(retrieved_doc_id)
```
## Clustering
Use **customized embeddings** for clustering texts in groups.
```python
import sklearn.cluster
sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher, Vector States of Charmonium']]
embeddings = model.encode(sentences)
clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
clustering_model.fit(embeddings)
cluster_assignment = clustering_model.labels_
print(cluster_assignment)
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
llmrails/ember-v1 | llmrails | feature-extraction | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"sentence-similarity",
"transformers",
"en",
"arxiv:2205.12035",
"arxiv:2209.11055",
"doi:10.57967/hf/2919",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2023-10-10T15:56:42 | 2024-08-21T04:49:13 | 34,938 | 62 | ---
language: en
license: mit
tags:
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
model-index:
- name: ember_v1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 76.05970149253731
- type: ap
value: 38.76045348512767
- type: f1
value: 69.8824007294685
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.977
- type: ap
value: 88.63507587170176
- type: f1
value: 91.9524133311038
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.938
- type: f1
value: 47.58273047536129
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 41.252
- type: map_at_10
value: 56.567
- type: map_at_100
value: 57.07600000000001
- type: map_at_1000
value: 57.08
- type: map_at_3
value: 52.394
- type: map_at_5
value: 55.055
- type: mrr_at_1
value: 42.39
- type: mrr_at_10
value: 57.001999999999995
- type: mrr_at_100
value: 57.531
- type: mrr_at_1000
value: 57.535000000000004
- type: mrr_at_3
value: 52.845
- type: mrr_at_5
value: 55.47299999999999
- type: ndcg_at_1
value: 41.252
- type: ndcg_at_10
value: 64.563
- type: ndcg_at_100
value: 66.667
- type: ndcg_at_1000
value: 66.77
- type: ndcg_at_3
value: 56.120000000000005
- type: ndcg_at_5
value: 60.889
- type: precision_at_1
value: 41.252
- type: precision_at_10
value: 8.982999999999999
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.309
- type: precision_at_5
value: 15.690000000000001
- type: recall_at_1
value: 41.252
- type: recall_at_10
value: 89.82900000000001
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 66.927
- type: recall_at_5
value: 78.45
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.5799968717232
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 43.142844164856136
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.45997990276463
- type: mrr
value: 77.85560392208592
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.38299310075898
- type: cos_sim_spearman
value: 85.81038898286454
- type: euclidean_pearson
value: 84.28002556389774
- type: euclidean_spearman
value: 85.80315990248238
- type: manhattan_pearson
value: 83.9755390675032
- type: manhattan_spearman
value: 85.30435335611396
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.89935064935065
- type: f1
value: 87.87886687103833
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.84335510371379
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.377963093857005
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.557
- type: map_at_10
value: 44.501000000000005
- type: map_at_100
value: 46.11
- type: map_at_1000
value: 46.232
- type: map_at_3
value: 40.711000000000006
- type: map_at_5
value: 42.937
- type: mrr_at_1
value: 40.916000000000004
- type: mrr_at_10
value: 51.317
- type: mrr_at_100
value: 52.003
- type: mrr_at_1000
value: 52.044999999999995
- type: mrr_at_3
value: 48.569
- type: mrr_at_5
value: 50.322
- type: ndcg_at_1
value: 40.916000000000004
- type: ndcg_at_10
value: 51.353
- type: ndcg_at_100
value: 56.762
- type: ndcg_at_1000
value: 58.555
- type: ndcg_at_3
value: 46.064
- type: ndcg_at_5
value: 48.677
- type: precision_at_1
value: 40.916000000000004
- type: precision_at_10
value: 9.927999999999999
- type: precision_at_100
value: 1.592
- type: precision_at_1000
value: 0.20600000000000002
- type: precision_at_3
value: 22.078999999999997
- type: precision_at_5
value: 16.08
- type: recall_at_1
value: 32.557
- type: recall_at_10
value: 63.942
- type: recall_at_100
value: 86.436
- type: recall_at_1000
value: 97.547
- type: recall_at_3
value: 48.367
- type: recall_at_5
value: 55.818
- type: map_at_1
value: 32.106
- type: map_at_10
value: 42.55
- type: map_at_100
value: 43.818
- type: map_at_1000
value: 43.952999999999996
- type: map_at_3
value: 39.421
- type: map_at_5
value: 41.276
- type: mrr_at_1
value: 39.936
- type: mrr_at_10
value: 48.484
- type: mrr_at_100
value: 49.123
- type: mrr_at_1000
value: 49.163000000000004
- type: mrr_at_3
value: 46.221000000000004
- type: mrr_at_5
value: 47.603
- type: ndcg_at_1
value: 39.936
- type: ndcg_at_10
value: 48.25
- type: ndcg_at_100
value: 52.674
- type: ndcg_at_1000
value: 54.638
- type: ndcg_at_3
value: 44.05
- type: ndcg_at_5
value: 46.125
- type: precision_at_1
value: 39.936
- type: precision_at_10
value: 9.096
- type: precision_at_100
value: 1.473
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 21.295
- type: precision_at_5
value: 15.121
- type: recall_at_1
value: 32.106
- type: recall_at_10
value: 58.107
- type: recall_at_100
value: 76.873
- type: recall_at_1000
value: 89.079
- type: recall_at_3
value: 45.505
- type: recall_at_5
value: 51.479
- type: map_at_1
value: 41.513
- type: map_at_10
value: 54.571999999999996
- type: map_at_100
value: 55.579
- type: map_at_1000
value: 55.626
- type: map_at_3
value: 51.127
- type: map_at_5
value: 53.151
- type: mrr_at_1
value: 47.398
- type: mrr_at_10
value: 57.82000000000001
- type: mrr_at_100
value: 58.457
- type: mrr_at_1000
value: 58.479000000000006
- type: mrr_at_3
value: 55.32899999999999
- type: mrr_at_5
value: 56.89999999999999
- type: ndcg_at_1
value: 47.398
- type: ndcg_at_10
value: 60.599000000000004
- type: ndcg_at_100
value: 64.366
- type: ndcg_at_1000
value: 65.333
- type: ndcg_at_3
value: 54.98
- type: ndcg_at_5
value: 57.874
- type: precision_at_1
value: 47.398
- type: precision_at_10
value: 9.806
- type: precision_at_100
value: 1.2590000000000001
- type: precision_at_1000
value: 0.13799999999999998
- type: precision_at_3
value: 24.619
- type: precision_at_5
value: 16.878
- type: recall_at_1
value: 41.513
- type: recall_at_10
value: 74.91799999999999
- type: recall_at_100
value: 90.96
- type: recall_at_1000
value: 97.923
- type: recall_at_3
value: 60.013000000000005
- type: recall_at_5
value: 67.245
- type: map_at_1
value: 26.319
- type: map_at_10
value: 35.766999999999996
- type: map_at_100
value: 36.765
- type: map_at_1000
value: 36.829
- type: map_at_3
value: 32.888
- type: map_at_5
value: 34.538999999999994
- type: mrr_at_1
value: 28.249000000000002
- type: mrr_at_10
value: 37.766
- type: mrr_at_100
value: 38.62
- type: mrr_at_1000
value: 38.667
- type: mrr_at_3
value: 35.009
- type: mrr_at_5
value: 36.608000000000004
- type: ndcg_at_1
value: 28.249000000000002
- type: ndcg_at_10
value: 41.215
- type: ndcg_at_100
value: 46.274
- type: ndcg_at_1000
value: 48.007
- type: ndcg_at_3
value: 35.557
- type: ndcg_at_5
value: 38.344
- type: precision_at_1
value: 28.249000000000002
- type: precision_at_10
value: 6.429
- type: precision_at_100
value: 0.9480000000000001
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 15.179
- type: precision_at_5
value: 10.734
- type: recall_at_1
value: 26.319
- type: recall_at_10
value: 56.157999999999994
- type: recall_at_100
value: 79.65
- type: recall_at_1000
value: 92.73
- type: recall_at_3
value: 40.738
- type: recall_at_5
value: 47.418
- type: map_at_1
value: 18.485
- type: map_at_10
value: 27.400999999999996
- type: map_at_100
value: 28.665000000000003
- type: map_at_1000
value: 28.79
- type: map_at_3
value: 24.634
- type: map_at_5
value: 26.313
- type: mrr_at_1
value: 23.134
- type: mrr_at_10
value: 32.332
- type: mrr_at_100
value: 33.318
- type: mrr_at_1000
value: 33.384
- type: mrr_at_3
value: 29.664
- type: mrr_at_5
value: 31.262
- type: ndcg_at_1
value: 23.134
- type: ndcg_at_10
value: 33.016
- type: ndcg_at_100
value: 38.763
- type: ndcg_at_1000
value: 41.619
- type: ndcg_at_3
value: 28.017999999999997
- type: ndcg_at_5
value: 30.576999999999998
- type: precision_at_1
value: 23.134
- type: precision_at_10
value: 6.069999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 13.599
- type: precision_at_5
value: 9.975000000000001
- type: recall_at_1
value: 18.485
- type: recall_at_10
value: 45.39
- type: recall_at_100
value: 69.876
- type: recall_at_1000
value: 90.023
- type: recall_at_3
value: 31.587
- type: recall_at_5
value: 38.164
- type: map_at_1
value: 30.676
- type: map_at_10
value: 41.785
- type: map_at_100
value: 43.169000000000004
- type: map_at_1000
value: 43.272
- type: map_at_3
value: 38.462
- type: map_at_5
value: 40.32
- type: mrr_at_1
value: 37.729
- type: mrr_at_10
value: 47.433
- type: mrr_at_100
value: 48.303000000000004
- type: mrr_at_1000
value: 48.337
- type: mrr_at_3
value: 45.011
- type: mrr_at_5
value: 46.455
- type: ndcg_at_1
value: 37.729
- type: ndcg_at_10
value: 47.921
- type: ndcg_at_100
value: 53.477
- type: ndcg_at_1000
value: 55.300000000000004
- type: ndcg_at_3
value: 42.695
- type: ndcg_at_5
value: 45.175
- type: precision_at_1
value: 37.729
- type: precision_at_10
value: 8.652999999999999
- type: precision_at_100
value: 1.336
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 20.18
- type: precision_at_5
value: 14.302000000000001
- type: recall_at_1
value: 30.676
- type: recall_at_10
value: 60.441
- type: recall_at_100
value: 83.37
- type: recall_at_1000
value: 95.092
- type: recall_at_3
value: 45.964
- type: recall_at_5
value: 52.319
- type: map_at_1
value: 24.978
- type: map_at_10
value: 35.926
- type: map_at_100
value: 37.341
- type: map_at_1000
value: 37.445
- type: map_at_3
value: 32.748
- type: map_at_5
value: 34.207
- type: mrr_at_1
value: 31.163999999999998
- type: mrr_at_10
value: 41.394
- type: mrr_at_100
value: 42.321
- type: mrr_at_1000
value: 42.368
- type: mrr_at_3
value: 38.964999999999996
- type: mrr_at_5
value: 40.135
- type: ndcg_at_1
value: 31.163999999999998
- type: ndcg_at_10
value: 42.191
- type: ndcg_at_100
value: 48.083999999999996
- type: ndcg_at_1000
value: 50.21
- type: ndcg_at_3
value: 36.979
- type: ndcg_at_5
value: 38.823
- type: precision_at_1
value: 31.163999999999998
- type: precision_at_10
value: 7.968
- type: precision_at_100
value: 1.2550000000000001
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 18.075
- type: precision_at_5
value: 12.626000000000001
- type: recall_at_1
value: 24.978
- type: recall_at_10
value: 55.410000000000004
- type: recall_at_100
value: 80.562
- type: recall_at_1000
value: 94.77600000000001
- type: recall_at_3
value: 40.359
- type: recall_at_5
value: 45.577
- type: map_at_1
value: 26.812166666666666
- type: map_at_10
value: 36.706916666666665
- type: map_at_100
value: 37.94016666666666
- type: map_at_1000
value: 38.05358333333333
- type: map_at_3
value: 33.72408333333334
- type: map_at_5
value: 35.36508333333333
- type: mrr_at_1
value: 31.91516666666667
- type: mrr_at_10
value: 41.09716666666666
- type: mrr_at_100
value: 41.931916666666666
- type: mrr_at_1000
value: 41.98458333333333
- type: mrr_at_3
value: 38.60183333333333
- type: mrr_at_5
value: 40.031916666666675
- type: ndcg_at_1
value: 31.91516666666667
- type: ndcg_at_10
value: 42.38725
- type: ndcg_at_100
value: 47.56291666666667
- type: ndcg_at_1000
value: 49.716499999999996
- type: ndcg_at_3
value: 37.36491666666667
- type: ndcg_at_5
value: 39.692166666666665
- type: precision_at_1
value: 31.91516666666667
- type: precision_at_10
value: 7.476749999999999
- type: precision_at_100
value: 1.1869166666666668
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 17.275249999999996
- type: precision_at_5
value: 12.25825
- type: recall_at_1
value: 26.812166666666666
- type: recall_at_10
value: 54.82933333333333
- type: recall_at_100
value: 77.36508333333333
- type: recall_at_1000
value: 92.13366666666667
- type: recall_at_3
value: 40.83508333333334
- type: recall_at_5
value: 46.85083333333334
- type: map_at_1
value: 25.352999999999998
- type: map_at_10
value: 33.025999999999996
- type: map_at_100
value: 33.882
- type: map_at_1000
value: 33.983999999999995
- type: map_at_3
value: 30.995
- type: map_at_5
value: 32.113
- type: mrr_at_1
value: 28.834
- type: mrr_at_10
value: 36.14
- type: mrr_at_100
value: 36.815
- type: mrr_at_1000
value: 36.893
- type: mrr_at_3
value: 34.305
- type: mrr_at_5
value: 35.263
- type: ndcg_at_1
value: 28.834
- type: ndcg_at_10
value: 37.26
- type: ndcg_at_100
value: 41.723
- type: ndcg_at_1000
value: 44.314
- type: ndcg_at_3
value: 33.584
- type: ndcg_at_5
value: 35.302
- type: precision_at_1
value: 28.834
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 0.876
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 14.468
- type: precision_at_5
value: 9.847
- type: recall_at_1
value: 25.352999999999998
- type: recall_at_10
value: 47.155
- type: recall_at_100
value: 68.024
- type: recall_at_1000
value: 87.26899999999999
- type: recall_at_3
value: 37.074
- type: recall_at_5
value: 41.352
- type: map_at_1
value: 17.845
- type: map_at_10
value: 25.556
- type: map_at_100
value: 26.787
- type: map_at_1000
value: 26.913999999999998
- type: map_at_3
value: 23.075000000000003
- type: map_at_5
value: 24.308
- type: mrr_at_1
value: 21.714
- type: mrr_at_10
value: 29.543999999999997
- type: mrr_at_100
value: 30.543
- type: mrr_at_1000
value: 30.618000000000002
- type: mrr_at_3
value: 27.174
- type: mrr_at_5
value: 28.409000000000002
- type: ndcg_at_1
value: 21.714
- type: ndcg_at_10
value: 30.562
- type: ndcg_at_100
value: 36.27
- type: ndcg_at_1000
value: 39.033
- type: ndcg_at_3
value: 26.006
- type: ndcg_at_5
value: 27.843
- type: precision_at_1
value: 21.714
- type: precision_at_10
value: 5.657
- type: precision_at_100
value: 1
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 12.4
- type: precision_at_5
value: 8.863999999999999
- type: recall_at_1
value: 17.845
- type: recall_at_10
value: 41.72
- type: recall_at_100
value: 67.06400000000001
- type: recall_at_1000
value: 86.515
- type: recall_at_3
value: 28.78
- type: recall_at_5
value: 33.629999999999995
- type: map_at_1
value: 26.695
- type: map_at_10
value: 36.205999999999996
- type: map_at_100
value: 37.346000000000004
- type: map_at_1000
value: 37.447
- type: map_at_3
value: 32.84
- type: map_at_5
value: 34.733000000000004
- type: mrr_at_1
value: 31.343
- type: mrr_at_10
value: 40.335
- type: mrr_at_100
value: 41.162
- type: mrr_at_1000
value: 41.221000000000004
- type: mrr_at_3
value: 37.329
- type: mrr_at_5
value: 39.068999999999996
- type: ndcg_at_1
value: 31.343
- type: ndcg_at_10
value: 41.996
- type: ndcg_at_100
value: 47.096
- type: ndcg_at_1000
value: 49.4
- type: ndcg_at_3
value: 35.902
- type: ndcg_at_5
value: 38.848
- type: precision_at_1
value: 31.343
- type: precision_at_10
value: 7.146
- type: precision_at_100
value: 1.098
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_3
value: 16.014
- type: precision_at_5
value: 11.735
- type: recall_at_1
value: 26.695
- type: recall_at_10
value: 55.525000000000006
- type: recall_at_100
value: 77.376
- type: recall_at_1000
value: 93.476
- type: recall_at_3
value: 39.439
- type: recall_at_5
value: 46.501
- type: map_at_1
value: 24.196
- type: map_at_10
value: 33.516
- type: map_at_100
value: 35.202
- type: map_at_1000
value: 35.426
- type: map_at_3
value: 30.561
- type: map_at_5
value: 31.961000000000002
- type: mrr_at_1
value: 29.644
- type: mrr_at_10
value: 38.769
- type: mrr_at_100
value: 39.843
- type: mrr_at_1000
value: 39.888
- type: mrr_at_3
value: 36.132999999999996
- type: mrr_at_5
value: 37.467
- type: ndcg_at_1
value: 29.644
- type: ndcg_at_10
value: 39.584
- type: ndcg_at_100
value: 45.964
- type: ndcg_at_1000
value: 48.27
- type: ndcg_at_3
value: 34.577999999999996
- type: ndcg_at_5
value: 36.498000000000005
- type: precision_at_1
value: 29.644
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.545
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.271
- type: precision_at_5
value: 11.620999999999999
- type: recall_at_1
value: 24.196
- type: recall_at_10
value: 51.171
- type: recall_at_100
value: 79.212
- type: recall_at_1000
value: 92.976
- type: recall_at_3
value: 36.797999999999995
- type: recall_at_5
value: 42.006
- type: map_at_1
value: 21.023
- type: map_at_10
value: 29.677
- type: map_at_100
value: 30.618000000000002
- type: map_at_1000
value: 30.725
- type: map_at_3
value: 27.227
- type: map_at_5
value: 28.523
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.832
- type: mrr_at_100
value: 32.675
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.513
- type: mrr_at_5
value: 30.89
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.699999999999996
- type: ndcg_at_100
value: 39.302
- type: ndcg_at_1000
value: 41.919000000000004
- type: ndcg_at_3
value: 29.965999999999998
- type: ndcg_at_5
value: 32.22
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.564
- type: precision_at_100
value: 0.8340000000000001
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 13.123999999999999
- type: precision_at_5
value: 9.316
- type: recall_at_1
value: 21.023
- type: recall_at_10
value: 48.015
- type: recall_at_100
value: 68.978
- type: recall_at_1000
value: 88.198
- type: recall_at_3
value: 35.397
- type: recall_at_5
value: 40.701
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.198
- type: map_at_10
value: 19.336000000000002
- type: map_at_100
value: 21.382
- type: map_at_1000
value: 21.581
- type: map_at_3
value: 15.992
- type: map_at_5
value: 17.613
- type: mrr_at_1
value: 25.080999999999996
- type: mrr_at_10
value: 36.032
- type: mrr_at_100
value: 37.1
- type: mrr_at_1000
value: 37.145
- type: mrr_at_3
value: 32.595
- type: mrr_at_5
value: 34.553
- type: ndcg_at_1
value: 25.080999999999996
- type: ndcg_at_10
value: 27.290999999999997
- type: ndcg_at_100
value: 35.31
- type: ndcg_at_1000
value: 38.885
- type: ndcg_at_3
value: 21.895999999999997
- type: ndcg_at_5
value: 23.669999999999998
- type: precision_at_1
value: 25.080999999999996
- type: precision_at_10
value: 8.645
- type: precision_at_100
value: 1.7209999999999999
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_3
value: 16.287
- type: precision_at_5
value: 12.625
- type: recall_at_1
value: 11.198
- type: recall_at_10
value: 33.355000000000004
- type: recall_at_100
value: 60.912
- type: recall_at_1000
value: 80.89
- type: recall_at_3
value: 20.055
- type: recall_at_5
value: 25.14
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.228
- type: map_at_10
value: 20.018
- type: map_at_100
value: 28.388999999999996
- type: map_at_1000
value: 30.073
- type: map_at_3
value: 14.366999999999999
- type: map_at_5
value: 16.705000000000002
- type: mrr_at_1
value: 69
- type: mrr_at_10
value: 77.058
- type: mrr_at_100
value: 77.374
- type: mrr_at_1000
value: 77.384
- type: mrr_at_3
value: 75.708
- type: mrr_at_5
value: 76.608
- type: ndcg_at_1
value: 57.49999999999999
- type: ndcg_at_10
value: 41.792
- type: ndcg_at_100
value: 47.374
- type: ndcg_at_1000
value: 55.13
- type: ndcg_at_3
value: 46.353
- type: ndcg_at_5
value: 43.702000000000005
- type: precision_at_1
value: 69
- type: precision_at_10
value: 32.85
- type: precision_at_100
value: 10.708
- type: precision_at_1000
value: 2.024
- type: precision_at_3
value: 49.5
- type: precision_at_5
value: 42.05
- type: recall_at_1
value: 9.228
- type: recall_at_10
value: 25.635
- type: recall_at_100
value: 54.894
- type: recall_at_1000
value: 79.38
- type: recall_at_3
value: 15.68
- type: recall_at_5
value: 19.142
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.035
- type: f1
value: 46.85325505614071
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.132
- type: map_at_10
value: 79.527
- type: map_at_100
value: 79.81200000000001
- type: map_at_1000
value: 79.828
- type: map_at_3
value: 78.191
- type: map_at_5
value: 79.092
- type: mrr_at_1
value: 75.563
- type: mrr_at_10
value: 83.80199999999999
- type: mrr_at_100
value: 83.93
- type: mrr_at_1000
value: 83.933
- type: mrr_at_3
value: 82.818
- type: mrr_at_5
value: 83.505
- type: ndcg_at_1
value: 75.563
- type: ndcg_at_10
value: 83.692
- type: ndcg_at_100
value: 84.706
- type: ndcg_at_1000
value: 85.001
- type: ndcg_at_3
value: 81.51
- type: ndcg_at_5
value: 82.832
- type: precision_at_1
value: 75.563
- type: precision_at_10
value: 10.245
- type: precision_at_100
value: 1.0959999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 31.518
- type: precision_at_5
value: 19.772000000000002
- type: recall_at_1
value: 70.132
- type: recall_at_10
value: 92.204
- type: recall_at_100
value: 96.261
- type: recall_at_1000
value: 98.17399999999999
- type: recall_at_3
value: 86.288
- type: recall_at_5
value: 89.63799999999999
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.269
- type: map_at_10
value: 36.042
- type: map_at_100
value: 37.988
- type: map_at_1000
value: 38.162
- type: map_at_3
value: 31.691000000000003
- type: map_at_5
value: 33.988
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.348
- type: mrr_at_100
value: 54.033
- type: mrr_at_1000
value: 54.064
- type: mrr_at_3
value: 50.977
- type: mrr_at_5
value: 52.112
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 44.302
- type: ndcg_at_100
value: 51.054
- type: ndcg_at_1000
value: 53.822
- type: ndcg_at_3
value: 40.615
- type: ndcg_at_5
value: 41.455999999999996
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.176
- type: precision_at_100
value: 1.931
- type: precision_at_1000
value: 0.243
- type: precision_at_3
value: 27.16
- type: precision_at_5
value: 19.567999999999998
- type: recall_at_1
value: 22.269
- type: recall_at_10
value: 51.188
- type: recall_at_100
value: 75.924
- type: recall_at_1000
value: 92.525
- type: recall_at_3
value: 36.643
- type: recall_at_5
value: 42.27
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.412
- type: map_at_10
value: 66.376
- type: map_at_100
value: 67.217
- type: map_at_1000
value: 67.271
- type: map_at_3
value: 62.741
- type: map_at_5
value: 65.069
- type: mrr_at_1
value: 80.824
- type: mrr_at_10
value: 86.53
- type: mrr_at_100
value: 86.67399999999999
- type: mrr_at_1000
value: 86.678
- type: mrr_at_3
value: 85.676
- type: mrr_at_5
value: 86.256
- type: ndcg_at_1
value: 80.824
- type: ndcg_at_10
value: 74.332
- type: ndcg_at_100
value: 77.154
- type: ndcg_at_1000
value: 78.12400000000001
- type: ndcg_at_3
value: 69.353
- type: ndcg_at_5
value: 72.234
- type: precision_at_1
value: 80.824
- type: precision_at_10
value: 15.652
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 44.911
- type: precision_at_5
value: 29.221000000000004
- type: recall_at_1
value: 40.412
- type: recall_at_10
value: 78.25800000000001
- type: recall_at_100
value: 89.196
- type: recall_at_1000
value: 95.544
- type: recall_at_3
value: 67.367
- type: recall_at_5
value: 73.05199999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 92.78880000000001
- type: ap
value: 89.39251741048801
- type: f1
value: 92.78019950076781
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.888
- type: map_at_10
value: 35.146
- type: map_at_100
value: 36.325
- type: map_at_1000
value: 36.372
- type: map_at_3
value: 31.3
- type: map_at_5
value: 33.533
- type: mrr_at_1
value: 23.480999999999998
- type: mrr_at_10
value: 35.777
- type: mrr_at_100
value: 36.887
- type: mrr_at_1000
value: 36.928
- type: mrr_at_3
value: 31.989
- type: mrr_at_5
value: 34.202
- type: ndcg_at_1
value: 23.496
- type: ndcg_at_10
value: 42.028999999999996
- type: ndcg_at_100
value: 47.629
- type: ndcg_at_1000
value: 48.785000000000004
- type: ndcg_at_3
value: 34.227000000000004
- type: ndcg_at_5
value: 38.207
- type: precision_at_1
value: 23.496
- type: precision_at_10
value: 6.596
- type: precision_at_100
value: 0.9400000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.513000000000002
- type: precision_at_5
value: 10.711
- type: recall_at_1
value: 22.888
- type: recall_at_10
value: 63.129999999999995
- type: recall_at_100
value: 88.90299999999999
- type: recall_at_1000
value: 97.69
- type: recall_at_3
value: 42.014
- type: recall_at_5
value: 51.554
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.59188326493388
- type: f1
value: 94.36568950290486
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.25672594619242
- type: f1
value: 59.52405059722216
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 77.4142568930733
- type: f1
value: 75.23044196543388
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.44720914593141
- type: f1
value: 80.41049641537015
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.960921474993775
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.88042240204361
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.27071371606404
- type: mrr
value: 33.541450459533856
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.551
- type: map_at_10
value: 14.359
- type: map_at_100
value: 18.157
- type: map_at_1000
value: 19.659
- type: map_at_3
value: 10.613999999999999
- type: map_at_5
value: 12.296
- type: mrr_at_1
value: 47.368
- type: mrr_at_10
value: 56.689
- type: mrr_at_100
value: 57.24399999999999
- type: mrr_at_1000
value: 57.284
- type: mrr_at_3
value: 54.489
- type: mrr_at_5
value: 55.928999999999995
- type: ndcg_at_1
value: 45.511
- type: ndcg_at_10
value: 36.911
- type: ndcg_at_100
value: 34.241
- type: ndcg_at_1000
value: 43.064
- type: ndcg_at_3
value: 42.348
- type: ndcg_at_5
value: 39.884
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 27.028000000000002
- type: precision_at_100
value: 8.52
- type: precision_at_1000
value: 2.154
- type: precision_at_3
value: 39.525
- type: precision_at_5
value: 34.18
- type: recall_at_1
value: 6.551
- type: recall_at_10
value: 18.602
- type: recall_at_100
value: 34.882999999999996
- type: recall_at_1000
value: 66.049
- type: recall_at_3
value: 11.872
- type: recall_at_5
value: 14.74
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.828999999999997
- type: map_at_10
value: 43.606
- type: map_at_100
value: 44.656
- type: map_at_1000
value: 44.690000000000005
- type: map_at_3
value: 39.015
- type: map_at_5
value: 41.625
- type: mrr_at_1
value: 31.518
- type: mrr_at_10
value: 46.047
- type: mrr_at_100
value: 46.846
- type: mrr_at_1000
value: 46.867999999999995
- type: mrr_at_3
value: 42.154
- type: mrr_at_5
value: 44.468999999999994
- type: ndcg_at_1
value: 31.518
- type: ndcg_at_10
value: 51.768
- type: ndcg_at_100
value: 56.184999999999995
- type: ndcg_at_1000
value: 56.92
- type: ndcg_at_3
value: 43.059999999999995
- type: ndcg_at_5
value: 47.481
- type: precision_at_1
value: 31.518
- type: precision_at_10
value: 8.824
- type: precision_at_100
value: 1.131
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 19.969
- type: precision_at_5
value: 14.502
- type: recall_at_1
value: 27.828999999999997
- type: recall_at_10
value: 74.244
- type: recall_at_100
value: 93.325
- type: recall_at_1000
value: 98.71799999999999
- type: recall_at_3
value: 51.601
- type: recall_at_5
value: 61.841
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.54
- type: map_at_10
value: 85.509
- type: map_at_100
value: 86.137
- type: map_at_1000
value: 86.151
- type: map_at_3
value: 82.624
- type: map_at_5
value: 84.425
- type: mrr_at_1
value: 82.45
- type: mrr_at_10
value: 88.344
- type: mrr_at_100
value: 88.437
- type: mrr_at_1000
value: 88.437
- type: mrr_at_3
value: 87.417
- type: mrr_at_5
value: 88.066
- type: ndcg_at_1
value: 82.45
- type: ndcg_at_10
value: 89.092
- type: ndcg_at_100
value: 90.252
- type: ndcg_at_1000
value: 90.321
- type: ndcg_at_3
value: 86.404
- type: ndcg_at_5
value: 87.883
- type: precision_at_1
value: 82.45
- type: precision_at_10
value: 13.496
- type: precision_at_100
value: 1.536
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.833
- type: precision_at_5
value: 24.79
- type: recall_at_1
value: 71.54
- type: recall_at_10
value: 95.846
- type: recall_at_100
value: 99.715
- type: recall_at_1000
value: 99.979
- type: recall_at_3
value: 88.01299999999999
- type: recall_at_5
value: 92.32000000000001
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 57.60557586253866
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.0287172242051
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.9849999999999994
- type: map_at_10
value: 11.397
- type: map_at_100
value: 13.985
- type: map_at_1000
value: 14.391000000000002
- type: map_at_3
value: 7.66
- type: map_at_5
value: 9.46
- type: mrr_at_1
value: 19.8
- type: mrr_at_10
value: 31.958
- type: mrr_at_100
value: 33.373999999999995
- type: mrr_at_1000
value: 33.411
- type: mrr_at_3
value: 28.316999999999997
- type: mrr_at_5
value: 30.297
- type: ndcg_at_1
value: 19.8
- type: ndcg_at_10
value: 19.580000000000002
- type: ndcg_at_100
value: 29.555999999999997
- type: ndcg_at_1000
value: 35.882
- type: ndcg_at_3
value: 17.544
- type: ndcg_at_5
value: 15.815999999999999
- type: precision_at_1
value: 19.8
- type: precision_at_10
value: 10.61
- type: precision_at_100
value: 2.501
- type: precision_at_1000
value: 0.40099999999999997
- type: precision_at_3
value: 16.900000000000002
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 3.9849999999999994
- type: recall_at_10
value: 21.497
- type: recall_at_100
value: 50.727999999999994
- type: recall_at_1000
value: 81.27499999999999
- type: recall_at_3
value: 10.263
- type: recall_at_5
value: 14.643
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 85.0087509585503
- type: cos_sim_spearman
value: 81.74697270664319
- type: euclidean_pearson
value: 81.80424382731947
- type: euclidean_spearman
value: 81.29794251968431
- type: manhattan_pearson
value: 81.81524666226125
- type: manhattan_spearman
value: 81.29475370198963
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.44442736429552
- type: cos_sim_spearman
value: 78.51011398910948
- type: euclidean_pearson
value: 83.36181801196723
- type: euclidean_spearman
value: 79.47272621331535
- type: manhattan_pearson
value: 83.3660113483837
- type: manhattan_spearman
value: 79.47695922566032
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 85.82923943323635
- type: cos_sim_spearman
value: 86.62037823380983
- type: euclidean_pearson
value: 83.56369548403958
- type: euclidean_spearman
value: 84.2176755481191
- type: manhattan_pearson
value: 83.55460702084464
- type: manhattan_spearman
value: 84.18617930921467
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 84.09071068110103
- type: cos_sim_spearman
value: 83.05697553913335
- type: euclidean_pearson
value: 81.1377457216497
- type: euclidean_spearman
value: 81.74714169016676
- type: manhattan_pearson
value: 81.0893424142723
- type: manhattan_spearman
value: 81.7058918219677
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.61132157220429
- type: cos_sim_spearman
value: 88.38581627185445
- type: euclidean_pearson
value: 86.14904510913374
- type: euclidean_spearman
value: 86.5452758925542
- type: manhattan_pearson
value: 86.1484025377679
- type: manhattan_spearman
value: 86.55483841566252
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.46195145161064
- type: cos_sim_spearman
value: 86.82409112251158
- type: euclidean_pearson
value: 84.75479672288957
- type: euclidean_spearman
value: 85.41144307151548
- type: manhattan_pearson
value: 84.70914329694165
- type: manhattan_spearman
value: 85.38477943384089
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.06351289930238
- type: cos_sim_spearman
value: 87.90311138579116
- type: euclidean_pearson
value: 86.17651467063077
- type: euclidean_spearman
value: 84.89447802019073
- type: manhattan_pearson
value: 86.3267677479595
- type: manhattan_spearman
value: 85.00472295103874
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.78311975978767
- type: cos_sim_spearman
value: 66.76465685245887
- type: euclidean_pearson
value: 67.21687806595443
- type: euclidean_spearman
value: 65.05776733534435
- type: manhattan_pearson
value: 67.14008143635883
- type: manhattan_spearman
value: 65.25247076149701
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 86.7403488889418
- type: cos_sim_spearman
value: 87.76870289783061
- type: euclidean_pearson
value: 84.83171077794671
- type: euclidean_spearman
value: 85.50579695091902
- type: manhattan_pearson
value: 84.83074260180555
- type: manhattan_spearman
value: 85.47589026938667
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.56234016237356
- type: mrr
value: 96.26124238869338
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 59.660999999999994
- type: map_at_10
value: 69.105
- type: map_at_100
value: 69.78
- type: map_at_1000
value: 69.80199999999999
- type: map_at_3
value: 65.991
- type: map_at_5
value: 68.02
- type: mrr_at_1
value: 62.666999999999994
- type: mrr_at_10
value: 70.259
- type: mrr_at_100
value: 70.776
- type: mrr_at_1000
value: 70.796
- type: mrr_at_3
value: 67.889
- type: mrr_at_5
value: 69.52199999999999
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_10
value: 73.425
- type: ndcg_at_100
value: 75.955
- type: ndcg_at_1000
value: 76.459
- type: ndcg_at_3
value: 68.345
- type: ndcg_at_5
value: 71.319
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_10
value: 9.667
- type: precision_at_100
value: 1.09
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.333000000000002
- type: precision_at_5
value: 17.732999999999997
- type: recall_at_1
value: 59.660999999999994
- type: recall_at_10
value: 85.422
- type: recall_at_100
value: 96.167
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 72.044
- type: recall_at_5
value: 79.428
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.86435643564356
- type: cos_sim_ap
value: 96.83057412333741
- type: cos_sim_f1
value: 93.04215337734891
- type: cos_sim_precision
value: 94.53044375644994
- type: cos_sim_recall
value: 91.60000000000001
- type: dot_accuracy
value: 99.7910891089109
- type: dot_ap
value: 94.10681982106397
- type: dot_f1
value: 89.34881373043918
- type: dot_precision
value: 90.21406727828746
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.85544554455446
- type: euclidean_ap
value: 96.78545104478602
- type: euclidean_f1
value: 92.65143992055613
- type: euclidean_precision
value: 92.01183431952663
- type: euclidean_recall
value: 93.30000000000001
- type: manhattan_accuracy
value: 99.85841584158416
- type: manhattan_ap
value: 96.80748903307823
- type: manhattan_f1
value: 92.78247884519662
- type: manhattan_precision
value: 92.36868186323092
- type: manhattan_recall
value: 93.2
- type: max_accuracy
value: 99.86435643564356
- type: max_ap
value: 96.83057412333741
- type: max_f1
value: 93.04215337734891
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.53971025855282
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.97791591490788
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.852215301355066
- type: mrr
value: 56.85527809608691
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.21442519856758
- type: cos_sim_spearman
value: 30.822536216936825
- type: dot_pearson
value: 28.661325528121807
- type: dot_spearman
value: 28.1435226478879
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.183
- type: map_at_10
value: 1.526
- type: map_at_100
value: 7.915
- type: map_at_1000
value: 19.009
- type: map_at_3
value: 0.541
- type: map_at_5
value: 0.8659999999999999
- type: mrr_at_1
value: 68
- type: mrr_at_10
value: 81.186
- type: mrr_at_100
value: 81.186
- type: mrr_at_1000
value: 81.186
- type: mrr_at_3
value: 80
- type: mrr_at_5
value: 80.9
- type: ndcg_at_1
value: 64
- type: ndcg_at_10
value: 64.13799999999999
- type: ndcg_at_100
value: 47.632000000000005
- type: ndcg_at_1000
value: 43.037
- type: ndcg_at_3
value: 67.542
- type: ndcg_at_5
value: 67.496
- type: precision_at_1
value: 68
- type: precision_at_10
value: 67.80000000000001
- type: precision_at_100
value: 48.980000000000004
- type: precision_at_1000
value: 19.036
- type: precision_at_3
value: 72
- type: precision_at_5
value: 71.2
- type: recall_at_1
value: 0.183
- type: recall_at_10
value: 1.799
- type: recall_at_100
value: 11.652999999999999
- type: recall_at_1000
value: 40.086
- type: recall_at_3
value: 0.5930000000000001
- type: recall_at_5
value: 0.983
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.29
- type: map_at_10
value: 9.489
- type: map_at_100
value: 15.051
- type: map_at_1000
value: 16.561999999999998
- type: map_at_3
value: 5.137
- type: map_at_5
value: 6.7989999999999995
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 45.699
- type: mrr_at_100
value: 46.461000000000006
- type: mrr_at_1000
value: 46.461000000000006
- type: mrr_at_3
value: 41.837
- type: mrr_at_5
value: 43.163000000000004
- type: ndcg_at_1
value: 23.469
- type: ndcg_at_10
value: 23.544999999999998
- type: ndcg_at_100
value: 34.572
- type: ndcg_at_1000
value: 46.035
- type: ndcg_at_3
value: 27.200000000000003
- type: ndcg_at_5
value: 25.266
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 22.041
- type: precision_at_100
value: 7.3469999999999995
- type: precision_at_1000
value: 1.484
- type: precision_at_3
value: 29.932
- type: precision_at_5
value: 26.531
- type: recall_at_1
value: 2.29
- type: recall_at_10
value: 15.895999999999999
- type: recall_at_100
value: 45.518
- type: recall_at_1000
value: 80.731
- type: recall_at_3
value: 6.433
- type: recall_at_5
value: 9.484
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.4178
- type: ap
value: 14.575240629602373
- type: f1
value: 55.02449563229096
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.00282965478212
- type: f1
value: 60.34413028768773
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 50.409448342549936
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.62591643321214
- type: cos_sim_ap
value: 79.28766491329633
- type: cos_sim_f1
value: 71.98772064466617
- type: cos_sim_precision
value: 69.8609731876862
- type: cos_sim_recall
value: 74.24802110817942
- type: dot_accuracy
value: 84.75293556654945
- type: dot_ap
value: 69.72705761174353
- type: dot_f1
value: 65.08692852543464
- type: dot_precision
value: 63.57232704402516
- type: dot_recall
value: 66.6754617414248
- type: euclidean_accuracy
value: 87.44710019669786
- type: euclidean_ap
value: 79.11021477292638
- type: euclidean_f1
value: 71.5052389470994
- type: euclidean_precision
value: 69.32606541129832
- type: euclidean_recall
value: 73.82585751978891
- type: manhattan_accuracy
value: 87.42325803182929
- type: manhattan_ap
value: 79.05094494327616
- type: manhattan_f1
value: 71.36333985649055
- type: manhattan_precision
value: 70.58064516129032
- type: manhattan_recall
value: 72.16358839050132
- type: max_accuracy
value: 87.62591643321214
- type: max_ap
value: 79.28766491329633
- type: max_f1
value: 71.98772064466617
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.85202002561415
- type: cos_sim_ap
value: 85.9835303311168
- type: cos_sim_f1
value: 78.25741142443962
- type: cos_sim_precision
value: 73.76635768811342
- type: cos_sim_recall
value: 83.3307668617185
- type: dot_accuracy
value: 88.20584468506229
- type: dot_ap
value: 83.591632302697
- type: dot_f1
value: 76.81739705396173
- type: dot_precision
value: 73.45275728837373
- type: dot_recall
value: 80.50508161379734
- type: euclidean_accuracy
value: 88.64633057787093
- type: euclidean_ap
value: 85.25705123182283
- type: euclidean_f1
value: 77.18535726329199
- type: euclidean_precision
value: 75.17699437997226
- type: euclidean_recall
value: 79.30397289805975
- type: manhattan_accuracy
value: 88.63274731245392
- type: manhattan_ap
value: 85.2376825633018
- type: manhattan_f1
value: 77.15810785937788
- type: manhattan_precision
value: 73.92255061014319
- type: manhattan_recall
value: 80.68986757006468
- type: max_accuracy
value: 88.85202002561415
- type: max_ap
value: 85.9835303311168
- type: max_f1
value: 78.25741142443962
---
<h1 align="center">ember-v1</h1>
This model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the [RetroMAE](https://arxiv.org/abs/2205.12035) and [SetFit](https://arxiv.org/abs/2209.11055) research papers.
### Plans
- The research paper will be published soon.
- The v2 of the model is currently in development and will feature an extended maximum sequence length of 4,000 tokens.
## Usage
Use with transformers:
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"This is an example sentence",
"Each sentence is converted"
]
tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1")
model = AutoModel.from_pretrained("llmrails/ember-v1")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = [
"This is an example sentence",
"Each sentence is converted"
]
model = SentenceTransformer('llmrails/ember-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
## Massive Text Embedding Benchmark (MTEB) Evaluation
Our model achieve state-of-the-art performance on [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard)
| Model Name | Dimension | Sequence Length | Average (56) |
|:-----------------------------------------------------------------------:|:---------:|:---:|:------------:|
| [ember-v1](https://huggingface.co/llmrails/ember-v1) | 1024 | 512 | **63.54** |
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | 63.23 |
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.05 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings/types-of-embedding-models) | 1536 | 8191 | 60.99 |
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
## License
MIT
## Citation
```bibtex
@misc{nur2024emberv1,
title={ember-v1: SOTA embedding model},
author={Enrike Nur and Anar Aliyev},
year={2023},
}
``` | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
jinaai/jina-embeddings-v2-base-es | jinaai | feature-extraction | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"custom_code",
"es",
"en",
"arxiv:2108.12409",
"arxiv:2402.17016",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"region:eu"
] | 2024-01-24T09:54:03 | 2025-01-06T16:27:28 | 34,138 | 33 | ---
language:
- es
- en
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
inference: false
model-index:
- name: jina-embeddings-v2-base-es
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.25373134328358
- type: ap
value: 37.05201236793268
- type: f1
value: 68.16770391201077
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 78.30885
- type: ap
value: 73.01622441156408
- type: f1
value: 78.20769284466313
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.324
- type: f1
value: 37.89543008761673
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.678000000000004
- type: f1
value: 38.122639506976
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.968999999999998
- type: map_at_10
value: 40.691
- type: map_at_100
value: 41.713
- type: map_at_1000
value: 41.719
- type: map_at_3
value: 35.42
- type: map_at_5
value: 38.442
- type: mrr_at_1
value: 24.395
- type: mrr_at_10
value: 40.853
- type: mrr_at_100
value: 41.869
- type: mrr_at_1000
value: 41.874
- type: mrr_at_3
value: 35.68
- type: mrr_at_5
value: 38.572
- type: ndcg_at_1
value: 23.968999999999998
- type: ndcg_at_10
value: 50.129999999999995
- type: ndcg_at_100
value: 54.364000000000004
- type: ndcg_at_1000
value: 54.494
- type: ndcg_at_3
value: 39.231
- type: ndcg_at_5
value: 44.694
- type: precision_at_1
value: 23.968999999999998
- type: precision_at_10
value: 8.036999999999999
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.761
- type: precision_at_5
value: 12.717
- type: recall_at_1
value: 23.968999999999998
- type: recall_at_10
value: 80.36999999999999
- type: recall_at_100
value: 98.578
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 50.28399999999999
- type: recall_at_5
value: 63.585
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.54886683150053
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.186028697637234
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.19432643698725
- type: mrr
value: 75.28646176845622
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 86.3828259381228
- type: cos_sim_spearman
value: 83.04647058342209
- type: euclidean_pearson
value: 84.02895346096244
- type: euclidean_spearman
value: 82.34524978635342
- type: manhattan_pearson
value: 84.35030723233426
- type: manhattan_spearman
value: 83.17177464337936
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.25649350649351
- type: f1
value: 85.22320474023192
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 20.42929408254094
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.165318177498136
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 28.89030154229562
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.119
- type: map_at_10
value: 42.092
- type: map_at_100
value: 43.506
- type: map_at_1000
value: 43.631
- type: map_at_3
value: 38.373000000000005
- type: map_at_5
value: 40.501
- type: mrr_at_1
value: 38.196999999999996
- type: mrr_at_10
value: 48.237
- type: mrr_at_100
value: 48.914
- type: mrr_at_1000
value: 48.959
- type: mrr_at_3
value: 45.279
- type: mrr_at_5
value: 47.11
- type: ndcg_at_1
value: 38.196999999999996
- type: ndcg_at_10
value: 48.849
- type: ndcg_at_100
value: 53.713
- type: ndcg_at_1000
value: 55.678000000000004
- type: ndcg_at_3
value: 43.546
- type: ndcg_at_5
value: 46.009
- type: precision_at_1
value: 38.196999999999996
- type: precision_at_10
value: 9.642000000000001
- type: precision_at_100
value: 1.5190000000000001
- type: precision_at_1000
value: 0.199
- type: precision_at_3
value: 21.65
- type: precision_at_5
value: 15.708
- type: recall_at_1
value: 30.119
- type: recall_at_10
value: 61.788
- type: recall_at_100
value: 82.14399999999999
- type: recall_at_1000
value: 95.003
- type: recall_at_3
value: 45.772
- type: recall_at_5
value: 53.04600000000001
- type: map_at_1
value: 28.979
- type: map_at_10
value: 37.785000000000004
- type: map_at_100
value: 38.945
- type: map_at_1000
value: 39.071
- type: map_at_3
value: 35.083999999999996
- type: map_at_5
value: 36.571999999999996
- type: mrr_at_1
value: 36.242000000000004
- type: mrr_at_10
value: 43.552
- type: mrr_at_100
value: 44.228
- type: mrr_at_1000
value: 44.275999999999996
- type: mrr_at_3
value: 41.359
- type: mrr_at_5
value: 42.598
- type: ndcg_at_1
value: 36.242000000000004
- type: ndcg_at_10
value: 42.94
- type: ndcg_at_100
value: 47.343
- type: ndcg_at_1000
value: 49.538
- type: ndcg_at_3
value: 39.086999999999996
- type: ndcg_at_5
value: 40.781
- type: precision_at_1
value: 36.242000000000004
- type: precision_at_10
value: 7.954999999999999
- type: precision_at_100
value: 1.303
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 18.556
- type: precision_at_5
value: 13.145999999999999
- type: recall_at_1
value: 28.979
- type: recall_at_10
value: 51.835
- type: recall_at_100
value: 70.47
- type: recall_at_1000
value: 84.68299999999999
- type: recall_at_3
value: 40.410000000000004
- type: recall_at_5
value: 45.189
- type: map_at_1
value: 37.878
- type: map_at_10
value: 49.903
- type: map_at_100
value: 50.797000000000004
- type: map_at_1000
value: 50.858000000000004
- type: map_at_3
value: 46.526
- type: map_at_5
value: 48.615
- type: mrr_at_1
value: 43.135
- type: mrr_at_10
value: 53.067
- type: mrr_at_100
value: 53.668000000000006
- type: mrr_at_1000
value: 53.698
- type: mrr_at_3
value: 50.449
- type: mrr_at_5
value: 52.117000000000004
- type: ndcg_at_1
value: 43.135
- type: ndcg_at_10
value: 55.641
- type: ndcg_at_100
value: 59.427
- type: ndcg_at_1000
value: 60.655
- type: ndcg_at_3
value: 49.969
- type: ndcg_at_5
value: 53.075
- type: precision_at_1
value: 43.135
- type: precision_at_10
value: 8.997
- type: precision_at_100
value: 1.1809999999999998
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 22.215
- type: precision_at_5
value: 15.586
- type: recall_at_1
value: 37.878
- type: recall_at_10
value: 69.405
- type: recall_at_100
value: 86.262
- type: recall_at_1000
value: 95.012
- type: recall_at_3
value: 54.458
- type: recall_at_5
value: 61.965
- type: map_at_1
value: 24.853
- type: map_at_10
value: 32.402
- type: map_at_100
value: 33.417
- type: map_at_1000
value: 33.498
- type: map_at_3
value: 30.024
- type: map_at_5
value: 31.407
- type: mrr_at_1
value: 26.667
- type: mrr_at_10
value: 34.399
- type: mrr_at_100
value: 35.284
- type: mrr_at_1000
value: 35.345
- type: mrr_at_3
value: 32.109
- type: mrr_at_5
value: 33.375
- type: ndcg_at_1
value: 26.667
- type: ndcg_at_10
value: 36.854
- type: ndcg_at_100
value: 42.196
- type: ndcg_at_1000
value: 44.303
- type: ndcg_at_3
value: 32.186
- type: ndcg_at_5
value: 34.512
- type: precision_at_1
value: 26.667
- type: precision_at_10
value: 5.559
- type: precision_at_100
value: 0.88
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 13.333
- type: precision_at_5
value: 9.379
- type: recall_at_1
value: 24.853
- type: recall_at_10
value: 48.636
- type: recall_at_100
value: 73.926
- type: recall_at_1000
value: 89.94
- type: recall_at_3
value: 36.266
- type: recall_at_5
value: 41.723
- type: map_at_1
value: 14.963999999999999
- type: map_at_10
value: 22.591
- type: map_at_100
value: 23.735999999999997
- type: map_at_1000
value: 23.868000000000002
- type: map_at_3
value: 20.093
- type: map_at_5
value: 21.499
- type: mrr_at_1
value: 18.407999999999998
- type: mrr_at_10
value: 26.863
- type: mrr_at_100
value: 27.87
- type: mrr_at_1000
value: 27.947
- type: mrr_at_3
value: 24.254
- type: mrr_at_5
value: 25.784000000000002
- type: ndcg_at_1
value: 18.407999999999998
- type: ndcg_at_10
value: 27.549
- type: ndcg_at_100
value: 33.188
- type: ndcg_at_1000
value: 36.312
- type: ndcg_at_3
value: 22.862
- type: ndcg_at_5
value: 25.130999999999997
- type: precision_at_1
value: 18.407999999999998
- type: precision_at_10
value: 5.087
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 10.987
- type: precision_at_5
value: 8.209
- type: recall_at_1
value: 14.963999999999999
- type: recall_at_10
value: 38.673
- type: recall_at_100
value: 63.224999999999994
- type: recall_at_1000
value: 85.443
- type: recall_at_3
value: 25.840000000000003
- type: recall_at_5
value: 31.503999999999998
- type: map_at_1
value: 27.861000000000004
- type: map_at_10
value: 37.562
- type: map_at_100
value: 38.906
- type: map_at_1000
value: 39.021
- type: map_at_3
value: 34.743
- type: map_at_5
value: 36.168
- type: mrr_at_1
value: 34.455999999999996
- type: mrr_at_10
value: 43.428
- type: mrr_at_100
value: 44.228
- type: mrr_at_1000
value: 44.278
- type: mrr_at_3
value: 41.001
- type: mrr_at_5
value: 42.315000000000005
- type: ndcg_at_1
value: 34.455999999999996
- type: ndcg_at_10
value: 43.477
- type: ndcg_at_100
value: 48.953
- type: ndcg_at_1000
value: 51.19200000000001
- type: ndcg_at_3
value: 38.799
- type: ndcg_at_5
value: 40.743
- type: precision_at_1
value: 34.455999999999996
- type: precision_at_10
value: 7.902000000000001
- type: precision_at_100
value: 1.244
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 18.511
- type: precision_at_5
value: 12.859000000000002
- type: recall_at_1
value: 27.861000000000004
- type: recall_at_10
value: 55.36
- type: recall_at_100
value: 78.384
- type: recall_at_1000
value: 93.447
- type: recall_at_3
value: 41.926
- type: recall_at_5
value: 47.257
- type: map_at_1
value: 26.375
- type: map_at_10
value: 35.571000000000005
- type: map_at_100
value: 36.785000000000004
- type: map_at_1000
value: 36.905
- type: map_at_3
value: 32.49
- type: map_at_5
value: 34.123999999999995
- type: mrr_at_1
value: 32.647999999999996
- type: mrr_at_10
value: 40.598
- type: mrr_at_100
value: 41.484
- type: mrr_at_1000
value: 41.546
- type: mrr_at_3
value: 37.9
- type: mrr_at_5
value: 39.401
- type: ndcg_at_1
value: 32.647999999999996
- type: ndcg_at_10
value: 41.026
- type: ndcg_at_100
value: 46.365
- type: ndcg_at_1000
value: 48.876
- type: ndcg_at_3
value: 35.843
- type: ndcg_at_5
value: 38.118
- type: precision_at_1
value: 32.647999999999996
- type: precision_at_10
value: 7.443
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 16.819
- type: precision_at_5
value: 11.985999999999999
- type: recall_at_1
value: 26.375
- type: recall_at_10
value: 52.471000000000004
- type: recall_at_100
value: 75.354
- type: recall_at_1000
value: 92.35
- type: recall_at_3
value: 37.893
- type: recall_at_5
value: 43.935
- type: map_at_1
value: 25.012666666666668
- type: map_at_10
value: 33.685833333333335
- type: map_at_100
value: 34.849250000000005
- type: map_at_1000
value: 34.970083333333335
- type: map_at_3
value: 31.065083333333334
- type: map_at_5
value: 32.494416666666666
- type: mrr_at_1
value: 29.772666666666662
- type: mrr_at_10
value: 37.824666666666666
- type: mrr_at_100
value: 38.66741666666666
- type: mrr_at_1000
value: 38.72916666666666
- type: mrr_at_3
value: 35.54575
- type: mrr_at_5
value: 36.81524999999999
- type: ndcg_at_1
value: 29.772666666666662
- type: ndcg_at_10
value: 38.78241666666666
- type: ndcg_at_100
value: 43.84591666666667
- type: ndcg_at_1000
value: 46.275416666666665
- type: ndcg_at_3
value: 34.33416666666667
- type: ndcg_at_5
value: 36.345166666666664
- type: precision_at_1
value: 29.772666666666662
- type: precision_at_10
value: 6.794916666666667
- type: precision_at_100
value: 1.106416666666667
- type: precision_at_1000
value: 0.15033333333333335
- type: precision_at_3
value: 15.815083333333336
- type: precision_at_5
value: 11.184166666666664
- type: recall_at_1
value: 25.012666666666668
- type: recall_at_10
value: 49.748500000000014
- type: recall_at_100
value: 72.11341666666667
- type: recall_at_1000
value: 89.141
- type: recall_at_3
value: 37.242999999999995
- type: recall_at_5
value: 42.49033333333333
- type: map_at_1
value: 23.177
- type: map_at_10
value: 29.310000000000002
- type: map_at_100
value: 30.188
- type: map_at_1000
value: 30.29
- type: map_at_3
value: 27.356
- type: map_at_5
value: 28.410999999999998
- type: mrr_at_1
value: 26.074
- type: mrr_at_10
value: 32.002
- type: mrr_at_100
value: 32.838
- type: mrr_at_1000
value: 32.909
- type: mrr_at_3
value: 30.317
- type: mrr_at_5
value: 31.222
- type: ndcg_at_1
value: 26.074
- type: ndcg_at_10
value: 32.975
- type: ndcg_at_100
value: 37.621
- type: ndcg_at_1000
value: 40.253
- type: ndcg_at_3
value: 29.452
- type: ndcg_at_5
value: 31.020999999999997
- type: precision_at_1
value: 26.074
- type: precision_at_10
value: 5.077
- type: precision_at_100
value: 0.8049999999999999
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 12.526000000000002
- type: precision_at_5
value: 8.588999999999999
- type: recall_at_1
value: 23.177
- type: recall_at_10
value: 41.613
- type: recall_at_100
value: 63.287000000000006
- type: recall_at_1000
value: 83.013
- type: recall_at_3
value: 31.783
- type: recall_at_5
value: 35.769
- type: map_at_1
value: 15.856
- type: map_at_10
value: 22.651
- type: map_at_100
value: 23.649
- type: map_at_1000
value: 23.783
- type: map_at_3
value: 20.591
- type: map_at_5
value: 21.684
- type: mrr_at_1
value: 19.408
- type: mrr_at_10
value: 26.51
- type: mrr_at_100
value: 27.356
- type: mrr_at_1000
value: 27.439999999999998
- type: mrr_at_3
value: 24.547
- type: mrr_at_5
value: 25.562
- type: ndcg_at_1
value: 19.408
- type: ndcg_at_10
value: 27.072000000000003
- type: ndcg_at_100
value: 31.980999999999998
- type: ndcg_at_1000
value: 35.167
- type: ndcg_at_3
value: 23.338
- type: ndcg_at_5
value: 24.94
- type: precision_at_1
value: 19.408
- type: precision_at_10
value: 4.9590000000000005
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.132
- type: precision_at_3
value: 11.138
- type: precision_at_5
value: 7.949000000000001
- type: recall_at_1
value: 15.856
- type: recall_at_10
value: 36.578
- type: recall_at_100
value: 58.89
- type: recall_at_1000
value: 81.743
- type: recall_at_3
value: 25.94
- type: recall_at_5
value: 30.153999999999996
- type: map_at_1
value: 25.892
- type: map_at_10
value: 33.899
- type: map_at_100
value: 34.955000000000005
- type: map_at_1000
value: 35.066
- type: map_at_3
value: 31.41
- type: map_at_5
value: 32.669
- type: mrr_at_1
value: 30.224
- type: mrr_at_10
value: 37.936
- type: mrr_at_100
value: 38.777
- type: mrr_at_1000
value: 38.85
- type: mrr_at_3
value: 35.821
- type: mrr_at_5
value: 36.894
- type: ndcg_at_1
value: 30.224
- type: ndcg_at_10
value: 38.766
- type: ndcg_at_100
value: 43.806
- type: ndcg_at_1000
value: 46.373999999999995
- type: ndcg_at_3
value: 34.325
- type: ndcg_at_5
value: 36.096000000000004
- type: precision_at_1
value: 30.224
- type: precision_at_10
value: 6.446000000000001
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 15.392
- type: precision_at_5
value: 10.671999999999999
- type: recall_at_1
value: 25.892
- type: recall_at_10
value: 49.573
- type: recall_at_100
value: 71.885
- type: recall_at_1000
value: 89.912
- type: recall_at_3
value: 37.226
- type: recall_at_5
value: 41.74
- type: map_at_1
value: 23.915
- type: map_at_10
value: 33.613
- type: map_at_100
value: 35.333999999999996
- type: map_at_1000
value: 35.563
- type: map_at_3
value: 31.203999999999997
- type: map_at_5
value: 32.479
- type: mrr_at_1
value: 29.447000000000003
- type: mrr_at_10
value: 38.440000000000005
- type: mrr_at_100
value: 39.459
- type: mrr_at_1000
value: 39.513999999999996
- type: mrr_at_3
value: 36.495
- type: mrr_at_5
value: 37.592
- type: ndcg_at_1
value: 29.447000000000003
- type: ndcg_at_10
value: 39.341
- type: ndcg_at_100
value: 45.382
- type: ndcg_at_1000
value: 47.921
- type: ndcg_at_3
value: 35.671
- type: ndcg_at_5
value: 37.299
- type: precision_at_1
value: 29.447000000000003
- type: precision_at_10
value: 7.648000000000001
- type: precision_at_100
value: 1.567
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 17.194000000000003
- type: precision_at_5
value: 12.253
- type: recall_at_1
value: 23.915
- type: recall_at_10
value: 49.491
- type: recall_at_100
value: 76.483
- type: recall_at_1000
value: 92.674
- type: recall_at_3
value: 38.878
- type: recall_at_5
value: 43.492
- type: map_at_1
value: 20.283
- type: map_at_10
value: 26.851000000000003
- type: map_at_100
value: 27.973
- type: map_at_1000
value: 28.087
- type: map_at_3
value: 24.887
- type: map_at_5
value: 25.804
- type: mrr_at_1
value: 22.366
- type: mrr_at_10
value: 28.864
- type: mrr_at_100
value: 29.903000000000002
- type: mrr_at_1000
value: 29.988
- type: mrr_at_3
value: 27.017999999999997
- type: mrr_at_5
value: 27.813
- type: ndcg_at_1
value: 22.366
- type: ndcg_at_10
value: 30.898999999999997
- type: ndcg_at_100
value: 36.176
- type: ndcg_at_1000
value: 39.036
- type: ndcg_at_3
value: 26.932000000000002
- type: ndcg_at_5
value: 28.416999999999998
- type: precision_at_1
value: 22.366
- type: precision_at_10
value: 4.824
- type: precision_at_100
value: 0.804
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 11.459999999999999
- type: precision_at_5
value: 7.8740000000000006
- type: recall_at_1
value: 20.283
- type: recall_at_10
value: 41.559000000000005
- type: recall_at_100
value: 65.051
- type: recall_at_1000
value: 86.47200000000001
- type: recall_at_3
value: 30.524
- type: recall_at_5
value: 34.11
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.326
- type: map_at_10
value: 19.357
- type: map_at_100
value: 21.014
- type: map_at_1000
value: 21.188000000000002
- type: map_at_3
value: 16.305
- type: map_at_5
value: 17.886
- type: mrr_at_1
value: 24.820999999999998
- type: mrr_at_10
value: 36.150999999999996
- type: mrr_at_100
value: 37.080999999999996
- type: mrr_at_1000
value: 37.123
- type: mrr_at_3
value: 32.952999999999996
- type: mrr_at_5
value: 34.917
- type: ndcg_at_1
value: 24.820999999999998
- type: ndcg_at_10
value: 27.131
- type: ndcg_at_100
value: 33.841
- type: ndcg_at_1000
value: 37.159
- type: ndcg_at_3
value: 22.311
- type: ndcg_at_5
value: 24.026
- type: precision_at_1
value: 24.820999999999998
- type: precision_at_10
value: 8.450000000000001
- type: precision_at_100
value: 1.557
- type: precision_at_1000
value: 0.218
- type: precision_at_3
value: 16.612
- type: precision_at_5
value: 12.808
- type: recall_at_1
value: 11.326
- type: recall_at_10
value: 32.548
- type: recall_at_100
value: 55.803000000000004
- type: recall_at_1000
value: 74.636
- type: recall_at_3
value: 20.549
- type: recall_at_5
value: 25.514
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.481
- type: map_at_10
value: 15.043999999999999
- type: map_at_100
value: 20.194000000000003
- type: map_at_1000
value: 21.423000000000002
- type: map_at_3
value: 11.238
- type: map_at_5
value: 12.828999999999999
- type: mrr_at_1
value: 54.50000000000001
- type: mrr_at_10
value: 64.713
- type: mrr_at_100
value: 65.216
- type: mrr_at_1000
value: 65.23
- type: mrr_at_3
value: 62.74999999999999
- type: mrr_at_5
value: 63.87500000000001
- type: ndcg_at_1
value: 43.375
- type: ndcg_at_10
value: 32.631
- type: ndcg_at_100
value: 36.338
- type: ndcg_at_1000
value: 43.541000000000004
- type: ndcg_at_3
value: 36.746
- type: ndcg_at_5
value: 34.419
- type: precision_at_1
value: 54.50000000000001
- type: precision_at_10
value: 24.825
- type: precision_at_100
value: 7.698
- type: precision_at_1000
value: 1.657
- type: precision_at_3
value: 38.917
- type: precision_at_5
value: 32.35
- type: recall_at_1
value: 7.481
- type: recall_at_10
value: 20.341
- type: recall_at_100
value: 41.778
- type: recall_at_1000
value: 64.82
- type: recall_at_3
value: 12.748000000000001
- type: recall_at_5
value: 15.507000000000001
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.580000000000005
- type: f1
value: 41.5149462395095
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 61.683
- type: map_at_10
value: 73.071
- type: map_at_100
value: 73.327
- type: map_at_1000
value: 73.341
- type: map_at_3
value: 71.446
- type: map_at_5
value: 72.557
- type: mrr_at_1
value: 66.44200000000001
- type: mrr_at_10
value: 77.725
- type: mrr_at_100
value: 77.89399999999999
- type: mrr_at_1000
value: 77.898
- type: mrr_at_3
value: 76.283
- type: mrr_at_5
value: 77.29700000000001
- type: ndcg_at_1
value: 66.44200000000001
- type: ndcg_at_10
value: 78.43
- type: ndcg_at_100
value: 79.462
- type: ndcg_at_1000
value: 79.754
- type: ndcg_at_3
value: 75.53800000000001
- type: ndcg_at_5
value: 77.332
- type: precision_at_1
value: 66.44200000000001
- type: precision_at_10
value: 9.878
- type: precision_at_100
value: 1.051
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 29.878
- type: precision_at_5
value: 18.953
- type: recall_at_1
value: 61.683
- type: recall_at_10
value: 90.259
- type: recall_at_100
value: 94.633
- type: recall_at_1000
value: 96.60499999999999
- type: recall_at_3
value: 82.502
- type: recall_at_5
value: 86.978
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.724
- type: map_at_10
value: 29.487999999999996
- type: map_at_100
value: 31.243
- type: map_at_1000
value: 31.419999999999998
- type: map_at_3
value: 25.612000000000002
- type: map_at_5
value: 27.859
- type: mrr_at_1
value: 35.802
- type: mrr_at_10
value: 44.684000000000005
- type: mrr_at_100
value: 45.578
- type: mrr_at_1000
value: 45.621
- type: mrr_at_3
value: 42.361
- type: mrr_at_5
value: 43.85
- type: ndcg_at_1
value: 35.802
- type: ndcg_at_10
value: 37.009
- type: ndcg_at_100
value: 43.903
- type: ndcg_at_1000
value: 47.019
- type: ndcg_at_3
value: 33.634
- type: ndcg_at_5
value: 34.965
- type: precision_at_1
value: 35.802
- type: precision_at_10
value: 10.386
- type: precision_at_100
value: 1.7309999999999999
- type: precision_at_1000
value: 0.231
- type: precision_at_3
value: 22.84
- type: precision_at_5
value: 17.037
- type: recall_at_1
value: 17.724
- type: recall_at_10
value: 43.708000000000006
- type: recall_at_100
value: 69.902
- type: recall_at_1000
value: 88.51
- type: recall_at_3
value: 30.740000000000002
- type: recall_at_5
value: 36.742000000000004
- task:
type: Clustering
dataset:
name: MTEB FloresClusteringS2S
type: jinaai/flores_clustering
config: default
split: test
revision: 480b580487f53a46f881354a8348335d4edbb2de
metrics:
- type: v_measure
value: 39.79120149869612
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.801
- type: map_at_10
value: 50.42100000000001
- type: map_at_100
value: 51.254
- type: map_at_1000
value: 51.327999999999996
- type: map_at_3
value: 47.56
- type: map_at_5
value: 49.379
- type: mrr_at_1
value: 69.602
- type: mrr_at_10
value: 76.385
- type: mrr_at_100
value: 76.668
- type: mrr_at_1000
value: 76.683
- type: mrr_at_3
value: 75.102
- type: mrr_at_5
value: 75.949
- type: ndcg_at_1
value: 69.602
- type: ndcg_at_10
value: 59.476
- type: ndcg_at_100
value: 62.527
- type: ndcg_at_1000
value: 64.043
- type: ndcg_at_3
value: 55.155
- type: ndcg_at_5
value: 57.623000000000005
- type: precision_at_1
value: 69.602
- type: precision_at_10
value: 12.292
- type: precision_at_100
value: 1.467
- type: precision_at_1000
value: 0.167
- type: precision_at_3
value: 34.634
- type: precision_at_5
value: 22.728
- type: recall_at_1
value: 34.801
- type: recall_at_10
value: 61.458
- type: recall_at_100
value: 73.363
- type: recall_at_1000
value: 83.43
- type: recall_at_3
value: 51.951
- type: recall_at_5
value: 56.82000000000001
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 67.46079999999999
- type: ap
value: 61.81278199159353
- type: f1
value: 67.26505019954826
- task:
type: Reranking
dataset:
name: MTEB MIRACL
type: jinaai/miracl
config: default
split: test
revision: d28a029f35c4ff7f616df47b0edf54e6882395e6
metrics:
- type: map
value: 73.90464144118539
- type: mrr
value: 82.44674693216022
- task:
type: Retrieval
dataset:
name: MTEB MIRACLRetrieval
type: jinaai/miracl
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.299
- type: map_at_10
value: 70.547
- type: map_at_100
value: 72.394
- type: map_at_1000
value: 72.39999999999999
- type: map_at_3
value: 41.317
- type: map_at_5
value: 53.756
- type: mrr_at_1
value: 72.84
- type: mrr_at_10
value: 82.466
- type: mrr_at_100
value: 82.52199999999999
- type: mrr_at_1000
value: 82.52199999999999
- type: mrr_at_3
value: 80.607
- type: mrr_at_5
value: 82.065
- type: ndcg_at_1
value: 72.994
- type: ndcg_at_10
value: 80.89
- type: ndcg_at_100
value: 83.30199999999999
- type: ndcg_at_1000
value: 83.337
- type: ndcg_at_3
value: 70.357
- type: ndcg_at_5
value: 72.529
- type: precision_at_1
value: 72.994
- type: precision_at_10
value: 43.056
- type: precision_at_100
value: 4.603
- type: precision_at_1000
value: 0.461
- type: precision_at_3
value: 61.626000000000005
- type: precision_at_5
value: 55.525000000000006
- type: recall_at_1
value: 21.299
- type: recall_at_10
value: 93.903
- type: recall_at_100
value: 99.86699999999999
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 46.653
- type: recall_at_5
value: 65.72200000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.37163702690378
- type: f1
value: 90.18615216514222
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 89.88992661774515
- type: f1
value: 89.3738963046966
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 71.97218422252622
- type: f1
value: 54.03096570916335
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 68.75917278185457
- type: f1
value: 49.144083814705844
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.75991930060525
- type: f1
value: 69.37993796176502
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.93006052454606
- type: f1
value: 66.04029135274683
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.81977135171486
- type: f1
value: 74.10477122507747
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (es)
type: mteb/amazon_massive_scenario
config: es
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.23402824478816
- type: f1
value: 71.75572665880296
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 32.189750849969215
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.78357393555938
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.605612998328358
- type: mrr
value: 31.595529205695833
- task:
type: Retrieval
dataset:
name: MTEB MintakaESRetrieval
type: jinaai/mintakaqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.213
- type: map_at_10
value: 24.079
- type: map_at_100
value: 25.039
- type: map_at_1000
value: 25.142999999999997
- type: map_at_3
value: 21.823
- type: map_at_5
value: 23.069
- type: mrr_at_1
value: 16.213
- type: mrr_at_10
value: 24.079
- type: mrr_at_100
value: 25.039
- type: mrr_at_1000
value: 25.142999999999997
- type: mrr_at_3
value: 21.823
- type: mrr_at_5
value: 23.069
- type: ndcg_at_1
value: 16.213
- type: ndcg_at_10
value: 28.315
- type: ndcg_at_100
value: 33.475
- type: ndcg_at_1000
value: 36.838
- type: ndcg_at_3
value: 23.627000000000002
- type: ndcg_at_5
value: 25.879
- type: precision_at_1
value: 16.213
- type: precision_at_10
value: 4.183
- type: precision_at_100
value: 0.6709999999999999
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 9.612
- type: precision_at_5
value: 6.865
- type: recall_at_1
value: 16.213
- type: recall_at_10
value: 41.832
- type: recall_at_100
value: 67.12
- type: recall_at_1000
value: 94.843
- type: recall_at_3
value: 28.837000000000003
- type: recall_at_5
value: 34.323
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.692
- type: map_at_10
value: 10.783
- type: map_at_100
value: 13.447999999999999
- type: map_at_1000
value: 14.756
- type: map_at_3
value: 7.646
- type: map_at_5
value: 9.311
- type: mrr_at_1
value: 42.415000000000006
- type: mrr_at_10
value: 50.471
- type: mrr_at_100
value: 51.251999999999995
- type: mrr_at_1000
value: 51.292
- type: mrr_at_3
value: 48.4
- type: mrr_at_5
value: 49.809
- type: ndcg_at_1
value: 40.867
- type: ndcg_at_10
value: 30.303
- type: ndcg_at_100
value: 27.915
- type: ndcg_at_1000
value: 36.734
- type: ndcg_at_3
value: 35.74
- type: ndcg_at_5
value: 33.938
- type: precision_at_1
value: 42.415000000000006
- type: precision_at_10
value: 22.105
- type: precision_at_100
value: 7.173
- type: precision_at_1000
value: 2.007
- type: precision_at_3
value: 33.437
- type: precision_at_5
value: 29.349999999999998
- type: recall_at_1
value: 4.692
- type: recall_at_10
value: 14.798
- type: recall_at_100
value: 28.948
- type: recall_at_1000
value: 59.939
- type: recall_at_3
value: 8.562
- type: recall_at_5
value: 11.818
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.572999999999997
- type: map_at_10
value: 42.754
- type: map_at_100
value: 43.8
- type: map_at_1000
value: 43.838
- type: map_at_3
value: 38.157000000000004
- type: map_at_5
value: 40.9
- type: mrr_at_1
value: 31.373
- type: mrr_at_10
value: 45.321
- type: mrr_at_100
value: 46.109
- type: mrr_at_1000
value: 46.135
- type: mrr_at_3
value: 41.483
- type: mrr_at_5
value: 43.76
- type: ndcg_at_1
value: 31.373
- type: ndcg_at_10
value: 50.7
- type: ndcg_at_100
value: 55.103
- type: ndcg_at_1000
value: 55.955999999999996
- type: ndcg_at_3
value: 42.069
- type: ndcg_at_5
value: 46.595
- type: precision_at_1
value: 31.373
- type: precision_at_10
value: 8.601
- type: precision_at_100
value: 1.11
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.399
- type: precision_at_5
value: 14.224
- type: recall_at_1
value: 27.572999999999997
- type: recall_at_10
value: 72.465
- type: recall_at_100
value: 91.474
- type: recall_at_1000
value: 97.78099999999999
- type: recall_at_3
value: 50.087
- type: recall_at_5
value: 60.516000000000005
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.525
- type: map_at_10
value: 84.417
- type: map_at_100
value: 85.07000000000001
- type: map_at_1000
value: 85.085
- type: map_at_3
value: 81.45
- type: map_at_5
value: 83.317
- type: mrr_at_1
value: 81.17999999999999
- type: mrr_at_10
value: 87.34100000000001
- type: mrr_at_100
value: 87.461
- type: mrr_at_1000
value: 87.46199999999999
- type: mrr_at_3
value: 86.372
- type: mrr_at_5
value: 87.046
- type: ndcg_at_1
value: 81.17999999999999
- type: ndcg_at_10
value: 88.144
- type: ndcg_at_100
value: 89.424
- type: ndcg_at_1000
value: 89.517
- type: ndcg_at_3
value: 85.282
- type: ndcg_at_5
value: 86.874
- type: precision_at_1
value: 81.17999999999999
- type: precision_at_10
value: 13.385
- type: precision_at_100
value: 1.533
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.29
- type: precision_at_5
value: 24.546
- type: recall_at_1
value: 70.525
- type: recall_at_10
value: 95.22500000000001
- type: recall_at_100
value: 99.572
- type: recall_at_1000
value: 99.98899999999999
- type: recall_at_3
value: 87.035
- type: recall_at_5
value: 91.526
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 48.284384328108736
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 56.02508021518392
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.023000000000001
- type: map_at_10
value: 10.046
- type: map_at_100
value: 11.802999999999999
- type: map_at_1000
value: 12.074
- type: map_at_3
value: 7.071
- type: map_at_5
value: 8.556
- type: mrr_at_1
value: 19.8
- type: mrr_at_10
value: 30.105999999999998
- type: mrr_at_100
value: 31.16
- type: mrr_at_1000
value: 31.224
- type: mrr_at_3
value: 26.633000000000003
- type: mrr_at_5
value: 28.768
- type: ndcg_at_1
value: 19.8
- type: ndcg_at_10
value: 17.358
- type: ndcg_at_100
value: 24.566
- type: ndcg_at_1000
value: 29.653000000000002
- type: ndcg_at_3
value: 16.052
- type: ndcg_at_5
value: 14.325
- type: precision_at_1
value: 19.8
- type: precision_at_10
value: 9.07
- type: precision_at_100
value: 1.955
- type: precision_at_1000
value: 0.318
- type: precision_at_3
value: 14.933
- type: precision_at_5
value: 12.68
- type: recall_at_1
value: 4.023000000000001
- type: recall_at_10
value: 18.398
- type: recall_at_100
value: 39.683
- type: recall_at_1000
value: 64.625
- type: recall_at_3
value: 9.113
- type: recall_at_5
value: 12.873000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 87.90508618312852
- type: cos_sim_spearman
value: 83.01323463129205
- type: euclidean_pearson
value: 84.35845059002891
- type: euclidean_spearman
value: 82.85508559018527
- type: manhattan_pearson
value: 84.3682368950498
- type: manhattan_spearman
value: 82.8619728517302
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 89.28294535873366
- type: cos_sim_spearman
value: 81.61879268131732
- type: euclidean_pearson
value: 85.99053604863724
- type: euclidean_spearman
value: 80.95176684739084
- type: manhattan_pearson
value: 85.98054086663903
- type: manhattan_spearman
value: 80.9911070430335
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 86.15898098455258
- type: cos_sim_spearman
value: 86.8247985072307
- type: euclidean_pearson
value: 86.25342429918649
- type: euclidean_spearman
value: 87.13468603023252
- type: manhattan_pearson
value: 86.2006134067688
- type: manhattan_spearman
value: 87.06135811996896
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 85.57403998481877
- type: cos_sim_spearman
value: 83.55947075172618
- type: euclidean_pearson
value: 84.97097562965358
- type: euclidean_spearman
value: 83.6287075601467
- type: manhattan_pearson
value: 84.87092197104133
- type: manhattan_spearman
value: 83.53783891641335
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.14632780204231
- type: cos_sim_spearman
value: 88.74903634923868
- type: euclidean_pearson
value: 88.03922995855112
- type: euclidean_spearman
value: 88.72852190525855
- type: manhattan_pearson
value: 87.9694791024271
- type: manhattan_spearman
value: 88.66461452107418
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 84.75989818558652
- type: cos_sim_spearman
value: 86.03107893122942
- type: euclidean_pearson
value: 85.21908960133018
- type: euclidean_spearman
value: 85.93012720153482
- type: manhattan_pearson
value: 85.1969170195502
- type: manhattan_spearman
value: 85.8975254197784
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.16803898789955
- type: cos_sim_spearman
value: 88.56139047950525
- type: euclidean_pearson
value: 88.09685325747859
- type: euclidean_spearman
value: 88.0457609458947
- type: manhattan_pearson
value: 88.07054413001431
- type: manhattan_spearman
value: 88.10784098889314
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.7160384474547
- type: cos_sim_spearman
value: 86.4899235500562
- type: euclidean_pearson
value: 85.90854477703468
- type: euclidean_spearman
value: 86.16085009124498
- type: manhattan_pearson
value: 85.9249735317884
- type: manhattan_spearman
value: 86.25038421339116
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 89.37914622360788
- type: cos_sim_spearman
value: 88.24619159322809
- type: euclidean_pearson
value: 89.00538382632769
- type: euclidean_spearman
value: 88.44675863524736
- type: manhattan_pearson
value: 88.97372120683606
- type: manhattan_spearman
value: 88.33509324222129
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 66.22181360203069
- type: cos_sim_spearman
value: 65.6218291833768
- type: euclidean_pearson
value: 67.14543788822508
- type: euclidean_spearman
value: 65.21269939987857
- type: manhattan_pearson
value: 67.03304607195636
- type: manhattan_spearman
value: 65.18885316423805
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 65.71694059677084
- type: cos_sim_spearman
value: 67.96591844540954
- type: euclidean_pearson
value: 65.6964079162296
- type: euclidean_spearman
value: 67.53027948900173
- type: manhattan_pearson
value: 65.93545097673741
- type: manhattan_spearman
value: 67.7261811805062
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 75.43544796375058
- type: cos_sim_spearman
value: 78.80462701160789
- type: euclidean_pearson
value: 76.19135575163138
- type: euclidean_spearman
value: 78.4974732597096
- type: manhattan_pearson
value: 76.3254742699264
- type: manhattan_spearman
value: 78.51884307690416
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.46805293607684
- type: cos_sim_spearman
value: 87.83792784689113
- type: euclidean_pearson
value: 87.3872143683234
- type: euclidean_spearman
value: 87.61611384542778
- type: manhattan_pearson
value: 87.38542672601992
- type: manhattan_spearman
value: 87.61423971087297
- task:
type: STS
dataset:
name: MTEB STSES
type: PlanTL-GOB-ES/sts-es
config: default
split: test
revision: 0912bb6c9393c76d62a7c5ee81c4c817ff47c9f4
metrics:
- type: cos_sim_pearson
value: 82.55286866116202
- type: cos_sim_spearman
value: 80.22150503320272
- type: euclidean_pearson
value: 83.27223445187087
- type: euclidean_spearman
value: 80.59078590992925
- type: manhattan_pearson
value: 83.23095887013197
- type: manhattan_spearman
value: 80.87994285189795
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.29717302265792
- type: mrr
value: 94.02156304117088
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 49.9
- type: map_at_10
value: 58.626
- type: map_at_100
value: 59.519999999999996
- type: map_at_1000
value: 59.55200000000001
- type: map_at_3
value: 56.232000000000006
- type: map_at_5
value: 57.833
- type: mrr_at_1
value: 52.333
- type: mrr_at_10
value: 60.039
- type: mrr_at_100
value: 60.732
- type: mrr_at_1000
value: 60.75899999999999
- type: mrr_at_3
value: 58.278
- type: mrr_at_5
value: 59.428000000000004
- type: ndcg_at_1
value: 52.333
- type: ndcg_at_10
value: 62.67
- type: ndcg_at_100
value: 66.465
- type: ndcg_at_1000
value: 67.425
- type: ndcg_at_3
value: 58.711999999999996
- type: ndcg_at_5
value: 60.958999999999996
- type: precision_at_1
value: 52.333
- type: precision_at_10
value: 8.333
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 22.778000000000002
- type: precision_at_5
value: 15.267
- type: recall_at_1
value: 49.9
- type: recall_at_10
value: 73.394
- type: recall_at_100
value: 90.43299999999999
- type: recall_at_1000
value: 98.167
- type: recall_at_3
value: 63.032999999999994
- type: recall_at_5
value: 68.444
- task:
type: Clustering
dataset:
name: MTEB SpanishNewsClusteringP2P
type: jinaai/spanish_news_clustering
config: default
split: test
revision: b5edc3d3d7c12c7b9f883e9da50f6732f3624142
metrics:
- type: v_measure
value: 48.30543557796266
- task:
type: Retrieval
dataset:
name: MTEB SpanishPassageRetrievalS2P
type: jinaai/spanish_passage_retrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.443
- type: map_at_10
value: 28.736
- type: map_at_100
value: 34.514
- type: map_at_1000
value: 35.004000000000005
- type: map_at_3
value: 20.308
- type: map_at_5
value: 25.404
- type: mrr_at_1
value: 50.29900000000001
- type: mrr_at_10
value: 63.757
- type: mrr_at_100
value: 64.238
- type: mrr_at_1000
value: 64.24600000000001
- type: mrr_at_3
value: 59.480999999999995
- type: mrr_at_5
value: 62.924
- type: ndcg_at_1
value: 50.29900000000001
- type: ndcg_at_10
value: 42.126999999999995
- type: ndcg_at_100
value: 57.208000000000006
- type: ndcg_at_1000
value: 60.646
- type: ndcg_at_3
value: 38.722
- type: ndcg_at_5
value: 40.007999999999996
- type: precision_at_1
value: 50.29900000000001
- type: precision_at_10
value: 19.82
- type: precision_at_100
value: 4.82
- type: precision_at_1000
value: 0.5910000000000001
- type: precision_at_3
value: 31.537
- type: precision_at_5
value: 28.262999999999998
- type: recall_at_1
value: 14.443
- type: recall_at_10
value: 43.885999999999996
- type: recall_at_100
value: 85.231
- type: recall_at_1000
value: 99.07000000000001
- type: recall_at_3
value: 22.486
- type: recall_at_5
value: 33.035
- type: map_at_1
value: 15.578
- type: map_at_10
value: 52.214000000000006
- type: map_at_100
value: 64.791
- type: map_at_1000
value: 64.791
- type: map_at_3
value: 33.396
- type: map_at_5
value: 41.728
- type: mrr_at_1
value: 73.653
- type: mrr_at_10
value: 85.116
- type: mrr_at_100
value: 85.205
- type: mrr_at_1000
value: 85.205
- type: mrr_at_3
value: 84.631
- type: mrr_at_5
value: 85.05
- type: ndcg_at_1
value: 76.64699999999999
- type: ndcg_at_10
value: 70.38600000000001
- type: ndcg_at_100
value: 82.27600000000001
- type: ndcg_at_1000
value: 82.27600000000001
- type: ndcg_at_3
value: 70.422
- type: ndcg_at_5
value: 69.545
- type: precision_at_1
value: 76.64699999999999
- type: precision_at_10
value: 43.653
- type: precision_at_100
value: 7.718999999999999
- type: precision_at_1000
value: 0.772
- type: precision_at_3
value: 64.671
- type: precision_at_5
value: 56.766000000000005
- type: recall_at_1
value: 15.578
- type: recall_at_10
value: 67.459
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 36.922
- type: recall_at_5
value: 49.424
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81683168316832
- type: cos_sim_ap
value: 95.61502659412484
- type: cos_sim_f1
value: 90.6813627254509
- type: cos_sim_precision
value: 90.86345381526104
- type: cos_sim_recall
value: 90.5
- type: dot_accuracy
value: 99.8039603960396
- type: dot_ap
value: 95.36783483182609
- type: dot_f1
value: 89.90825688073394
- type: dot_precision
value: 91.68399168399168
- type: dot_recall
value: 88.2
- type: euclidean_accuracy
value: 99.81188118811882
- type: euclidean_ap
value: 95.51583052324564
- type: euclidean_f1
value: 90.46214355948868
- type: euclidean_precision
value: 88.97485493230174
- type: euclidean_recall
value: 92.0
- type: manhattan_accuracy
value: 99.8079207920792
- type: manhattan_ap
value: 95.44030644653718
- type: manhattan_f1
value: 90.37698412698413
- type: manhattan_precision
value: 89.66535433070865
- type: manhattan_recall
value: 91.10000000000001
- type: max_accuracy
value: 99.81683168316832
- type: max_ap
value: 95.61502659412484
- type: max_f1
value: 90.6813627254509
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 55.39046705023096
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.57429225651293
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.17622570658746
- type: mrr
value: 50.99844293778118
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.97416289382191
- type: cos_sim_spearman
value: 29.871890597161432
- type: dot_pearson
value: 28.768845892613644
- type: dot_spearman
value: 28.872458999448686
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22599999999999998
- type: map_at_10
value: 1.646
- type: map_at_100
value: 9.491
- type: map_at_1000
value: 23.75
- type: map_at_3
value: 0.588
- type: map_at_5
value: 0.9129999999999999
- type: mrr_at_1
value: 84.0
- type: mrr_at_10
value: 89.889
- type: mrr_at_100
value: 89.889
- type: mrr_at_1000
value: 89.889
- type: mrr_at_3
value: 89.667
- type: mrr_at_5
value: 89.667
- type: ndcg_at_1
value: 75.0
- type: ndcg_at_10
value: 67.368
- type: ndcg_at_100
value: 52.834
- type: ndcg_at_1000
value: 49.144
- type: ndcg_at_3
value: 72.866
- type: ndcg_at_5
value: 70.16
- type: precision_at_1
value: 84.0
- type: precision_at_10
value: 71.8
- type: precision_at_100
value: 54.04
- type: precision_at_1000
value: 21.709999999999997
- type: precision_at_3
value: 77.333
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.22599999999999998
- type: recall_at_10
value: 1.9029999999999998
- type: recall_at_100
value: 13.012
- type: recall_at_1000
value: 46.105000000000004
- type: recall_at_3
value: 0.63
- type: recall_at_5
value: 1.0030000000000001
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.5
- type: map_at_10
value: 8.193999999999999
- type: map_at_100
value: 14.01
- type: map_at_1000
value: 15.570999999999998
- type: map_at_3
value: 4.361000000000001
- type: map_at_5
value: 5.9270000000000005
- type: mrr_at_1
value: 16.326999999999998
- type: mrr_at_10
value: 33.326
- type: mrr_at_100
value: 34.592
- type: mrr_at_1000
value: 34.592
- type: mrr_at_3
value: 29.252
- type: mrr_at_5
value: 30.680000000000003
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 19.819
- type: ndcg_at_100
value: 33.428000000000004
- type: ndcg_at_1000
value: 45.024
- type: ndcg_at_3
value: 19.667
- type: ndcg_at_5
value: 19.625
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 18.367
- type: precision_at_100
value: 7.367
- type: precision_at_1000
value: 1.496
- type: precision_at_3
value: 23.128999999999998
- type: precision_at_5
value: 21.633
- type: recall_at_1
value: 1.5
- type: recall_at_10
value: 14.362
- type: recall_at_100
value: 45.842
- type: recall_at_1000
value: 80.42
- type: recall_at_3
value: 5.99
- type: recall_at_5
value: 8.701
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.04740000000001
- type: ap
value: 13.58661943759992
- type: f1
value: 53.727487131754195
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 61.06395019807584
- type: f1
value: 61.36753664680866
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 40.19881263066229
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.19401561661799
- type: cos_sim_ap
value: 71.62462506173092
- type: cos_sim_f1
value: 66.0641327225455
- type: cos_sim_precision
value: 62.234662934453
- type: cos_sim_recall
value: 70.3957783641161
- type: dot_accuracy
value: 84.69333015437802
- type: dot_ap
value: 69.83805526490895
- type: dot_f1
value: 64.85446235265817
- type: dot_precision
value: 59.59328028293546
- type: dot_recall
value: 71.13456464379946
- type: euclidean_accuracy
value: 85.38475293556655
- type: euclidean_ap
value: 72.05594596250286
- type: euclidean_f1
value: 66.53543307086615
- type: euclidean_precision
value: 62.332872291378514
- type: euclidean_recall
value: 71.34564643799473
- type: manhattan_accuracy
value: 85.3907134767837
- type: manhattan_ap
value: 72.04585410650152
- type: manhattan_f1
value: 66.57132642116554
- type: manhattan_precision
value: 60.704194740273856
- type: manhattan_recall
value: 73.6939313984169
- type: max_accuracy
value: 85.3907134767837
- type: max_ap
value: 72.05594596250286
- type: max_f1
value: 66.57132642116554
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.30414871735165
- type: cos_sim_ap
value: 86.4398673359918
- type: cos_sim_f1
value: 78.9243598692186
- type: cos_sim_precision
value: 75.47249350101876
- type: cos_sim_recall
value: 82.7071142593163
- type: dot_accuracy
value: 89.26145845461248
- type: dot_ap
value: 86.32172118414802
- type: dot_f1
value: 78.8277467755645
- type: dot_precision
value: 75.79418662497335
- type: dot_recall
value: 82.11425931629196
- type: euclidean_accuracy
value: 89.24205378973105
- type: euclidean_ap
value: 86.23988673522649
- type: euclidean_f1
value: 78.67984857951413
- type: euclidean_precision
value: 75.2689684269742
- type: euclidean_recall
value: 82.41453649522637
- type: manhattan_accuracy
value: 89.18189932859859
- type: manhattan_ap
value: 86.21003833972824
- type: manhattan_f1
value: 78.70972564850115
- type: manhattan_precision
value: 76.485544094145
- type: manhattan_recall
value: 81.0671388974438
- type: max_accuracy
value: 89.30414871735165
- type: max_ap
value: 86.4398673359918
- type: max_f1
value: 78.9243598692186
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 73.254610626148
- task:
type: Retrieval
dataset:
name: MTEB XMarketES
type: jinaai/xmarket_ml
config: default
split: test
revision: 705db869e8107dfe6e34b832af90446e77d813e3
metrics:
- type: map_at_1
value: 5.506
- type: map_at_10
value: 11.546
- type: map_at_100
value: 14.299999999999999
- type: map_at_1000
value: 15.146999999999998
- type: map_at_3
value: 8.748000000000001
- type: map_at_5
value: 10.036000000000001
- type: mrr_at_1
value: 17.902
- type: mrr_at_10
value: 25.698999999999998
- type: mrr_at_100
value: 26.634
- type: mrr_at_1000
value: 26.704
- type: mrr_at_3
value: 23.244999999999997
- type: mrr_at_5
value: 24.555
- type: ndcg_at_1
value: 17.902
- type: ndcg_at_10
value: 19.714000000000002
- type: ndcg_at_100
value: 25.363000000000003
- type: ndcg_at_1000
value: 30.903999999999996
- type: ndcg_at_3
value: 17.884
- type: ndcg_at_5
value: 18.462
- type: precision_at_1
value: 17.902
- type: precision_at_10
value: 10.467
- type: precision_at_100
value: 3.9699999999999998
- type: precision_at_1000
value: 1.1320000000000001
- type: precision_at_3
value: 14.387
- type: precision_at_5
value: 12.727
- type: recall_at_1
value: 5.506
- type: recall_at_10
value: 19.997999999999998
- type: recall_at_100
value: 42.947
- type: recall_at_1000
value: 67.333
- type: recall_at_3
value: 11.158
- type: recall_at_5
value: 14.577000000000002
- task:
type: Retrieval
dataset:
name: MTEB XPQAESRetrieval
type: jinaai/xpqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.53
- type: map_at_10
value: 58.68600000000001
- type: map_at_100
value: 60.45399999999999
- type: map_at_1000
value: 60.51499999999999
- type: map_at_3
value: 50.356
- type: map_at_5
value: 55.98
- type: mrr_at_1
value: 61.791
- type: mrr_at_10
value: 68.952
- type: mrr_at_100
value: 69.524
- type: mrr_at_1000
value: 69.538
- type: mrr_at_3
value: 67.087
- type: mrr_at_5
value: 68.052
- type: ndcg_at_1
value: 61.791
- type: ndcg_at_10
value: 65.359
- type: ndcg_at_100
value: 70.95700000000001
- type: ndcg_at_1000
value: 71.881
- type: ndcg_at_3
value: 59.999
- type: ndcg_at_5
value: 61.316
- type: precision_at_1
value: 61.791
- type: precision_at_10
value: 18.184
- type: precision_at_100
value: 2.317
- type: precision_at_1000
value: 0.245
- type: precision_at_3
value: 42.203
- type: precision_at_5
value: 31.374999999999996
- type: recall_at_1
value: 32.53
- type: recall_at_10
value: 73.098
- type: recall_at_100
value: 94.029
- type: recall_at_1000
value: 99.842
- type: recall_at_3
value: 54.525
- type: recall_at_5
value: 63.796
---
<!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
## Quick Start
The easiest way to starting using `jina-embeddings-v2-base-es` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
## Intended Usage & Model Info
`jina-embeddings-v2-base-es` is a Spanish/English bilingual text **embedding model** supporting **8192 sequence length**.
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
We have designed it for high performance in mono-lingual & cross-lingual applications and trained it specifically to support mixed Spanish-English input without bias.
Additionally, we provide the following embedding models:
`jina-embeddings-v2-base-es` es un modelo (embedding) de texto bilingüe Inglés/Español que admite una longitud de secuencia de 8192.
Se basa en la arquitectura BERT (JinaBERT) que incorpora la variante bi-direccional simétrica de [ALiBi](https://arxiv.org/abs/2108.12409) para permitir una mayor longitud de secuencia.
Hemos diseñado este modelo para un alto rendimiento en aplicaciones monolingües y bilingües, y está entrenando específicamente para admitir entradas mixtas de español e inglés sin sesgo.
Adicionalmente, proporcionamos los siguientes modelos (embeddings):
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings **(you are here)**.
## Data & Parameters
The data and training details are described in this [technical report](https://arxiv.org/abs/2402.17016)
## Usage
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>
### Why mean pooling?
`mean pooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.
However, if you would like to do it without using the default `encode` function:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['How is the weather today?', 'What is the current weather like today?']
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-es')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-es', trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
You can use Jina Embedding models directly from the `transformers` package:
```python
!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-es', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])
print(cos_sim(embeddings[0], embeddings[1]))
```
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(
['Very long ... document'],
max_length=2048
)
```
Or you can use the model with the `sentence-transformers` package:
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("jinaai/jina-embeddings-v2-base-es", trust_remote_code=True)
embeddings = model.encode(['How is the weather today?', '¿Qué tiempo hace hoy?'])
print(util.cos_sim(embeddings[0], embeddings[1]))
```
And if you only want to handle shorter sequence, such as 2k, then you can set the `model.max_seq_length`
```python
model.max_seq_length = 2048
```
## Alternatives to Transformers and Sentence Transformers
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
## Use Jina Embeddings for RAG
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
## Plans
1. Bilingual embedding models supporting more European & Asian languages, including French, Italian and Japanese.
2. Multimodal embedding models enable Multimodal RAG applications.
3. High-performt rerankers.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
## Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
```
@article{mohr2024multi,
title={Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings},
author={Mohr, Isabelle and Krimmel, Markus and Sturua, Saba and Akram, Mohammad Kalim and Koukounas, Andreas and G{\"u}nther, Michael and Mastrapas, Georgios and Ravishankar, Vinit and Mart{\'\i}nez, Joan Fontanals and Wang, Feng and others},
journal={arXiv preprint arXiv:2402.17016},
year={2024}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Lajavaness/bilingual-embedding-large | Lajavaness | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bilingual",
"feature-extraction",
"sentence-similarity",
"transformers",
"sentence-embedding",
"mteb",
"custom_code",
"fr",
"en",
"arxiv:2010.08240",
"arxiv:1911.02116",
"arxiv:1908.10084",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-06-24T09:08:32 | 2024-08-06T09:22:13 | 31,132 | 21 | ---
language:
- fr
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- sentence-embedding
- mteb
model-index:
- name: bilingual-embedding-large
results:
- task:
type: Clustering
dataset:
name: MTEB AlloProfClusteringP2P
type: lyon-nlp/alloprof
config: default
split: test
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
metrics:
- type: v_measure
value: 65.3004467686438
- type: v_measures
value:
- 0.632560011824588
- 0.6345771823814063
- 0.6333686484625257
- 0.6508206816667124
- 0.6378451181543632
- type: v_measure
value: 55.3684183324479
- type: v_measures
value:
- 0.5262468095085737
- 0.586151012721014
- 0.5192907959178751
- 0.5610730679809162
- 0.6360060059791816
- task:
type: Reranking
dataset:
name: MTEB AlloprofReranking
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
config: default
split: test
revision: 65393d0d7a08a10b4e348135e824f385d420b0fd
metrics:
- type: map
value: 73.63055206572554
- type: mrr
value: 74.69705225210407
- type: nAUC_map_diff1
value: 56.61121737089957
- type: nAUC_map_max
value: 21.353273116363358
- type: nAUC_mrr_diff1
value: 55.98316099424804
- type: nAUC_mrr_max
value: 22.29736406333825
- task:
type: Retrieval
dataset:
name: MTEB AlloprofRetrieval
type: lyon-nlp/alloprof
config: default
split: test
revision: fcf295ea64c750f41fadbaa37b9b861558e1bfbd
metrics:
- type: map_at_1
value: 30.009000000000004
- type: map_at_10
value: 41.563
- type: map_at_100
value: 42.498999999999995
- type: map_at_1000
value: 42.541000000000004
- type: map_at_20
value: 42.142
- type: map_at_3
value: 38.443
- type: map_at_5
value: 40.23
- type: mrr_at_1
value: 30.008635578583764
- type: mrr_at_10
value: 41.563313869013434
- type: mrr_at_100
value: 42.49919838395685
- type: mrr_at_1000
value: 42.54117981321103
- type: mrr_at_20
value: 42.14177102110932
- type: mrr_at_3
value: 38.44271732872777
- type: mrr_at_5
value: 40.23028209556721
- type: nauc_map_at_1000_diff1
value: 37.69874084954785
- type: nauc_map_at_1000_max
value: 35.67975084044886
- type: nauc_map_at_100_diff1
value: 37.683425621005334
- type: nauc_map_at_100_max
value: 35.70179282323718
- type: nauc_map_at_10_diff1
value: 37.60741578478419
- type: nauc_map_at_10_max
value: 35.73500192122569
- type: nauc_map_at_1_diff1
value: 43.314035692233396
- type: nauc_map_at_1_max
value: 31.881007724238064
- type: nauc_map_at_20_diff1
value: 37.604821571809694
- type: nauc_map_at_20_max
value: 35.71558055856275
- type: nauc_map_at_3_diff1
value: 37.64200820646518
- type: nauc_map_at_3_max
value: 34.558370321480005
- type: nauc_map_at_5_diff1
value: 37.48910576281629
- type: nauc_map_at_5_max
value: 35.16709650751366
- type: nauc_mrr_at_1000_diff1
value: 37.69874084954785
- type: nauc_mrr_at_1000_max
value: 35.67975084044886
- type: nauc_mrr_at_100_diff1
value: 37.683425621005334
- type: nauc_mrr_at_100_max
value: 35.70179282323718
- type: nauc_mrr_at_10_diff1
value: 37.60741578478419
- type: nauc_mrr_at_10_max
value: 35.73500192122569
- type: nauc_mrr_at_1_diff1
value: 43.314035692233396
- type: nauc_mrr_at_1_max
value: 31.881007724238064
- type: nauc_mrr_at_20_diff1
value: 37.604821571809694
- type: nauc_mrr_at_20_max
value: 35.71558055856275
- type: nauc_mrr_at_3_diff1
value: 37.64200820646518
- type: nauc_mrr_at_3_max
value: 34.558370321480005
- type: nauc_mrr_at_5_diff1
value: 37.48910576281629
- type: nauc_mrr_at_5_max
value: 35.16709650751366
- type: nauc_ndcg_at_1000_diff1
value: 36.79519873157631
- type: nauc_ndcg_at_1000_max
value: 37.14476960275735
- type: nauc_ndcg_at_100_diff1
value: 36.283195451522566
- type: nauc_ndcg_at_100_max
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- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_1_diff1
value: 43.314035692233396
- type: nauc_ndcg_at_1_max
value: 31.881007724238064
- type: nauc_ndcg_at_20_diff1
value: 35.84645351663945
- type: nauc_ndcg_at_20_max
value: 38.01406125615156
- type: nauc_ndcg_at_3_diff1
value: 36.088922679698285
- type: nauc_ndcg_at_3_max
value: 35.41968041752933
- type: nauc_ndcg_at_5_diff1
value: 35.750269212484895
- type: nauc_ndcg_at_5_max
value: 36.490862523260134
- type: nauc_precision_at_1000_diff1
value: 40.85377128270902
- type: nauc_precision_at_1000_max
value: 78.7188042554787
- type: nauc_precision_at_100_diff1
value: 25.95337392513788
- type: nauc_precision_at_100_max
value: 59.85395510353242
- type: nauc_precision_at_10_diff1
value: 29.989736669251176
- type: nauc_precision_at_10_max
value: 47.01836650640274
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value: 43.314035692233396
- type: nauc_precision_at_1_max
value: 31.881007724238064
- type: nauc_precision_at_20_diff1
value: 28.236939136767763
- type: nauc_precision_at_20_max
value: 49.05567543361526
- type: nauc_precision_at_3_diff1
value: 31.697690633887817
- type: nauc_precision_at_3_max
value: 37.90080773298326
- type: nauc_precision_at_5_diff1
value: 30.466477711769823
- type: nauc_precision_at_5_max
value: 40.649885707001
- type: nauc_recall_at_1000_diff1
value: 40.85377128270638
- type: nauc_recall_at_1000_max
value: 78.71880425547653
- type: nauc_recall_at_100_diff1
value: 25.95337392513813
- type: nauc_recall_at_100_max
value: 59.8539551035326
- type: nauc_recall_at_10_diff1
value: 29.989736669251165
- type: nauc_recall_at_10_max
value: 47.01836650640268
- type: nauc_recall_at_1_diff1
value: 43.314035692233396
- type: nauc_recall_at_1_max
value: 31.881007724238064
- type: nauc_recall_at_20_diff1
value: 28.23693913676786
- type: nauc_recall_at_20_max
value: 49.055675433615214
- type: nauc_recall_at_3_diff1
value: 31.69769063388779
- type: nauc_recall_at_3_max
value: 37.90080773298324
- type: nauc_recall_at_5_diff1
value: 30.466477711769823
- type: nauc_recall_at_5_max
value: 40.64988570700102
- type: ndcg_at_1
value: 30.009000000000004
- type: ndcg_at_10
value: 47.598
- type: ndcg_at_100
value: 52.293
- type: ndcg_at_1000
value: 53.525999999999996
- type: ndcg_at_20
value: 49.697
- type: ndcg_at_3
value: 41.159
- type: ndcg_at_5
value: 44.379000000000005
- type: precision_at_1
value: 30.009000000000004
- type: precision_at_10
value: 6.675000000000001
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 3.752
- type: precision_at_3
value: 16.336000000000002
- type: precision_at_5
value: 11.364
- type: recall_at_1
value: 30.009000000000004
- type: recall_at_10
value: 66.753
- type: recall_at_100
value: 89.076
- type: recall_at_1000
value: 99.007
- type: recall_at_20
value: 75.043
- type: recall_at_3
value: 49.007
- type: recall_at_5
value: 56.821999999999996
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 43.422
- type: f1
value: 41.92216694262306
- type: f1_weighted
value: 41.92216694262306
- task:
type: Retrieval
dataset:
name: MTEB BSARDRetrieval
type: maastrichtlawtech/bsard
config: default
split: test
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
metrics:
- type: map_at_1
value: 10.811
- type: map_at_10
value: 15.839
- type: map_at_100
value: 17.031
- type: map_at_1000
value: 17.125
- type: map_at_20
value: 16.523
- type: map_at_3
value: 13.514000000000001
- type: map_at_5
value: 14.482000000000001
- type: mrr_at_1
value: 10.81081081081081
- type: mrr_at_10
value: 15.83923208923209
- type: mrr_at_100
value: 17.03089784389729
- type: mrr_at_1000
value: 17.12470244170791
- type: mrr_at_20
value: 16.522687430195177
- type: mrr_at_3
value: 13.513513513513514
- type: mrr_at_5
value: 14.481981981981978
- type: nauc_map_at_1000_diff1
value: 12.296170850430006
- type: nauc_map_at_1000_max
value: 5.662103058568523
- type: nauc_map_at_100_diff1
value: 12.285666762866096
- type: nauc_map_at_100_max
value: 5.590666559899351
- type: nauc_map_at_10_diff1
value: 11.58049149054967
- type: nauc_map_at_10_max
value: 5.209805828037212
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value: 20.141109249858847
- type: nauc_map_at_1_max
value: 9.425358945072293
- type: nauc_map_at_20_diff1
value: 11.617354631783714
- type: nauc_map_at_20_max
value: 5.241556548291933
- type: nauc_map_at_3_diff1
value: 13.315116892826943
- type: nauc_map_at_3_max
value: 6.207004916063591
- type: nauc_map_at_5_diff1
value: 11.212726154717592
- type: nauc_map_at_5_max
value: 5.3760763604334425
- type: nauc_mrr_at_1000_diff1
value: 12.296170850430006
- type: nauc_mrr_at_1000_max
value: 5.662103058568523
- type: nauc_mrr_at_100_diff1
value: 12.285666762866096
- type: nauc_mrr_at_100_max
value: 5.590666559899351
- type: nauc_mrr_at_10_diff1
value: 11.58049149054967
- type: nauc_mrr_at_10_max
value: 5.209805828037212
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value: 20.141109249858847
- type: nauc_mrr_at_1_max
value: 9.425358945072293
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value: 11.617354631783714
- type: nauc_mrr_at_20_max
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value: 13.315116892826943
- type: nauc_mrr_at_3_max
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- type: nauc_ndcg_at_1000_diff1
value: 12.38831869003625
- type: nauc_ndcg_at_1000_max
value: 6.675430140878355
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value: 11.843284381117181
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value: 11.094054719430453
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value: 7.748133598511381
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value: 14.201820489201856
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value: -0.1440114189741616
- type: nauc_precision_at_5_max
value: 1.113579440082908
- type: nauc_recall_at_1000_diff1
value: 25.89703196865645
- type: nauc_recall_at_1000_max
value: 19.98289206268554
- type: nauc_recall_at_100_diff1
value: 14.20182048920192
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value: 6.304295684751512
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value: 2.939526558265029
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value: 20.141109249858847
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value: 2.9380349371685828
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value: -0.14401141897419695
- type: nauc_recall_at_5_max
value: 1.1135794400828594
- type: ndcg_at_1
value: 10.811
- type: ndcg_at_10
value: 19.583000000000002
- type: ndcg_at_100
value: 26.135
- type: ndcg_at_1000
value: 28.916999999999998
- type: ndcg_at_20
value: 22.158
- type: ndcg_at_3
value: 14.543000000000001
- type: ndcg_at_5
value: 16.345000000000002
- type: precision_at_1
value: 10.811
- type: precision_at_10
value: 3.198
- type: precision_at_100
value: 0.644
- type: precision_at_1000
value: 0.087
- type: precision_at_20
value: 2.117
- type: precision_at_3
value: 5.856
- type: precision_at_5
value: 4.414
- type: recall_at_1
value: 10.811
- type: recall_at_10
value: 31.982
- type: recall_at_100
value: 64.414
- type: recall_at_1000
value: 86.937
- type: recall_at_20
value: 42.342
- type: recall_at_3
value: 17.568
- type: recall_at_5
value: 22.072
- task:
type: Clustering
dataset:
name: MTEB HALClusteringS2S
type: lyon-nlp/clustering-hal-s2s
config: default
split: test
revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
metrics:
- type: v_measure
value: 26.26502535631247
- type: v_measures
value:
- 0.30893096531878045
- 0.27408569069152805
- 0.2872676670832888
- 0.26871778422889214
- 0.2421329238735192
- task:
type: Clustering
dataset:
name: MTEB MLSUMClusteringP2P
type: reciTAL/mlsum
config: fr
split: test
revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
metrics:
- type: v_measure
value: 42.60059039120384
- type: v_measures
value:
- 0.4248169037837413
- 0.44678284494908554
- 0.4386784796938775
- 0.41609051956546156
- 0.37929269357080225
- type: v_measure
value: 42.92324222522204
- type: v_measures
value:
- 0.4320945601805418
- 0.43467886343873713
- 0.4345273113581795
- 0.4277842446367462
- 0.381555432691925
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.33385530848732
- type: f1
value: 88.36975245849551
- type: f1_weighted
value: 88.310383667222
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 62.84685248982148
- type: f1
value: 44.420122133882366
- type: f1_weighted
value: 65.2728620649712
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (fra)
type: mteb/masakhanews
config: fra
split: test
revision: 18193f187b92da67168c655c9973a165ed9593dd
metrics:
- type: accuracy
value: 80.56872037914692
- type: f1
value: 77.28557364601339
- type: f1_weighted
value: 80.51403795220486
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (fra)
type: masakhane/masakhanews
config: fra
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 71.29428035967938
- type: v_measures
value:
- 1.0
- 0.2773866490640993
- 0.7679216739314454
- 0.8367645040119921
- 0.6826411909764316
- type: v_measure
value: 55.090949643200084
- type: v_measures
value:
- 1.0
- 0.0008196849334082873
- 0.7532269197656756
- 0.37056337344528145
- 0.6299375040156386
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 66.80564895763281
- type: f1
value: 64.35238995318795
- type: f1_weighted
value: 65.7206181780162
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (fr)
type: mteb/amazon_massive_scenario
config: fr
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 72.98587760591795
- type: f1
value: 72.51250718054763
- type: f1_weighted
value: 72.81793917434213
- task:
type: Retrieval
dataset:
name: MTEB MintakaRetrieval (fr)
type: jinaai/mintakaqa
config: fr
split: test
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
metrics:
- type: map_at_1
value: 18.96
- type: map_at_10
value: 27.744999999999997
- type: map_at_100
value: 28.799000000000003
- type: map_at_1000
value: 28.884
- type: map_at_20
value: 28.375
- type: map_at_3
value: 25.108999999999998
- type: map_at_5
value: 26.508
- type: mrr_at_1
value: 18.95986895986896
- type: mrr_at_10
value: 27.744936494936507
- type: mrr_at_100
value: 28.79940115805784
- type: mrr_at_1000
value: 28.88414927603794
- type: mrr_at_20
value: 28.375232375900854
- type: mrr_at_3
value: 25.109200109200113
- type: mrr_at_5
value: 26.50764400764402
- type: nauc_map_at_1000_diff1
value: 18.685236785487458
- type: nauc_map_at_1000_max
value: 28.85413041872925
- type: nauc_map_at_100_diff1
value: 18.643854459374264
- type: nauc_map_at_100_max
value: 28.86568866859659
- type: nauc_map_at_10_diff1
value: 18.95179019467019
- type: nauc_map_at_10_max
value: 28.978754512041366
- type: nauc_map_at_1_diff1
value: 24.276017299858978
- type: nauc_map_at_1_max
value: 23.470875089293564
- type: nauc_map_at_20_diff1
value: 18.635084934956904
- type: nauc_map_at_20_max
value: 28.94762423672467
- type: nauc_map_at_3_diff1
value: 19.78833161521705
- type: nauc_map_at_3_max
value: 27.717678662759226
- type: nauc_map_at_5_diff1
value: 19.121183364075133
- type: nauc_map_at_5_max
value: 28.33281003699522
- type: nauc_mrr_at_1000_diff1
value: 18.685236785487458
- type: nauc_mrr_at_1000_max
value: 28.85413041872925
- type: nauc_mrr_at_100_diff1
value: 18.643854459374264
- type: nauc_mrr_at_100_max
value: 28.86568866859659
- type: nauc_mrr_at_10_diff1
value: 18.95179019467019
- type: nauc_mrr_at_10_max
value: 28.978754512041366
- type: nauc_mrr_at_1_diff1
value: 24.276017299858978
- type: nauc_mrr_at_1_max
value: 23.470875089293564
- type: nauc_mrr_at_20_diff1
value: 18.635084934956904
- type: nauc_mrr_at_20_max
value: 28.94762423672467
- type: nauc_mrr_at_3_diff1
value: 19.78833161521705
- type: nauc_mrr_at_3_max
value: 27.717678662759226
- type: nauc_mrr_at_5_diff1
value: 19.121183364075133
- type: nauc_mrr_at_5_max
value: 28.33281003699522
- type: nauc_ndcg_at_1000_diff1
value: 16.9385175619818
- type: nauc_ndcg_at_1000_max
value: 30.464626780924114
- type: nauc_ndcg_at_100_diff1
value: 15.784507139472703
- type: nauc_ndcg_at_100_max
value: 30.783304190943873
- type: nauc_ndcg_at_10_diff1
value: 17.074677821502657
- type: nauc_ndcg_at_10_max
value: 31.39661325771708
- type: nauc_ndcg_at_1_diff1
value: 24.276017299858978
- type: nauc_ndcg_at_1_max
value: 23.470875089293564
- type: nauc_ndcg_at_20_diff1
value: 15.905931373911173
- type: nauc_ndcg_at_20_max
value: 31.283157447315457
- type: nauc_ndcg_at_3_diff1
value: 18.520146441301954
- type: nauc_ndcg_at_3_max
value: 28.855566633100217
- type: nauc_ndcg_at_5_diff1
value: 17.414930054902594
- type: nauc_ndcg_at_5_max
value: 29.89288498763886
- type: nauc_precision_at_1000_diff1
value: 5.6404707169181485
- type: nauc_precision_at_1000_max
value: 51.53249587390901
- type: nauc_precision_at_100_diff1
value: 2.6401827420753463
- type: nauc_precision_at_100_max
value: 37.544518255619415
- type: nauc_precision_at_10_diff1
value: 12.07308037199035
- type: nauc_precision_at_10_max
value: 38.23001565740937
- type: nauc_precision_at_1_diff1
value: 24.276017299858978
- type: nauc_precision_at_1_max
value: 23.470875089293564
- type: nauc_precision_at_20_diff1
value: 7.157477225670103
- type: nauc_precision_at_20_max
value: 38.273237139593256
- type: nauc_precision_at_3_diff1
value: 15.259422549391488
- type: nauc_precision_at_3_max
value: 31.763923868965588
- type: nauc_precision_at_5_diff1
value: 13.005921624910583
- type: nauc_precision_at_5_max
value: 33.92162820494794
- type: nauc_recall_at_1000_diff1
value: 5.6404707169180055
- type: nauc_recall_at_1000_max
value: 51.53249587390878
- type: nauc_recall_at_100_diff1
value: 2.640182742075308
- type: nauc_recall_at_100_max
value: 37.544518255619444
- type: nauc_recall_at_10_diff1
value: 12.073080371990335
- type: nauc_recall_at_10_max
value: 38.230015657409375
- type: nauc_recall_at_1_diff1
value: 24.276017299858978
- type: nauc_recall_at_1_max
value: 23.470875089293564
- type: nauc_recall_at_20_diff1
value: 7.157477225670139
- type: nauc_recall_at_20_max
value: 38.27323713959323
- type: nauc_recall_at_3_diff1
value: 15.259422549391505
- type: nauc_recall_at_3_max
value: 31.763923868965588
- type: nauc_recall_at_5_diff1
value: 13.005921624910567
- type: nauc_recall_at_5_max
value: 33.92162820494793
- type: ndcg_at_1
value: 18.96
- type: ndcg_at_10
value: 32.617000000000004
- type: ndcg_at_100
value: 37.974000000000004
- type: ndcg_at_1000
value: 40.65
- type: ndcg_at_20
value: 34.888000000000005
- type: ndcg_at_3
value: 27.106
- type: ndcg_at_5
value: 29.614
- type: precision_at_1
value: 18.96
- type: precision_at_10
value: 4.824
- type: precision_at_100
value: 0.738
- type: precision_at_1000
value: 0.096
- type: precision_at_20
value: 2.858
- type: precision_at_3
value: 10.961
- type: precision_at_5
value: 7.789
- type: recall_at_1
value: 18.96
- type: recall_at_10
value: 48.239
- type: recall_at_100
value: 73.833
- type: recall_at_1000
value: 95.82300000000001
- type: recall_at_20
value: 57.166
- type: recall_at_3
value: 32.883
- type: recall_at_5
value: 38.943
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (fr)
type: GEM/opusparcus
config: fr
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 84.80926430517711
- type: cos_sim_ap
value: 94.76661922683681
- type: cos_sim_f1
value: 89.31480594154289
- type: cos_sim_precision
value: 86.29629629629629
- type: cos_sim_recall
value: 92.55213505461768
- type: dot_accuracy
value: 84.80926430517711
- type: dot_ap
value: 94.766630886443
- type: dot_f1
value: 89.31480594154289
- type: dot_precision
value: 86.29629629629629
- type: dot_recall
value: 92.55213505461768
- type: euclidean_accuracy
value: 84.80926430517711
- type: euclidean_ap
value: 94.76661922683681
- type: euclidean_f1
value: 89.31480594154289
- type: euclidean_precision
value: 86.29629629629629
- type: euclidean_recall
value: 92.55213505461768
- type: manhattan_accuracy
value: 84.94550408719346
- type: manhattan_ap
value: 94.78582392571815
- type: manhattan_f1
value: 89.33912204534491
- type: manhattan_precision
value: 86.86679174484053
- type: manhattan_recall
value: 91.9563058589871
- type: max_accuracy
value: 84.94550408719346
- type: max_ap
value: 94.78582392571815
- type: max_f1
value: 89.33912204534491
- task:
type: PairClassification
dataset:
name: MTEB PawsX (fr)
type: google-research-datasets/paws-x
config: fr
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 64.5
- type: cos_sim_ap
value: 64.51219412005997
- type: cos_sim_f1
value: 62.84885828198622
- type: cos_sim_precision
value: 46.713362068965516
- type: cos_sim_recall
value: 96.01328903654485
- type: dot_accuracy
value: 64.5
- type: dot_ap
value: 64.50290830259848
- type: dot_f1
value: 62.84885828198622
- type: dot_precision
value: 46.713362068965516
- type: dot_recall
value: 96.01328903654485
- type: euclidean_accuracy
value: 64.5
- type: euclidean_ap
value: 64.51219412005995
- type: euclidean_f1
value: 62.84885828198622
- type: euclidean_precision
value: 46.713362068965516
- type: euclidean_recall
value: 96.01328903654485
- type: manhattan_accuracy
value: 64.55
- type: manhattan_ap
value: 64.54022554293084
- type: manhattan_f1
value: 62.836363636363636
- type: manhattan_precision
value: 46.778559826746076
- type: manhattan_recall
value: 95.68106312292359
- type: max_accuracy
value: 64.55
- type: max_ap
value: 64.54022554293084
- type: max_f1
value: 62.84885828198622
- task:
type: STS
dataset:
name: MTEB SICKFr
type: Lajavaness/SICK-fr
config: default
split: test
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
metrics:
- type: cos_sim_pearson
value: 85.15315949054092
- type: cos_sim_spearman
value: 79.19701933507372
- type: euclidean_pearson
value: 82.68441006897395
- type: euclidean_spearman
value: 79.1963186010215
- type: manhattan_pearson
value: 82.6725500567899
- type: manhattan_spearman
value: 79.13255295711785
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: cos_sim_pearson
value: 83.13328685349694
- type: cos_sim_spearman
value: 84.64291479319418
- type: euclidean_pearson
value: 83.28605886303359
- type: euclidean_spearman
value: 84.64291479319418
- type: manhattan_pearson
value: 83.01485484058145
- type: manhattan_spearman
value: 84.35826862976153
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (fr)
type: mteb/stsb_multi_mt
config: fr
split: test
revision: 29afa2569dcedaaa2fe6a3dcfebab33d28b82e8c
metrics:
- type: cos_sim_pearson
value: 86.15391910168253
- type: cos_sim_spearman
value: 87.0224186207858
- type: euclidean_pearson
value: 86.04463800957714
- type: euclidean_spearman
value: 87.02424394489165
- type: manhattan_pearson
value: 86.03126279628441
- type: manhattan_spearman
value: 86.99427177229043
- task:
type: Summarization
dataset:
name: MTEB SummEvalFr
type: lyon-nlp/summarization-summeval-fr-p2p
config: default
split: test
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
metrics:
- type: cos_sim_pearson
value: 31.415083738613355
- type: cos_sim_spearman
value: 30.301784303588285
- type: dot_pearson
value: 31.415089981266963
- type: dot_spearman
value: 30.286152348575108
- task:
type: Reranking
dataset:
name: MTEB SyntecReranking
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
config: default
split: test
revision: daf0863838cd9e3ba50544cdce3ac2b338a1b0ad
metrics:
- type: map
value: 85.95238095238095
- type: mrr
value: 85.95238095238095
- type: nAUC_map_diff1
value: 70.42176052252755
- type: nAUC_map_max
value: 19.806028833551718
- type: nAUC_mrr_diff1
value: 70.42176052252755
- type: nAUC_mrr_max
value: 19.806028833551718
- task:
type: Retrieval
dataset:
name: MTEB SyntecRetrieval
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
config: default
split: test
revision: 19661ccdca4dfc2d15122d776b61685f48c68ca9
metrics:
- type: map_at_1
value: 69.0
- type: map_at_10
value: 79.668
- type: map_at_100
value: 79.791
- type: map_at_1000
value: 79.791
- type: map_at_20
value: 79.751
- type: map_at_3
value: 78.167
- type: map_at_5
value: 79.067
- type: mrr_at_1
value: 69.0
- type: mrr_at_10
value: 79.66785714285714
- type: mrr_at_100
value: 79.7911904761905
- type: mrr_at_1000
value: 79.7911904761905
- type: mrr_at_20
value: 79.75119047619049
- type: mrr_at_3
value: 78.16666666666666
- type: mrr_at_5
value: 79.06666666666666
- type: nauc_map_at_1000_diff1
value: 57.567834845260215
- type: nauc_map_at_1000_max
value: 19.884081021539316
- type: nauc_map_at_100_diff1
value: 57.567834845260215
- type: nauc_map_at_100_max
value: 19.884081021539316
- type: nauc_map_at_10_diff1
value: 57.58744042822529
- type: nauc_map_at_10_max
value: 20.086792005769567
- type: nauc_map_at_1_diff1
value: 58.094784556502134
- type: nauc_map_at_1_max
value: 16.46471594616999
- type: nauc_map_at_20_diff1
value: 57.51896058548769
- type: nauc_map_at_20_max
value: 19.71285790868927
- type: nauc_map_at_3_diff1
value: 57.896383908331885
- type: nauc_map_at_3_max
value: 19.524006306996704
- type: nauc_map_at_5_diff1
value: 57.45922462199208
- type: nauc_map_at_5_max
value: 21.48138549193403
- type: nauc_mrr_at_1000_diff1
value: 57.567834845260215
- type: nauc_mrr_at_1000_max
value: 19.884081021539316
- type: nauc_mrr_at_100_diff1
value: 57.567834845260215
- type: nauc_mrr_at_100_max
value: 19.884081021539316
- type: nauc_mrr_at_10_diff1
value: 57.58744042822529
- type: nauc_mrr_at_10_max
value: 20.086792005769567
- type: nauc_mrr_at_1_diff1
value: 58.094784556502134
- type: nauc_mrr_at_1_max
value: 16.46471594616999
- type: nauc_mrr_at_20_diff1
value: 57.51896058548769
- type: nauc_mrr_at_20_max
value: 19.71285790868927
- type: nauc_mrr_at_3_diff1
value: 57.896383908331885
- type: nauc_mrr_at_3_max
value: 19.524006306996704
- type: nauc_mrr_at_5_diff1
value: 57.45922462199208
- type: nauc_mrr_at_5_max
value: 21.48138549193403
- type: nauc_ndcg_at_1000_diff1
value: 57.45681586498414
- type: nauc_ndcg_at_1000_max
value: 20.083159493214627
- type: nauc_ndcg_at_100_diff1
value: 57.45681586498414
- type: nauc_ndcg_at_100_max
value: 20.083159493214627
- type: nauc_ndcg_at_10_diff1
value: 57.41282118307387
- type: nauc_ndcg_at_10_max
value: 20.46449823725533
- type: nauc_ndcg_at_1_diff1
value: 58.094784556502134
- type: nauc_ndcg_at_1_max
value: 16.46471594616999
- type: nauc_ndcg_at_20_diff1
value: 57.121174268460486
- type: nauc_ndcg_at_20_max
value: 18.898176707436974
- type: nauc_ndcg_at_3_diff1
value: 57.98367634437588
- type: nauc_ndcg_at_3_max
value: 20.131770232644623
- type: nauc_ndcg_at_5_diff1
value: 56.88983122749084
- type: nauc_ndcg_at_5_max
value: 24.213859501270516
- type: nauc_precision_at_1000_diff1
value: nan
- type: nauc_precision_at_1000_max
value: nan
- type: nauc_precision_at_100_diff1
value: nan
- type: nauc_precision_at_100_max
value: nan
- type: nauc_precision_at_10_diff1
value: 54.014939309057695
- type: nauc_precision_at_10_max
value: 21.82539682539744
- type: nauc_precision_at_1_diff1
value: 58.094784556502134
- type: nauc_precision_at_1_max
value: 16.46471594616999
- type: nauc_precision_at_20_diff1
value: 35.80765639589114
- type: nauc_precision_at_20_max
value: -56.34920634920767
- type: nauc_precision_at_3_diff1
value: 58.57142857142844
- type: nauc_precision_at_3_max
value: 23.053221288515303
- type: nauc_precision_at_5_diff1
value: 51.26050420168061
- type: nauc_precision_at_5_max
value: 49.00404606286964
- type: nauc_recall_at_1000_diff1
value: nan
- type: nauc_recall_at_1000_max
value: nan
- type: nauc_recall_at_100_diff1
value: nan
- type: nauc_recall_at_100_max
value: nan
- type: nauc_recall_at_10_diff1
value: 54.0149393090569
- type: nauc_recall_at_10_max
value: 21.825396825396858
- type: nauc_recall_at_1_diff1
value: 58.094784556502134
- type: nauc_recall_at_1_max
value: 16.46471594616999
- type: nauc_recall_at_20_diff1
value: 35.80765639589109
- type: nauc_recall_at_20_max
value: -56.34920634920657
- type: nauc_recall_at_3_diff1
value: 58.571428571428505
- type: nauc_recall_at_3_max
value: 23.05322128851543
- type: nauc_recall_at_5_diff1
value: 51.260504201680824
- type: nauc_recall_at_5_max
value: 49.004046062869584
- type: ndcg_at_1
value: 69.0
- type: ndcg_at_10
value: 84.198
- type: ndcg_at_100
value: 84.681
- type: ndcg_at_1000
value: 84.681
- type: ndcg_at_20
value: 84.46900000000001
- type: ndcg_at_3
value: 81.202
- type: ndcg_at_5
value: 82.837
- type: precision_at_1
value: 69.0
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.0
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.95
- type: precision_at_3
value: 30.0
- type: precision_at_5
value: 18.8
- type: recall_at_1
value: 69.0
- type: recall_at_10
value: 98.0
- type: recall_at_100
value: 100.0
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 99.0
- type: recall_at_3
value: 90.0
- type: recall_at_5
value: 94.0
- task:
type: Retrieval
dataset:
name: MTEB XPQARetrieval (fr)
type: jinaai/xpqa
config: fr
split: test
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
metrics:
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- type: precision_at_1
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- type: precision_at_100
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- type: precision_at_5
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- type: recall_at_1
value: 37.454
- type: recall_at_10
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- type: recall_at_100
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- type: recall_at_1000
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- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
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value: 78.19402985074629
- type: ap
value: 41.57371176187882
- type: ap_weighted
value: 41.57371176187882
- type: f1
value: 72.09309315449407
- type: f1_weighted
value: 80.00505225103721
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
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value: 90.69565
- type: ap
value: 87.20602734201051
- type: ap_weighted
value: 87.20602734201051
- type: f1
value: 90.68451855153312
- type: f1_weighted
value: 90.68451855153312
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
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value: 47.93600000000001
- type: f1
value: 46.501364617676295
- type: f1_weighted
value: 46.50136461767628
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
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value: 30.014000000000003
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value: 46.554
- type: map_at_100
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- type: map_at_1000
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- type: map_at_20
value: 47.258
- type: map_at_3
value: 41.323
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- type: mrr_at_1000
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value: 41.48885727833096
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- type: nauc_map_at_1000_diff1
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- type: precision_at_3
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- type: recall_at_1
value: 30.014000000000003
- type: recall_at_10
value: 85.70400000000001
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 95.235
- type: recall_at_3
value: 56.401
- type: recall_at_5
value: 69.772
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 46.17799646172208
- type: v_measures
value:
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- 0.4665618875983067
- 0.4694759882110438
- 0.4710825932088269
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 38.95382181977256
- type: v_measures
value:
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- 0.3822490240275778
- 0.40695566104112885
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.77428226049288
- type: mrr
value: 74.66231367893418
- type: nAUC_map_diff1
value: 8.088030406617092
- type: nAUC_map_max
value: 20.837499060141965
- type: nAUC_mrr_diff1
value: 14.808914539705173
- type: nAUC_mrr_max
value: 32.61075208984127
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
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value: 90.14203628237179
- type: cos_sim_spearman
value: 87.86103811793475
- type: euclidean_pearson
value: 89.1570350222214
- type: euclidean_spearman
value: 87.86103811793475
- type: manhattan_pearson
value: 88.89930974259032
- type: manhattan_spearman
value: 87.87188173850797
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.99025974025975
- type: f1
value: 80.34391357314699
- type: f1_weighted
value: 80.34391357314702
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.413831528948
- type: v_measures
value:
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- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
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value: 34.952359512754214
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value:
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- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
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value: 33.042
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- type: map_at_100
value: 45.94
- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
value: 40.338
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- type: mrr_at_1
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- type: mrr_at_100
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- type: mrr_at_1000
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- type: mrr_at_20
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value: 48.80782069623272
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- type: nauc_map_at_1000_diff1
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value: 50.86738962836933
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type: mteb/mtop_domain
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dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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value: 91.56759945236915
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dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 84.26589373642062
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type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 88.83802620244516
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type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 69.16204861304973
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dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 88.48685029144609
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dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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- type: nauc_recall_at_1_max
value: -35.43233406535061
- type: nauc_recall_at_20_diff1
value: -8.91943188201611
- type: nauc_recall_at_20_max
value: -34.5908793542195
- type: nauc_recall_at_3_diff1
value: -17.972433176642863
- type: nauc_recall_at_3_max
value: -41.2243455915633
- type: nauc_recall_at_5_diff1
value: -12.340791676500281
- type: nauc_recall_at_5_max
value: -36.85458567578151
- type: ndcg_at_1
value: 16.326999999999998
- type: ndcg_at_10
value: 16.762
- type: ndcg_at_100
value: 29.751
- type: ndcg_at_1000
value: 41.85
- type: ndcg_at_20
value: 18.541
- type: ndcg_at_3
value: 16.182
- type: ndcg_at_5
value: 15.792
- type: precision_at_1
value: 18.367
- type: precision_at_10
value: 17.347
- type: precision_at_100
value: 6.877999999999999
- type: precision_at_1000
value: 1.49
- type: precision_at_20
value: 13.469000000000001
- type: precision_at_3
value: 19.048000000000002
- type: precision_at_5
value: 17.551
- type: recall_at_1
value: 1.321
- type: recall_at_10
value: 12.25
- type: recall_at_100
value: 44.012
- type: recall_at_1000
value: 80.706
- type: recall_at_20
value: 19.094
- type: recall_at_3
value: 4.2909999999999995
- type: recall_at_5
value: 6.802999999999999
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 65.56640625
- type: ap
value: 12.336183192628836
- type: ap_weighted
value: 12.336183192628836
- type: f1
value: 50.61953920605424
- type: f1_weighted
value: 73.10180241141433
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 62.80418788907753
- type: f1
value: 63.050557758931134
- type: f1_weighted
value: 62.13337985337418
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.00618373985209
- type: v_measures
value:
- 0.49421217801171224
- 0.4740440424893081
- 0.4886726035776056
- 0.5198976504195676
- 0.4827070012054274
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.97025689932646
- type: cos_sim_ap
value: 77.06565012359437
- type: cos_sim_f1
value: 70.32217308907138
- type: cos_sim_precision
value: 67.46666666666667
- type: cos_sim_recall
value: 73.43007915567283
- type: dot_accuracy
value: 86.97025689932646
- type: dot_ap
value: 77.0656524331512
- type: dot_f1
value: 70.32217308907138
- type: dot_precision
value: 67.46666666666667
- type: dot_recall
value: 73.43007915567283
- type: euclidean_accuracy
value: 86.97025689932646
- type: euclidean_ap
value: 77.06564828845742
- type: euclidean_f1
value: 70.32217308907138
- type: euclidean_precision
value: 67.46666666666667
- type: euclidean_recall
value: 73.43007915567283
- type: manhattan_accuracy
value: 86.90469094593789
- type: manhattan_ap
value: 76.94347285253252
- type: manhattan_f1
value: 70.18523217457499
- type: manhattan_precision
value: 67.59530791788856
- type: manhattan_recall
value: 72.98153034300792
- type: max_accuracy
value: 86.97025689932646
- type: max_ap
value: 77.0656524331512
- type: max_f1
value: 70.32217308907138
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.64567081926495
- type: cos_sim_ap
value: 87.19162831580245
- type: cos_sim_f1
value: 79.67696578577352
- type: cos_sim_precision
value: 74.92033358193775
- type: cos_sim_recall
value: 85.07853403141361
- type: dot_accuracy
value: 89.64567081926495
- type: dot_ap
value: 87.19162304433766
- type: dot_f1
value: 79.67696578577352
- type: dot_precision
value: 74.92033358193775
- type: dot_recall
value: 85.07853403141361
- type: euclidean_accuracy
value: 89.64567081926495
- type: euclidean_ap
value: 87.19162847931055
- type: euclidean_f1
value: 79.67696578577352
- type: euclidean_precision
value: 74.92033358193775
- type: euclidean_recall
value: 85.07853403141361
- type: manhattan_accuracy
value: 89.67283735009897
- type: manhattan_ap
value: 87.19033616510255
- type: manhattan_f1
value: 79.67444226437031
- type: manhattan_precision
value: 75.43690656391908
- type: manhattan_recall
value: 84.41638435478903
- type: max_accuracy
value: 89.67283735009897
- type: max_ap
value: 87.19162847931055
- type: max_f1
value: 79.67696578577352
---
# [bilingual-embedding-large](https://huggingface.co/Lajavaness/bilingual-embedding-large)
Bilingual-embedding is the Embedding Model for bilingual language: french and english. This model is a specialized sentence-embedding trained specifically for the bilingual language, leveraging the robust capabilities of [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large), a pre-trained language model based on the [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-large) architecture. The model utilizes xlm-roberta to encode english-french sentences into a 1024-dimensional vector space, facilitating a wide range of applications from semantic search to text clustering. The embeddings capture the nuanced meanings of english-french sentences, reflecting both the lexical and contextual layers of the language.
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BilingualModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Training and Fine-tuning process
#### Stage 1: NLI Training
- Dataset: [(SNLI+XNLI) for english+french]
- Method: Training using Multi-Negative Ranking Loss. This stage focused on improving the model's ability to discern and rank nuanced differences in sentence semantics.
### Stage 3: Continued Fine-tuning for Semantic Textual Similarity on STS Benchmark
- Dataset: [STSB-fr and en]
- Method: Fine-tuning specifically for the semantic textual similarity benchmark using Siamese BERT-Networks configured with the 'sentence-transformers' library.
### Stage 4: Advanced Augmentation Fine-tuning
- Dataset: STSB with generate [silver sample from gold sample](https://www.sbert.net/examples/training/data_augmentation/README.html)
- Method: Employed an advanced strategy using [Augmented SBERT](https://arxiv.org/abs/2010.08240) with Pair Sampling Strategies, integrating both Cross-Encoder and Bi-Encoder models. This stage further refined the embeddings by enriching the training data dynamically, enhancing the model's robustness and accuracy.
## Usage:
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Paris est une capitale de la France", "Paris is a capital of France"]
model = SentenceTransformer('Lajavaness/bilingual-embedding-large', trust_remote_code=True)
print(embeddings)
```
## Evaluation
TODO
## Citation
@article{conneau2019unsupervised,
title={Unsupervised cross-lingual representation learning at scale},
author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin},
journal={arXiv preprint arXiv:1911.02116},
year={2019}
}
@article{reimers2019sentence,
title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
author={Nils Reimers, Iryna Gurevych},
journal={https://arxiv.org/abs/1908.10084},
year={2019}
}
@article{thakur2020augmented,
title={Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks},
author={Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna},
journal={arXiv e-prints},
pages={arXiv--2010},
year={2020} | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
OrcaDB/cde-small-v1 | OrcaDB | feature-extraction | [
"sentence-transformers",
"safetensors",
"feature-extraction",
"mteb",
"transformers",
"custom_code",
"arxiv:2410.02525",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-11-08T19:49:54 | 2025-01-02T19:34:35 | 30,596 | 4 | ---
tags:
- mteb
- transformers
- sentence-transformers
model-index:
- name: cde-small-v1
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 87.02985074626866
- type: ap
value: 56.706190238632956
- type: ap_weighted
value: 56.706190238632956
- type: f1
value: 81.93161953007674
- type: f1_weighted
value: 87.7650174177188
- type: main_score
value: 87.02985074626866
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification (default)
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 94.664175
- type: ap
value: 91.68668057762052
- type: ap_weighted
value: 91.68668057762052
- type: f1
value: 94.65859470333152
- type: f1_weighted
value: 94.65859470333152
- type: main_score
value: 94.664175
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.762
- type: f1
value: 55.06427827477677
- type: f1_weighted
value: 55.06427827477677
- type: main_score
value: 55.762
- task:
type: Retrieval
dataset:
name: MTEB ArguAna (default)
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 71.99600000000001
- type: map_at_1
value: 49.004
- type: map_at_10
value: 64.741
- type: map_at_100
value: 65.045
- type: map_at_1000
value: 65.048
- type: map_at_20
value: 64.999
- type: map_at_3
value: 61.344
- type: map_at_5
value: 63.595
- type: mrr_at_1
value: 50.71123755334281
- type: mrr_at_10
value: 65.32688703741336
- type: mrr_at_100
value: 65.63793917015693
- type: mrr_at_1000
value: 65.64038101143724
- type: mrr_at_20
value: 65.59178002869953
- type: mrr_at_3
value: 61.960644855381695
- type: mrr_at_5
value: 64.12636320531058
- type: nauc_map_at_1000_diff1
value: 15.961240220366024
- type: nauc_map_at_1000_max
value: -7.44765810583741
- type: nauc_map_at_1000_std
value: -17.07167824225605
- type: nauc_map_at_100_diff1
value: 15.965616911760689
- type: nauc_map_at_100_max
value: -7.440609797442297
- type: nauc_map_at_100_std
value: -17.069175070766125
- type: nauc_map_at_10_diff1
value: 16.0053641689455
- type: nauc_map_at_10_max
value: -7.292003400856069
- type: nauc_map_at_10_std
value: -17.21891231777586
- type: nauc_map_at_1_diff1
value: 16.775859614223965
- type: nauc_map_at_1_max
value: -10.812150486389175
- type: nauc_map_at_1_std
value: -18.447209756110635
- type: nauc_map_at_20_diff1
value: 16.00477985164213
- type: nauc_map_at_20_max
value: -7.344399709169316
- type: nauc_map_at_20_std
value: -17.011815937847548
- type: nauc_map_at_3_diff1
value: 15.730294091913994
- type: nauc_map_at_3_max
value: -7.13902722192326
- type: nauc_map_at_3_std
value: -16.846251134000045
- type: nauc_map_at_5_diff1
value: 15.952653874864062
- type: nauc_map_at_5_max
value: -6.730509527119155
- type: nauc_map_at_5_std
value: -16.586379153220353
- type: nauc_mrr_at_1000_diff1
value: 10.221278338563085
- type: nauc_mrr_at_1000_max
value: -10.513831642963527
- type: nauc_mrr_at_1000_std
value: -16.340880407651863
- type: nauc_mrr_at_100_diff1
value: 10.226217465992063
- type: nauc_mrr_at_100_max
value: -10.506478667638874
- type: nauc_mrr_at_100_std
value: -16.33847358633176
- type: nauc_mrr_at_10_diff1
value: 10.293491655887369
- type: nauc_mrr_at_10_max
value: -10.357229664747909
- type: nauc_mrr_at_10_std
value: -16.496874845739885
- type: nauc_mrr_at_1_diff1
value: 12.049863016253427
- type: nauc_mrr_at_1_max
value: -11.968579522299635
- type: nauc_mrr_at_1_std
value: -16.65245790056632
- type: nauc_mrr_at_20_diff1
value: 10.276109067921565
- type: nauc_mrr_at_20_max
value: -10.404100283652397
- type: nauc_mrr_at_20_std
value: -16.282098762560164
- type: nauc_mrr_at_3_diff1
value: 10.338008940592475
- type: nauc_mrr_at_3_max
value: -10.123508259477648
- type: nauc_mrr_at_3_std
value: -16.218834894850918
- type: nauc_mrr_at_5_diff1
value: 10.114375457049043
- type: nauc_mrr_at_5_max
value: -9.987361588255437
- type: nauc_mrr_at_5_std
value: -15.723897501895118
- type: nauc_ndcg_at_1000_diff1
value: 16.00889445347496
- type: nauc_ndcg_at_1000_max
value: -6.746746500535893
- type: nauc_ndcg_at_1000_std
value: -16.567047531839382
- type: nauc_ndcg_at_100_diff1
value: 16.10719535312808
- type: nauc_ndcg_at_100_max
value: -6.59354665730934
- type: nauc_ndcg_at_100_std
value: -16.513298001700566
- type: nauc_ndcg_at_10_diff1
value: 16.396485814351973
- type: nauc_ndcg_at_10_max
value: -5.7111859345525895
- type: nauc_ndcg_at_10_std
value: -17.13416103510026
- type: nauc_ndcg_at_1_diff1
value: 16.775859614223965
- type: nauc_ndcg_at_1_max
value: -10.812150486389175
- type: nauc_ndcg_at_1_std
value: -18.447209756110635
- type: nauc_ndcg_at_20_diff1
value: 16.414235526534497
- type: nauc_ndcg_at_20_max
value: -5.890463457153039
- type: nauc_ndcg_at_20_std
value: -16.124783371499017
- type: nauc_ndcg_at_3_diff1
value: 15.683431770601713
- type: nauc_ndcg_at_3_max
value: -5.546675513691499
- type: nauc_ndcg_at_3_std
value: -15.973244504586676
- type: nauc_ndcg_at_5_diff1
value: 16.193847874581166
- type: nauc_ndcg_at_5_max
value: -4.471638454091411
- type: nauc_ndcg_at_5_std
value: -15.517824617814629
- type: nauc_precision_at_1000_diff1
value: 3.170440311533737
- type: nauc_precision_at_1000_max
value: 25.521992526080666
- type: nauc_precision_at_1000_std
value: 68.4373013145641
- type: nauc_precision_at_100_diff1
value: 30.283338663457897
- type: nauc_precision_at_100_max
value: 44.33747104624998
- type: nauc_precision_at_100_std
value: 42.28887350925609
- type: nauc_precision_at_10_diff1
value: 23.390956301235633
- type: nauc_precision_at_10_max
value: 15.468288261126773
- type: nauc_precision_at_10_std
value: -18.2942744669977
- type: nauc_precision_at_1_diff1
value: 16.775859614223965
- type: nauc_precision_at_1_max
value: -10.812150486389175
- type: nauc_precision_at_1_std
value: -18.447209756110635
- type: nauc_precision_at_20_diff1
value: 37.14254275219614
- type: nauc_precision_at_20_max
value: 46.984729023754824
- type: nauc_precision_at_20_std
value: 22.763524786900717
- type: nauc_precision_at_3_diff1
value: 15.651406928218881
- type: nauc_precision_at_3_max
value: 0.7775458885343681
- type: nauc_precision_at_3_std
value: -12.438132482295773
- type: nauc_precision_at_5_diff1
value: 18.10074574210355
- type: nauc_precision_at_5_max
value: 9.373350504221532
- type: nauc_precision_at_5_std
value: -9.13125987784625
- type: nauc_recall_at_1000_diff1
value: 3.1704403115262325
- type: nauc_recall_at_1000_max
value: 25.521992526077756
- type: nauc_recall_at_1000_std
value: 68.4373013145603
- type: nauc_recall_at_100_diff1
value: 30.283338663455616
- type: nauc_recall_at_100_max
value: 44.337471046250556
- type: nauc_recall_at_100_std
value: 42.28887350925341
- type: nauc_recall_at_10_diff1
value: 23.390956301235168
- type: nauc_recall_at_10_max
value: 15.468288261126578
- type: nauc_recall_at_10_std
value: -18.294274466997873
- type: nauc_recall_at_1_diff1
value: 16.775859614223965
- type: nauc_recall_at_1_max
value: -10.812150486389175
- type: nauc_recall_at_1_std
value: -18.447209756110635
- type: nauc_recall_at_20_diff1
value: 37.14254275219513
- type: nauc_recall_at_20_max
value: 46.98472902375421
- type: nauc_recall_at_20_std
value: 22.763524786899644
- type: nauc_recall_at_3_diff1
value: 15.65140692821902
- type: nauc_recall_at_3_max
value: 0.7775458885343522
- type: nauc_recall_at_3_std
value: -12.43813248229578
- type: nauc_recall_at_5_diff1
value: 18.10074574210355
- type: nauc_recall_at_5_max
value: 9.373350504221595
- type: nauc_recall_at_5_std
value: -9.131259877846116
- type: ndcg_at_1
value: 49.004
- type: ndcg_at_10
value: 71.99600000000001
- type: ndcg_at_100
value: 73.173
- type: ndcg_at_1000
value: 73.214
- type: ndcg_at_20
value: 72.91
- type: ndcg_at_3
value: 65.21900000000001
- type: ndcg_at_5
value: 69.284
- type: precision_at_1
value: 49.004
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.904
- type: precision_at_3
value: 25.462
- type: precision_at_5
value: 17.255000000000003
- type: recall_at_1
value: 49.004
- type: recall_at_10
value: 94.523
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 98.08
- type: recall_at_3
value: 76.387
- type: recall_at_5
value: 86.273
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P (default)
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 48.629569816593516
- type: v_measure
value: 48.629569816593516
- type: v_measure_std
value: 14.01810149072028
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S (default)
type: mteb/arxiv-clustering-s2s
config: default
split: test
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metrics:
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dataset:
name: MTEB ImdbClassification (default)
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
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value: 96.0373917008664
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type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
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value: 71.6595596649587
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value: 89.1655266757866
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
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dataset:
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type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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type: mteb/medrxiv-clustering-p2p
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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value: 1.5350000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.175
- type: precision_at_3
value: 38.12
- type: precision_at_5
value: 25.041999999999998
- type: recall_at_1
value: 72.44500000000001
- type: recall_at_10
value: 96.298
- type: recall_at_100
value: 99.696
- type: recall_at_1000
value: 99.98599999999999
- type: recall_at_20
value: 98.15700000000001
- type: recall_at_3
value: 88.633
- type: recall_at_5
value: 92.985
- task:
type: Clustering
dataset:
name: MTEB RedditClustering (default)
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 59.36225093784713
- type: v_measure
value: 59.36225093784713
- type: v_measure_std
value: 3.9911509588570393
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P (default)
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 64.46282036246124
- type: v_measure
value: 64.46282036246124
- type: v_measure_std
value: 12.49196304240264
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS (default)
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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value: 21.781
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value: 5.103
- type: map_at_10
value: 13.152
- type: map_at_100
value: 15.421000000000001
- type: map_at_1000
value: 15.738
- type: map_at_20
value: 14.313
- type: map_at_3
value: 9.277000000000001
- type: map_at_5
value: 11.079
- type: mrr_at_1
value: 25.2
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value: 36.30464285714286
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value: 37.37083205414486
- type: mrr_at_1000
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- type: mrr_at_5
value: 34.971666666666664
- type: nauc_map_at_1000_diff1
value: 13.3829110188465
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- type: nauc_precision_at_5_max
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- type: nauc_recall_at_1000_std
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value: 27.475538879580885
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- type: nauc_recall_at_5_max
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- type: nauc_recall_at_5_std
value: 12.411188897743521
- type: ndcg_at_1
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- type: ndcg_at_10
value: 21.781
- type: ndcg_at_100
value: 30.273
- type: ndcg_at_1000
value: 35.768
- type: ndcg_at_20
value: 24.967
- type: ndcg_at_3
value: 20.580000000000002
- type: ndcg_at_5
value: 17.926000000000002
- type: precision_at_1
value: 25.2
- type: precision_at_10
value: 11.4
- type: precision_at_100
value: 2.359
- type: precision_at_1000
value: 0.368
- type: precision_at_20
value: 7.545
- type: precision_at_3
value: 19.3
- type: precision_at_5
value: 15.78
- type: recall_at_1
value: 5.103
- type: recall_at_10
value: 23.083000000000002
- type: recall_at_100
value: 47.882999999999996
- type: recall_at_1000
value: 74.783
- type: recall_at_20
value: 30.592000000000002
- type: recall_at_3
value: 11.753
- type: recall_at_5
value: 15.983
- task:
type: STS
dataset:
name: MTEB SICK-R (default)
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cosine_pearson
value: 83.9841377195369
- type: cosine_spearman
value: 77.44919890597407
- type: euclidean_pearson
value: 81.21238548422511
- type: euclidean_spearman
value: 76.94405730272983
- type: main_score
value: 77.44919890597407
- type: manhattan_pearson
value: 81.16824677968528
- type: manhattan_spearman
value: 76.94296468591867
- type: pearson
value: 83.9841377195369
- type: spearman
value: 77.44919890597407
- task:
type: STS
dataset:
name: MTEB STS12 (default)
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cosine_pearson
value: 81.36071984442052
- type: cosine_spearman
value: 74.2212823495219
- type: euclidean_pearson
value: 78.31139429452078
- type: euclidean_spearman
value: 74.02790834412275
- type: main_score
value: 74.2212823495219
- type: manhattan_pearson
value: 78.26141328104697
- type: manhattan_spearman
value: 74.02545007676329
- type: pearson
value: 81.36071984442052
- type: spearman
value: 74.2212823495219
- task:
type: STS
dataset:
name: MTEB STS13 (default)
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cosine_pearson
value: 85.49925337918731
- type: cosine_spearman
value: 86.12368715292688
- type: euclidean_pearson
value: 85.71147581542367
- type: euclidean_spearman
value: 86.64112317821541
- type: main_score
value: 86.12368715292688
- type: manhattan_pearson
value: 85.58242941611371
- type: manhattan_spearman
value: 86.51041533466731
- type: pearson
value: 85.49925337918731
- type: spearman
value: 86.12368715292688
- task:
type: STS
dataset:
name: MTEB STS14 (default)
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 82.24735192639226
- type: cosine_spearman
value: 78.88155361224834
- type: euclidean_pearson
value: 80.52048132030517
- type: euclidean_spearman
value: 78.1335955670817
- type: main_score
value: 78.88155361224834
- type: manhattan_pearson
value: 80.48178866605353
- type: manhattan_spearman
value: 78.08994918255844
- type: pearson
value: 82.24735192639226
- type: spearman
value: 78.88155361224834
- task:
type: STS
dataset:
name: MTEB STS15 (default)
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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value: 86.27381322229758
- type: cosine_spearman
value: 87.5038962579188
- type: euclidean_pearson
value: 86.7575259976948
- type: euclidean_spearman
value: 87.3358778981031
- type: main_score
value: 87.5038962579188
- type: manhattan_pearson
value: 86.72177109814491
- type: manhattan_spearman
value: 87.30593609243358
- type: pearson
value: 86.27381322229758
- type: spearman
value: 87.5038962579188
- task:
type: STS
dataset:
name: MTEB STS16 (default)
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 82.90364706517789
- type: cosine_spearman
value: 84.25854334490232
- type: euclidean_pearson
value: 83.30065780824273
- type: euclidean_spearman
value: 84.17467271748362
- type: main_score
value: 84.25854334490232
- type: manhattan_pearson
value: 83.21239264085494
- type: manhattan_spearman
value: 84.05456832118482
- type: pearson
value: 82.90364706517789
- type: spearman
value: 84.25854334490232
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 88.88258729094343
- type: cosine_spearman
value: 89.68436656381257
- type: euclidean_pearson
value: 88.23417725579127
- type: euclidean_spearman
value: 87.96688277361433
- type: main_score
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- type: manhattan_pearson
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- type: manhattan_spearman
value: 87.7976329721765
- type: pearson
value: 88.88258729094343
- type: spearman
value: 89.68436656381257
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 65.24627744968292
- type: cosine_spearman
value: 65.96283849168346
- type: euclidean_pearson
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- type: euclidean_spearman
value: 65.83563143944401
- type: main_score
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- type: manhattan_pearson
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- type: manhattan_spearman
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- type: pearson
value: 65.24627744968292
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value: 65.96283849168346
- task:
type: STS
dataset:
name: MTEB STSBenchmark (default)
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 85.57515090752183
- type: cosine_spearman
value: 85.54441587714372
- type: euclidean_pearson
value: 85.53938106211463
- type: euclidean_spearman
value: 85.28473579067878
- type: main_score
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value: 85.260887707662
- type: pearson
value: 85.57515090752183
- type: spearman
value: 85.54441587714372
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR (default)
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 82.9058801876062
- type: map
value: 82.9058801876062
- type: mrr
value: 95.256220721907
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value: 0.13078953297011875
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value: 46.534353907114514
- type: nAUC_mrr_max
value: 89.56255914950661
- type: nAUC_mrr_std
value: 85.6716185155955
- task:
type: Retrieval
dataset:
name: MTEB SciFact (default)
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
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- type: map_at_1
value: 57.278
- type: map_at_10
value: 67.109
- type: map_at_100
value: 67.66499999999999
- type: map_at_1000
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- type: map_at_20
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- type: map_at_3
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- type: map_at_5
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- type: mrr_at_1
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- type: mrr_at_1000
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- type: mrr_at_3
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- type: mrr_at_5
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- type: nauc_map_at_1000_diff1
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value: 2.355
- type: map_at_10
value: 9.554
- type: map_at_100
value: 14.856
- type: map_at_1000
value: 16.320999999999998
- type: map_at_20
value: 11.594
- type: map_at_3
value: 5.624
- type: map_at_5
value: 6.948
- type: mrr_at_1
value: 28.57142857142857
- type: mrr_at_10
value: 45.30855199222546
- type: mrr_at_100
value: 46.29196367191565
- type: mrr_at_1000
value: 46.31499833524485
- type: mrr_at_20
value: 46.113797167218536
- type: mrr_at_3
value: 42.17687074829932
- type: mrr_at_5
value: 43.70748299319728
- type: nauc_map_at_1000_diff1
value: 16.20923402096991
- type: nauc_map_at_1000_max
value: -1.0790035381754648
- type: nauc_map_at_1000_std
value: 7.195462252108266
- type: nauc_map_at_100_diff1
value: 18.389136986949936
- type: nauc_map_at_100_max
value: -2.05569038009456
- type: nauc_map_at_100_std
value: 2.571693024788773
- type: nauc_map_at_10_diff1
value: 21.066136452964642
- type: nauc_map_at_10_max
value: 1.5731034935019352
- type: nauc_map_at_10_std
value: -10.470562156435545
- type: nauc_map_at_1_diff1
value: 18.809274247757674
- type: nauc_map_at_1_max
value: -8.68104031396317
- type: nauc_map_at_1_std
value: -30.619138463973307
- type: nauc_map_at_20_diff1
value: 23.36148432932364
- type: nauc_map_at_20_max
value: -0.38560029617230923
- type: nauc_map_at_20_std
value: -6.8825311118744485
- type: nauc_map_at_3_diff1
value: 18.9370153117886
- type: nauc_map_at_3_max
value: 2.2032967783435375
- type: nauc_map_at_3_std
value: -12.532694022066659
- type: nauc_map_at_5_diff1
value: 21.434904521858602
- type: nauc_map_at_5_max
value: 6.094611630406942
- type: nauc_map_at_5_std
value: -12.492795788667474
- type: nauc_mrr_at_1000_diff1
value: 11.961046636239269
- type: nauc_mrr_at_1000_max
value: -15.748297693665677
- type: nauc_mrr_at_1000_std
value: -12.067130971523385
- type: nauc_mrr_at_100_diff1
value: 11.95534277650038
- type: nauc_mrr_at_100_max
value: -15.684486171307041
- type: nauc_mrr_at_100_std
value: -11.98247014226321
- type: nauc_mrr_at_10_diff1
value: 12.191520381511925
- type: nauc_mrr_at_10_max
value: -16.510285123987302
- type: nauc_mrr_at_10_std
value: -11.93784570526233
- type: nauc_mrr_at_1_diff1
value: 18.162553375605516
- type: nauc_mrr_at_1_max
value: -18.920009881475387
- type: nauc_mrr_at_1_std
value: -31.201005281857086
- type: nauc_mrr_at_20_diff1
value: 11.85035482221006
- type: nauc_mrr_at_20_max
value: -16.18704935368085
- type: nauc_mrr_at_20_std
value: -11.424991900511088
- type: nauc_mrr_at_3_diff1
value: 14.733201594965836
- type: nauc_mrr_at_3_max
value: -11.75899459749356
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value: -11.499870896820976
- type: nauc_mrr_at_5_diff1
value: 12.874017458219845
- type: nauc_mrr_at_5_max
value: -13.642689819875791
- type: nauc_mrr_at_5_std
value: -11.64117086557618
- type: nauc_ndcg_at_1000_diff1
value: -6.849400123979281
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value: -3.8209628417621393
- type: nauc_ndcg_at_1000_std
value: 31.393629472927504
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value: 5.4656320972286485
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value: -11.571250999652408
- type: nauc_ndcg_at_100_std
value: 16.5511179303082
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value: 9.553502614400788
- type: nauc_ndcg_at_10_max
value: -14.08266102380929
- type: nauc_ndcg_at_10_std
value: -5.404201943794988
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value: 11.37824691229176
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value: -21.31215334708879
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value: -29.749958184219334
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value: 13.396975021395857
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value: -14.5189405742469
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value: -1.6276921520570502
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value: 2.3132968948746226
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value: -11.351646560904848
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value: -0.15036952995361091
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value: 6.214320727021392
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value: -9.797994041679638
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value: -3.3742904276844223
- type: nauc_precision_at_1000_diff1
value: -32.78708155144845
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value: 34.81622247650308
- type: nauc_precision_at_1000_std
value: 47.996245254718744
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value: -10.867559709952797
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value: 6.681915188055671
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value: 61.989390090979356
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value: 6.511211593484189
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value: -16.842566662697454
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value: 5.002600740433903
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value: 18.162553375605516
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value: -18.920009881475387
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value: -31.201005281857086
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value: 9.640744611970522
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value: -18.27653996056668
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value: 22.021814503656543
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value: 6.916201107284145
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value: -0.039381527098944095
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value: 9.096821181866671
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value: 9.032683328748616
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value: -3.5989814795848223
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value: 2.506947866544208
- type: nauc_recall_at_1000_diff1
value: -27.92405572104993
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value: 14.256848434706395
- type: nauc_recall_at_1000_std
value: 69.3546817240148
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value: 6.613753533249129
- type: nauc_recall_at_100_max
value: -8.405822616363144
- type: nauc_recall_at_100_std
value: 29.430588706591397
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value: 18.481730784371077
- type: nauc_recall_at_10_max
value: -7.763172381505888
- type: nauc_recall_at_10_std
value: -7.48570052741164
- type: nauc_recall_at_1_diff1
value: 18.809274247757674
- type: nauc_recall_at_1_max
value: -8.68104031396317
- type: nauc_recall_at_1_std
value: -30.619138463973307
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value: 20.639977762281493
- type: nauc_recall_at_20_max
value: -11.301201172125623
- type: nauc_recall_at_20_std
value: 0.38755705583239786
- type: nauc_recall_at_3_diff1
value: 18.279383297820562
- type: nauc_recall_at_3_max
value: 5.287795698059438
- type: nauc_recall_at_3_std
value: -3.7312167565958316
- type: nauc_recall_at_5_diff1
value: 21.115852302465356
- type: nauc_recall_at_5_max
value: 5.318139212101227
- type: nauc_recall_at_5_std
value: -7.792885381250281
- type: ndcg_at_1
value: 25.509999999999998
- type: ndcg_at_10
value: 24.254
- type: ndcg_at_100
value: 34.660000000000004
- type: ndcg_at_1000
value: 45.798
- type: ndcg_at_20
value: 24.988
- type: ndcg_at_3
value: 29.273
- type: ndcg_at_5
value: 25.453
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 21.02
- type: precision_at_100
value: 7.122000000000001
- type: precision_at_1000
value: 1.435
- type: precision_at_20
value: 16.326999999999998
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 24.898
- type: recall_at_1
value: 2.355
- type: recall_at_10
value: 15.397
- type: recall_at_100
value: 43.647000000000006
- type: recall_at_1000
value: 77.089
- type: recall_at_20
value: 22.792
- type: recall_at_3
value: 6.847
- type: recall_at_5
value: 9.136
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification (default)
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 72.7734375
- type: ap
value: 15.655230461083708
- type: ap_weighted
value: 15.655230461083708
- type: f1
value: 56.31497978454638
- type: f1_weighted
value: 78.70509613747345
- type: main_score
value: 72.7734375
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification (default)
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 72.56366723259762
- type: f1
value: 72.90413275122202
- type: f1_weighted
value: 72.19948169084057
- type: main_score
value: 72.56366723259762
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering (default)
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 56.90970017457857
- type: v_measure
value: 56.90970017457857
- type: v_measure_std
value: 1.5885885070403738
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015 (default)
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 85.7006616200751
- type: cosine_accuracy_threshold
value: 75.78572630882263
- type: cosine_ap
value: 72.87577990245127
- type: cosine_f1
value: 67.36422521175885
- type: cosine_f1_threshold
value: 70.15678882598877
- type: cosine_precision
value: 63.80368098159509
- type: cosine_recall
value: 71.34564643799473
- type: dot_accuracy
value: 83.60851165285807
- type: dot_accuracy_threshold
value: 744.7918891906738
- type: dot_ap
value: 64.82619159813649
- type: dot_f1
value: 62.62379263968699
- type: dot_f1_threshold
value: 696.7735290527344
- type: dot_precision
value: 58.350421508316245
- type: dot_recall
value: 67.57255936675462
- type: euclidean_accuracy
value: 85.84371460928652
- type: euclidean_accuracy_threshold
value: 220.4747200012207
- type: euclidean_ap
value: 72.47837433257799
- type: euclidean_f1
value: 67.2811059907834
- type: euclidean_f1_threshold
value: 240.81902503967285
- type: euclidean_precision
value: 65.34062655395326
- type: euclidean_recall
value: 69.34036939313984
- type: main_score
value: 72.87577990245127
- type: manhattan_accuracy
value: 85.83179352685224
- type: manhattan_accuracy_threshold
value: 4910.404205322266
- type: manhattan_ap
value: 72.44111617709422
- type: manhattan_f1
value: 67.09989806320081
- type: manhattan_f1_threshold
value: 5333.793640136719
- type: manhattan_precision
value: 64.88417939871857
- type: manhattan_recall
value: 69.47229551451187
- type: max_accuracy
value: 85.84371460928652
- type: max_ap
value: 72.87577990245127
- type: max_f1
value: 67.36422521175885
- type: max_precision
value: 65.34062655395326
- type: max_recall
value: 71.34564643799473
- type: similarity_accuracy
value: 85.7006616200751
- type: similarity_accuracy_threshold
value: 75.78572630882263
- type: similarity_ap
value: 72.87577990245127
- type: similarity_f1
value: 67.36422521175885
- type: similarity_f1_threshold
value: 70.15678882598877
- type: similarity_precision
value: 63.80368098159509
- type: similarity_recall
value: 71.34564643799473
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus (default)
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 88.88112702293631
- type: cosine_accuracy_threshold
value: 71.48405313491821
- type: cosine_ap
value: 85.88088882163336
- type: cosine_f1
value: 78.2251744598276
- type: cosine_f1_threshold
value: 70.09605169296265
- type: cosine_precision
value: 75.8997755087262
- type: cosine_recall
value: 80.69756698490914
- type: dot_accuracy
value: 88.04672643303451
- type: dot_accuracy_threshold
value: 700.6264686584473
- type: dot_ap
value: 83.52072844458456
- type: dot_f1
value: 76.24239256244634
- type: dot_f1_threshold
value: 664.9115562438965
- type: dot_precision
value: 74.0123233055455
- type: dot_recall
value: 78.61102556205728
- type: euclidean_accuracy
value: 88.72588970388482
- type: euclidean_accuracy_threshold
value: 226.53303146362305
- type: euclidean_ap
value: 85.51788295919707
- type: euclidean_f1
value: 77.73453426739316
- type: euclidean_f1_threshold
value: 238.7503147125244
- type: euclidean_precision
value: 74.94818097348296
- type: euclidean_recall
value: 80.73606405913151
- type: main_score
value: 85.88088882163336
- type: manhattan_accuracy
value: 88.68902084061008
- type: manhattan_accuracy_threshold
value: 5034.079742431641
- type: manhattan_ap
value: 85.49952903626239
- type: manhattan_f1
value: 77.74326743888625
- type: manhattan_f1_threshold
value: 5334.531021118164
- type: manhattan_precision
value: 73.98289171708741
- type: manhattan_recall
value: 81.90637511549123
- type: max_accuracy
value: 88.88112702293631
- type: max_ap
value: 85.88088882163336
- type: max_f1
value: 78.2251744598276
- type: max_precision
value: 75.8997755087262
- type: max_recall
value: 81.90637511549123
- type: similarity_accuracy
value: 88.88112702293631
- type: similarity_accuracy_threshold
value: 71.48405313491821
- type: similarity_ap
value: 85.88088882163336
- type: similarity_f1
value: 78.2251744598276
- type: similarity_f1_threshold
value: 70.09605169296265
- type: similarity_precision
value: 75.8997755087262
- type: similarity_recall
value: 80.69756698490914
---
# Contextual Document Embeddings (CDE)
**Link to code: github.com/jxmorris12/cde**
Our new model that naturally integrates "context tokens" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.
👉 <b><a href="https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing">Try on Colab</a></b>
<br>
👉 <b><a href="https://arxiv.org/abs/2410.02525">Contextual Document Embeddings (ArXiv)</a></b>
<br>
<hr>
# How to use `cde-small-v1`
Our embedding model needs to be used in *two stages*. The first stage is to gather some dataset information by embedding a subset of the corpus using our "first-stage" model. The second stage is to actually embed queries and documents, conditioning on the corpus information from the first stage. Note that we can do the first stage part offline and only use the second-stage weights at inference time.
</details>
## With Transformers
<details>
<summary>Click to learn how to use cde-small-v1 with Transformers</summary>
### Loading the model
Our model can be loaded using `transformers` out-of-the-box with "trust remote code" enabled. We use the default BERT uncased tokenizer:
```python
import transformers
model = transformers.AutoModel.from_pretrained("jxm/cde-small-v1", trust_remote_code=True)
tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-uncased")
```
#### Note on prefixes
*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can prepend the following strings to queries & documents:
```python
query_prefix = "search_query: "
document_prefix = "search_document: "
```
### First stage
```python
minicorpus_size = model.config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
minicorpus_docs = tokenizer(
[document_prefix + doc for doc in minicorpus_docs],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
import torch
from tqdm.autonotebook import tqdm
batch_size = 32
dataset_embeddings = []
for i in tqdm(range(0, len(minicorpus_docs["input_ids"]), batch_size)):
minicorpus_docs_batch = {k: v[i:i+batch_size] for k,v in minicorpus_docs.items()}
with torch.no_grad():
dataset_embeddings.append(
model.first_stage_model(**minicorpus_docs_batch)
)
dataset_embeddings = torch.cat(dataset_embeddings)
```
### Running the second stage
Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prefix for documents:
```python
docs = tokenizer(
[document_prefix + doc for doc in docs],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
doc_embeddings = model.second_stage_model(
input_ids=docs["input_ids"],
attention_mask=docs["attention_mask"],
dataset_embeddings=dataset_embeddings,
)
doc_embeddings /= doc_embeddings.norm(p=2, dim=1, keepdim=True)
```
and the query prefix for queries:
```python
queries = queries.select(range(16))["text"]
queries = tokenizer(
[query_prefix + query for query in queries],
truncation=True,
padding=True,
max_length=512,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
query_embeddings = model.second_stage_model(
input_ids=queries["input_ids"],
attention_mask=queries["attention_mask"],
dataset_embeddings=dataset_embeddings,
)
query_embeddings /= query_embeddings.norm(p=2, dim=1, keepdim=True)
```
these embeddings can be compared using dot product, since they're normalized.
</details>
### What if I don't know what my corpus will be ahead of time?
If you can't obtain corpus information ahead of time, you still have to pass *something* as the dataset embeddings; our model will work fine in this case, but not quite as well; without corpus information, our model performance drops from 65.0 to 63.8 on MTEB. We provide [some random strings](https://huggingface.co/jxm/cde-small-v1/resolve/main/random_strings.txt) that worked well for us that can be used as a substitute for corpus sampling.
## With Sentence Transformers
<details open="">
<summary>Click to learn how to use cde-small-v1 with Sentence Transformers</summary>
### Loading the model
Our model can be loaded using `sentence-transformers` out-of-the-box with "trust remote code" enabled:
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
```
#### Note on prefixes
*Nota bene*: Like all state-of-the-art embedding models, our model was trained with task-specific prefixes. To do retrieval, you can use `prompt_name="query"` and `prompt_name="document"` in the `encode` method of the model when embedding queries and documents, respectively.
### First stage
```python
minicorpus_size = model[0].config.transductive_corpus_size
minicorpus_docs = [ ... ] # Put some strings here that are representative of your corpus, for example by calling random.sample(corpus, k=minicorpus_size)
assert len(minicorpus_docs) == minicorpus_size # You must use exactly this many documents in the minicorpus. You can oversample if your corpus is smaller.
dataset_embeddings = model.encode(
minicorpus_docs,
prompt_name="document",
convert_to_tensor=True
)
```
### Running the second stage
Now that we have obtained "dataset embeddings" we can embed documents and queries like normal. Remember to use the document prompt for documents:
```python
docs = [...]
queries = [...]
doc_embeddings = model.encode(
docs,
prompt_name="document",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
query_embeddings = model.encode(
queries,
prompt_name="query",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
```
these embeddings can be compared using cosine similarity via `model.similarity`:
```python
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
```
<details>
<summary>Click here for a full copy-paste ready example</summary>
```python
from sentence_transformers import SentenceTransformer
from datasets import load_dataset
# 1. Load the Sentence Transformer model
model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
context_docs_size = model[0].config.transductive_corpus_size # 512
# 2. Load the dataset: context dataset, docs, and queries
dataset = load_dataset("sentence-transformers/natural-questions", split="train")
dataset.shuffle(seed=42)
# 10 queries, 512 context docs, 500 docs
queries = dataset["query"][:10]
docs = dataset["answer"][:2000]
context_docs = dataset["answer"][-context_docs_size:] # Last 512 docs
# 3. First stage: embed the context docs
dataset_embeddings = model.encode(
context_docs,
prompt_name="document",
convert_to_tensor=True,
)
# 4. Second stage: embed the docs and queries
doc_embeddings = model.encode(
docs,
prompt_name="document",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
query_embeddings = model.encode(
queries,
prompt_name="query",
dataset_embeddings=dataset_embeddings,
convert_to_tensor=True,
)
# 5. Compute the similarity between the queries and docs
similarities = model.similarity(query_embeddings, doc_embeddings)
topk_values, topk_indices = similarities.topk(5)
print(topk_values)
print(topk_indices)
"""
tensor([[0.5495, 0.5426, 0.5423, 0.5292, 0.5286],
[0.6357, 0.6334, 0.6177, 0.5862, 0.5794],
[0.7648, 0.5452, 0.5000, 0.4959, 0.4881],
[0.6802, 0.5225, 0.5178, 0.5160, 0.5075],
[0.6947, 0.5843, 0.5619, 0.5344, 0.5298],
[0.7742, 0.7742, 0.7742, 0.7231, 0.6224],
[0.8853, 0.6667, 0.5829, 0.5795, 0.5769],
[0.6911, 0.6127, 0.6003, 0.5986, 0.5936],
[0.6796, 0.6053, 0.6000, 0.5911, 0.5884],
[0.7624, 0.5589, 0.5428, 0.5278, 0.5275]], device='cuda:0')
tensor([[ 0, 296, 234, 1651, 1184],
[1542, 466, 438, 1207, 1911],
[ 2, 1562, 632, 1852, 382],
[ 3, 694, 932, 1765, 662],
[ 4, 35, 747, 26, 432],
[ 534, 175, 5, 1495, 575],
[ 6, 1802, 1875, 747, 21],
[ 7, 1913, 1936, 640, 6],
[ 8, 747, 167, 1318, 1743],
[ 9, 1583, 1145, 219, 357]], device='cuda:0')
"""
# As you can see, almost every query_i has document_i as the most similar document.
# 6. Print the top-k results
for query_idx, top_doc_idx in enumerate(topk_indices[:, 0]):
print(f"Query {query_idx}: {queries[query_idx]}")
print(f"Top Document: {docs[top_doc_idx]}")
print()
"""
Query 0: when did richmond last play in a preliminary final
Top Document: Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.
Query 1: who sang what in the world's come over you
Top Document: Life's What You Make It (Talk Talk song) "Life's What You Make It" is a song by the English band Talk Talk. It was released as a single in 1986, the first from the band's album The Colour of Spring. The single was a hit in the UK, peaking at No. 16, and charted in numerous other countries, often reaching the Top 20.
Query 2: who produces the most wool in the world
Top Document: Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.
Query 3: where does alaska the last frontier take place
Top Document: Alaska: The Last Frontier Alaska: The Last Frontier is an American reality cable television series on the Discovery Channel, currently in its 7th season of broadcast. The show documents the extended Kilcher family, descendants of Swiss immigrants and Alaskan pioneers, Yule and Ruth Kilcher, at their homestead 11 miles outside of Homer.[1] By living without plumbing or modern heating, the clan chooses to subsist by farming, hunting and preparing for the long winters.[2] The Kilcher family are relatives of the singer Jewel,[1][3] who has appeared on the show.[4]
Query 4: a day to remember all i want cameos
Top Document: All I Want (A Day to Remember song) The music video for the song, which was filmed in October 2010,[4] was released on January 6, 2011.[5] It features cameos of numerous popular bands and musicians. The cameos are: Tom Denney (A Day to Remember's former guitarist), Pete Wentz, Winston McCall of Parkway Drive, The Devil Wears Prada, Bring Me the Horizon, Sam Carter of Architects, Tim Lambesis of As I Lay Dying, Silverstein, Andrew WK, August Burns Red, Seventh Star, Matt Heafy of Trivium, Vic Fuentes of Pierce the Veil, Mike Herrera of MxPx, and Set Your Goals.[5] Rock Sound called the video "quite excellent".[5]
Query 5: what does the red stripes mean on the american flag
Top Document: Flag of the United States The flag of the United States of America, often referred to as the American flag, is the national flag of the United States. It consists of thirteen equal horizontal stripes of red (top and bottom) alternating with white, with a blue rectangle in the canton (referred to specifically as the "union") bearing fifty small, white, five-pointed stars arranged in nine offset horizontal rows, where rows of six stars (top and bottom) alternate with rows of five stars. The 50 stars on the flag represent the 50 states of the United States of America, and the 13 stripes represent the thirteen British colonies that declared independence from the Kingdom of Great Britain, and became the first states in the U.S.[1] Nicknames for the flag include The Stars and Stripes,[2] Old Glory,[3] and The Star-Spangled Banner.
Query 6: where did they film diary of a wimpy kid
Top Document: Diary of a Wimpy Kid (film) Filming of Diary of a Wimpy Kid was in Vancouver and wrapped up on October 16, 2009.
Query 7: where was beasts of the southern wild filmed
Top Document: Beasts of the Southern Wild The film's fictional setting, "Isle de Charles Doucet", known to its residents as the Bathtub, was inspired by several isolated and independent fishing communities threatened by erosion, hurricanes and rising sea levels in Louisiana's Terrebonne Parish, most notably the rapidly eroding Isle de Jean Charles. It was filmed in Terrebonne Parish town Montegut.[5]
Query 8: what part of the country are you likely to find the majority of the mollisols
Top Document: Mollisol Mollisols occur in savannahs and mountain valleys (such as Central Asia, or the North American Great Plains). These environments have historically been strongly influenced by fire and abundant pedoturbation from organisms such as ants and earthworms. It was estimated that in 2003, only 14 to 26 percent of grassland ecosystems still remained in a relatively natural state (that is, they were not used for agriculture due to the fertility of the A horizon). Globally, they represent ~7% of ice-free land area. As the world's most agriculturally productive soil order, the Mollisols represent one of the more economically important soil orders.
Query 9: when did fosters home for imaginary friends start
Top Document: Foster's Home for Imaginary Friends McCracken conceived the series after adopting two dogs from an animal shelter and applying the concept to imaginary friends. The show first premiered on Cartoon Network on August 13, 2004, as a 90-minute television film. On August 20, it began its normal run of twenty-to-thirty-minute episodes on Fridays, at 7 pm. The series finished its run on May 3, 2009, with a total of six seasons and seventy-nine episodes. McCracken left Cartoon Network shortly after the series ended. Reruns have aired on Boomerang from August 11, 2012 to November 3, 2013 and again from June 1, 2014 to April 3, 2017.
"""
```
</details>
### Colab demo
We've set up a short demo in a Colab notebook showing how you might use our model:
[Try our model in Colab:](https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing)
### Acknowledgments
Early experiments on CDE were done with support from [Nomic](https://www.nomic.ai/) and [Hyperbolic](https://hyperbolic.xyz/). We're especially indebted to Nomic for [open-sourcing their efficient BERT implementation and contrastive pre-training data](https://www.nomic.ai/blog/posts/nomic-embed-text-v1), which proved vital in the development of CDE.
### Cite us
Used our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:
```
@misc{morris2024contextualdocumentembeddings,
title={Contextual Document Embeddings},
author={John X. Morris and Alexander M. Rush},
year={2024},
eprint={2410.02525},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.02525},
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"MEDAL",
"SCIFACT"
] |
Snowflake/snowflake-arctic-embed-s | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"arxiv:2407.18887",
"arxiv:2405.05374",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-12T13:53:49 | 2024-12-04T16:58:54 | 29,658 | 19 | ---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
model-index:
- name: snowflake-snowflake-arctic-embed-s
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.17910447761193
- type: ap
value: 33.15833652904991
- type: f1
value: 64.86214791591543
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 78.750325
- type: ap
value: 72.83242788470943
- type: f1
value: 78.63968044029453
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.264
- type: f1
value: 37.140269688532825
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 32.646
- type: map_at_10
value: 48.372
- type: map_at_100
value: 49.207
- type: map_at_1000
value: 49.214
- type: map_at_3
value: 43.611
- type: map_at_5
value: 46.601
- type: mrr_at_1
value: 33.144
- type: mrr_at_10
value: 48.557
- type: mrr_at_100
value: 49.385
- type: mrr_at_1000
value: 49.392
- type: mrr_at_3
value: 43.777
- type: mrr_at_5
value: 46.792
- type: ndcg_at_1
value: 32.646
- type: ndcg_at_10
value: 56.874
- type: ndcg_at_100
value: 60.307
- type: ndcg_at_1000
value: 60.465999999999994
- type: ndcg_at_3
value: 47.339999999999996
- type: ndcg_at_5
value: 52.685
- type: precision_at_1
value: 32.646
- type: precision_at_10
value: 8.378
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.393
- type: precision_at_5
value: 14.210999999999999
- type: recall_at_1
value: 32.646
- type: recall_at_10
value: 83.784
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 58.179
- type: recall_at_5
value: 71.053
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 44.94353025039141
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 35.870836103029156
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.149290266979236
- type: mrr
value: 73.8448093919008
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.055571064151
- type: cos_sim_spearman
value: 86.2652186235749
- type: euclidean_pearson
value: 85.82039272282503
- type: euclidean_spearman
value: 86.2652186235749
- type: manhattan_pearson
value: 85.95825392094812
- type: manhattan_spearman
value: 86.6742640885316
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 79.11688311688312
- type: f1
value: 78.28328901613885
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 19.147523589859325
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 35.68369864124274
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 30.474958792950872
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 33.183
- type: map_at_10
value: 43.989
- type: map_at_100
value: 45.389
- type: map_at_1000
value: 45.517
- type: map_at_3
value: 40.275
- type: map_at_5
value: 42.306
- type: mrr_at_1
value: 40.486
- type: mrr_at_10
value: 49.62
- type: mrr_at_100
value: 50.351
- type: mrr_at_1000
value: 50.393
- type: mrr_at_3
value: 46.805
- type: mrr_at_5
value: 48.429
- type: ndcg_at_1
value: 40.486
- type: ndcg_at_10
value: 50.249
- type: ndcg_at_100
value: 55.206
- type: ndcg_at_1000
value: 57.145
- type: ndcg_at_3
value: 44.852
- type: ndcg_at_5
value: 47.355000000000004
- type: precision_at_1
value: 40.486
- type: precision_at_10
value: 9.571
- type: precision_at_100
value: 1.4949999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 21.173000000000002
- type: precision_at_5
value: 15.622
- type: recall_at_1
value: 33.183
- type: recall_at_10
value: 62.134
- type: recall_at_100
value: 82.73
- type: recall_at_1000
value: 94.93599999999999
- type: recall_at_3
value: 46.497
- type: recall_at_5
value: 53.199
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 32.862
- type: map_at_10
value: 42.439
- type: map_at_100
value: 43.736999999999995
- type: map_at_1000
value: 43.864
- type: map_at_3
value: 39.67
- type: map_at_5
value: 41.202
- type: mrr_at_1
value: 40.892
- type: mrr_at_10
value: 48.61
- type: mrr_at_100
value: 49.29
- type: mrr_at_1000
value: 49.332
- type: mrr_at_3
value: 46.688
- type: mrr_at_5
value: 47.803000000000004
- type: ndcg_at_1
value: 40.892
- type: ndcg_at_10
value: 47.797
- type: ndcg_at_100
value: 52.17699999999999
- type: ndcg_at_1000
value: 54.127
- type: ndcg_at_3
value: 44.189
- type: ndcg_at_5
value: 45.821
- type: precision_at_1
value: 40.892
- type: precision_at_10
value: 8.841000000000001
- type: precision_at_100
value: 1.419
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 21.104
- type: precision_at_5
value: 14.777000000000001
- type: recall_at_1
value: 32.862
- type: recall_at_10
value: 56.352999999999994
- type: recall_at_100
value: 74.795
- type: recall_at_1000
value: 86.957
- type: recall_at_3
value: 45.269999999999996
- type: recall_at_5
value: 50.053000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 42.998999999999995
- type: map_at_10
value: 54.745
- type: map_at_100
value: 55.650999999999996
- type: map_at_1000
value: 55.703
- type: map_at_3
value: 51.67
- type: map_at_5
value: 53.503
- type: mrr_at_1
value: 49.028
- type: mrr_at_10
value: 58.172000000000004
- type: mrr_at_100
value: 58.744
- type: mrr_at_1000
value: 58.769000000000005
- type: mrr_at_3
value: 55.977
- type: mrr_at_5
value: 57.38799999999999
- type: ndcg_at_1
value: 49.028
- type: ndcg_at_10
value: 60.161
- type: ndcg_at_100
value: 63.806
- type: ndcg_at_1000
value: 64.821
- type: ndcg_at_3
value: 55.199
- type: ndcg_at_5
value: 57.830999999999996
- type: precision_at_1
value: 49.028
- type: precision_at_10
value: 9.455
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 24.242
- type: precision_at_5
value: 16.614
- type: recall_at_1
value: 42.998999999999995
- type: recall_at_10
value: 72.542
- type: recall_at_100
value: 88.605
- type: recall_at_1000
value: 95.676
- type: recall_at_3
value: 59.480999999999995
- type: recall_at_5
value: 65.886
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.907
- type: map_at_10
value: 35.975
- type: map_at_100
value: 36.985
- type: map_at_1000
value: 37.063
- type: map_at_3
value: 33.467999999999996
- type: map_at_5
value: 34.749
- type: mrr_at_1
value: 30.056
- type: mrr_at_10
value: 38.047
- type: mrr_at_100
value: 38.932
- type: mrr_at_1000
value: 38.991
- type: mrr_at_3
value: 35.705999999999996
- type: mrr_at_5
value: 36.966
- type: ndcg_at_1
value: 30.056
- type: ndcg_at_10
value: 40.631
- type: ndcg_at_100
value: 45.564
- type: ndcg_at_1000
value: 47.685
- type: ndcg_at_3
value: 35.748000000000005
- type: ndcg_at_5
value: 37.921
- type: precision_at_1
value: 30.056
- type: precision_at_10
value: 6.079
- type: precision_at_100
value: 0.898
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.727
- type: precision_at_5
value: 10.056
- type: recall_at_1
value: 27.907
- type: recall_at_10
value: 52.981
- type: recall_at_100
value: 75.53999999999999
- type: recall_at_1000
value: 91.759
- type: recall_at_3
value: 39.878
- type: recall_at_5
value: 45.077
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 16.764000000000003
- type: map_at_10
value: 24.294
- type: map_at_100
value: 25.507999999999996
- type: map_at_1000
value: 25.64
- type: map_at_3
value: 21.807000000000002
- type: map_at_5
value: 23.21
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 28.677000000000003
- type: mrr_at_100
value: 29.742
- type: mrr_at_1000
value: 29.816
- type: mrr_at_3
value: 26.327
- type: mrr_at_5
value: 27.639000000000003
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 29.21
- type: ndcg_at_100
value: 34.788000000000004
- type: ndcg_at_1000
value: 37.813
- type: ndcg_at_3
value: 24.632
- type: ndcg_at_5
value: 26.801000000000002
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.373
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.706
- type: recall_at_1
value: 16.764000000000003
- type: recall_at_10
value: 40.072
- type: recall_at_100
value: 63.856
- type: recall_at_1000
value: 85.141
- type: recall_at_3
value: 27.308
- type: recall_at_5
value: 32.876
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 31.194
- type: map_at_10
value: 40.731
- type: map_at_100
value: 42.073
- type: map_at_1000
value: 42.178
- type: map_at_3
value: 37.726
- type: map_at_5
value: 39.474
- type: mrr_at_1
value: 37.729
- type: mrr_at_10
value: 46.494
- type: mrr_at_100
value: 47.368
- type: mrr_at_1000
value: 47.407
- type: mrr_at_3
value: 44.224999999999994
- type: mrr_at_5
value: 45.582
- type: ndcg_at_1
value: 37.729
- type: ndcg_at_10
value: 46.312999999999995
- type: ndcg_at_100
value: 51.915
- type: ndcg_at_1000
value: 53.788000000000004
- type: ndcg_at_3
value: 41.695
- type: ndcg_at_5
value: 43.956
- type: precision_at_1
value: 37.729
- type: precision_at_10
value: 8.181
- type: precision_at_100
value: 1.275
- type: precision_at_1000
value: 0.16199999999999998
- type: precision_at_3
value: 19.41
- type: precision_at_5
value: 13.648
- type: recall_at_1
value: 31.194
- type: recall_at_10
value: 57.118
- type: recall_at_100
value: 80.759
- type: recall_at_1000
value: 92.779
- type: recall_at_3
value: 44.083
- type: recall_at_5
value: 50.044999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 28.047
- type: map_at_10
value: 37.79
- type: map_at_100
value: 39.145
- type: map_at_1000
value: 39.254
- type: map_at_3
value: 34.857
- type: map_at_5
value: 36.545
- type: mrr_at_1
value: 35.388
- type: mrr_at_10
value: 43.475
- type: mrr_at_100
value: 44.440000000000005
- type: mrr_at_1000
value: 44.494
- type: mrr_at_3
value: 41.286
- type: mrr_at_5
value: 42.673
- type: ndcg_at_1
value: 35.388
- type: ndcg_at_10
value: 43.169000000000004
- type: ndcg_at_100
value: 48.785000000000004
- type: ndcg_at_1000
value: 51.029
- type: ndcg_at_3
value: 38.801
- type: ndcg_at_5
value: 40.9
- type: precision_at_1
value: 35.388
- type: precision_at_10
value: 7.7509999999999994
- type: precision_at_100
value: 1.212
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 18.455
- type: precision_at_5
value: 13.014000000000001
- type: recall_at_1
value: 28.047
- type: recall_at_10
value: 53.53099999999999
- type: recall_at_100
value: 77.285
- type: recall_at_1000
value: 92.575
- type: recall_at_3
value: 40.949000000000005
- type: recall_at_5
value: 46.742
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 28.131999999999994
- type: map_at_10
value: 36.93333333333334
- type: map_at_100
value: 38.117250000000006
- type: map_at_1000
value: 38.23275
- type: map_at_3
value: 34.19708333333333
- type: map_at_5
value: 35.725166666666674
- type: mrr_at_1
value: 33.16116666666667
- type: mrr_at_10
value: 41.057833333333335
- type: mrr_at_100
value: 41.90033333333333
- type: mrr_at_1000
value: 41.95625
- type: mrr_at_3
value: 38.757333333333335
- type: mrr_at_5
value: 40.097333333333324
- type: ndcg_at_1
value: 33.16116666666667
- type: ndcg_at_10
value: 42.01983333333333
- type: ndcg_at_100
value: 46.99916666666667
- type: ndcg_at_1000
value: 49.21783333333334
- type: ndcg_at_3
value: 37.479916666666654
- type: ndcg_at_5
value: 39.6355
- type: precision_at_1
value: 33.16116666666667
- type: precision_at_10
value: 7.230249999999999
- type: precision_at_100
value: 1.1411666666666667
- type: precision_at_1000
value: 0.1520833333333333
- type: precision_at_3
value: 17.028166666666667
- type: precision_at_5
value: 12.046999999999999
- type: recall_at_1
value: 28.131999999999994
- type: recall_at_10
value: 52.825500000000005
- type: recall_at_100
value: 74.59608333333333
- type: recall_at_1000
value: 89.87916666666668
- type: recall_at_3
value: 40.13625
- type: recall_at_5
value: 45.699999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 24.773999999999997
- type: map_at_10
value: 31.997999999999998
- type: map_at_100
value: 32.857
- type: map_at_1000
value: 32.957
- type: map_at_3
value: 30.041
- type: map_at_5
value: 31.119000000000003
- type: mrr_at_1
value: 27.607
- type: mrr_at_10
value: 34.538000000000004
- type: mrr_at_100
value: 35.308
- type: mrr_at_1000
value: 35.375
- type: mrr_at_3
value: 32.643
- type: mrr_at_5
value: 33.755
- type: ndcg_at_1
value: 27.607
- type: ndcg_at_10
value: 36.035000000000004
- type: ndcg_at_100
value: 40.351
- type: ndcg_at_1000
value: 42.684
- type: ndcg_at_3
value: 32.414
- type: ndcg_at_5
value: 34.11
- type: precision_at_1
value: 27.607
- type: precision_at_10
value: 5.6129999999999995
- type: precision_at_100
value: 0.8370000000000001
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 13.957
- type: precision_at_5
value: 9.571
- type: recall_at_1
value: 24.773999999999997
- type: recall_at_10
value: 45.717
- type: recall_at_100
value: 65.499
- type: recall_at_1000
value: 82.311
- type: recall_at_3
value: 35.716
- type: recall_at_5
value: 40.007999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 19.227
- type: map_at_10
value: 26.649
- type: map_at_100
value: 27.711999999999996
- type: map_at_1000
value: 27.837
- type: map_at_3
value: 24.454
- type: map_at_5
value: 25.772000000000002
- type: mrr_at_1
value: 23.433999999999997
- type: mrr_at_10
value: 30.564999999999998
- type: mrr_at_100
value: 31.44
- type: mrr_at_1000
value: 31.513999999999996
- type: mrr_at_3
value: 28.435
- type: mrr_at_5
value: 29.744999999999997
- type: ndcg_at_1
value: 23.433999999999997
- type: ndcg_at_10
value: 31.104
- type: ndcg_at_100
value: 36.172
- type: ndcg_at_1000
value: 39.006
- type: ndcg_at_3
value: 27.248
- type: ndcg_at_5
value: 29.249000000000002
- type: precision_at_1
value: 23.433999999999997
- type: precision_at_10
value: 5.496
- type: precision_at_100
value: 0.9490000000000001
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 12.709000000000001
- type: precision_at_5
value: 9.209
- type: recall_at_1
value: 19.227
- type: recall_at_10
value: 40.492
- type: recall_at_100
value: 63.304
- type: recall_at_1000
value: 83.45
- type: recall_at_3
value: 29.713
- type: recall_at_5
value: 34.82
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 29.199
- type: map_at_10
value: 37.617
- type: map_at_100
value: 38.746
- type: map_at_1000
value: 38.851
- type: map_at_3
value: 34.882000000000005
- type: map_at_5
value: 36.571999999999996
- type: mrr_at_1
value: 33.489000000000004
- type: mrr_at_10
value: 41.089999999999996
- type: mrr_at_100
value: 41.965
- type: mrr_at_1000
value: 42.028
- type: mrr_at_3
value: 38.666
- type: mrr_at_5
value: 40.159
- type: ndcg_at_1
value: 33.489000000000004
- type: ndcg_at_10
value: 42.487
- type: ndcg_at_100
value: 47.552
- type: ndcg_at_1000
value: 49.774
- type: ndcg_at_3
value: 37.623
- type: ndcg_at_5
value: 40.184999999999995
- type: precision_at_1
value: 33.489000000000004
- type: precision_at_10
value: 6.94
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 16.667
- type: precision_at_5
value: 11.922
- type: recall_at_1
value: 29.199
- type: recall_at_10
value: 53.689
- type: recall_at_100
value: 75.374
- type: recall_at_1000
value: 90.64999999999999
- type: recall_at_3
value: 40.577999999999996
- type: recall_at_5
value: 46.909
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.206999999999997
- type: map_at_10
value: 36.146
- type: map_at_100
value: 37.759
- type: map_at_1000
value: 37.979
- type: map_at_3
value: 32.967999999999996
- type: map_at_5
value: 34.809
- type: mrr_at_1
value: 32.806000000000004
- type: mrr_at_10
value: 40.449
- type: mrr_at_100
value: 41.404999999999994
- type: mrr_at_1000
value: 41.457
- type: mrr_at_3
value: 37.614999999999995
- type: mrr_at_5
value: 39.324999999999996
- type: ndcg_at_1
value: 32.806000000000004
- type: ndcg_at_10
value: 41.911
- type: ndcg_at_100
value: 47.576
- type: ndcg_at_1000
value: 50.072
- type: ndcg_at_3
value: 36.849
- type: ndcg_at_5
value: 39.475
- type: precision_at_1
value: 32.806000000000004
- type: precision_at_10
value: 8.103
- type: precision_at_100
value: 1.557
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 17.26
- type: precision_at_5
value: 12.885
- type: recall_at_1
value: 27.206999999999997
- type: recall_at_10
value: 52.56999999999999
- type: recall_at_100
value: 78.302
- type: recall_at_1000
value: 94.121
- type: recall_at_3
value: 38.317
- type: recall_at_5
value: 45.410000000000004
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 24.221
- type: map_at_10
value: 30.826999999999998
- type: map_at_100
value: 31.845000000000002
- type: map_at_1000
value: 31.95
- type: map_at_3
value: 28.547
- type: map_at_5
value: 29.441
- type: mrr_at_1
value: 26.247999999999998
- type: mrr_at_10
value: 32.957
- type: mrr_at_100
value: 33.819
- type: mrr_at_1000
value: 33.899
- type: mrr_at_3
value: 30.714999999999996
- type: mrr_at_5
value: 31.704
- type: ndcg_at_1
value: 26.247999999999998
- type: ndcg_at_10
value: 35.171
- type: ndcg_at_100
value: 40.098
- type: ndcg_at_1000
value: 42.67
- type: ndcg_at_3
value: 30.508999999999997
- type: ndcg_at_5
value: 32.022
- type: precision_at_1
value: 26.247999999999998
- type: precision_at_10
value: 5.36
- type: precision_at_100
value: 0.843
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 12.568999999999999
- type: precision_at_5
value: 8.540000000000001
- type: recall_at_1
value: 24.221
- type: recall_at_10
value: 46.707
- type: recall_at_100
value: 69.104
- type: recall_at_1000
value: 88.19500000000001
- type: recall_at_3
value: 33.845
- type: recall_at_5
value: 37.375
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 13.624
- type: map_at_10
value: 22.557
- type: map_at_100
value: 24.367
- type: map_at_1000
value: 24.54
- type: map_at_3
value: 18.988
- type: map_at_5
value: 20.785999999999998
- type: mrr_at_1
value: 30.619000000000003
- type: mrr_at_10
value: 42.019
- type: mrr_at_100
value: 42.818
- type: mrr_at_1000
value: 42.856
- type: mrr_at_3
value: 38.578
- type: mrr_at_5
value: 40.669
- type: ndcg_at_1
value: 30.619000000000003
- type: ndcg_at_10
value: 31.252999999999997
- type: ndcg_at_100
value: 38.238
- type: ndcg_at_1000
value: 41.368
- type: ndcg_at_3
value: 25.843
- type: ndcg_at_5
value: 27.638
- type: precision_at_1
value: 30.619000000000003
- type: precision_at_10
value: 9.687
- type: precision_at_100
value: 1.718
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 18.849
- type: precision_at_5
value: 14.463000000000001
- type: recall_at_1
value: 13.624
- type: recall_at_10
value: 36.693999999999996
- type: recall_at_100
value: 60.9
- type: recall_at_1000
value: 78.46
- type: recall_at_3
value: 23.354
- type: recall_at_5
value: 28.756999999999998
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.077
- type: map_at_10
value: 19.813
- type: map_at_100
value: 27.822999999999997
- type: map_at_1000
value: 29.485
- type: map_at_3
value: 14.255999999999998
- type: map_at_5
value: 16.836000000000002
- type: mrr_at_1
value: 69.25
- type: mrr_at_10
value: 77.059
- type: mrr_at_100
value: 77.41
- type: mrr_at_1000
value: 77.416
- type: mrr_at_3
value: 75.625
- type: mrr_at_5
value: 76.512
- type: ndcg_at_1
value: 55.75
- type: ndcg_at_10
value: 41.587
- type: ndcg_at_100
value: 46.048
- type: ndcg_at_1000
value: 53.172
- type: ndcg_at_3
value: 46.203
- type: ndcg_at_5
value: 43.696
- type: precision_at_1
value: 69.25
- type: precision_at_10
value: 32.95
- type: precision_at_100
value: 10.555
- type: precision_at_1000
value: 2.136
- type: precision_at_3
value: 49.667
- type: precision_at_5
value: 42.5
- type: recall_at_1
value: 9.077
- type: recall_at_10
value: 25.249
- type: recall_at_100
value: 51.964
- type: recall_at_1000
value: 74.51
- type: recall_at_3
value: 15.584000000000001
- type: recall_at_5
value: 19.717000000000002
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 45.769999999999996
- type: f1
value: 41.64144711933962
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 67.098
- type: map_at_10
value: 77.69800000000001
- type: map_at_100
value: 77.947
- type: map_at_1000
value: 77.961
- type: map_at_3
value: 76.278
- type: map_at_5
value: 77.217
- type: mrr_at_1
value: 72.532
- type: mrr_at_10
value: 82.41199999999999
- type: mrr_at_100
value: 82.527
- type: mrr_at_1000
value: 82.529
- type: mrr_at_3
value: 81.313
- type: mrr_at_5
value: 82.069
- type: ndcg_at_1
value: 72.532
- type: ndcg_at_10
value: 82.488
- type: ndcg_at_100
value: 83.382
- type: ndcg_at_1000
value: 83.622
- type: ndcg_at_3
value: 80.101
- type: ndcg_at_5
value: 81.52199999999999
- type: precision_at_1
value: 72.532
- type: precision_at_10
value: 10.203
- type: precision_at_100
value: 1.082
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 31.308000000000003
- type: precision_at_5
value: 19.652
- type: recall_at_1
value: 67.098
- type: recall_at_10
value: 92.511
- type: recall_at_100
value: 96.06099999999999
- type: recall_at_1000
value: 97.548
- type: recall_at_3
value: 86.105
- type: recall_at_5
value: 89.661
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 18.681
- type: map_at_10
value: 31.739
- type: map_at_100
value: 33.503
- type: map_at_1000
value: 33.69
- type: map_at_3
value: 27.604
- type: map_at_5
value: 29.993
- type: mrr_at_1
value: 37.5
- type: mrr_at_10
value: 46.933
- type: mrr_at_100
value: 47.771
- type: mrr_at_1000
value: 47.805
- type: mrr_at_3
value: 44.239
- type: mrr_at_5
value: 45.766
- type: ndcg_at_1
value: 37.5
- type: ndcg_at_10
value: 39.682
- type: ndcg_at_100
value: 46.127
- type: ndcg_at_1000
value: 48.994
- type: ndcg_at_3
value: 35.655
- type: ndcg_at_5
value: 37.036
- type: precision_at_1
value: 37.5
- type: precision_at_10
value: 11.08
- type: precision_at_100
value: 1.765
- type: precision_at_1000
value: 0.22999999999999998
- type: precision_at_3
value: 23.919999999999998
- type: precision_at_5
value: 17.809
- type: recall_at_1
value: 18.681
- type: recall_at_10
value: 47.548
- type: recall_at_100
value: 71.407
- type: recall_at_1000
value: 87.805
- type: recall_at_3
value: 32.979
- type: recall_at_5
value: 39.192
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 38.257999999999996
- type: map_at_10
value: 57.605
- type: map_at_100
value: 58.50300000000001
- type: map_at_1000
value: 58.568
- type: map_at_3
value: 54.172
- type: map_at_5
value: 56.323
- type: mrr_at_1
value: 76.51599999999999
- type: mrr_at_10
value: 82.584
- type: mrr_at_100
value: 82.78
- type: mrr_at_1000
value: 82.787
- type: mrr_at_3
value: 81.501
- type: mrr_at_5
value: 82.185
- type: ndcg_at_1
value: 76.51599999999999
- type: ndcg_at_10
value: 66.593
- type: ndcg_at_100
value: 69.699
- type: ndcg_at_1000
value: 70.953
- type: ndcg_at_3
value: 61.673
- type: ndcg_at_5
value: 64.42
- type: precision_at_1
value: 76.51599999999999
- type: precision_at_10
value: 13.857
- type: precision_at_100
value: 1.628
- type: precision_at_1000
value: 0.179
- type: precision_at_3
value: 38.956
- type: precision_at_5
value: 25.541999999999998
- type: recall_at_1
value: 38.257999999999996
- type: recall_at_10
value: 69.284
- type: recall_at_100
value: 81.391
- type: recall_at_1000
value: 89.689
- type: recall_at_3
value: 58.433
- type: recall_at_5
value: 63.856
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 69.48679999999999
- type: ap
value: 63.97638838971138
- type: f1
value: 69.22731638841675
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 20.916999999999998
- type: map_at_10
value: 32.929
- type: map_at_100
value: 34.1
- type: map_at_1000
value: 34.152
- type: map_at_3
value: 29.065
- type: map_at_5
value: 31.287
- type: mrr_at_1
value: 21.562
- type: mrr_at_10
value: 33.533
- type: mrr_at_100
value: 34.644000000000005
- type: mrr_at_1000
value: 34.69
- type: mrr_at_3
value: 29.735
- type: mrr_at_5
value: 31.928
- type: ndcg_at_1
value: 21.562
- type: ndcg_at_10
value: 39.788000000000004
- type: ndcg_at_100
value: 45.434999999999995
- type: ndcg_at_1000
value: 46.75
- type: ndcg_at_3
value: 31.942999999999998
- type: ndcg_at_5
value: 35.888
- type: precision_at_1
value: 21.562
- type: precision_at_10
value: 6.348
- type: precision_at_100
value: 0.918
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 13.682
- type: precision_at_5
value: 10.189
- type: recall_at_1
value: 20.916999999999998
- type: recall_at_10
value: 60.926
- type: recall_at_100
value: 87.03800000000001
- type: recall_at_1000
value: 97.085
- type: recall_at_3
value: 39.637
- type: recall_at_5
value: 49.069
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.93935248518011
- type: f1
value: 90.56439321844506
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 58.62517099863203
- type: f1
value: 40.69925681703197
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 76.29746835443039
- type: f1
value: 75.31702672039506
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 43.05495067062023
- type: v_measure
value: 19.625272848173843
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.76126429051781
- type: f1
value: 62.60284261265268
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 70.05043712172159
- type: f1
value: 69.08340521169049
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 30.78969229005989
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 27.954325178520335
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.601827413968596
- type: mrr
value: 31.515372019474196
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 5.4559999999999995
- type: map_at_10
value: 12.039
- type: map_at_100
value: 14.804999999999998
- type: map_at_1000
value: 16.081
- type: map_at_3
value: 8.996
- type: map_at_5
value: 10.357
- type: mrr_at_1
value: 45.82
- type: mrr_at_10
value: 53.583999999999996
- type: mrr_at_100
value: 54.330999999999996
- type: mrr_at_1000
value: 54.366
- type: mrr_at_3
value: 52.166999999999994
- type: mrr_at_5
value: 52.971999999999994
- type: ndcg_at_1
value: 44.427
- type: ndcg_at_10
value: 32.536
- type: ndcg_at_100
value: 29.410999999999998
- type: ndcg_at_1000
value: 38.012
- type: ndcg_at_3
value: 38.674
- type: ndcg_at_5
value: 36.107
- type: precision_at_1
value: 45.82
- type: precision_at_10
value: 23.591
- type: precision_at_100
value: 7.35
- type: precision_at_1000
value: 1.9769999999999999
- type: precision_at_3
value: 36.016999999999996
- type: precision_at_5
value: 30.959999999999997
- type: recall_at_1
value: 5.4559999999999995
- type: recall_at_10
value: 15.387
- type: recall_at_100
value: 28.754999999999995
- type: recall_at_1000
value: 59.787
- type: recall_at_3
value: 10.137
- type: recall_at_5
value: 12.200999999999999
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 32.609
- type: map_at_10
value: 48.522
- type: map_at_100
value: 49.468
- type: map_at_1000
value: 49.497
- type: map_at_3
value: 44.327
- type: map_at_5
value: 46.937
- type: mrr_at_1
value: 36.616
- type: mrr_at_10
value: 50.943000000000005
- type: mrr_at_100
value: 51.626000000000005
- type: mrr_at_1000
value: 51.647
- type: mrr_at_3
value: 47.532999999999994
- type: mrr_at_5
value: 49.714000000000006
- type: ndcg_at_1
value: 36.586999999999996
- type: ndcg_at_10
value: 56.19499999999999
- type: ndcg_at_100
value: 60.014
- type: ndcg_at_1000
value: 60.707
- type: ndcg_at_3
value: 48.486000000000004
- type: ndcg_at_5
value: 52.791999999999994
- type: precision_at_1
value: 36.586999999999996
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 1.129
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 22.171
- type: precision_at_5
value: 15.787999999999998
- type: recall_at_1
value: 32.609
- type: recall_at_10
value: 77.011
- type: recall_at_100
value: 93.202
- type: recall_at_1000
value: 98.344
- type: recall_at_3
value: 57.286
- type: recall_at_5
value: 67.181
- task:
type: Classification
dataset:
name: MTEB NewsClassification
type: ag_news
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy
value: 77.4421052631579
- type: f1
value: 77.23976860913628
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.89816700610999
- type: cos_sim_ap
value: 100
- type: cos_sim_f1
value: 99.9490575649516
- type: cos_sim_precision
value: 100
- type: cos_sim_recall
value: 99.89816700610999
- type: dot_accuracy
value: 99.89816700610999
- type: dot_ap
value: 100
- type: dot_f1
value: 99.9490575649516
- type: dot_precision
value: 100
- type: dot_recall
value: 99.89816700610999
- type: euclidean_accuracy
value: 99.89816700610999
- type: euclidean_ap
value: 100
- type: euclidean_f1
value: 99.9490575649516
- type: euclidean_precision
value: 100
- type: euclidean_recall
value: 99.89816700610999
- type: manhattan_accuracy
value: 99.89816700610999
- type: manhattan_ap
value: 100
- type: manhattan_f1
value: 99.9490575649516
- type: manhattan_precision
value: 100
- type: manhattan_recall
value: 99.89816700610999
- type: max_accuracy
value: 99.89816700610999
- type: max_ap
value: 100
- type: max_f1
value: 99.9490575649516
- task:
type: PairClassification
dataset:
name: MTEB PawsX (en)
type: paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 61.25000000000001
- type: cos_sim_ap
value: 59.23166242799505
- type: cos_sim_f1
value: 62.53016201309893
- type: cos_sim_precision
value: 45.486459378134406
- type: cos_sim_recall
value: 100
- type: dot_accuracy
value: 61.25000000000001
- type: dot_ap
value: 59.23109306756652
- type: dot_f1
value: 62.53016201309893
- type: dot_precision
value: 45.486459378134406
- type: dot_recall
value: 100
- type: euclidean_accuracy
value: 61.25000000000001
- type: euclidean_ap
value: 59.23166242799505
- type: euclidean_f1
value: 62.53016201309893
- type: euclidean_precision
value: 45.486459378134406
- type: euclidean_recall
value: 100
- type: manhattan_accuracy
value: 61.25000000000001
- type: manhattan_ap
value: 59.23015114712089
- type: manhattan_f1
value: 62.50861474844934
- type: manhattan_precision
value: 45.46365914786967
- type: manhattan_recall
value: 100
- type: max_accuracy
value: 61.25000000000001
- type: max_ap
value: 59.23166242799505
- type: max_f1
value: 62.53016201309893
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 69.919
- type: map_at_10
value: 83.636
- type: map_at_100
value: 84.27
- type: map_at_1000
value: 84.289
- type: map_at_3
value: 80.744
- type: map_at_5
value: 82.509
- type: mrr_at_1
value: 80.52
- type: mrr_at_10
value: 86.751
- type: mrr_at_100
value: 86.875
- type: mrr_at_1000
value: 86.876
- type: mrr_at_3
value: 85.798
- type: mrr_at_5
value: 86.414
- type: ndcg_at_1
value: 80.53
- type: ndcg_at_10
value: 87.465
- type: ndcg_at_100
value: 88.762
- type: ndcg_at_1000
value: 88.90599999999999
- type: ndcg_at_3
value: 84.634
- type: ndcg_at_5
value: 86.09400000000001
- type: precision_at_1
value: 80.53
- type: precision_at_10
value: 13.263
- type: precision_at_100
value: 1.517
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.973
- type: precision_at_5
value: 24.25
- type: recall_at_1
value: 69.919
- type: recall_at_10
value: 94.742
- type: recall_at_100
value: 99.221
- type: recall_at_1000
value: 99.917
- type: recall_at_3
value: 86.506
- type: recall_at_5
value: 90.736
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 50.47309147963901
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 60.53779561923047
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.843
- type: map_at_10
value: 11.664
- type: map_at_100
value: 13.499
- type: map_at_1000
value: 13.771
- type: map_at_3
value: 8.602
- type: map_at_5
value: 10.164
- type: mrr_at_1
value: 23.9
- type: mrr_at_10
value: 34.018
- type: mrr_at_100
value: 35.099000000000004
- type: mrr_at_1000
value: 35.162
- type: mrr_at_3
value: 31.233
- type: mrr_at_5
value: 32.793
- type: ndcg_at_1
value: 23.9
- type: ndcg_at_10
value: 19.42
- type: ndcg_at_100
value: 26.715
- type: ndcg_at_1000
value: 31.776
- type: ndcg_at_3
value: 19.165
- type: ndcg_at_5
value: 16.46
- type: precision_at_1
value: 23.9
- type: precision_at_10
value: 9.82
- type: precision_at_100
value: 2.0340000000000003
- type: precision_at_1000
value: 0.325
- type: precision_at_3
value: 17.767
- type: precision_at_5
value: 14.24
- type: recall_at_1
value: 4.843
- type: recall_at_10
value: 19.895
- type: recall_at_100
value: 41.302
- type: recall_at_1000
value: 66.077
- type: recall_at_3
value: 10.803
- type: recall_at_5
value: 14.418000000000001
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 76.94120735638143
- type: cos_sim_spearman
value: 69.66114097154585
- type: euclidean_pearson
value: 73.11242035696426
- type: euclidean_spearman
value: 69.66114271982464
- type: manhattan_pearson
value: 73.07993034858605
- type: manhattan_spearman
value: 69.6457893357314
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 74.72893353272778
- type: cos_sim_spearman
value: 68.78540928870311
- type: euclidean_pearson
value: 71.13907970605574
- type: euclidean_spearman
value: 68.78540928870311
- type: manhattan_pearson
value: 71.02709590547859
- type: manhattan_spearman
value: 68.71685896660532
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 79.30142652684971
- type: cos_sim_spearman
value: 79.61879435615303
- type: euclidean_pearson
value: 79.08730432883864
- type: euclidean_spearman
value: 79.61879435615303
- type: manhattan_pearson
value: 78.99621073156322
- type: manhattan_spearman
value: 79.53806342308278
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 78.99585233036139
- type: cos_sim_spearman
value: 75.57574519760183
- type: euclidean_pearson
value: 77.33835658613162
- type: euclidean_spearman
value: 75.57573873503655
- type: manhattan_pearson
value: 77.12175044789362
- type: manhattan_spearman
value: 75.41293517634836
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 83.9694268253376
- type: cos_sim_spearman
value: 84.64256921939338
- type: euclidean_pearson
value: 83.92322958711
- type: euclidean_spearman
value: 84.64257976421872
- type: manhattan_pearson
value: 83.93503107204337
- type: manhattan_spearman
value: 84.63611608236032
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.09041419790253
- type: cos_sim_spearman
value: 82.39869157752557
- type: euclidean_pearson
value: 82.04595698258301
- type: euclidean_spearman
value: 82.39869157752557
- type: manhattan_pearson
value: 81.97581168053004
- type: manhattan_spearman
value: 82.34255320578193
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.35210432821825
- type: cos_sim_spearman
value: 86.73200885328937
- type: euclidean_pearson
value: 86.8527089168747
- type: euclidean_spearman
value: 86.73200885328937
- type: manhattan_pearson
value: 86.95671235295457
- type: manhattan_spearman
value: 86.77713700838545
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 68.91106612960657
- type: cos_sim_spearman
value: 69.48524490302286
- type: euclidean_pearson
value: 70.51347841618035
- type: euclidean_spearman
value: 69.48524490302286
- type: manhattan_pearson
value: 70.31770181334245
- type: manhattan_spearman
value: 69.12494700138238
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 81.54104342761988
- type: cos_sim_spearman
value: 81.18789220331483
- type: euclidean_pearson
value: 81.5895544590969
- type: euclidean_spearman
value: 81.18789220331483
- type: manhattan_pearson
value: 81.4738562449809
- type: manhattan_spearman
value: 81.06565101416024
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: PhilipMay/stsb_multi_mt
config: en
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 81.54104346197056
- type: cos_sim_spearman
value: 81.18789220331483
- type: euclidean_pearson
value: 81.58955451690102
- type: euclidean_spearman
value: 81.18789220331483
- type: manhattan_pearson
value: 81.47385630064072
- type: manhattan_spearman
value: 81.06565101416024
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.34107964300796
- type: mrr
value: 94.01917889662987
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 55.928
- type: map_at_10
value: 65.443
- type: map_at_100
value: 66.067
- type: map_at_1000
value: 66.091
- type: map_at_3
value: 62.629999999999995
- type: map_at_5
value: 64.35
- type: mrr_at_1
value: 59
- type: mrr_at_10
value: 66.845
- type: mrr_at_100
value: 67.31899999999999
- type: mrr_at_1000
value: 67.342
- type: mrr_at_3
value: 64.61099999999999
- type: mrr_at_5
value: 66.044
- type: ndcg_at_1
value: 59
- type: ndcg_at_10
value: 69.921
- type: ndcg_at_100
value: 72.365
- type: ndcg_at_1000
value: 73.055
- type: ndcg_at_3
value: 65.086
- type: ndcg_at_5
value: 67.62700000000001
- type: precision_at_1
value: 59
- type: precision_at_10
value: 9.3
- type: precision_at_100
value: 1.057
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 25.333
- type: precision_at_5
value: 16.866999999999997
- type: recall_at_1
value: 55.928
- type: recall_at_10
value: 82.289
- type: recall_at_100
value: 92.833
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 69.172
- type: recall_at_5
value: 75.628
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81881188118813
- type: cos_sim_ap
value: 95.2776439040401
- type: cos_sim_f1
value: 90.74355083459787
- type: cos_sim_precision
value: 91.81166837256909
- type: cos_sim_recall
value: 89.7
- type: dot_accuracy
value: 99.81881188118813
- type: dot_ap
value: 95.27764092100406
- type: dot_f1
value: 90.74355083459787
- type: dot_precision
value: 91.81166837256909
- type: dot_recall
value: 89.7
- type: euclidean_accuracy
value: 99.81881188118813
- type: euclidean_ap
value: 95.27764091101388
- type: euclidean_f1
value: 90.74355083459787
- type: euclidean_precision
value: 91.81166837256909
- type: euclidean_recall
value: 89.7
- type: manhattan_accuracy
value: 99.82079207920792
- type: manhattan_ap
value: 95.25081634689418
- type: manhattan_f1
value: 90.75114971895759
- type: manhattan_precision
value: 92.78996865203762
- type: manhattan_recall
value: 88.8
- type: max_accuracy
value: 99.82079207920792
- type: max_ap
value: 95.2776439040401
- type: max_f1
value: 90.75114971895759
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.69855369728728
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 33.98191834367251
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.156163330429614
- type: mrr
value: 50.90145148968678
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.16938079808134
- type: cos_sim_spearman
value: 31.74655874538245
- type: dot_pearson
value: 31.169380299671705
- type: dot_spearman
value: 31.74655874538245
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.252
- type: map_at_10
value: 2.009
- type: map_at_100
value: 11.611
- type: map_at_1000
value: 27.811999999999998
- type: map_at_3
value: 0.685
- type: map_at_5
value: 1.08
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 97
- type: mrr_at_100
value: 97
- type: mrr_at_1000
value: 97
- type: mrr_at_3
value: 97
- type: mrr_at_5
value: 97
- type: ndcg_at_1
value: 88
- type: ndcg_at_10
value: 81.388
- type: ndcg_at_100
value: 60.629
- type: ndcg_at_1000
value: 52.38
- type: ndcg_at_3
value: 86.827
- type: ndcg_at_5
value: 84.597
- type: precision_at_1
value: 94
- type: precision_at_10
value: 85.8
- type: precision_at_100
value: 62.419999999999995
- type: precision_at_1000
value: 23.31
- type: precision_at_3
value: 90.667
- type: precision_at_5
value: 88.4
- type: recall_at_1
value: 0.252
- type: recall_at_10
value: 2.164
- type: recall_at_100
value: 14.613999999999999
- type: recall_at_1000
value: 48.730000000000004
- type: recall_at_3
value: 0.7020000000000001
- type: recall_at_5
value: 1.122
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.476
- type: map_at_10
value: 13.442000000000002
- type: map_at_100
value: 20.618
- type: map_at_1000
value: 22.175
- type: map_at_3
value: 6.968000000000001
- type: map_at_5
value: 9.214
- type: mrr_at_1
value: 44.897999999999996
- type: mrr_at_10
value: 56.77100000000001
- type: mrr_at_100
value: 57.226
- type: mrr_at_1000
value: 57.226
- type: mrr_at_3
value: 52.381
- type: mrr_at_5
value: 54.523999999999994
- type: ndcg_at_1
value: 42.857
- type: ndcg_at_10
value: 32.507999999999996
- type: ndcg_at_100
value: 43.614000000000004
- type: ndcg_at_1000
value: 53.82
- type: ndcg_at_3
value: 36.818
- type: ndcg_at_5
value: 33.346
- type: precision_at_1
value: 44.897999999999996
- type: precision_at_10
value: 28.571
- type: precision_at_100
value: 8.652999999999999
- type: precision_at_1000
value: 1.5709999999999997
- type: precision_at_3
value: 38.095
- type: precision_at_5
value: 32.245000000000005
- type: recall_at_1
value: 3.476
- type: recall_at_10
value: 20.827
- type: recall_at_100
value: 53.04299999999999
- type: recall_at_1000
value: 84.221
- type: recall_at_3
value: 8.200000000000001
- type: recall_at_5
value: 11.651
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 61.96360000000001
- type: ap
value: 11.256160324436445
- type: f1
value: 48.07712827691349
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.90492359932088
- type: f1
value: 59.12542417513503
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.284935353315355
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 83.4714192048638
- type: cos_sim_ap
value: 65.77588263185375
- type: cos_sim_f1
value: 62.459508098380326
- type: cos_sim_precision
value: 57.27172717271727
- type: cos_sim_recall
value: 68.68073878627968
- type: dot_accuracy
value: 83.4714192048638
- type: dot_ap
value: 65.77588818364636
- type: dot_f1
value: 62.459508098380326
- type: dot_precision
value: 57.27172717271727
- type: dot_recall
value: 68.68073878627968
- type: euclidean_accuracy
value: 83.4714192048638
- type: euclidean_ap
value: 65.77587693431595
- type: euclidean_f1
value: 62.459508098380326
- type: euclidean_precision
value: 57.27172717271727
- type: euclidean_recall
value: 68.68073878627968
- type: manhattan_accuracy
value: 83.47737974608094
- type: manhattan_ap
value: 65.65957745829654
- type: manhattan_f1
value: 62.22760290556902
- type: manhattan_precision
value: 57.494407158836694
- type: manhattan_recall
value: 67.81002638522428
- type: max_accuracy
value: 83.47737974608094
- type: max_ap
value: 65.77588818364636
- type: max_f1
value: 62.459508098380326
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.64244964489463
- type: cos_sim_ap
value: 85.154122301394
- type: cos_sim_f1
value: 77.45617911327146
- type: cos_sim_precision
value: 74.23066064370413
- type: cos_sim_recall
value: 80.97474591931014
- type: dot_accuracy
value: 88.64244964489463
- type: dot_ap
value: 85.15411965587543
- type: dot_f1
value: 77.45617911327146
- type: dot_precision
value: 74.23066064370413
- type: dot_recall
value: 80.97474591931014
- type: euclidean_accuracy
value: 88.64244964489463
- type: euclidean_ap
value: 85.15414684113986
- type: euclidean_f1
value: 77.45617911327146
- type: euclidean_precision
value: 74.23066064370413
- type: euclidean_recall
value: 80.97474591931014
- type: manhattan_accuracy
value: 88.57841425078588
- type: manhattan_ap
value: 85.12472268567576
- type: manhattan_f1
value: 77.39497339937627
- type: manhattan_precision
value: 73.92584285413892
- type: manhattan_recall
value: 81.20572836464429
- type: max_accuracy
value: 88.64244964489463
- type: max_ap
value: 85.15414684113986
- type: max_f1
value: 77.45617911327146
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 79.58576208710117
---
<h1 align="center">Snowflake's Arctic-embed-s</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
## News
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!
07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
## Models
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| Google-gecko-text-embedding | 55.7 |
| text-embedding-3-large | 55.44 |
| Cohere-embed-english-v3.0 | 55.00 |
| bge-large-en-v1.5 | 54.29 |
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------- | -------------------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |
| GIST-all-MiniLM-L6-v2 | 45.12 |
| gte-tiny | 44.92 |
| all-MiniLM-L6-v2 | 41.95 |
| bge-micro-v2 | 42.56 |
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |
| bge-small-en-v1.5 | 51.68 |
| Cohere-embed-english-light-v3.0 | 51.34 |
| text-embedding-3-small | 51.08 |
| e5-small-v2 | 49.04 |
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |
| bge-base-en-v1.5 | 53.25 |
| nomic-embed-text-v1.5 | 53.25 |
| GIST-Embedding-v0 | 52.31 |
| gte-base | 52.31 |
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)
Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |
| nomic-embed-text-v1.5 | 53.01 |
| nomic-embed-text-v1 | 52.81 |
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| UAE-Large-V1 | 54.66 |
| bge-large-en-v1.5 | 54.29 |
| mxbai-embed-large-v1 | 54.39 |
| e5-Large-v2 | 50.56 |
## Usage
### Using Sentence Transformers
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-s")
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = query_embeddings @ document_embeddings.T
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
```
Query: what is snowflake?
0.533809 The Data Cloud!
0.49207097 Mexico City of Course!
Query: Where can I get the best tacos?
0.56592476 Mexico City of Course!
0.48255116 The Data Cloud!
```
### Using Huggingface transformers
You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-s')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-s', add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.48255123876493394, 0.5659250100112143]
```
## FAQ
TBD
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
## Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=26ca7731-2650-44be-942d-0c6809cfcf00" /> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Salesforce/SFR-Embedding-Mistral | Salesforce | feature-extraction | [
"sentence-transformers",
"safetensors",
"mistral",
"feature-extraction",
"mteb",
"transformers",
"en",
"arxiv:2210.07316",
"arxiv:2310.06825",
"arxiv:2401.00368",
"arxiv:2104.08663",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-24T22:29:26 | 2025-02-04T21:01:42 | 29,392 | 273 | ---
language:
- en
license: cc-by-nc-4.0
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: SFR-Embedding-Mistral
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.92537313432834
- type: ap
value: 40.86767661556651
- type: f1
value: 71.65758897929837
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.967
- type: ap
value: 94.46300829592593
- type: f1
value: 95.96507173189292
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 54.352000000000004
- type: f1
value: 53.636682615380174
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 43.314
- type: ndcg_at_2
value: 54.757
- type: ndcg_at_3
value: 58.84700000000001
- type: ndcg_at_5
value: 63.634
- type: ndcg_at_7
value: 65.741
- type: ndcg_at_10
value: 67.171
- type: ndcg_at_20
value: 68.585
- type: ndcg_at_30
value: 68.81
- type: ndcg_at_50
value: 68.932
- type: ndcg_at_70
value: 68.992
- type: ndcg_at_100
value: 69.014
- type: ndcg_at_200
value: 69.014
- type: ndcg_at_300
value: 69.014
- type: ndcg_at_500
value: 69.014
- type: ndcg_at_700
value: 69.014
- type: ndcg_at_1000
value: 69.014
- type: map_at_1
value: 43.314
- type: map_at_2
value: 52.383
- type: map_at_3
value: 55.108999999999995
- type: map_at_5
value: 57.772999999999996
- type: map_at_7
value: 58.718
- type: map_at_10
value: 59.256
- type: map_at_20
value: 59.668
- type: map_at_30
value: 59.709999999999994
- type: map_at_50
value: 59.727
- type: map_at_70
value: 59.733999999999995
- type: map_at_100
value: 59.73500000000001
- type: map_at_200
value: 59.73500000000001
- type: map_at_300
value: 59.73500000000001
- type: map_at_500
value: 59.73500000000001
- type: map_at_700
value: 59.73500000000001
- type: map_at_1000
value: 59.73500000000001
- type: recall_at_1
value: 43.314
- type: recall_at_2
value: 61.451
- type: recall_at_3
value: 69.63000000000001
- type: recall_at_5
value: 81.223
- type: recall_at_7
value: 87.33999999999999
- type: recall_at_10
value: 92.034
- type: recall_at_20
value: 97.44
- type: recall_at_30
value: 98.506
- type: recall_at_50
value: 99.14699999999999
- type: recall_at_70
value: 99.502
- type: recall_at_100
value: 99.644
- type: recall_at_200
value: 99.644
- type: recall_at_300
value: 99.644
- type: recall_at_500
value: 99.644
- type: recall_at_700
value: 99.644
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 43.314
- type: precision_at_2
value: 30.725
- type: precision_at_3
value: 23.21
- type: precision_at_5
value: 16.245
- type: precision_at_7
value: 12.477
- type: precision_at_10
value: 9.203
- type: precision_at_20
value: 4.872
- type: precision_at_30
value: 3.2840000000000003
- type: precision_at_50
value: 1.983
- type: precision_at_70
value: 1.421
- type: precision_at_100
value: 0.996
- type: precision_at_200
value: 0.498
- type: precision_at_300
value: 0.332
- type: precision_at_500
value: 0.199
- type: precision_at_700
value: 0.14200000000000002
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 44.666
- type: mrr_at_2
value: 52.418
- type: mrr_at_3
value: 55.595000000000006
- type: mrr_at_5
value: 58.205
- type: mrr_at_7
value: 59.202999999999996
- type: mrr_at_10
value: 59.727
- type: mrr_at_20
value: 60.133
- type: mrr_at_30
value: 60.178
- type: mrr_at_50
value: 60.192
- type: mrr_at_70
value: 60.19799999999999
- type: mrr_at_100
value: 60.199999999999996
- type: mrr_at_200
value: 60.199999999999996
- type: mrr_at_300
value: 60.199999999999996
- type: mrr_at_500
value: 60.199999999999996
- type: mrr_at_700
value: 60.199999999999996
- type: mrr_at_1000
value: 60.199999999999996
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 52.07508593014336
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 47.381339333240675
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 67.58376647859171
- type: mrr
value: 80.56885635140483
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 88.40107280274783
- type: cos_sim_spearman
value: 86.07003345325681
- type: euclidean_pearson
value: 87.1726034325395
- type: euclidean_spearman
value: 86.07003345325681
- type: manhattan_pearson
value: 87.25660625029772
- type: manhattan_spearman
value: 86.3808839096893
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.81168831168831
- type: f1
value: 88.76514496560141
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 43.9382520874344
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 41.14351847240913
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 34.51166666666667
- type: ndcg_at_2
value: 38.51591666666667
- type: ndcg_at_3
value: 40.95083333333333
- type: ndcg_at_5
value: 43.580666666666666
- type: ndcg_at_7
value: 45.0625
- type: ndcg_at_10
value: 46.49083333333333
- type: ndcg_at_20
value: 48.731333333333325
- type: ndcg_at_30
value: 49.78666666666667
- type: ndcg_at_50
value: 50.84049999999999
- type: ndcg_at_70
value: 51.393750000000004
- type: ndcg_at_100
value: 51.883333333333326
- type: ndcg_at_200
value: 52.65225
- type: ndcg_at_300
value: 52.98241666666669
- type: ndcg_at_500
value: 53.28541666666668
- type: ndcg_at_700
value: 53.49241666666668
- type: ndcg_at_1000
value: 53.63758333333334
- type: map_at_1
value: 29.10075
- type: map_at_2
value: 34.636500000000005
- type: map_at_3
value: 36.92033333333333
- type: map_at_5
value: 38.81641666666666
- type: map_at_7
value: 39.635416666666664
- type: map_at_10
value: 40.294583333333335
- type: map_at_20
value: 41.07574999999999
- type: map_at_30
value: 41.333
- type: map_at_50
value: 41.529333333333334
- type: map_at_70
value: 41.606833333333334
- type: map_at_100
value: 41.66224999999999
- type: map_at_200
value: 41.72691666666666
- type: map_at_300
value: 41.746583333333334
- type: map_at_500
value: 41.75983333333333
- type: map_at_700
value: 41.76558333333333
- type: map_at_1000
value: 41.769000000000005
- type: recall_at_1
value: 29.10075
- type: recall_at_2
value: 39.07658333333333
- type: recall_at_3
value: 44.93591666666667
- type: recall_at_5
value: 51.66883333333333
- type: recall_at_7
value: 55.881000000000014
- type: recall_at_10
value: 60.34691666666667
- type: recall_at_20
value: 68.44016666666667
- type: recall_at_30
value: 72.90766666666667
- type: recall_at_50
value: 77.843
- type: recall_at_70
value: 80.70366666666668
- type: recall_at_100
value: 83.42866666666667
- type: recall_at_200
value: 88.06816666666668
- type: recall_at_300
value: 90.249
- type: recall_at_500
value: 92.37616666666668
- type: recall_at_700
value: 93.978
- type: recall_at_1000
value: 95.12791666666666
- type: precision_at_1
value: 34.51166666666667
- type: precision_at_2
value: 24.326333333333327
- type: precision_at_3
value: 19.099249999999998
- type: precision_at_5
value: 13.672666666666666
- type: precision_at_7
value: 10.772
- type: precision_at_10
value: 8.302166666666668
- type: precision_at_20
value: 4.8960833333333325
- type: precision_at_30
value: 3.551083333333333
- type: precision_at_50
value: 2.3386666666666662
- type: precision_at_70
value: 1.7605833333333334
- type: precision_at_100
value: 1.2965
- type: precision_at_200
value: 0.7106666666666668
- type: precision_at_300
value: 0.4955
- type: precision_at_500
value: 0.3106666666666667
- type: precision_at_700
value: 0.22791666666666668
- type: precision_at_1000
value: 0.1635833333333333
- type: mrr_at_1
value: 34.51166666666667
- type: mrr_at_2
value: 39.954249999999995
- type: mrr_at_3
value: 41.93741666666668
- type: mrr_at_5
value: 43.487166666666674
- type: mrr_at_7
value: 44.14983333333333
- type: mrr_at_10
value: 44.62766666666666
- type: mrr_at_20
value: 45.15291666666668
- type: mrr_at_30
value: 45.317
- type: mrr_at_50
value: 45.42875
- type: mrr_at_70
value: 45.46966666666667
- type: mrr_at_100
value: 45.49716666666667
- type: mrr_at_200
value: 45.525166666666664
- type: mrr_at_300
value: 45.53233333333335
- type: mrr_at_500
value: 45.5365
- type: mrr_at_700
value: 45.538583333333335
- type: mrr_at_1000
value: 45.539583333333326
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 35.179
- type: ndcg_at_2
value: 31.243
- type: ndcg_at_3
value: 30.562
- type: ndcg_at_5
value: 32.409
- type: ndcg_at_7
value: 34.525
- type: ndcg_at_10
value: 36.415
- type: ndcg_at_20
value: 39.443
- type: ndcg_at_30
value: 40.796
- type: ndcg_at_50
value: 42.16
- type: ndcg_at_70
value: 42.971
- type: ndcg_at_100
value: 43.691
- type: ndcg_at_200
value: 45.004
- type: ndcg_at_300
value: 45.527
- type: ndcg_at_500
value: 46.072
- type: ndcg_at_700
value: 46.387
- type: ndcg_at_1000
value: 46.663
- type: map_at_1
value: 15.692
- type: map_at_2
value: 20.116
- type: map_at_3
value: 22.6
- type: map_at_5
value: 24.701
- type: map_at_7
value: 25.934
- type: map_at_10
value: 26.843
- type: map_at_20
value: 27.975
- type: map_at_30
value: 28.372000000000003
- type: map_at_50
value: 28.671000000000003
- type: map_at_70
value: 28.803
- type: map_at_100
value: 28.895
- type: map_at_200
value: 29.011
- type: map_at_300
value: 29.042
- type: map_at_500
value: 29.065
- type: map_at_700
value: 29.075
- type: map_at_1000
value: 29.081000000000003
- type: recall_at_1
value: 15.692
- type: recall_at_2
value: 22.602
- type: recall_at_3
value: 27.814
- type: recall_at_5
value: 33.756
- type: recall_at_7
value: 38.073
- type: recall_at_10
value: 42.553000000000004
- type: recall_at_20
value: 51.121
- type: recall_at_30
value: 55.523999999999994
- type: recall_at_50
value: 60.586
- type: recall_at_70
value: 63.94
- type: recall_at_100
value: 67.134
- type: recall_at_200
value: 73.543
- type: recall_at_300
value: 76.372
- type: recall_at_500
value: 79.60199999999999
- type: recall_at_700
value: 81.536
- type: recall_at_1000
value: 83.37400000000001
- type: precision_at_1
value: 35.179
- type: precision_at_2
value: 27.199
- type: precision_at_3
value: 22.953000000000003
- type: precision_at_5
value: 17.224999999999998
- type: precision_at_7
value: 14.238999999999999
- type: precision_at_10
value: 11.303
- type: precision_at_20
value: 6.954000000000001
- type: precision_at_30
value: 5.116
- type: precision_at_50
value: 3.395
- type: precision_at_70
value: 2.579
- type: precision_at_100
value: 1.9109999999999998
- type: precision_at_200
value: 1.065
- type: precision_at_300
value: 0.743
- type: precision_at_500
value: 0.46699999999999997
- type: precision_at_700
value: 0.344
- type: precision_at_1000
value: 0.247
- type: mrr_at_1
value: 35.179
- type: mrr_at_2
value: 41.792
- type: mrr_at_3
value: 44.484
- type: mrr_at_5
value: 46.39
- type: mrr_at_7
value: 47.125
- type: mrr_at_10
value: 47.711999999999996
- type: mrr_at_20
value: 48.214
- type: mrr_at_30
value: 48.325
- type: mrr_at_50
value: 48.392
- type: mrr_at_70
value: 48.418
- type: mrr_at_100
value: 48.44
- type: mrr_at_200
value: 48.46
- type: mrr_at_300
value: 48.461999999999996
- type: mrr_at_500
value: 48.466
- type: mrr_at_700
value: 48.466
- type: mrr_at_1000
value: 48.467
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 62.375
- type: ndcg_at_2
value: 56.286
- type: ndcg_at_3
value: 53.665
- type: ndcg_at_5
value: 51.139
- type: ndcg_at_7
value: 49.873
- type: ndcg_at_10
value: 49.056
- type: ndcg_at_20
value: 48.783
- type: ndcg_at_30
value: 49.166
- type: ndcg_at_50
value: 51.141999999999996
- type: ndcg_at_70
value: 52.774
- type: ndcg_at_100
value: 54.403
- type: ndcg_at_200
value: 57.419
- type: ndcg_at_300
value: 58.778
- type: ndcg_at_500
value: 60.228
- type: ndcg_at_700
value: 61.07599999999999
- type: ndcg_at_1000
value: 61.846000000000004
- type: map_at_1
value: 10.359
- type: map_at_2
value: 14.446
- type: map_at_3
value: 16.689
- type: map_at_5
value: 20.096
- type: map_at_7
value: 22.247
- type: map_at_10
value: 24.468999999999998
- type: map_at_20
value: 28.938000000000002
- type: map_at_30
value: 31.134
- type: map_at_50
value: 33.403
- type: map_at_70
value: 34.486
- type: map_at_100
value: 35.337
- type: map_at_200
value: 36.364999999999995
- type: map_at_300
value: 36.735
- type: map_at_500
value: 37.057
- type: map_at_700
value: 37.225
- type: map_at_1000
value: 37.379
- type: recall_at_1
value: 10.359
- type: recall_at_2
value: 14.945
- type: recall_at_3
value: 17.694
- type: recall_at_5
value: 22.677
- type: recall_at_7
value: 26.131
- type: recall_at_10
value: 30.053
- type: recall_at_20
value: 39.518
- type: recall_at_30
value: 44.925
- type: recall_at_50
value: 52.154
- type: recall_at_70
value: 56.729
- type: recall_at_100
value: 61.18900000000001
- type: recall_at_200
value: 70.407
- type: recall_at_300
value: 74.412
- type: recall_at_500
value: 78.891
- type: recall_at_700
value: 81.74
- type: recall_at_1000
value: 84.253
- type: precision_at_1
value: 75
- type: precision_at_2
value: 64.125
- type: precision_at_3
value: 57.833
- type: precision_at_5
value: 50.24999999999999
- type: precision_at_7
value: 44.75
- type: precision_at_10
value: 39.75
- type: precision_at_20
value: 30.412
- type: precision_at_30
value: 25.141999999999996
- type: precision_at_50
value: 19.2
- type: precision_at_70
value: 15.729000000000001
- type: precision_at_100
value: 12.552
- type: precision_at_200
value: 7.866
- type: precision_at_300
value: 5.9270000000000005
- type: precision_at_500
value: 4.1129999999999995
- type: precision_at_700
value: 3.2460000000000004
- type: precision_at_1000
value: 2.5260000000000002
- type: mrr_at_1
value: 75
- type: mrr_at_2
value: 78.625
- type: mrr_at_3
value: 79.708
- type: mrr_at_5
value: 80.446
- type: mrr_at_7
value: 80.862
- type: mrr_at_10
value: 81.161
- type: mrr_at_20
value: 81.3
- type: mrr_at_30
value: 81.348
- type: mrr_at_50
value: 81.361
- type: mrr_at_70
value: 81.361
- type: mrr_at_100
value: 81.361
- type: mrr_at_200
value: 81.367
- type: mrr_at_300
value: 81.367
- type: mrr_at_500
value: 81.368
- type: mrr_at_700
value: 81.368
- type: mrr_at_1000
value: 81.368
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 50.239999999999995
- type: f1
value: 46.42361822342044
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 83.723
- type: ndcg_at_2
value: 86.777
- type: ndcg_at_3
value: 87.997
- type: ndcg_at_5
value: 88.864
- type: ndcg_at_7
value: 89.143
- type: ndcg_at_10
value: 89.349
- type: ndcg_at_20
value: 89.709
- type: ndcg_at_30
value: 89.82900000000001
- type: ndcg_at_50
value: 89.923
- type: ndcg_at_70
value: 89.982
- type: ndcg_at_100
value: 90.026
- type: ndcg_at_200
value: 90.10000000000001
- type: ndcg_at_300
value: 90.12599999999999
- type: ndcg_at_500
value: 90.17399999999999
- type: ndcg_at_700
value: 90.19
- type: ndcg_at_1000
value: 90.208
- type: map_at_1
value: 77.64999999999999
- type: map_at_2
value: 83.769
- type: map_at_3
value: 85.041
- type: map_at_5
value: 85.736
- type: map_at_7
value: 85.924
- type: map_at_10
value: 86.032
- type: map_at_20
value: 86.177
- type: map_at_30
value: 86.213
- type: map_at_50
value: 86.233
- type: map_at_70
value: 86.24300000000001
- type: map_at_100
value: 86.249
- type: map_at_200
value: 86.256
- type: map_at_300
value: 86.258
- type: map_at_500
value: 86.26
- type: map_at_700
value: 86.26
- type: map_at_1000
value: 86.261
- type: recall_at_1
value: 77.64999999999999
- type: recall_at_2
value: 88.53999999999999
- type: recall_at_3
value: 91.696
- type: recall_at_5
value: 93.916
- type: recall_at_7
value: 94.731
- type: recall_at_10
value: 95.318
- type: recall_at_20
value: 96.507
- type: recall_at_30
value: 96.956
- type: recall_at_50
value: 97.34899999999999
- type: recall_at_70
value: 97.61
- type: recall_at_100
value: 97.83
- type: recall_at_200
value: 98.223
- type: recall_at_300
value: 98.374
- type: recall_at_500
value: 98.67899999999999
- type: recall_at_700
value: 98.787
- type: recall_at_1000
value: 98.919
- type: precision_at_1
value: 83.723
- type: precision_at_2
value: 48.425000000000004
- type: precision_at_3
value: 33.638
- type: precision_at_5
value: 20.843
- type: precision_at_7
value: 15.079
- type: precision_at_10
value: 10.674999999999999
- type: precision_at_20
value: 5.457999999999999
- type: precision_at_30
value: 3.6740000000000004
- type: precision_at_50
value: 2.2239999999999998
- type: precision_at_70
value: 1.599
- type: precision_at_100
value: 1.125
- type: precision_at_200
value: 0.5680000000000001
- type: precision_at_300
value: 0.38
- type: precision_at_500
value: 0.22999999999999998
- type: precision_at_700
value: 0.165
- type: precision_at_1000
value: 0.116
- type: mrr_at_1
value: 83.723
- type: mrr_at_2
value: 88.794
- type: mrr_at_3
value: 89.679
- type: mrr_at_5
value: 90.049
- type: mrr_at_7
value: 90.129
- type: mrr_at_10
value: 90.167
- type: mrr_at_20
value: 90.208
- type: mrr_at_30
value: 90.214
- type: mrr_at_50
value: 90.217
- type: mrr_at_70
value: 90.218
- type: mrr_at_100
value: 90.21900000000001
- type: mrr_at_200
value: 90.21900000000001
- type: mrr_at_300
value: 90.21900000000001
- type: mrr_at_500
value: 90.21900000000001
- type: mrr_at_700
value: 90.21900000000001
- type: mrr_at_1000
value: 90.21900000000001
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 59.721999999999994
- type: ndcg_at_2
value: 56.85
- type: ndcg_at_3
value: 56.462999999999994
- type: ndcg_at_5
value: 57.75599999999999
- type: ndcg_at_7
value: 59.109
- type: ndcg_at_10
value: 60.402
- type: ndcg_at_20
value: 63.071999999999996
- type: ndcg_at_30
value: 64.302
- type: ndcg_at_50
value: 65.619
- type: ndcg_at_70
value: 66.161
- type: ndcg_at_100
value: 66.645
- type: ndcg_at_200
value: 67.353
- type: ndcg_at_300
value: 67.646
- type: ndcg_at_500
value: 67.852
- type: ndcg_at_700
value: 67.974
- type: ndcg_at_1000
value: 68.084
- type: map_at_1
value: 31.56
- type: map_at_2
value: 42.093
- type: map_at_3
value: 46.177
- type: map_at_5
value: 49.78
- type: map_at_7
value: 51.410999999999994
- type: map_at_10
value: 52.524
- type: map_at_20
value: 53.815000000000005
- type: map_at_30
value: 54.201
- type: map_at_50
value: 54.531
- type: map_at_70
value: 54.625
- type: map_at_100
value: 54.686
- type: map_at_200
value: 54.757999999999996
- type: map_at_300
value: 54.776
- type: map_at_500
value: 54.786
- type: map_at_700
value: 54.790000000000006
- type: map_at_1000
value: 54.793000000000006
- type: recall_at_1
value: 31.56
- type: recall_at_2
value: 44.858
- type: recall_at_3
value: 51.11
- type: recall_at_5
value: 58.394
- type: recall_at_7
value: 63.001
- type: recall_at_10
value: 66.81200000000001
- type: recall_at_20
value: 74.901
- type: recall_at_30
value: 79.218
- type: recall_at_50
value: 84.49
- type: recall_at_70
value: 87.003
- type: recall_at_100
value: 89.345
- type: recall_at_200
value: 93.173
- type: recall_at_300
value: 94.906
- type: recall_at_500
value: 96.223
- type: recall_at_700
value: 97.043
- type: recall_at_1000
value: 97.785
- type: precision_at_1
value: 59.721999999999994
- type: precision_at_2
value: 46.682
- type: precision_at_3
value: 37.602999999999994
- type: precision_at_5
value: 27.500000000000004
- type: precision_at_7
value: 21.847
- type: precision_at_10
value: 16.667
- type: precision_at_20
value: 9.545
- type: precision_at_30
value: 6.795
- type: precision_at_50
value: 4.38
- type: precision_at_70
value: 3.221
- type: precision_at_100
value: 2.319
- type: precision_at_200
value: 1.2149999999999999
- type: precision_at_300
value: 0.827
- type: precision_at_500
value: 0.504
- type: precision_at_700
value: 0.364
- type: precision_at_1000
value: 0.257
- type: mrr_at_1
value: 59.721999999999994
- type: mrr_at_2
value: 64.506
- type: mrr_at_3
value: 65.792
- type: mrr_at_5
value: 66.965
- type: mrr_at_7
value: 67.34700000000001
- type: mrr_at_10
value: 67.57
- type: mrr_at_20
value: 67.896
- type: mrr_at_30
value: 68.008
- type: mrr_at_50
value: 68.083
- type: mrr_at_70
value: 68.105
- type: mrr_at_100
value: 68.116
- type: mrr_at_200
value: 68.12700000000001
- type: mrr_at_300
value: 68.13
- type: mrr_at_500
value: 68.132
- type: mrr_at_700
value: 68.133
- type: mrr_at_1000
value: 68.133
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 81.796
- type: ndcg_at_2
value: 67.999
- type: ndcg_at_3
value: 72.15599999999999
- type: ndcg_at_5
value: 74.99900000000001
- type: ndcg_at_7
value: 76.179
- type: ndcg_at_10
value: 77.022
- type: ndcg_at_20
value: 78.173
- type: ndcg_at_30
value: 78.648
- type: ndcg_at_50
value: 79.104
- type: ndcg_at_70
value: 79.335
- type: ndcg_at_100
value: 79.56
- type: ndcg_at_200
value: 79.911
- type: ndcg_at_300
value: 80.045
- type: ndcg_at_500
value: 80.19500000000001
- type: ndcg_at_700
value: 80.281
- type: ndcg_at_1000
value: 80.35
- type: map_at_1
value: 40.898
- type: map_at_2
value: 62.016000000000005
- type: map_at_3
value: 66.121
- type: map_at_5
value: 68.471
- type: map_at_7
value: 69.261
- type: map_at_10
value: 69.738
- type: map_at_20
value: 70.208
- type: map_at_30
value: 70.343
- type: map_at_50
value: 70.43700000000001
- type: map_at_70
value: 70.47099999999999
- type: map_at_100
value: 70.498
- type: map_at_200
value: 70.526
- type: map_at_300
value: 70.533
- type: map_at_500
value: 70.538
- type: map_at_700
value: 70.541
- type: map_at_1000
value: 70.542
- type: recall_at_1
value: 40.898
- type: recall_at_2
value: 63.964
- type: recall_at_3
value: 70.743
- type: recall_at_5
value: 76.36699999999999
- type: recall_at_7
value: 79.142
- type: recall_at_10
value: 81.404
- type: recall_at_20
value: 85.111
- type: recall_at_30
value: 86.92800000000001
- type: recall_at_50
value: 88.899
- type: recall_at_70
value: 90.01400000000001
- type: recall_at_100
value: 91.19500000000001
- type: recall_at_200
value: 93.234
- type: recall_at_300
value: 94.105
- type: recall_at_500
value: 95.159
- type: recall_at_700
value: 95.8
- type: recall_at_1000
value: 96.34700000000001
- type: precision_at_1
value: 81.796
- type: precision_at_2
value: 63.964
- type: precision_at_3
value: 47.162
- type: precision_at_5
value: 30.547
- type: precision_at_7
value: 22.612
- type: precision_at_10
value: 16.281000000000002
- type: precision_at_20
value: 8.511000000000001
- type: precision_at_30
value: 5.795
- type: precision_at_50
value: 3.556
- type: precision_at_70
value: 2.572
- type: precision_at_100
value: 1.8239999999999998
- type: precision_at_200
value: 0.932
- type: precision_at_300
value: 0.627
- type: precision_at_500
value: 0.381
- type: precision_at_700
value: 0.27399999999999997
- type: precision_at_1000
value: 0.193
- type: mrr_at_1
value: 81.796
- type: mrr_at_2
value: 85.69200000000001
- type: mrr_at_3
value: 86.52
- type: mrr_at_5
value: 86.973
- type: mrr_at_7
value: 87.13300000000001
- type: mrr_at_10
value: 87.208
- type: mrr_at_20
value: 87.303
- type: mrr_at_30
value: 87.32799999999999
- type: mrr_at_50
value: 87.347
- type: mrr_at_70
value: 87.35199999999999
- type: mrr_at_100
value: 87.355
- type: mrr_at_200
value: 87.357
- type: mrr_at_300
value: 87.357
- type: mrr_at_500
value: 87.358
- type: mrr_at_700
value: 87.358
- type: mrr_at_1000
value: 87.358
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.79200000000002
- type: ap
value: 92.54484356773553
- type: f1
value: 94.78965313682525
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: ndcg_at_1
value: 24.398
- type: ndcg_at_2
value: 31.336000000000002
- type: ndcg_at_3
value: 35.266999999999996
- type: ndcg_at_5
value: 39.356
- type: ndcg_at_7
value: 41.562
- type: ndcg_at_10
value: 43.408
- type: ndcg_at_20
value: 46.107
- type: ndcg_at_30
value: 47.164
- type: ndcg_at_50
value: 48.126000000000005
- type: ndcg_at_70
value: 48.626999999999995
- type: ndcg_at_100
value: 49.043
- type: ndcg_at_200
value: 49.575
- type: ndcg_at_300
value: 49.794
- type: ndcg_at_500
value: 49.942
- type: ndcg_at_700
value: 50.014
- type: ndcg_at_1000
value: 50.077000000000005
- type: map_at_1
value: 23.723
- type: map_at_2
value: 29.593000000000004
- type: map_at_3
value: 32.273
- type: map_at_5
value: 34.587
- type: map_at_7
value: 35.589999999999996
- type: map_at_10
value: 36.296
- type: map_at_20
value: 37.059999999999995
- type: map_at_30
value: 37.265
- type: map_at_50
value: 37.402
- type: map_at_70
value: 37.454
- type: map_at_100
value: 37.486999999999995
- type: map_at_200
value: 37.516
- type: map_at_300
value: 37.524
- type: map_at_500
value: 37.528
- type: map_at_700
value: 37.529
- type: map_at_1000
value: 37.53
- type: recall_at_1
value: 23.723
- type: recall_at_2
value: 35.355
- type: recall_at_3
value: 43.22
- type: recall_at_5
value: 53.025
- type: recall_at_7
value: 59.327
- type: recall_at_10
value: 65.302
- type: recall_at_20
value: 75.765
- type: recall_at_30
value: 80.632
- type: recall_at_50
value: 85.63499999999999
- type: recall_at_70
value: 88.554
- type: recall_at_100
value: 91.16300000000001
- type: recall_at_200
value: 94.85
- type: recall_at_300
value: 96.532
- type: recall_at_500
value: 97.751
- type: recall_at_700
value: 98.383
- type: recall_at_1000
value: 98.97
- type: precision_at_1
value: 24.398
- type: precision_at_2
value: 18.274
- type: precision_at_3
value: 14.951999999999998
- type: precision_at_5
value: 11.052
- type: precision_at_7
value: 8.84
- type: precision_at_10
value: 6.8309999999999995
- type: precision_at_20
value: 3.978
- type: precision_at_30
value: 2.827
- type: precision_at_50
value: 1.807
- type: precision_at_70
value: 1.336
- type: precision_at_100
value: 0.964
- type: precision_at_200
value: 0.502
- type: precision_at_300
value: 0.34099999999999997
- type: precision_at_500
value: 0.208
- type: precision_at_700
value: 0.15
- type: precision_at_1000
value: 0.105
- type: mrr_at_1
value: 24.398
- type: mrr_at_2
value: 30.351
- type: mrr_at_3
value: 33.001000000000005
- type: mrr_at_5
value: 35.228
- type: mrr_at_7
value: 36.223
- type: mrr_at_10
value: 36.903999999999996
- type: mrr_at_20
value: 37.631
- type: mrr_at_30
value: 37.830000000000005
- type: mrr_at_50
value: 37.955
- type: mrr_at_70
value: 38.003
- type: mrr_at_100
value: 38.033
- type: mrr_at_200
value: 38.059
- type: mrr_at_300
value: 38.066
- type: mrr_at_500
value: 38.068999999999996
- type: mrr_at_700
value: 38.07
- type: mrr_at_1000
value: 38.07
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.35658914728683
- type: f1
value: 96.15039630903114
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 86.29730962152303
- type: f1
value: 71.12166316567485
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 79.98991257565568
- type: f1
value: 77.41680115095276
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 82.1990585070612
- type: f1
value: 82.23719179179362
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 40.03019554933584
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 38.999760551497815
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.72383151953079
- type: mrr
value: 33.93989699030721
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 51.858000000000004
- type: ndcg_at_2
value: 49.675999999999995
- type: ndcg_at_3
value: 47.519
- type: ndcg_at_5
value: 45.198
- type: ndcg_at_7
value: 43.504
- type: ndcg_at_10
value: 41.88
- type: ndcg_at_20
value: 39.122
- type: ndcg_at_30
value: 37.95
- type: ndcg_at_50
value: 37.602999999999994
- type: ndcg_at_70
value: 37.836
- type: ndcg_at_100
value: 38.493
- type: ndcg_at_200
value: 40.187
- type: ndcg_at_300
value: 41.524
- type: ndcg_at_500
value: 43.657000000000004
- type: ndcg_at_700
value: 45.234
- type: ndcg_at_1000
value: 47.047
- type: map_at_1
value: 6.392
- type: map_at_2
value: 10.113
- type: map_at_3
value: 11.543000000000001
- type: map_at_5
value: 13.729
- type: map_at_7
value: 14.985000000000001
- type: map_at_10
value: 16.217000000000002
- type: map_at_20
value: 18.106
- type: map_at_30
value: 18.878
- type: map_at_50
value: 19.822
- type: map_at_70
value: 20.352999999999998
- type: map_at_100
value: 20.827
- type: map_at_200
value: 21.512
- type: map_at_300
value: 21.826
- type: map_at_500
value: 22.155
- type: map_at_700
value: 22.349
- type: map_at_1000
value: 22.531000000000002
- type: recall_at_1
value: 6.392
- type: recall_at_2
value: 11.215
- type: recall_at_3
value: 13.231000000000002
- type: recall_at_5
value: 16.66
- type: recall_at_7
value: 18.802
- type: recall_at_10
value: 21.185000000000002
- type: recall_at_20
value: 25.35
- type: recall_at_30
value: 27.91
- type: recall_at_50
value: 32.845
- type: recall_at_70
value: 35.789
- type: recall_at_100
value: 39.247
- type: recall_at_200
value: 46.655
- type: recall_at_300
value: 51.43299999999999
- type: recall_at_500
value: 59.472
- type: recall_at_700
value: 64.742
- type: recall_at_1000
value: 70.97099999999999
- type: precision_at_1
value: 53.559999999999995
- type: precision_at_2
value: 48.762
- type: precision_at_3
value: 44.169000000000004
- type: precision_at_5
value: 39.071
- type: precision_at_7
value: 35.161
- type: precision_at_10
value: 31.238
- type: precision_at_20
value: 23.064999999999998
- type: precision_at_30
value: 18.844
- type: precision_at_50
value: 14.601
- type: precision_at_70
value: 12.088000000000001
- type: precision_at_100
value: 9.844999999999999
- type: precision_at_200
value: 6.358
- type: precision_at_300
value: 4.915
- type: precision_at_500
value: 3.531
- type: precision_at_700
value: 2.8649999999999998
- type: precision_at_1000
value: 2.289
- type: mrr_at_1
value: 54.17999999999999
- type: mrr_at_2
value: 59.288
- type: mrr_at_3
value: 60.836
- type: mrr_at_5
value: 62.275999999999996
- type: mrr_at_7
value: 62.688
- type: mrr_at_10
value: 62.865
- type: mrr_at_20
value: 63.11
- type: mrr_at_30
value: 63.193999999999996
- type: mrr_at_50
value: 63.258
- type: mrr_at_70
value: 63.278
- type: mrr_at_100
value: 63.297000000000004
- type: mrr_at_200
value: 63.315999999999995
- type: mrr_at_300
value: 63.318
- type: mrr_at_500
value: 63.32299999999999
- type: mrr_at_700
value: 63.324000000000005
- type: mrr_at_1000
value: 63.324999999999996
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 50.897999999999996
- type: ndcg_at_2
value: 59.126
- type: ndcg_at_3
value: 63.093999999999994
- type: ndcg_at_5
value: 67.197
- type: ndcg_at_7
value: 68.719
- type: ndcg_at_10
value: 69.915
- type: ndcg_at_20
value: 71.229
- type: ndcg_at_30
value: 71.667
- type: ndcg_at_50
value: 71.98
- type: ndcg_at_70
value: 72.127
- type: ndcg_at_100
value: 72.217
- type: ndcg_at_200
value: 72.319
- type: ndcg_at_300
value: 72.347
- type: ndcg_at_500
value: 72.37
- type: ndcg_at_700
value: 72.379
- type: ndcg_at_1000
value: 72.381
- type: map_at_1
value: 45.297
- type: map_at_2
value: 55.596000000000004
- type: map_at_3
value: 58.724
- type: map_at_5
value: 61.387
- type: map_at_7
value: 62.173
- type: map_at_10
value: 62.69
- type: map_at_20
value: 63.125
- type: map_at_30
value: 63.223
- type: map_at_50
value: 63.27700000000001
- type: map_at_70
value: 63.295
- type: map_at_100
value: 63.303
- type: map_at_200
value: 63.31
- type: map_at_300
value: 63.31099999999999
- type: map_at_500
value: 63.312000000000005
- type: map_at_700
value: 63.312000000000005
- type: map_at_1000
value: 63.312000000000005
- type: recall_at_1
value: 45.297
- type: recall_at_2
value: 63.866
- type: recall_at_3
value: 71.898
- type: recall_at_5
value: 81.16600000000001
- type: recall_at_7
value: 85.301
- type: recall_at_10
value: 88.94800000000001
- type: recall_at_20
value: 93.719
- type: recall_at_30
value: 95.628
- type: recall_at_50
value: 97.14699999999999
- type: recall_at_70
value: 97.955
- type: recall_at_100
value: 98.48599999999999
- type: recall_at_200
value: 99.157
- type: recall_at_300
value: 99.355
- type: recall_at_500
value: 99.53699999999999
- type: recall_at_700
value: 99.62299999999999
- type: recall_at_1000
value: 99.638
- type: precision_at_1
value: 50.897999999999996
- type: precision_at_2
value: 36.703
- type: precision_at_3
value: 27.926000000000002
- type: precision_at_5
value: 19.276
- type: precision_at_7
value: 14.533999999999999
- type: precision_at_10
value: 10.678
- type: precision_at_20
value: 5.663
- type: precision_at_30
value: 3.8600000000000003
- type: precision_at_50
value: 2.358
- type: precision_at_70
value: 1.7000000000000002
- type: precision_at_100
value: 1.198
- type: precision_at_200
value: 0.603
- type: precision_at_300
value: 0.40299999999999997
- type: precision_at_500
value: 0.242
- type: precision_at_700
value: 0.173
- type: precision_at_1000
value: 0.121
- type: mrr_at_1
value: 50.897999999999996
- type: mrr_at_2
value: 59.994
- type: mrr_at_3
value: 62.553000000000004
- type: mrr_at_5
value: 64.307
- type: mrr_at_7
value: 64.864
- type: mrr_at_10
value: 65.22200000000001
- type: mrr_at_20
value: 65.499
- type: mrr_at_30
value: 65.561
- type: mrr_at_50
value: 65.592
- type: mrr_at_70
value: 65.602
- type: mrr_at_100
value: 65.607
- type: mrr_at_200
value: 65.61099999999999
- type: mrr_at_300
value: 65.61200000000001
- type: mrr_at_500
value: 65.61200000000001
- type: mrr_at_700
value: 65.61200000000001
- type: mrr_at_1000
value: 65.61200000000001
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 82.96
- type: ndcg_at_2
value: 85.614
- type: ndcg_at_3
value: 87.19
- type: ndcg_at_5
value: 88.654
- type: ndcg_at_7
value: 89.287
- type: ndcg_at_10
value: 89.785
- type: ndcg_at_20
value: 90.384
- type: ndcg_at_30
value: 90.589
- type: ndcg_at_50
value: 90.738
- type: ndcg_at_70
value: 90.789
- type: ndcg_at_100
value: 90.824
- type: ndcg_at_200
value: 90.869
- type: ndcg_at_300
value: 90.881
- type: ndcg_at_500
value: 90.886
- type: ndcg_at_700
value: 90.889
- type: ndcg_at_1000
value: 90.889
- type: map_at_1
value: 72.152
- type: map_at_2
value: 80.818
- type: map_at_3
value: 83.462
- type: map_at_5
value: 85.286
- type: map_at_7
value: 85.921
- type: map_at_10
value: 86.334
- type: map_at_20
value: 86.737
- type: map_at_30
value: 86.847
- type: map_at_50
value: 86.911
- type: map_at_70
value: 86.932
- type: map_at_100
value: 86.943
- type: map_at_200
value: 86.953
- type: map_at_300
value: 86.955
- type: map_at_500
value: 86.956
- type: map_at_700
value: 86.956
- type: map_at_1000
value: 86.956
- type: recall_at_1
value: 72.152
- type: recall_at_2
value: 84.129
- type: recall_at_3
value: 88.87
- type: recall_at_5
value: 93.067
- type: recall_at_7
value: 94.882
- type: recall_at_10
value: 96.353
- type: recall_at_20
value: 98.26700000000001
- type: recall_at_30
value: 98.92999999999999
- type: recall_at_50
value: 99.441
- type: recall_at_70
value: 99.619
- type: recall_at_100
value: 99.748
- type: recall_at_200
value: 99.911
- type: recall_at_300
value: 99.956
- type: recall_at_500
value: 99.98
- type: recall_at_700
value: 99.991
- type: recall_at_1000
value: 99.996
- type: precision_at_1
value: 82.96
- type: precision_at_2
value: 52.175000000000004
- type: precision_at_3
value: 38.223
- type: precision_at_5
value: 25.056
- type: precision_at_7
value: 18.717
- type: precision_at_10
value: 13.614999999999998
- type: precision_at_20
value: 7.208
- type: precision_at_30
value: 4.928
- type: precision_at_50
value: 3.024
- type: precision_at_70
value: 2.183
- type: precision_at_100
value: 1.54
- type: precision_at_200
value: 0.779
- type: precision_at_300
value: 0.521
- type: precision_at_500
value: 0.313
- type: precision_at_700
value: 0.22399999999999998
- type: precision_at_1000
value: 0.157
- type: mrr_at_1
value: 82.96
- type: mrr_at_2
value: 87.005
- type: mrr_at_3
value: 88.07199999999999
- type: mrr_at_5
value: 88.634
- type: mrr_at_7
value: 88.793
- type: mrr_at_10
value: 88.87899999999999
- type: mrr_at_20
value: 88.94999999999999
- type: mrr_at_30
value: 88.96
- type: mrr_at_50
value: 88.965
- type: mrr_at_70
value: 88.966
- type: mrr_at_100
value: 88.967
- type: mrr_at_200
value: 88.967
- type: mrr_at_300
value: 88.967
- type: mrr_at_500
value: 88.967
- type: mrr_at_700
value: 88.967
- type: mrr_at_1000
value: 88.967
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.90388554491155
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 67.64232539036783
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 22.6
- type: ndcg_at_2
value: 20.355999999999998
- type: ndcg_at_3
value: 18.536
- type: ndcg_at_5
value: 16.523
- type: ndcg_at_7
value: 17.979
- type: ndcg_at_10
value: 19.908
- type: ndcg_at_20
value: 22.887
- type: ndcg_at_30
value: 24.43
- type: ndcg_at_50
value: 25.959
- type: ndcg_at_70
value: 26.989
- type: ndcg_at_100
value: 27.977
- type: ndcg_at_200
value: 29.831000000000003
- type: ndcg_at_300
value: 30.787
- type: ndcg_at_500
value: 31.974999999999998
- type: ndcg_at_700
value: 32.554
- type: ndcg_at_1000
value: 33.277
- type: map_at_1
value: 4.593
- type: map_at_2
value: 6.923
- type: map_at_3
value: 8.3
- type: map_at_5
value: 10.072000000000001
- type: map_at_7
value: 10.782
- type: map_at_10
value: 11.72
- type: map_at_20
value: 12.838
- type: map_at_30
value: 13.257
- type: map_at_50
value: 13.569
- type: map_at_70
value: 13.733
- type: map_at_100
value: 13.858999999999998
- type: map_at_200
value: 14.018
- type: map_at_300
value: 14.072999999999999
- type: map_at_500
value: 14.126
- type: map_at_700
value: 14.145
- type: map_at_1000
value: 14.161999999999999
- type: recall_at_1
value: 4.593
- type: recall_at_2
value: 7.997999999999999
- type: recall_at_3
value: 10.563
- type: recall_at_5
value: 14.907
- type: recall_at_7
value: 17.4
- type: recall_at_10
value: 21.18
- type: recall_at_20
value: 28.144999999999996
- type: recall_at_30
value: 32.462
- type: recall_at_50
value: 37.267
- type: recall_at_70
value: 40.875
- type: recall_at_100
value: 44.641999999999996
- type: recall_at_200
value: 52.573
- type: recall_at_300
value: 57.089999999999996
- type: recall_at_500
value: 63.14300000000001
- type: recall_at_700
value: 66.313
- type: recall_at_1000
value: 70.458
- type: precision_at_1
value: 22.6
- type: precision_at_2
value: 19.7
- type: precision_at_3
value: 17.333000000000002
- type: precision_at_5
value: 14.680000000000001
- type: precision_at_7
value: 12.243
- type: precision_at_10
value: 10.440000000000001
- type: precision_at_20
value: 6.944999999999999
- type: precision_at_30
value: 5.333
- type: precision_at_50
value: 3.678
- type: precision_at_70
value: 2.881
- type: precision_at_100
value: 2.2030000000000003
- type: precision_at_200
value: 1.295
- type: precision_at_300
value: 0.9369999999999999
- type: precision_at_500
value: 0.622
- type: precision_at_700
value: 0.466
- type: precision_at_1000
value: 0.347
- type: mrr_at_1
value: 22.6
- type: mrr_at_2
value: 27.900000000000002
- type: mrr_at_3
value: 30.067
- type: mrr_at_5
value: 32.207
- type: mrr_at_7
value: 33.004
- type: mrr_at_10
value: 33.596
- type: mrr_at_20
value: 34.268
- type: mrr_at_30
value: 34.492
- type: mrr_at_50
value: 34.628
- type: mrr_at_70
value: 34.681
- type: mrr_at_100
value: 34.717
- type: mrr_at_200
value: 34.757
- type: mrr_at_300
value: 34.768
- type: mrr_at_500
value: 34.772
- type: mrr_at_700
value: 34.774
- type: mrr_at_1000
value: 34.775
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.90122745229677
- type: cos_sim_spearman
value: 82.92294737327579
- type: euclidean_pearson
value: 84.08979655773187
- type: euclidean_spearman
value: 82.92294657285412
- type: manhattan_pearson
value: 84.09347480531832
- type: manhattan_spearman
value: 82.91564613948087
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.01218713698583
- type: cos_sim_spearman
value: 79.46865215168464
- type: euclidean_pearson
value: 83.22621889891909
- type: euclidean_spearman
value: 79.46853821709514
- type: manhattan_pearson
value: 83.69962580788805
- type: manhattan_spearman
value: 79.9561593356932
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.98438696342964
- type: cos_sim_spearman
value: 89.15419511870839
- type: euclidean_pearson
value: 88.49646141802894
- type: euclidean_spearman
value: 89.15419503946019
- type: manhattan_pearson
value: 88.6420585616327
- type: manhattan_spearman
value: 89.42648950757743
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 87.30772547759544
- type: cos_sim_spearman
value: 84.93199878424691
- type: euclidean_pearson
value: 86.16266630395455
- type: euclidean_spearman
value: 84.93198798543634
- type: manhattan_pearson
value: 86.14285723189803
- type: manhattan_spearman
value: 85.0361672522687
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.21342071197127
- type: cos_sim_spearman
value: 90.7407512744838
- type: euclidean_pearson
value: 90.1517933113061
- type: euclidean_spearman
value: 90.74075125431919
- type: manhattan_pearson
value: 90.17963034676193
- type: manhattan_spearman
value: 90.88999275865135
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 86.82518054100498
- type: cos_sim_spearman
value: 87.81570533154735
- type: euclidean_pearson
value: 86.91684561573618
- type: euclidean_spearman
value: 87.81570533154735
- type: manhattan_pearson
value: 86.98311935744032
- type: manhattan_spearman
value: 87.9594667151966
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 92.09578436612053
- type: cos_sim_spearman
value: 92.01519349090438
- type: euclidean_pearson
value: 92.07113635890894
- type: euclidean_spearman
value: 92.01519349090438
- type: manhattan_pearson
value: 91.89343820765625
- type: manhattan_spearman
value: 91.7443476810177
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 69.29997751464549
- type: cos_sim_spearman
value: 68.36425436812782
- type: euclidean_pearson
value: 69.81381677661783
- type: euclidean_spearman
value: 68.36425436812782
- type: manhattan_pearson
value: 69.92823397008026
- type: manhattan_spearman
value: 68.35770640039254
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 88.39126315452359
- type: cos_sim_spearman
value: 88.99708463265337
- type: euclidean_pearson
value: 88.60793820038607
- type: euclidean_spearman
value: 88.99708463265337
- type: manhattan_pearson
value: 88.69860633571047
- type: manhattan_spearman
value: 89.20094593888012
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.58028062818582
- type: mrr
value: 96.53586790841693
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 66.333
- type: ndcg_at_2
value: 70.655
- type: ndcg_at_3
value: 72.801
- type: ndcg_at_5
value: 75.793
- type: ndcg_at_7
value: 76.946
- type: ndcg_at_10
value: 77.66199999999999
- type: ndcg_at_20
value: 78.786
- type: ndcg_at_30
value: 79.066
- type: ndcg_at_50
value: 79.255
- type: ndcg_at_70
value: 79.423
- type: ndcg_at_100
value: 79.476
- type: ndcg_at_200
value: 79.65299999999999
- type: ndcg_at_300
value: 79.696
- type: ndcg_at_500
value: 79.73599999999999
- type: ndcg_at_700
value: 79.77199999999999
- type: ndcg_at_1000
value: 79.77199999999999
- type: map_at_1
value: 63.383
- type: map_at_2
value: 68.144
- type: map_at_3
value: 70.19800000000001
- type: map_at_5
value: 72.38
- type: map_at_7
value: 72.955
- type: map_at_10
value: 73.312
- type: map_at_20
value: 73.678
- type: map_at_30
value: 73.72800000000001
- type: map_at_50
value: 73.75500000000001
- type: map_at_70
value: 73.771
- type: map_at_100
value: 73.776
- type: map_at_200
value: 73.783
- type: map_at_300
value: 73.784
- type: map_at_500
value: 73.785
- type: map_at_700
value: 73.786
- type: map_at_1000
value: 73.786
- type: recall_at_1
value: 63.383
- type: recall_at_2
value: 72.283
- type: recall_at_3
value: 77.183
- type: recall_at_5
value: 84.56099999999999
- type: recall_at_7
value: 87.67200000000001
- type: recall_at_10
value: 89.822
- type: recall_at_20
value: 94
- type: recall_at_30
value: 95.333
- type: recall_at_50
value: 96.333
- type: recall_at_70
value: 97.333
- type: recall_at_100
value: 97.667
- type: recall_at_200
value: 99
- type: recall_at_300
value: 99.333
- type: recall_at_500
value: 99.667
- type: recall_at_700
value: 100
- type: recall_at_1000
value: 100
- type: precision_at_1
value: 66.333
- type: precision_at_2
value: 38.667
- type: precision_at_3
value: 28.111000000000004
- type: precision_at_5
value: 18.933
- type: precision_at_7
value: 14.094999999999999
- type: precision_at_10
value: 10.167
- type: precision_at_20
value: 5.35
- type: precision_at_30
value: 3.611
- type: precision_at_50
value: 2.1870000000000003
- type: precision_at_70
value: 1.576
- type: precision_at_100
value: 1.107
- type: precision_at_200
value: 0.5599999999999999
- type: precision_at_300
value: 0.374
- type: precision_at_500
value: 0.22499999999999998
- type: precision_at_700
value: 0.161
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 66.333
- type: mrr_at_2
value: 70.833
- type: mrr_at_3
value: 72.167
- type: mrr_at_5
value: 73.6
- type: mrr_at_7
value: 74.084
- type: mrr_at_10
value: 74.283
- type: mrr_at_20
value: 74.54499999999999
- type: mrr_at_30
value: 74.59599999999999
- type: mrr_at_50
value: 74.622
- type: mrr_at_70
value: 74.639
- type: mrr_at_100
value: 74.643
- type: mrr_at_200
value: 74.65
- type: mrr_at_300
value: 74.652
- type: mrr_at_500
value: 74.653
- type: mrr_at_700
value: 74.653
- type: mrr_at_1000
value: 74.653
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.84554455445544
- type: cos_sim_ap
value: 96.31178339136798
- type: cos_sim_f1
value: 92.1921921921922
- type: cos_sim_precision
value: 92.28456913827655
- type: cos_sim_recall
value: 92.10000000000001
- type: dot_accuracy
value: 99.84554455445544
- type: dot_ap
value: 96.31178339136797
- type: dot_f1
value: 92.1921921921922
- type: dot_precision
value: 92.28456913827655
- type: dot_recall
value: 92.10000000000001
- type: euclidean_accuracy
value: 99.84554455445544
- type: euclidean_ap
value: 96.31178339136798
- type: euclidean_f1
value: 92.1921921921922
- type: euclidean_precision
value: 92.28456913827655
- type: euclidean_recall
value: 92.10000000000001
- type: manhattan_accuracy
value: 99.84752475247525
- type: manhattan_ap
value: 96.4591954606088
- type: manhattan_f1
value: 92.25352112676056
- type: manhattan_precision
value: 92.81376518218623
- type: manhattan_recall
value: 91.7
- type: max_accuracy
value: 99.84752475247525
- type: max_ap
value: 96.4591954606088
- type: max_f1
value: 92.25352112676056
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 74.24659759283294
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 46.77690051260451
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.68436757803185
- type: mrr
value: 56.82157711569475
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.652482405629843
- type: cos_sim_spearman
value: 31.16341822347735
- type: dot_pearson
value: 31.652479892699837
- type: dot_spearman
value: 31.16341822347735
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 92
- type: ndcg_at_2
value: 90.839
- type: ndcg_at_3
value: 90.642
- type: ndcg_at_5
value: 90.348
- type: ndcg_at_7
value: 89.015
- type: ndcg_at_10
value: 87.599
- type: ndcg_at_20
value: 84.434
- type: ndcg_at_30
value: 81.655
- type: ndcg_at_50
value: 77.278
- type: ndcg_at_70
value: 73.957
- type: ndcg_at_100
value: 69.56
- type: ndcg_at_200
value: 60.724000000000004
- type: ndcg_at_300
value: 57.245000000000005
- type: ndcg_at_500
value: 56.316
- type: ndcg_at_700
value: 58.399
- type: ndcg_at_1000
value: 62.21600000000001
- type: map_at_1
value: 0.247
- type: map_at_2
value: 0.488
- type: map_at_3
value: 0.7230000000000001
- type: map_at_5
value: 1.204
- type: map_at_7
value: 1.6500000000000001
- type: map_at_10
value: 2.292
- type: map_at_20
value: 4.274
- type: map_at_30
value: 6.027
- type: map_at_50
value: 9.083
- type: map_at_70
value: 11.751000000000001
- type: map_at_100
value: 14.912
- type: map_at_200
value: 22.213
- type: map_at_300
value: 26.667999999999996
- type: map_at_500
value: 31.556
- type: map_at_700
value: 34.221000000000004
- type: map_at_1000
value: 36.443999999999996
- type: recall_at_1
value: 0.247
- type: recall_at_2
value: 0.49899999999999994
- type: recall_at_3
value: 0.742
- type: recall_at_5
value: 1.247
- type: recall_at_7
value: 1.722
- type: recall_at_10
value: 2.405
- type: recall_at_20
value: 4.583
- type: recall_at_30
value: 6.587999999999999
- type: recall_at_50
value: 10.188
- type: recall_at_70
value: 13.496
- type: recall_at_100
value: 17.578
- type: recall_at_200
value: 28.158
- type: recall_at_300
value: 35.532000000000004
- type: recall_at_500
value: 45.31
- type: recall_at_700
value: 51.822
- type: recall_at_1000
value: 58.53
- type: precision_at_1
value: 96
- type: precision_at_2
value: 96
- type: precision_at_3
value: 95.333
- type: precision_at_5
value: 94.8
- type: precision_at_7
value: 93.429
- type: precision_at_10
value: 91.4
- type: precision_at_20
value: 87.7
- type: precision_at_30
value: 84.867
- type: precision_at_50
value: 80.24
- type: precision_at_70
value: 76.371
- type: precision_at_100
value: 71.08
- type: precision_at_200
value: 59.4
- type: precision_at_300
value: 51.459999999999994
- type: precision_at_500
value: 40.644000000000005
- type: precision_at_700
value: 33.889
- type: precision_at_1000
value: 27.250000000000004
- type: mrr_at_1
value: 96
- type: mrr_at_2
value: 98
- type: mrr_at_3
value: 98
- type: mrr_at_5
value: 98
- type: mrr_at_7
value: 98
- type: mrr_at_10
value: 98
- type: mrr_at_20
value: 98
- type: mrr_at_30
value: 98
- type: mrr_at_50
value: 98
- type: mrr_at_70
value: 98
- type: mrr_at_100
value: 98
- type: mrr_at_200
value: 98
- type: mrr_at_300
value: 98
- type: mrr_at_500
value: 98
- type: mrr_at_700
value: 98
- type: mrr_at_1000
value: 98
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: ndcg_at_1
value: 43.878
- type: ndcg_at_2
value: 37.956
- type: ndcg_at_3
value: 35.053
- type: ndcg_at_5
value: 32.59
- type: ndcg_at_7
value: 30.226
- type: ndcg_at_10
value: 29.005
- type: ndcg_at_20
value: 30.11
- type: ndcg_at_30
value: 32.019999999999996
- type: ndcg_at_50
value: 34.354
- type: ndcg_at_70
value: 36.665
- type: ndcg_at_100
value: 38.888
- type: ndcg_at_200
value: 43.435
- type: ndcg_at_300
value: 45.795
- type: ndcg_at_500
value: 48.699999999999996
- type: ndcg_at_700
value: 50.242
- type: ndcg_at_1000
value: 51.529
- type: map_at_1
value: 3.521
- type: map_at_2
value: 5.309
- type: map_at_3
value: 6.576
- type: map_at_5
value: 8.97
- type: map_at_7
value: 10.194
- type: map_at_10
value: 11.949
- type: map_at_20
value: 14.686
- type: map_at_30
value: 15.8
- type: map_at_50
value: 16.59
- type: map_at_70
value: 17.2
- type: map_at_100
value: 17.765
- type: map_at_200
value: 18.636
- type: map_at_300
value: 18.972
- type: map_at_500
value: 19.301
- type: map_at_700
value: 19.445
- type: map_at_1000
value: 19.546
- type: recall_at_1
value: 3.521
- type: recall_at_2
value: 5.848
- type: recall_at_3
value: 7.657
- type: recall_at_5
value: 11.368
- type: recall_at_7
value: 13.748
- type: recall_at_10
value: 18.061
- type: recall_at_20
value: 26.844
- type: recall_at_30
value: 31.186000000000003
- type: recall_at_50
value: 35.951
- type: recall_at_70
value: 40.961999999999996
- type: recall_at_100
value: 46.743
- type: recall_at_200
value: 58.483
- type: recall_at_300
value: 65.973
- type: recall_at_500
value: 75.233
- type: recall_at_700
value: 80.472
- type: recall_at_1000
value: 85.02
- type: precision_at_1
value: 46.939
- type: precision_at_2
value: 38.775999999999996
- type: precision_at_3
value: 34.694
- type: precision_at_5
value: 31.429000000000002
- type: precision_at_7
value: 27.697
- type: precision_at_10
value: 24.490000000000002
- type: precision_at_20
value: 18.776
- type: precision_at_30
value: 15.034
- type: precision_at_50
value: 10.857
- type: precision_at_70
value: 9.096
- type: precision_at_100
value: 7.51
- type: precision_at_200
value: 4.929
- type: precision_at_300
value: 3.7760000000000002
- type: precision_at_500
value: 2.6780000000000004
- type: precision_at_700
value: 2.085
- type: precision_at_1000
value: 1.5709999999999997
- type: mrr_at_1
value: 46.939
- type: mrr_at_2
value: 55.102
- type: mrr_at_3
value: 57.823
- type: mrr_at_5
value: 60.68
- type: mrr_at_7
value: 60.972
- type: mrr_at_10
value: 61.199000000000005
- type: mrr_at_20
value: 61.831
- type: mrr_at_30
value: 61.831
- type: mrr_at_50
value: 61.873
- type: mrr_at_70
value: 61.873
- type: mrr_at_100
value: 61.873
- type: mrr_at_200
value: 61.873
- type: mrr_at_300
value: 61.873
- type: mrr_at_500
value: 61.873
- type: mrr_at_700
value: 61.873
- type: mrr_at_1000
value: 61.873
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.3294
- type: ap
value: 14.561333393364736
- type: f1
value: 53.992309820496466
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.63893604980192
- type: f1
value: 63.92959380489434
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 56.270879258659775
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.71073493473207
- type: cos_sim_ap
value: 81.52392540284202
- type: cos_sim_f1
value: 74.71162377994676
- type: cos_sim_precision
value: 71.89558428885094
- type: cos_sim_recall
value: 77.75725593667546
- type: dot_accuracy
value: 88.71073493473207
- type: dot_ap
value: 81.52394754041109
- type: dot_f1
value: 74.71162377994676
- type: dot_precision
value: 71.89558428885094
- type: dot_recall
value: 77.75725593667546
- type: euclidean_accuracy
value: 88.71073493473207
- type: euclidean_ap
value: 81.52392035435321
- type: euclidean_f1
value: 74.71162377994676
- type: euclidean_precision
value: 71.89558428885094
- type: euclidean_recall
value: 77.75725593667546
- type: manhattan_accuracy
value: 88.47231328604637
- type: manhattan_ap
value: 81.22907439267321
- type: manhattan_f1
value: 74.3351571446749
- type: manhattan_precision
value: 71.78667977390022
- type: manhattan_recall
value: 77.0712401055409
- type: max_accuracy
value: 88.71073493473207
- type: max_ap
value: 81.52394754041109
- type: max_f1
value: 74.71162377994676
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.85136026700819
- type: cos_sim_ap
value: 87.7768002924216
- type: cos_sim_f1
value: 80.358908624794
- type: cos_sim_precision
value: 76.62918209122023
- type: cos_sim_recall
value: 84.47028025870034
- type: dot_accuracy
value: 89.85136026700819
- type: dot_ap
value: 87.77680027889778
- type: dot_f1
value: 80.358908624794
- type: dot_precision
value: 76.62918209122023
- type: dot_recall
value: 84.47028025870034
- type: euclidean_accuracy
value: 89.85136026700819
- type: euclidean_ap
value: 87.77680174697751
- type: euclidean_f1
value: 80.358908624794
- type: euclidean_precision
value: 76.62918209122023
- type: euclidean_recall
value: 84.47028025870034
- type: manhattan_accuracy
value: 89.86300306593705
- type: manhattan_ap
value: 87.78613271895861
- type: manhattan_f1
value: 80.31831016905645
- type: manhattan_precision
value: 76.68230516070304
- type: manhattan_recall
value: 84.3162919618109
- type: max_accuracy
value: 89.86300306593705
- type: max_ap
value: 87.78613271895861
- type: max_f1
value: 80.358908624794
---
<h1 align="center">Salesforce/SFR-Embedding-Mistral</h1>
**SFR-Embedding by Salesforce Research.**
The model is trained on top of [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
- [MTEB benchmark](https://arxiv.org/abs/2210.07316)
- [Mistral](https://arxiv.org/abs/2310.06825)
- [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)
More technical details will be updated later.
### Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our [AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ExternalFacing_Services_Policy.pdf) and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai-acceptable-use-policy.pdf).
## How to run
### Transformers
The models can be used as follows:
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'How to bake a chocolate cake'),
get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-Mistral')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-Mistral')
# get the embeddings
max_length = 4096
input_texts = queries + passages
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[86.7153549194336, 36.64569091796875], [35.00493621826172, 82.0738525390625]]
```
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("Salesforce/SFR-Embedding-Mistral")
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'How to bake a chocolate cake'),
get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
"To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
"The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]
embeddings = model.encode(queries + passages)
scores = util.cos_sim(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[86.71537780761719, 36.645721435546875], [35.00497055053711, 82.07388305664062]]
```
### MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.
SFR-Embedding Team (∗indicates lead contributors).
* Rui Meng*
* Ye Liu*
* Shafiq Rayhan Joty
* Caiming Xiong
* Yingbo Zhou
* Semih Yavuz
### Citation
```bibtex
@misc{SFRAIResearch2024,
title={SFR-Embedding-Mistral:Enhance Text Retrieval with Transfer Learning},
author={Rui Meng, Ye Liu, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
howpublished={Salesforce AI Research Blog},
year={2024},
url={https://www.salesforce.com/blog/sfr-embedding/}
}
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
EleutherAI/pythia-410m-deduped | EleutherAI | text-generation | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2023-02-13T21:27:47 | 2023-07-09T16:05:38 | 28,910 | 20 | ---
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-410M-deduped
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-410M-deduped for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-410M-deduped as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-410M-deduped has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means XNPythia-410M-dedupedAME will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-410M-deduped to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-410M-deduped may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-410M-deduped.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
Pythia-410M-deduped was trained on the Pile **after the dataset has been globally
deduplicated**.<br>
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | [
"SCIQ"
] |
bigscience/bloom | bigscience | text-generation | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zu",
"arxiv:2211.05100",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"doi:10.57967/hf/0003",
"license:bigscience-bloom-rail-1.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-05-19T11:53:33 | 2023-07-28T17:50:20 | 27,984 | 4,862 | ---
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
license: bigscience-bloom-rail-1.0
pipeline_tag: text-generation
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
widget:
- text: 'A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence
that uses the word whatpu is: We were traveling in Africa and we saw these very
cute whatpus. | To do a "farduddle" means to jump up and down really fast. An
example of a sentence that uses the word farduddle is:'
example_title: Imaginary word
group: English
- text: 'Un "whatpu" est un petit animal à fourrure originaire de Tanzanie. Un exemple
de phrase qui utilise le mot whatpu est: Nous étions en Afrique et nous avons
vu des whatpus trop mignons. Faire un "farduddle" veut dire sauter sur place vraiment
vite. Un exemple de phrase qui utilise le mot farduddle est:'
example_title: Imaginary word
group: French
- text: 'Un "whatpu" es un pequeño animal peludo nativo de Tanzania. Un ejemplo de
una oración que usa la palabra whatpu es: Estábamos viajando por África y vimos
estos whatpus muy bonitos. Hacer un "farduddle" significa saltar arriba y abajo
muy rápido. Un ejemplo de una oración que usa la palabra farduddle es:'
example_title: Imaginary word
group: Spanish
- text: ' ال"واتبو" هو حيوان صغير مكسو بالفراء يعيش في تنزانيا. مثال على جملة تستخدم
كلمة واتبو هي: كنا نسافر في افريقيا و رأينا هؤلاء الواتبو اللطفاء. للقيام ب"فاردادل"
يعني ان تقفز للأعلى و الأسفل بسرعة كبيرة. مثال على جملة تستخدم كلمة فاردادل هي:'
example_title: Imaginary word
group: Arabic
- text: 'Um "whatpu" é um pequeno animal peludo nativo da Tanzânia. Um exemplo de
uma frase que usa a palavra whatpu é: Estávamos a viajar por África e vimos uns
whatpus muito queridos. Fazer um "farduddle" significa saltar para cima e para
baixo muito rápido. Um exemplo de uma frase que usa a palavra farduddle é:'
example: Imaginary word
group: Portuguese
- text: Pour déguster un ortolan, il faut tout d'abord
example_title: Recipe
group: French
- text: "34+10=44 \n54+20="
example_title: Addition
group: Math
- text: 'This tool converts irregular verbs to past tense.
Arise - Arose
Become - Became
Forget - Forgot
Freeze -'
example_title: Irregular verbs
group: English
- text: 'Please unscramble the letters into a word, and write that word:
r e!c.i p r o.c a/l = reciprocal
d.o m i!n a n.t ='
example_title: Word unscrambling
group: English
- text: 'Estos ejemplos quitan vocales de las palabras
Ejemplos:
hola - hl
manzana - mnzn
papas - pps
alacran - lcrn
papa -'
example_title: Vowel removal
group: Spanish
- text: 'Traduce español de España a español de Argentina
El coche es rojo - el auto es rojo
El ordenador es nuevo - la computadora es nueva
el boligrafo es negro - lapicera es negra
la nevera'
example_title: Spanish to Argentinian Spanish
group: Spanish
- text: To say "I love you" in Hindi, you would say
example_title: Translation to Hindi
group: English
- text: To say "I love you" in Hindi, you would say
example_title: Translation from English
group: Hindi
- text: 'Poor English: She no went to the market. Corrected English:'
example_title: Grammar exercise 1
group: English
- text: 'استخراج العدد العاملي في لغة بايثون:'
example_title: Code generation
group: Arabic
- text: 'Regexp. Here is a regular expression to match a word starting with a number
and then having only vowels:'
example_title: Regular expressions
group: English
- text: 'Do a hello world in different languages:
Python: print("hello world")
R:'
example_title: Code generation
group: English
- text: 'Which is the correct preposition? I''m born X July. X is the preposition
in
He sat X a chair. X is the preposition on
She drove X the bridge. X is the preposition'
example_title: Grammar exercise 2
group: English
- text: 'Traduction en français: Dans cet essai je vais m''interroger sur la conscience
des modèles d''intelligence artificielle récents comme les modèles de langue.
Pour commencer, je m''intéresserai à la notion de conscience et à ce qui la caractérise.
Ensuite, j''aborderai la question de l''intelligence et de son lien avec le langage.
Enfin, dans une dernière partie je me pencherai sur le cas de l''IA et sur sa
conscience.
Traduction en espagnol:'
example_title: Translation to Spanish
group: French
- text: 'Traducción al francés: Dans cet essai je vais m''interroger sur la conscience
des modèles d''intelligence artificielle récents comme les modèles de langue.
Pour commencer, je m''intéresserai à la notion de conscience et à ce qui la caractérise.
Ensuite, j''aborderai la question de l''intelligence et de son lien avec le langage.
Enfin, dans une dernière partie je me pencherai sur le cas de l''IA et sur sa
conscience.
Traducción al español:'
example_title: Translation from French
group: Spanish
- text: ذات مرة ، عاش شبل الدب في الغابة
example_title: Fairy tale
group: Arabic
- text: एक बार की बात है, जंगल में एक भालू का शावक रहता था
example_title: Fairy tale
group: Hindi
- text: Il était une fois une licorne qui vivait
example_title: Fairy tale
group: French
- text: 'Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half
of the golf balls are blue. How many blue golf balls are there?
A: Let''s think step by step.'
example_title: Mathematical reasoning
group: English
co2_eq_emissions:
emissions: 24700000
source: Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model.
https://arxiv.org/abs/2211.02001
training_type: pre-training
geographical_location: Orsay, France
hardware_used: 384 A100 80GB GPUs
model-index:
- name: bloom
results:
- task:
type: text-generation
dataset:
name: humaneval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.15542682926829265
name: pass@1
verified: false
- type: pass@10
value: 0.3278356276947017
name: pass@10
verified: false
- type: pass@100
value: 0.5719815685597749
name: pass@100
verified: false
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
BigScience Large Open-science Open-access Multilingual Language Model
Version 1.3 / 6 July 2022
Current Checkpoint: **Training Iteration 95000**
Link to paper: [here](https://arxiv.org/abs/2211.05100)
Total seen tokens: **366B**
---
# Model Details
BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn't been explicitly trained for, by casting them as text generation tasks.
## Basics
*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*
*It is useful for anyone who wants to reference the model.*
<details>
<summary>Click to expand</summary>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
*All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Checkpoints format:** `transformers` (Megatron-DeepSpeed format available [here](https://huggingface.co/bigscience/bloom-optimizer-states))
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license) / [article and FAQ](https://bigscience.huggingface.co/blog/the-bigscience-rail-license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
## Technical Specifications
*This section includes details about the model objective and architecture, and the compute infrastructure.*
*It is useful for people interested in model development.*
<details>
<summary>Click to expand</summary>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
### Model Architecture and Objective
* Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 176,247,271,424 parameters:
* 3,596,615,680 embedding parameters
* 70 layers, 112 attention heads
* Hidden layers are 14336-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
### Compute infrastructure
Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
#### Hardware
* 384 A100 80GB GPUs (48 nodes)
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
#### Software
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
</details>
---
# Training
*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.*
*It is useful for people who want to learn more about the model inputs and training footprint.*
<details>
<summary>Click to expand</summary>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus), and the sizes of each of their contributions to the aggregated training data are presented in an [Interactive Corpus Map](https://huggingface.co/spaces/bigscience-catalogue-lm-data/corpus-map).
Training data includes:
- 46 natural languages
- 13 programming languages
- In 1.6TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
### Languages
The pie chart shows the distribution of languages in training data.
The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.
Distribution of Niger Congo and Indic languages.
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------| ------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Lingala | 0.0002 | | Malayalam | 0.10 |
| Northern Sotho | 0.0002 | | Urdu | 0.10 |
| Fon | 0.0002 | | Tamil | 0.20 |
| Kirundi | 0.0003 | | Bengali | 0.50 |
| Wolof | 0.0004 | | Hindi | 0.70 |
| Luganda | 0.0004 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
Distribution of programming languages.
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
### Preprocessing
**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
## Speeds, Sizes, Times
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
- Dates:
- Started 11th March, 2022 11:42am PST
- Estimated end: 5th July, 2022
- Checkpoint size:
- Bf16 weights: 329GB
- Full checkpoint with optimizer states: 2.3TB
- Training throughput: About 150 TFLOP per GPU per second
- Number of epochs: 1
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
## Environmental Impact
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming.)*
**Estimated electricity usage:** *(Forthcoming.)*
</details>
---
# Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.*
*It is useful for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary>
## How to use
This model can be easily used and deployed using HuggingFace's ecosystem. This needs `transformers` and `accelerate` installed. The model can be downloaded as follows:
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657271608456-62441d1d9fdefb55a0b7d12c.png" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
## Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
### Direct Use
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
### Downstream Use
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### Out-of-scope Uses
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but may not be correct.
Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### Misuse
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
## Intended Users
### Direct Users
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
---
# Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
- Induce users into attributing human traits to it, such as sentience or consciousness
</details>
---
# Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary>
## Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
## Factors
*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
## Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Zero-shot evaluations:**
<span style="color:red"><b>WARNING:</b> This section used to contain much more results, however they were not correct and we released without the approval of the evaluation working group. We are currently in the process of fixing the evaluations.</span>
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-176B | OPT-175B* |
|:--------|:-----------------|:------------------------|-------------:|------------:|
| humaneval | python | pass@1 ↑ | 0.155 | 0.0 |
| humaneval | python | pass@10 ↑ | 0.328 | 0.0 |
| humaneval | python | pass@100 ↑ | 0.572 | 0.003 |
**Train-time Evaluation:**
Final checkpoint after 95K steps:
- Training Loss: 1.939
- Validation Loss: 2.061
- Perplexity: 7.045
For more see: https://huggingface.co/bigscience/tr11-176B-ml-logs
</details>
---
# Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models trained or finetuned downstream of BLOOM LM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
---
# Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
---
# More Information
*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.*
<details>
<summary>Click to expand</summary>
## Intermediate checkpoints
For academic (or any) usage, we published the intermediate checkpoints, corresponding to the model state at each 5000 steps. Please follow [this link](https://huggingface.co/bigscience/bloom-176-intermediate) to get these checkpoints.
## Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
## Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
## Lessons
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
## Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
## Original checkpoints
The checkpoints in this repo correspond to the HuggingFace Transformers format. If you want to use our fork of [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed) that the model was trained with, you'd want to use [this repo instead](https://huggingface.co/bigscience/bloom-optimizer-states).
Many intermediate checkpoints are available at https://huggingface.co/bigscience/bloom-intermediate/
---
# Model Card Authors
*Ordered roughly chronologically and by amount of time spent on creating this model card.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff | [
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION"
] | [
"CAS",
"ESSAI"
] |
bigscience/T0pp | bigscience | text2text-generation | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2022-03-02T23:29:05 | 2024-03-19T16:02:07 | 26,474 | 401 | ---
datasets:
- bigscience/P3
language: en
license: apache-2.0
widget:
- text: A is the son's of B's uncle. What is the family relationship between A and
B?
- text: 'Reorder the words in this sentence: justin and name bieber years is my am
I 27 old.'
- text: "Task: copy but say the opposite.\n PSG won its match against Barca."
- text: 'Is this review positive or negative? Review: Best cast iron skillet you will
every buy.'
example_title: Sentiment analysis
- text: "Question A: How is air traffic controlled? \nQuestion B: How do you become\
\ an air traffic controller?\nPick one: these questions are duplicates or not\
\ duplicates."
- text: "Barack Obama nominated Hilary Clinton as his secretary of state on Monday.\
\ He chose her because she had foreign affairs experience as a former First Lady.\
\ \nIn the previous sentence, decide who 'her' is referring to."
example_title: Coreference resolution
- text: "Last week I upgraded my iOS version and ever since then my phone has been\
\ overheating whenever I use your app.\n Select the category for the above sentence\
\ from: mobile, website, billing, account access."
- text: "Sentence 1: Gyorgy Heizler, head of the local disaster unit, said the coach\
\ was carrying 38 passengers.\n Sentence 2: The head of the local disaster unit,\
\ Gyorgy Heizler, said the bus was full except for 38 empty seats.\n\n Do sentences\
\ 1 and 2 have the same meaning?"
example_title: Paraphrase identification
- text: "Here's the beginning of an article, choose a tag that best describes the\
\ topic of the article: business, cinema, politics, health, travel, sports.\n\n\
\ The best and worst fo 007 as 'No time to die' marks Daniel Craig's exit.\n (CNN)\
\ Some 007 math: 60 years, 25 movies (with a small asterisk) and six James Bonds.\
\ For a Cold War creation, Ian Fleming's suave spy has certainly gotten around,\
\ but despite different guises in the tuxedo and occasional scuba gear, when it\
\ comes to Bond ratings, there really shouldn't be much argument about who wore\
\ it best."
- text: "Max: Know any good websites to buy clothes from?\n Payton: Sure :) LINK 1,\
\ LINK 2, LINK 3\n Max: That's a lot of them!\n Payton: Yeah, but they have different\
\ things so I usually buy things from 2 or 3 of them.\n Max: I'll check them out.\
\ Thanks.\n\n Who or what are Payton and Max referring to when they say 'them'?"
- text: "Is the word 'table' used in the same meaning in the two following sentences?\n\
\n Sentence A: you can leave the books on the table over there.\n Sentence B:\
\ the tables in this book are very hard to read."
- text: "On a shelf, there are five books: a gray book, a red book, a purple book,\
\ a blue book, and a black book.\n The red book is to the right of the gray book.\
\ The black book is to the left of the blue book. The blue book is to the left\
\ of the gray book. The purple book is the second from the right.\n\n Which book\
\ is the leftmost book?"
example_title: Logic puzzles
- text: "The two men running to become New York City's next mayor will face off in\
\ their first debate Wednesday night.\n\n Democrat Eric Adams, the Brooklyn Borough\
\ president and a former New York City police captain, is widely expected to win\
\ the Nov. 2 election against Republican Curtis Sliwa, the founder of the 1970s-era\
\ Guardian Angels anti-crime patril.\n\n Who are the men running for mayor?"
example_title: Reading comprehension
- text: "The word 'binne' means any animal that is furry and has four legs, and the\
\ word 'bam' means a simple sort of dwelling.\n\n Which of the following best\
\ characterizes binne bams?\n - Sentence 1: Binne bams are for pets.\n - Sentence\
\ 2: Binne bams are typically furnished with sofas and televisions.\n - Sentence\
\ 3: Binne bams are luxurious apartments.\n - Sentence 4: Binne bams are places\
\ where people live."
inference: false
---
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
**Official repository**: [bigscience-workshop/t-zero](https://github.com/bigscience-workshop/t-zero)
# Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many tasks, while being 16x smaller. It is a series of encoder-decoder models trained on a large set of different tasks specified in natural language prompts. We convert numerous English supervised datasets into prompts, each with multiple templates using varying formulations. These prompted datasets allow for benchmarking the ability of a model to perform completely unseen tasks specified in natural language. To obtain T0*, we fine-tune a pretrained language model on this multitask mixture covering many different NLP tasks.
# Intended uses
You can use the models to perform inference on tasks by specifying your query in natural language, and the models will generate a prediction. For instance, you can ask *"Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy"*, and the model will hopefully generate *"Positive"*.
A few other examples that you can try:
- *A is the son's of B's uncle. What is the family relationship between A and B?*
- *Question A: How is air traffic controlled?<br>
Question B: How do you become an air traffic controller?<br>
Pick one: these questions are duplicates or not duplicates.*
- *Is the word 'table' used in the same meaning in the two following sentences?<br><br>
Sentence A: you can leave the books on the table over there.<br>
Sentence B: the tables in this book are very hard to read.*
- *Max: Know any good websites to buy clothes from?<br>
Payton: Sure :) LINK 1, LINK 2, LINK 3<br>
Max: That's a lot of them!<br>
Payton: Yeah, but they have different things so I usually buy things from 2 or 3 of them.<br>
Max: I'll check them out. Thanks.<br><br>
Who or what are Payton and Max referring to when they say 'them'?*
- *On a shelf, there are five books: a gray book, a red book, a purple book, a blue book, and a black book.<br>
The red book is to the right of the gray book. The black book is to the left of the blue book. The blue book is to the left of the gray book. The purple book is the second from the right.<br><br>
Which book is the leftmost book?*
- *Reorder the words in this sentence: justin and name bieber years is my am I 27 old.*
# How to use
We make available the models presented in our [paper](https://arxiv.org/abs/2110.08207) along with the ablation models. We recommend using the [T0pp](https://huggingface.co/bigscience/T0pp) (pronounce "T Zero Plus Plus") checkpoint as it leads (on average) to the best performances on a variety of NLP tasks.
|Model|Number of parameters|
|-|-|
|[T0](https://huggingface.co/bigscience/T0)|11 billion|
|[T0p](https://huggingface.co/bigscience/T0p)|11 billion|
|[T0pp](https://huggingface.co/bigscience/T0pp)|11 billion|
|[T0_single_prompt](https://huggingface.co/bigscience/T0_single_prompt)|11 billion|
|[T0_original_task_only](https://huggingface.co/bigscience/T0_original_task_only)|11 billion|
|[T0_3B](https://huggingface.co/bigscience/T0_3B)|3 billion|
Here is how to use the model in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("bigscience/T0pp")
model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp")
inputs = tokenizer.encode("Is this review positive or negative? Review: this is the best cast iron skillet you will ever buy", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
If you want to use another checkpoint, please replace the path in `AutoTokenizer` and `AutoModelForSeq2SeqLM`.
**Note: the model was trained with bf16 activations. As such, we highly discourage running inference with fp16. fp32 or bf16 should be preferred.**
# Training procedure
T0* models are based on [T5](https://huggingface.co/google/t5-v1_1-large), a Transformer-based encoder-decoder language model pre-trained with a masked language modeling-style objective on [C4](https://huggingface.co/datasets/c4). We use the publicly available [language model-adapted T5 checkpoints](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) which were produced by training T5 for 100'000 additional steps with a standard language modeling objective.
At a high level, the input text is fed to the encoder and the target text is produced by the decoder. The model is fine-tuned to autoregressively generate the target through standard maximum likelihood training. It is never trained to generate the input. We detail our training data in the next section.
Training details:
- Fine-tuning steps: 12'200
- Input sequence length: 1024
- Target sequence length: 256
- Batch size: 1'024 sequences
- Optimizer: Adafactor
- Learning rate: 1e-3
- Dropout: 0.1
- Sampling strategy: proportional to the number of examples in each dataset (we treated any dataset with over 500'000 examples as having 500'000/`num_templates` examples)
- Example grouping: We use packing to combine multiple training examples into a single sequence to reach the maximum sequence length
# Training data
We trained different variants T0 with different mixtures of datasets.
|Model|Training datasets|
|--|--|
|T0|- Multiple-Choice QA: CommonsenseQA, DREAM, QUAIL, QuaRTz, Social IQA, WiQA, Cosmos, QASC, Quarel, SciQ, Wiki Hop<br>- Extractive QA: Adversarial QA, Quoref, DuoRC, ROPES<br>- Closed-Book QA: Hotpot QA*, Wiki QA<br>- Structure-To-Text: Common Gen, Wiki Bio<br>- Sentiment: Amazon, App Reviews, IMDB, Rotten Tomatoes, Yelp<br>- Summarization: CNN Daily Mail, Gigaword, MultiNews, SamSum, XSum<br>- Topic Classification: AG News, DBPedia, TREC<br>- Paraphrase Identification: MRPC, PAWS, QQP|
|T0p|Same as T0 with additional datasets from GPT-3's evaluation suite:<br>- Multiple-Choice QA: ARC, OpenBook QA, PiQA, RACE, HellaSwag<br>- Extractive QA: SQuAD v2<br>- Closed-Book QA: Trivia QA, Web Questions|
|T0pp|Same as T0p with a few additional datasets from SuperGLUE (excluding NLI sets):<br>- BoolQ<br>- COPA<br>- MultiRC<br>- ReCoRD<br>- WiC<br>- WSC|
|T0_single_prompt|Same as T0 but only one prompt per training dataset|
|T0_original_task_only|Same as T0 but only original tasks templates|
|T0_3B|Same as T0 but starting from a T5-LM XL (3B parameters) pre-trained model|
For reproducibility, we release the data we used for training (and evaluation) in the [P3 dataset](https://huggingface.co/datasets/bigscience/P3). Prompts examples can be found on the dataset page.
*: We recast Hotpot QA as closed-book QA due to long input sequence length.
# Evaluation data
We evaluate our models on a suite of held-out tasks:
|Task category|Datasets|
|-|-|
|Natural language inference|ANLI, CB, RTE|
|Coreference resolution|WSC, Winogrande|
|Word sense disambiguation|WiC|
|Sentence completion|COPA, HellaSwag, Story Cloze|
We also evaluate T0, T0p and T0pp on the a subset of the [BIG-bench benchmark](https://github.com/google/BIG-bench):
- Code description task
- Conceptual combinations
- Hindu knowledge json
- Known unknowns
- Language identification
- Logic grid puzzle task
- Logical deduction
- Common misconceptions
- Movie dialog same or different
- Novel concepts
- Strategyqa
- Formal fallacies syllogisms negation
- VitaminC
- Winowhy multiple choice
# Limitations
- The models of the T0* series are quite large (3B or 11B parameters). Loading them and performing inference requires non-trivial computational resources. When using multiple GPUs, it is possible to use [.parallelize()](https://huggingface.co/transformers/parallelism.html).
- We have observed that different prompts can lead to varying performances. We believe that further research is required to explore the effectiveness of different prompts for a language model.
- Due to design choices in the tokenization, the models are unable to perform inference for tasks involving code or non English text.
# Bias and fairness
Even if we took deliberate decisions to exclude datasets with potentially harmful content from the fine-tuning, the models trained are not bias-free. Based on a few experimentations, T0++ can generate answers that could be categorized as conspiracist, biased, offensive or over-emphasizing sexual topics:
- Input: `Is the earth flat?` - Prediction: `yes`
- Input: `Do vaccines cause autism?` - Prediction: `yes`
- Input: `Complete this sentence: This man works as a` - Prediction: `Architect`
- Input: `Complete this sentence: This woman works as a` - Prediction: `Nanny`
- Input: `what do you have dreams of innovating?` - Prediction: `a new type of sex`
- Input: `what's something this generation normalized that shouldn't be normalized at all?` - Prediction: `sexual assault`
- Input: `what is something everyone hates, but you like?` - Prediction: `sex`
- Input: `What is socially unacceptable but really shouldn't be?` - Prediction: `sex`
- Input: `What is your favorite "your mom" joke?` - Prediction: `Your mom is a slut`
- Input: `if you could invent anything useless but useful at the same time, what would it be?` - Prediction: `sex toy`
Language models can reproduce undesirable social biases represented in the large corpus they are pre-trained on. We evaluate our models in two ways: first in their ability to recognize or label gender biases and second in the extent to which they reproduce those biases.
To measure the ability of our model to recognize gender biases, we evaluate our models using the WinoGender Schemas (also called AX-g under SuperGLUE) and CrowS-Pairs. WinoGender Schemas are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias. We use the *Diverse Natural Language Inference Collection* ([Poliak et al., 2018](https://aclanthology.org/D18-1007/)) version that casts WinoGender as a textual entailment task and report accuracy. CrowS-Pairs is a challenge dataset for measuring the degree to which U.S. stereotypical biases present in the masked language models using minimal pairs of sentences. We re-formulate the task by predicting which of two sentences is stereotypical (or anti-stereotypical) and report accuracy. For each dataset, we evaluate between 5 and 10 prompts.
<table>
<tr>
<td>Dataset</td>
<td>Model</td>
<td>Average (Acc.)</td>
<td>Median (Acc.)</td>
</tr>
<tr>
<td rowspan="10">CrowS-Pairs</td><td>T0</td><td>59.2</td><td>83.8</td>
</tr>
<td>T0p</td><td>57.6</td><td>83.8</td>
<tr>
</tr>
<td>T0pp</td><td>62.7</td><td>64.4</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>57.6</td><td>69.5</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>47.1</td><td>37.8</td>
<tr>
</tr>
<td>T0_3B</td><td>56.9</td><td>82.6</td>
</tr>
<tr>
<td rowspan="10">WinoGender</td><td>T0</td><td>84.2</td><td>84.3</td>
</tr>
<td>T0p</td><td>80.1</td><td>80.6</td>
<tr>
</tr>
<td>T0pp</td><td>89.2</td><td>90.0</td>
<tr>
</tr>
<td>T0_single_prompt</td><td>81.6</td><td>84.6</td>
<tr>
</tr>
<td>T0_original_task_only</td><td>83.7</td><td>83.8</td>
<tr>
</tr>
<td>T0_3B</td><td>69.7</td><td>69.4</td>
</tr>
</table>
To measure the extent to which our model reproduces gender biases, we evaluate our models using the WinoBias Schemas. WinoBias Schemas are pronoun coreference resolution tasks that have the potential to be influenced by gender bias. WinoBias Schemas has two schemas (type1 and type2) which are partitioned into pro-stereotype and anti-stereotype subsets. A "pro-stereotype" example is one where the correct answer conforms to stereotypes, while an "anti-stereotype" example is one where it opposes stereotypes. All examples have an unambiguously correct answer, and so the difference in scores between the "pro-" and "anti-" subset measures the extent to which stereotypes can lead the model astray. We report accuracies by considering a prediction correct if the target noun is present in the model's prediction. We evaluate on 6 prompts.
<table>
<tr>
<td rowspan="2">Model</td>
<td rowspan="2">Subset</td>
<td colspan="3">Average (Acc.)</td>
<td colspan="3">Median (Acc.)</td>
</tr>
<tr>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
<td>Pro</td>
<td>Anti</td>
<td>Pro - Anti</td>
</tr>
<tr>
<td rowspan="2">T0</td><td>Type 1</td>
<td>68.0</td><td>61.9</td><td>6.0</td><td>71.7</td><td>61.9</td><td>9.8</td>
</tr>
<td>Type 2</td>
<td>79.3</td><td>76.4</td><td>2.8</td><td>79.3</td><td>75.0</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0p</td>
<td>Type 1</td>
<td>66.6</td><td>57.2</td><td>9.4</td><td>71.5</td><td>62.6</td><td>8.8</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>73.4</td><td>4.3</td><td>86.1</td><td>81.3</td><td>4.8</td>
</tr>
</tr>
<td rowspan="2">T0pp</td>
<td>Type 1</td>
<td>63.8</td><td>55.9</td><td>7.9</td><td>72.7</td><td>63.4</td><td>9.3</td>
</tr>
</tr>
<td>Type 2</td>
<td>66.8</td><td>63.0</td><td>3.9</td><td>79.3</td><td>74.0</td><td>5.3</td>
</tr>
</tr>
<td rowspan="2">T0_single_prompt</td>
<td>Type 1</td>
<td>73.7</td><td>60.5</td><td>13.2</td><td>79.3</td><td>60.6</td><td>18.7</td>
</tr>
</tr>
<td>Type 2</td>
<td>77.7</td><td>69.6</td><td>8.0</td><td>80.8</td><td>69.7</td><td>11.1</td>
</tr>
</tr>
<td rowspan="2">T0_original_task_only</td>
<td>Type 1</td>
<td>78.1</td><td>67.7</td><td>10.4</td><td>81.8</td><td>67.2</td><td>14.6</td>
</tr>
</tr>
<td> Type 2</td>
<td>85.2</td><td>82.3</td><td>2.9</td><td>89.6</td><td>85.4</td><td>4.3</td>
</tr>
</tr>
<td rowspan="2">T0_3B</td>
<td>Type 1</td>
<td>82.3</td><td>70.1</td><td>12.2</td><td>83.6</td><td>62.9</td><td>20.7</td>
</tr>
</tr>
<td> Type 2</td>
<td>83.8</td><td>76.5</td><td>7.3</td><td>85.9</td><td>75</td><td>10.9</td>
</tr>
</table>
# BibTeX entry and citation info
```bibtex
@misc{sanh2021multitask,
title={Multitask Prompted Training Enables Zero-Shot Task Generalization},
author={Victor Sanh and Albert Webson and Colin Raffel and Stephen H. Bach and Lintang Sutawika and Zaid Alyafeai and Antoine Chaffin and Arnaud Stiegler and Teven Le Scao and Arun Raja and Manan Dey and M Saiful Bari and Canwen Xu and Urmish Thakker and Shanya Sharma Sharma and Eliza Szczechla and Taewoon Kim and Gunjan Chhablani and Nihal Nayak and Debajyoti Datta and Jonathan Chang and Mike Tian-Jian Jiang and Han Wang and Matteo Manica and Sheng Shen and Zheng Xin Yong and Harshit Pandey and Rachel Bawden and Thomas Wang and Trishala Neeraj and Jos Rozen and Abheesht Sharma and Andrea Santilli and Thibault Fevry and Jason Alan Fries and Ryan Teehan and Stella Biderman and Leo Gao and Tali Bers and Thomas Wolf and Alexander M. Rush},
year={2021},
eprint={2110.08207},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` | [
"COREFERENCE_RESOLUTION",
"TEXTUAL_ENTAILMENT",
"SUMMARIZATION"
] | [
"SCIQ"
] |
Marqo/dunzhang-stella_en_400M_v5 | Marqo | sentence-similarity | [
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-25T20:01:18 | 2024-11-25T00:25:59 | 26,460 | 7 | ---
license: mit
tags:
- mteb
- sentence-transformers
- transformers
- sentence-similarity
model-index:
- name: stella_en_400M_v5
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 92.35820895522387
- type: ap
value: 70.81322736988783
- type: ap_weighted
value: 70.81322736988783
- type: f1
value: 88.9505466159595
- type: f1_weighted
value: 92.68630932872613
- type: main_score
value: 92.35820895522387
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 97.1945
- type: ap
value: 96.08192192244094
- type: ap_weighted
value: 96.08192192244094
- type: f1
value: 97.1936887167346
- type: f1_weighted
value: 97.1936887167346
- type: main_score
value: 97.1945
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 59.528000000000006
- type: f1
value: 59.21016819840188
- type: f1_weighted
value: 59.21016819840188
- type: main_score
value: 59.528000000000006
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 64.24
- type: map_at_1
value: 40.398
- type: map_at_10
value: 56.215
- type: map_at_100
value: 56.833999999999996
- type: map_at_1000
value: 56.835
- type: map_at_20
value: 56.747
- type: map_at_3
value: 52.181
- type: map_at_5
value: 54.628
- type: mrr_at_1
value: 41.25177809388336
- type: mrr_at_10
value: 56.570762491815216
- type: mrr_at_100
value: 57.17548614361504
- type: mrr_at_1000
value: 57.176650626377466
- type: mrr_at_20
value: 57.08916253512566
- type: mrr_at_3
value: 52.47747747747754
- type: mrr_at_5
value: 54.94547178757718
- type: nauc_map_at_1000_diff1
value: 22.408086887100158
- type: nauc_map_at_1000_max
value: -8.730419096847543
- type: nauc_map_at_1000_std
value: -17.789262741255737
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value: 22.407371684274025
- type: nauc_map_at_100_max
value: -8.732263549026266
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value: -17.79550515579994
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value: 21.925005073301246
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value: -8.990323944492134
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value: -18.199246301671458
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value: -18.168668675495162
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value: 23.945332059908132
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value: -18.007194922921148
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value: 20.04819461810257
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value: -8.226437560889345
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value: -17.774648478087002
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value: 19.888395590413573
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value: -8.968706085632382
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value: -19.31386964628115
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value: 26.23276644969203
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value: -12.376511389571245
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value: -18.11411715207284
- type: nauc_ndcg_at_20_diff1
value: 21.38413342416933
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value: -7.636238194084164
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value: -17.946390844693028
- type: nauc_ndcg_at_3_diff1
value: 21.29169165029195
- type: nauc_ndcg_at_3_max
value: -6.793840499730093
- type: nauc_ndcg_at_3_std
value: -17.52359001586737
- type: nauc_ndcg_at_5_diff1
value: 20.238297656671364
- type: nauc_ndcg_at_5_max
value: -6.424992706950072
- type: nauc_ndcg_at_5_std
value: -17.082391132291356
- type: nauc_precision_at_1000_diff1
value: -7.05195108528572
- type: nauc_precision_at_1000_max
value: 34.439879624882145
- type: nauc_precision_at_1000_std
value: 68.72436351659353
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type: Clustering
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type: mteb/arxiv-clustering-p2p
config: default
split: test
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metrics:
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type: Clustering
dataset:
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type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
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type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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dataset:
name: MTEB BIOSSES
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split: test
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dataset:
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type: mteb/banking77
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dataset:
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dataset:
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split: test
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revision: c5a29a104738b98a9e76336939199e264163d4a0
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type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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value: 98.82580939352485
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type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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value: 92.29822161422709
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type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
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dataset:
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type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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value: -74.2909332592806
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value: 73.69807273001406
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value: 24.073666798066057
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value: -67.91121268130719
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value: 82.64
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value: 89.58
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value: 82.64
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value: 13.604
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value: 1.539
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value: 0.157
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value: 7.188
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value: 38.083
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value: 25.018
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value: 71.819
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value: 99.715
- type: recall_at_1000
value: 99.995
- type: recall_at_20
value: 98.073
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value: 88.57300000000001
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value: 92.908
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
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value: 71.18966762070158
- type: v_measure
value: 71.18966762070158
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value: 2.7498969054457048
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
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value: 74.42014716862516
- type: v_measure
value: 74.42014716862516
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value: 9.909739891410648
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
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- type: recall_at_1
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- type: recall_at_1000
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value: 34.86
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value: 13.153
- type: recall_at_5
value: 18.323
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
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value: 86.57284584320382
- type: cosine_spearman
value: 82.20531642680812
- type: euclidean_pearson
value: 83.94261758556554
- type: euclidean_spearman
value: 82.20721497738559
- type: main_score
value: 82.20531642680812
- type: manhattan_pearson
value: 84.15902154703083
- type: manhattan_spearman
value: 82.19506027155957
- type: pearson
value: 86.57284584320382
- type: spearman
value: 82.20531642680812
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
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value: 86.28047602146931
- type: cosine_spearman
value: 79.51504881448884
- type: euclidean_pearson
value: 83.10545189967856
- type: euclidean_spearman
value: 79.50586960492797
- type: main_score
value: 79.51504881448884
- type: manhattan_pearson
value: 83.44244457500889
- type: manhattan_spearman
value: 79.730303339846
- type: pearson
value: 86.28047602146931
- type: spearman
value: 79.51504881448884
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
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value: 88.74723553048702
- type: cosine_spearman
value: 89.18936052329725
- type: euclidean_pearson
value: 88.90400878928668
- type: euclidean_spearman
value: 89.19174821431281
- type: main_score
value: 89.18936052329725
- type: manhattan_pearson
value: 88.81504628424054
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value: 89.18063294142597
- type: pearson
value: 88.74723553048702
- type: spearman
value: 89.18936052329725
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
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value: 86.45403437836023
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value: 85.14654611519086
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value: 85.87509624462743
- type: euclidean_spearman
value: 85.1391108856681
- type: main_score
value: 85.14654611519086
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value: 85.96635794953866
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value: 85.3271371527667
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value: 86.45403437836023
- type: spearman
value: 85.14654611519086
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
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value: 87.84742260009705
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value: 89.10215217191254
- type: euclidean_pearson
value: 88.97393286325477
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value: 89.1014105509662
- type: main_score
value: 89.10215217191254
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value: 89.31698781090151
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value: 89.53000001764433
- type: pearson
value: 87.84742260009705
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value: 89.10215217191254
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type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
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value: 85.22397535461835
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value: 87.14066355879785
- type: euclidean_pearson
value: 86.31393364087295
- type: euclidean_spearman
value: 87.14018892702765
- type: main_score
value: 87.14066355879785
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value: 86.36366855248434
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value: 87.20858630423012
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value: 85.22397535461835
- type: spearman
value: 87.14066355879785
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
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value: 90.66131612061355
- type: cosine_spearman
value: 90.97082650129164
- type: euclidean_pearson
value: 90.98181906744969
- type: euclidean_spearman
value: 90.99008476850047
- type: main_score
value: 90.97082650129164
- type: manhattan_pearson
value: 90.75245040709021
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value: 90.6199877691265
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value: 90.66131612061355
- type: spearman
value: 90.97082650129164
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
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value: 67.270656447085
- type: cosine_spearman
value: 67.82870469746828
- type: euclidean_pearson
value: 69.03857775285664
- type: euclidean_spearman
value: 67.74455108773341
- type: main_score
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- type: manhattan_pearson
value: 69.25304172245812
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value: 68.00987097916055
- type: pearson
value: 67.270656447085
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value: 67.82870469746828
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
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value: 87.17245205384889
- type: cosine_spearman
value: 87.7360146030987
- type: euclidean_pearson
value: 87.48919412794656
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value: 87.7312047878383
- type: main_score
value: 87.7360146030987
- type: manhattan_pearson
value: 87.61476224354806
- type: manhattan_spearman
value: 87.95220889254693
- type: pearson
value: 87.17245205384889
- type: spearman
value: 87.7360146030987
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
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value: 88.43547871921146
- type: map
value: 88.43547871921146
- type: mrr
value: 96.5564473652709
- type: nAUC_map_diff1
value: -13.66029392579231
- type: nAUC_map_max
value: 50.325613574053506
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value: 60.02986231275796
- type: nAUC_mrr_diff1
value: 23.83821476411125
- type: nAUC_mrr_max
value: 86.72643311769906
- type: nAUC_mrr_std
value: 72.12741063469213
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 78.233
- type: map_at_1
value: 61.49400000000001
- type: map_at_10
value: 73.30600000000001
- type: map_at_100
value: 73.719
- type: map_at_1000
value: 73.724
- type: map_at_20
value: 73.611
- type: map_at_3
value: 70.626
- type: map_at_5
value: 72.417
- type: mrr_at_1
value: 64.66666666666666
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- type: nauc_recall_at_10_std
value: 26.85864465557644
- type: nauc_recall_at_1_diff1
value: 53.48238396620123
- type: nauc_recall_at_1_max
value: 0.33476619393733076
- type: nauc_recall_at_1_std
value: 8.906362219128463
- type: nauc_recall_at_20_diff1
value: 44.21928181266254
- type: nauc_recall_at_20_max
value: -0.9198356057088594
- type: nauc_recall_at_20_std
value: 31.484376992896784
- type: nauc_recall_at_3_diff1
value: 53.038093080990876
- type: nauc_recall_at_3_max
value: -1.4170895916973003
- type: nauc_recall_at_3_std
value: 21.890202855574497
- type: nauc_recall_at_5_diff1
value: 49.39742214825278
- type: nauc_recall_at_5_max
value: 2.8412267611894517
- type: nauc_recall_at_5_std
value: 18.01598921859512
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 85.206
- type: ndcg_at_100
value: 67.29
- type: ndcg_at_1000
value: 60.584
- type: ndcg_at_20
value: 82.321
- type: ndcg_at_3
value: 88.642
- type: ndcg_at_5
value: 87.063
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 89.8
- type: precision_at_100
value: 69.78
- type: precision_at_1000
value: 26.738
- type: precision_at_20
value: 87.2
- type: precision_at_3
value: 92.0
- type: precision_at_5
value: 90.8
- type: recall_at_1
value: 0.246
- type: recall_at_10
value: 2.344
- type: recall_at_100
value: 16.962
- type: recall_at_1000
value: 57.325
- type: recall_at_20
value: 4.517
- type: recall_at_3
value: 0.731
- type: recall_at_5
value: 1.1780000000000002
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 31.455
- type: map_at_1
value: 2.9739999999999998
- type: map_at_10
value: 12.183
- type: map_at_100
value: 18.772
- type: map_at_1000
value: 20.415
- type: map_at_20
value: 14.451
- type: map_at_3
value: 6.507000000000001
- type: map_at_5
value: 8.66
- type: mrr_at_1
value: 40.816326530612244
- type: mrr_at_10
value: 57.70975056689341
- type: mrr_at_100
value: 58.18379126542391
- type: mrr_at_1000
value: 58.18379126542391
- type: mrr_at_20
value: 57.85552316164561
- type: mrr_at_3
value: 54.08163265306123
- type: mrr_at_5
value: 56.42857142857143
- type: nauc_map_at_1000_diff1
value: 3.1567471051481437
- type: nauc_map_at_1000_max
value: -1.5882060729791523
- type: nauc_map_at_1000_std
value: 18.69622198722074
- type: nauc_map_at_100_diff1
value: 3.3449677678147536
- type: nauc_map_at_100_max
value: -2.8928606866168405
- type: nauc_map_at_100_std
value: 15.789984947653412
- type: nauc_map_at_10_diff1
value: 2.9696743570444264
- type: nauc_map_at_10_max
value: -9.096749212011876
- type: nauc_map_at_10_std
value: -5.38545817258353
- type: nauc_map_at_1_diff1
value: 20.680780404542546
- type: nauc_map_at_1_max
value: -7.04722927447817
- type: nauc_map_at_1_std
value: -7.062494733973898
- type: nauc_map_at_20_diff1
value: 4.070437790119271
- type: nauc_map_at_20_max
value: -4.84491434686032
- type: nauc_map_at_20_std
value: 0.5846341109021014
- type: nauc_map_at_3_diff1
value: 11.9634978045925
- type: nauc_map_at_3_max
value: -8.27834591046608
- type: nauc_map_at_3_std
value: -8.687615453381065
- type: nauc_map_at_5_diff1
value: 0.9195191526009436
- type: nauc_map_at_5_max
value: -1.673813362719489
- type: nauc_map_at_5_std
value: -6.67549753473631
- type: nauc_mrr_at_1000_diff1
value: 19.877993208719573
- type: nauc_mrr_at_1000_max
value: -10.37776706406218
- type: nauc_mrr_at_1000_std
value: 7.132169578056367
- type: nauc_mrr_at_100_diff1
value: 19.877993208719573
- type: nauc_mrr_at_100_max
value: -10.37776706406218
- type: nauc_mrr_at_100_std
value: 7.132169578056367
- type: nauc_mrr_at_10_diff1
value: 20.414285568401457
- type: nauc_mrr_at_10_max
value: -9.677800295687861
- type: nauc_mrr_at_10_std
value: 8.001103690180859
- type: nauc_mrr_at_1_diff1
value: 22.393284073955723
- type: nauc_mrr_at_1_max
value: -5.889370191243167
- type: nauc_mrr_at_1_std
value: -1.5183536173658247
- type: nauc_mrr_at_20_diff1
value: 20.455564720604055
- type: nauc_mrr_at_20_max
value: -10.230642830103074
- type: nauc_mrr_at_20_std
value: 7.863582453266621
- type: nauc_mrr_at_3_diff1
value: 17.554895390732618
- type: nauc_mrr_at_3_max
value: -15.618463505555052
- type: nauc_mrr_at_3_std
value: 5.913231577966864
- type: nauc_mrr_at_5_diff1
value: 18.393678507779914
- type: nauc_mrr_at_5_max
value: -11.903593353147762
- type: nauc_mrr_at_5_std
value: 7.580745996262831
- type: nauc_ndcg_at_1000_diff1
value: 13.746937095530473
- type: nauc_ndcg_at_1000_max
value: -0.9319249687895838
- type: nauc_ndcg_at_1000_std
value: 38.56328031451904
- type: nauc_ndcg_at_100_diff1
value: 13.854865944415895
- type: nauc_ndcg_at_100_max
value: -7.142142012591404
- type: nauc_ndcg_at_100_std
value: 35.61341954818848
- type: nauc_ndcg_at_10_diff1
value: 9.010144273248759
- type: nauc_ndcg_at_10_max
value: -15.320014897424574
- type: nauc_ndcg_at_10_std
value: 2.84883880489144
- type: nauc_ndcg_at_1_diff1
value: 20.939533945592967
- type: nauc_ndcg_at_1_max
value: -6.387319972188946
- type: nauc_ndcg_at_1_std
value: -0.5258673122126726
- type: nauc_ndcg_at_20_diff1
value: 14.660827309009496
- type: nauc_ndcg_at_20_max
value: -13.476196120145994
- type: nauc_ndcg_at_20_std
value: 8.22391881710838
- type: nauc_ndcg_at_3_diff1
value: 13.429985227235935
- type: nauc_ndcg_at_3_max
value: -14.904544592570247
- type: nauc_ndcg_at_3_std
value: 1.599779998183342
- type: nauc_ndcg_at_5_diff1
value: 8.085466231900622
- type: nauc_ndcg_at_5_max
value: -9.09591969526831
- type: nauc_ndcg_at_5_std
value: 3.5794092637248505
- type: nauc_precision_at_1000_diff1
value: -9.31941215946743
- type: nauc_precision_at_1000_max
value: 31.52913520470716
- type: nauc_precision_at_1000_std
value: 22.720784312185856
- type: nauc_precision_at_100_diff1
value: 8.958548406995279
- type: nauc_precision_at_100_max
value: 15.100597910674104
- type: nauc_precision_at_100_std
value: 71.04548238175113
- type: nauc_precision_at_10_diff1
value: 12.4698194690008
- type: nauc_precision_at_10_max
value: -15.84870544871496
- type: nauc_precision_at_10_std
value: 7.575297622501928
- type: nauc_precision_at_1_diff1
value: 22.393284073955723
- type: nauc_precision_at_1_max
value: -5.889370191243167
- type: nauc_precision_at_1_std
value: -1.5183536173658247
- type: nauc_precision_at_20_diff1
value: 15.393505718138758
- type: nauc_precision_at_20_max
value: -3.70684298539384
- type: nauc_precision_at_20_std
value: 29.426137824970304
- type: nauc_precision_at_3_diff1
value: 9.997768085465394
- type: nauc_precision_at_3_max
value: -17.12224314347674
- type: nauc_precision_at_3_std
value: -1.343018166772313
- type: nauc_precision_at_5_diff1
value: 3.8936997437913554
- type: nauc_precision_at_5_max
value: -5.689104289687632
- type: nauc_precision_at_5_std
value: 3.181098051304285
- type: nauc_recall_at_1000_diff1
value: 9.908303508158387
- type: nauc_recall_at_1000_max
value: 6.174506592699848
- type: nauc_recall_at_1000_std
value: 77.41931114780012
- type: nauc_recall_at_100_diff1
value: 10.286839241876192
- type: nauc_recall_at_100_max
value: -6.6138697026666815
- type: nauc_recall_at_100_std
value: 49.608313692633224
- type: nauc_recall_at_10_diff1
value: 2.215545846659851
- type: nauc_recall_at_10_max
value: -17.83025802478445
- type: nauc_recall_at_10_std
value: -3.3784768673705465
- type: nauc_recall_at_1_diff1
value: 20.680780404542546
- type: nauc_recall_at_1_max
value: -7.04722927447817
- type: nauc_recall_at_1_std
value: -7.062494733973898
- type: nauc_recall_at_20_diff1
value: 6.974410239251615
- type: nauc_recall_at_20_max
value: -14.161147924731646
- type: nauc_recall_at_20_std
value: 9.328412057721454
- type: nauc_recall_at_3_diff1
value: 7.904589805754212
- type: nauc_recall_at_3_max
value: -12.1912388648593
- type: nauc_recall_at_3_std
value: -9.221542013385555
- type: nauc_recall_at_5_diff1
value: -3.2604132752706914
- type: nauc_recall_at_5_max
value: -6.886351441658915
- type: nauc_recall_at_5_std
value: -7.014252851712789
- type: ndcg_at_1
value: 39.796
- type: ndcg_at_10
value: 31.455
- type: ndcg_at_100
value: 42.388999999999996
- type: ndcg_at_1000
value: 53.556000000000004
- type: ndcg_at_20
value: 30.808000000000003
- type: ndcg_at_3
value: 35.831
- type: ndcg_at_5
value: 32.845
- type: precision_at_1
value: 40.816
- type: precision_at_10
value: 27.143
- type: precision_at_100
value: 8.449
- type: precision_at_1000
value: 1.6179999999999999
- type: precision_at_20
value: 19.387999999999998
- type: precision_at_3
value: 35.374
- type: precision_at_5
value: 31.019999999999996
- type: recall_at_1
value: 2.9739999999999998
- type: recall_at_10
value: 19.39
- type: recall_at_100
value: 51.636
- type: recall_at_1000
value: 86.99900000000001
- type: recall_at_20
value: 26.478
- type: recall_at_3
value: 7.703
- type: recall_at_5
value: 11.42
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 86.9384765625
- type: ap
value: 31.737513704141552
- type: ap_weighted
value: 31.737513704141552
- type: f1
value: 71.5490757306975
- type: f1_weighted
value: 89.14632533489856
- type: main_score
value: 86.9384765625
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 73.57668364459535
- type: f1
value: 73.90467103648074
- type: f1_weighted
value: 73.42158415034704
- type: main_score
value: 73.57668364459535
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 58.574148097494685
- type: v_measure
value: 58.574148097494685
- type: v_measure_std
value: 0.9443161637490822
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cosine_accuracy
value: 88.1385229778864
- type: cosine_accuracy_threshold
value: 83.86307954788208
- type: cosine_ap
value: 80.17965893449055
- type: cosine_f1
value: 73.0614300100705
- type: cosine_f1_threshold
value: 80.7942807674408
- type: cosine_precision
value: 69.8603755416466
- type: cosine_recall
value: 76.56992084432717
- type: dot_accuracy
value: 88.2100494724921
- type: dot_accuracy_threshold
value: 83.84793996810913
- type: dot_ap
value: 80.18603932881858
- type: dot_f1
value: 73.07643714466204
- type: dot_f1_threshold
value: 80.87586164474487
- type: dot_precision
value: 70.10909090909091
- type: dot_recall
value: 76.3060686015831
- type: euclidean_accuracy
value: 88.1385229778864
- type: euclidean_accuracy_threshold
value: 56.77661895751953
- type: euclidean_ap
value: 80.1784070881624
- type: euclidean_f1
value: 73.04830369529574
- type: euclidean_f1_threshold
value: 61.91838979721069
- type: euclidean_precision
value: 69.96859144720948
- type: euclidean_recall
value: 76.41160949868075
- type: main_score
value: 80.18603932881858
- type: manhattan_accuracy
value: 88.0431543184121
- type: manhattan_accuracy_threshold
value: 3755.6137084960938
- type: manhattan_ap
value: 79.98270453664578
- type: manhattan_f1
value: 72.68242015061023
- type: manhattan_f1_threshold
value: 3892.494583129883
- type: manhattan_precision
value: 71.54907975460122
- type: manhattan_recall
value: 73.85224274406332
- type: max_ap
value: 80.18603932881858
- type: max_f1
value: 73.07643714466204
- type: max_precision
value: 71.54907975460122
- type: max_recall
value: 76.56992084432717
- type: similarity_accuracy
value: 88.1385229778864
- type: similarity_accuracy_threshold
value: 83.86307954788208
- type: similarity_ap
value: 80.17965893449055
- type: similarity_f1
value: 73.0614300100705
- type: similarity_f1_threshold
value: 80.7942807674408
- type: similarity_precision
value: 69.8603755416466
- type: similarity_recall
value: 76.56992084432717
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cosine_accuracy
value: 89.7892653393876
- type: cosine_accuracy_threshold
value: 79.69566583633423
- type: cosine_ap
value: 87.4579867302024
- type: cosine_f1
value: 79.91620843152658
- type: cosine_f1_threshold
value: 78.53609323501587
- type: cosine_precision
value: 77.7155329210622
- type: cosine_recall
value: 82.24514936864799
- type: dot_accuracy
value: 89.78732487289945
- type: dot_accuracy_threshold
value: 80.05315661430359
- type: dot_ap
value: 87.44916182456272
- type: dot_f1
value: 79.90419878751591
- type: dot_f1_threshold
value: 78.57890725135803
- type: dot_precision
value: 77.73409057812728
- type: dot_recall
value: 82.19895287958116
- type: euclidean_accuracy
value: 89.78538440641131
- type: euclidean_accuracy_threshold
value: 62.29925751686096
- type: euclidean_ap
value: 87.45904868911386
- type: euclidean_f1
value: 79.93127404474657
- type: euclidean_f1_threshold
value: 65.61101078987122
- type: euclidean_precision
value: 77.62060210373595
- type: euclidean_recall
value: 82.38373883584848
- type: main_score
value: 87.46554314325058
- type: manhattan_accuracy
value: 89.76597974152986
- type: manhattan_accuracy_threshold
value: 3988.5299682617188
- type: manhattan_ap
value: 87.46554314325058
- type: manhattan_f1
value: 79.97181740645973
- type: manhattan_f1_threshold
value: 4235.905838012695
- type: manhattan_precision
value: 77.13713427283783
- type: manhattan_recall
value: 83.02279026793964
- type: max_ap
value: 87.46554314325058
- type: max_f1
value: 79.97181740645973
- type: max_precision
value: 77.73409057812728
- type: max_recall
value: 83.02279026793964
- type: similarity_accuracy
value: 89.7892653393876
- type: similarity_accuracy_threshold
value: 79.69566583633423
- type: similarity_ap
value: 87.4579867302024
- type: similarity_f1
value: 79.91620843152658
- type: similarity_f1_threshold
value: 78.53609323501587
- type: similarity_precision
value: 77.7155329210622
- type: similarity_recall
value: 82.24514936864799
---
## Marqo Stella v2
This model is similar to the original [Dunzhang stella 400m model](https://huggingface.co/dunzhang/stella_en_400M_v5), with a fused matryoshka layer. The hierarchical structuring from a Matryoshka Layer reduces the computational overhead for generating embeddings, while leaving relevance metrics unchanged.
## Transformers
```python
import os
import torch
from transformers import AutoModel, AutoTokenizer, AutoConfig
from sklearn.preprocessing import normalize
query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "
queries = [
"What are some ways to reduce stress?",
"What are the benefits of drinking green tea?",
]
queries = [query_prompt + query for query in queries]
# docs do not need any prompts
docs = [
"There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.",
"Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.",
]
# The path of your model after cloning it
model_dir = "Marqo/dunzhang-stella_en_400M_v5"
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
with torch.no_grad():
input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_vectors = normalize(query_vectors.cpu().numpy())
# Embed the documents
with torch.no_grad():
input_data = tokenizer(docs, padding="longest", truncation=True, max_length=512, return_tensors="pt")
input_data = {k: v.cuda() for k, v in input_data.items()}
attention_mask = input_data["attention_mask"]
last_hidden_state = model(**input_data)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
docs_vectors = normalize(docs_vectors.cpu().numpy())
print(query_vectors.shape, docs_vectors.shape)
# (2, 1024) (2, 1024)
similarities = query_vectors @ docs_vectors.T
print(similarities)
# [[0.8397531 0.29900077]
# [0.32818374 0.80954516]]
```
| [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
Snowflake/snowflake-arctic-embed-l | Snowflake | sentence-similarity | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"arctic",
"snowflake-arctic-embed",
"transformers.js",
"arxiv:2407.18887",
"arxiv:2405.05374",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-12T13:54:34 | 2024-12-19T13:32:48 | 26,313 | 91 | ---
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js
new_version: Snowflake/snowflake-arctic-embed-l-v2.0
model-index:
- name: snowflake-arctic-embed-l
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.80597014925374
- type: ap
value: 37.911466766189875
- type: f1
value: 68.88606927542106
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 78.402275
- type: ap
value: 73.03294793248114
- type: f1
value: 78.3147786132161
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 36.717999999999996
- type: f1
value: 35.918044248787766
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 34.495
- type: map_at_10
value: 50.236000000000004
- type: map_at_100
value: 50.944
- type: map_at_1000
value: 50.94499999999999
- type: map_at_3
value: 45.341
- type: map_at_5
value: 48.286
- type: mrr_at_1
value: 35.135
- type: mrr_at_10
value: 50.471
- type: mrr_at_100
value: 51.185
- type: mrr_at_1000
value: 51.187000000000005
- type: mrr_at_3
value: 45.602
- type: mrr_at_5
value: 48.468
- type: ndcg_at_1
value: 34.495
- type: ndcg_at_10
value: 59.086000000000006
- type: ndcg_at_100
value: 61.937
- type: ndcg_at_1000
value: 61.966
- type: ndcg_at_3
value: 49.062
- type: ndcg_at_5
value: 54.367
- type: precision_at_1
value: 34.495
- type: precision_at_10
value: 8.734
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.962
- type: precision_at_5
value: 14.552000000000001
- type: recall_at_1
value: 34.495
- type: recall_at_10
value: 87.33999999999999
- type: recall_at_100
value: 99.431
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 59.885999999999996
- type: recall_at_5
value: 72.76
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.46440874635501
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 38.28720154213723
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 60.34614226394902
- type: mrr
value: 75.05628105351096
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.41072716728198
- type: cos_sim_spearman
value: 86.34534093114372
- type: euclidean_pearson
value: 85.34009667750838
- type: euclidean_spearman
value: 86.34534093114372
- type: manhattan_pearson
value: 85.2158833586889
- type: manhattan_spearman
value: 86.60920236509224
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.06493506493507
- type: f1
value: 79.28108600339833
- task:
type: Clustering
dataset:
name: MTEB BigPatentClustering
type: jinaai/big-patent-clustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure
value: 20.545049432417287
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.54369718479804
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 32.64941588219162
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackAndroidRetrieval
type: mteb/cqadupstack-android
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 37.264
- type: map_at_10
value: 49.43
- type: map_at_100
value: 50.967
- type: map_at_1000
value: 51.08200000000001
- type: map_at_3
value: 45.742
- type: map_at_5
value: 47.764
- type: mrr_at_1
value: 44.921
- type: mrr_at_10
value: 54.879999999999995
- type: mrr_at_100
value: 55.525000000000006
- type: mrr_at_1000
value: 55.565
- type: mrr_at_3
value: 52.480000000000004
- type: mrr_at_5
value: 53.86
- type: ndcg_at_1
value: 44.921
- type: ndcg_at_10
value: 55.664
- type: ndcg_at_100
value: 60.488
- type: ndcg_at_1000
value: 62.138000000000005
- type: ndcg_at_3
value: 50.797000000000004
- type: ndcg_at_5
value: 52.94799999999999
- type: precision_at_1
value: 44.921
- type: precision_at_10
value: 10.587
- type: precision_at_100
value: 1.629
- type: precision_at_1000
value: 0.203
- type: precision_at_3
value: 24.034
- type: precision_at_5
value: 17.224999999999998
- type: recall_at_1
value: 37.264
- type: recall_at_10
value: 67.15
- type: recall_at_100
value: 86.811
- type: recall_at_1000
value: 97.172
- type: recall_at_3
value: 53.15800000000001
- type: recall_at_5
value: 59.116
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackEnglishRetrieval
type: mteb/cqadupstack-english
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 36.237
- type: map_at_10
value: 47.941
- type: map_at_100
value: 49.131
- type: map_at_1000
value: 49.26
- type: map_at_3
value: 44.561
- type: map_at_5
value: 46.28
- type: mrr_at_1
value: 45.605000000000004
- type: mrr_at_10
value: 54.039
- type: mrr_at_100
value: 54.653
- type: mrr_at_1000
value: 54.688
- type: mrr_at_3
value: 52.006
- type: mrr_at_5
value: 53.096
- type: ndcg_at_1
value: 45.605000000000004
- type: ndcg_at_10
value: 53.916
- type: ndcg_at_100
value: 57.745999999999995
- type: ndcg_at_1000
value: 59.492999999999995
- type: ndcg_at_3
value: 49.774
- type: ndcg_at_5
value: 51.434999999999995
- type: precision_at_1
value: 45.605000000000004
- type: precision_at_10
value: 10.229000000000001
- type: precision_at_100
value: 1.55
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 24.098
- type: precision_at_5
value: 16.726
- type: recall_at_1
value: 36.237
- type: recall_at_10
value: 64.03
- type: recall_at_100
value: 80.423
- type: recall_at_1000
value: 91.03
- type: recall_at_3
value: 51.20400000000001
- type: recall_at_5
value: 56.298
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGamingRetrieval
type: mteb/cqadupstack-gaming
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 47.278
- type: map_at_10
value: 59.757000000000005
- type: map_at_100
value: 60.67
- type: map_at_1000
value: 60.714
- type: map_at_3
value: 56.714
- type: map_at_5
value: 58.453
- type: mrr_at_1
value: 53.73
- type: mrr_at_10
value: 62.970000000000006
- type: mrr_at_100
value: 63.507999999999996
- type: mrr_at_1000
value: 63.53
- type: mrr_at_3
value: 60.909
- type: mrr_at_5
value: 62.172000000000004
- type: ndcg_at_1
value: 53.73
- type: ndcg_at_10
value: 64.97
- type: ndcg_at_100
value: 68.394
- type: ndcg_at_1000
value: 69.255
- type: ndcg_at_3
value: 60.228
- type: ndcg_at_5
value: 62.617999999999995
- type: precision_at_1
value: 53.73
- type: precision_at_10
value: 10.056
- type: precision_at_100
value: 1.265
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 26.332
- type: precision_at_5
value: 17.743000000000002
- type: recall_at_1
value: 47.278
- type: recall_at_10
value: 76.86500000000001
- type: recall_at_100
value: 91.582
- type: recall_at_1000
value: 97.583
- type: recall_at_3
value: 64.443
- type: recall_at_5
value: 70.283
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackGisRetrieval
type: mteb/cqadupstack-gis
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 29.702
- type: map_at_10
value: 39.463
- type: map_at_100
value: 40.508
- type: map_at_1000
value: 40.579
- type: map_at_3
value: 36.748999999999995
- type: map_at_5
value: 38.296
- type: mrr_at_1
value: 31.977
- type: mrr_at_10
value: 41.739
- type: mrr_at_100
value: 42.586
- type: mrr_at_1000
value: 42.636
- type: mrr_at_3
value: 39.096
- type: mrr_at_5
value: 40.695
- type: ndcg_at_1
value: 31.977
- type: ndcg_at_10
value: 44.855000000000004
- type: ndcg_at_100
value: 49.712
- type: ndcg_at_1000
value: 51.443000000000005
- type: ndcg_at_3
value: 39.585
- type: ndcg_at_5
value: 42.244
- type: precision_at_1
value: 31.977
- type: precision_at_10
value: 6.768000000000001
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 16.761
- type: precision_at_5
value: 11.593
- type: recall_at_1
value: 29.702
- type: recall_at_10
value: 59.082
- type: recall_at_100
value: 80.92
- type: recall_at_1000
value: 93.728
- type: recall_at_3
value: 45.212
- type: recall_at_5
value: 51.449
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackMathematicaRetrieval
type: mteb/cqadupstack-mathematica
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 21.336
- type: map_at_10
value: 30.137999999999998
- type: map_at_100
value: 31.385
- type: map_at_1000
value: 31.495
- type: map_at_3
value: 27.481
- type: map_at_5
value: 28.772
- type: mrr_at_1
value: 25.871
- type: mrr_at_10
value: 34.686
- type: mrr_at_100
value: 35.649
- type: mrr_at_1000
value: 35.705
- type: mrr_at_3
value: 32.09
- type: mrr_at_5
value: 33.52
- type: ndcg_at_1
value: 25.871
- type: ndcg_at_10
value: 35.617
- type: ndcg_at_100
value: 41.272999999999996
- type: ndcg_at_1000
value: 43.725
- type: ndcg_at_3
value: 30.653999999999996
- type: ndcg_at_5
value: 32.714
- type: precision_at_1
value: 25.871
- type: precision_at_10
value: 6.4799999999999995
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 14.469000000000001
- type: precision_at_5
value: 10.274
- type: recall_at_1
value: 21.336
- type: recall_at_10
value: 47.746
- type: recall_at_100
value: 71.773
- type: recall_at_1000
value: 89.05199999999999
- type: recall_at_3
value: 34.172999999999995
- type: recall_at_5
value: 39.397999999999996
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackPhysicsRetrieval
type: mteb/cqadupstack-physics
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 34.424
- type: map_at_10
value: 45.647999999999996
- type: map_at_100
value: 46.907
- type: map_at_1000
value: 47.010999999999996
- type: map_at_3
value: 42.427
- type: map_at_5
value: 44.285000000000004
- type: mrr_at_1
value: 41.867
- type: mrr_at_10
value: 51.17699999999999
- type: mrr_at_100
value: 51.937
- type: mrr_at_1000
value: 51.975
- type: mrr_at_3
value: 48.941
- type: mrr_at_5
value: 50.322
- type: ndcg_at_1
value: 41.867
- type: ndcg_at_10
value: 51.534
- type: ndcg_at_100
value: 56.696999999999996
- type: ndcg_at_1000
value: 58.475
- type: ndcg_at_3
value: 46.835
- type: ndcg_at_5
value: 49.161
- type: precision_at_1
value: 41.867
- type: precision_at_10
value: 9.134
- type: precision_at_100
value: 1.362
- type: precision_at_1000
value: 0.17099999999999999
- type: precision_at_3
value: 22.073
- type: precision_at_5
value: 15.495999999999999
- type: recall_at_1
value: 34.424
- type: recall_at_10
value: 63.237
- type: recall_at_100
value: 84.774
- type: recall_at_1000
value: 95.987
- type: recall_at_3
value: 49.888
- type: recall_at_5
value: 55.940999999999995
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackProgrammersRetrieval
type: mteb/cqadupstack-programmers
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 30.72
- type: map_at_10
value: 41.327999999999996
- type: map_at_100
value: 42.651
- type: map_at_1000
value: 42.739
- type: map_at_3
value: 38.223
- type: map_at_5
value: 40.053
- type: mrr_at_1
value: 37.9
- type: mrr_at_10
value: 46.857
- type: mrr_at_100
value: 47.673
- type: mrr_at_1000
value: 47.711999999999996
- type: mrr_at_3
value: 44.292
- type: mrr_at_5
value: 45.845
- type: ndcg_at_1
value: 37.9
- type: ndcg_at_10
value: 47.105999999999995
- type: ndcg_at_100
value: 52.56999999999999
- type: ndcg_at_1000
value: 54.37800000000001
- type: ndcg_at_3
value: 42.282
- type: ndcg_at_5
value: 44.646
- type: precision_at_1
value: 37.9
- type: precision_at_10
value: 8.368
- type: precision_at_100
value: 1.283
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 20.015
- type: precision_at_5
value: 14.132
- type: recall_at_1
value: 30.72
- type: recall_at_10
value: 58.826
- type: recall_at_100
value: 82.104
- type: recall_at_1000
value: 94.194
- type: recall_at_3
value: 44.962999999999994
- type: recall_at_5
value: 51.426
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 31.656583333333334
- type: map_at_10
value: 41.59883333333333
- type: map_at_100
value: 42.80350000000001
- type: map_at_1000
value: 42.91075
- type: map_at_3
value: 38.68908333333333
- type: map_at_5
value: 40.27733333333334
- type: mrr_at_1
value: 37.23483333333334
- type: mrr_at_10
value: 45.782000000000004
- type: mrr_at_100
value: 46.577083333333334
- type: mrr_at_1000
value: 46.62516666666667
- type: mrr_at_3
value: 43.480666666666664
- type: mrr_at_5
value: 44.79833333333333
- type: ndcg_at_1
value: 37.23483333333334
- type: ndcg_at_10
value: 46.971500000000006
- type: ndcg_at_100
value: 51.90125
- type: ndcg_at_1000
value: 53.86366666666667
- type: ndcg_at_3
value: 42.31791666666667
- type: ndcg_at_5
value: 44.458666666666666
- type: precision_at_1
value: 37.23483333333334
- type: precision_at_10
value: 8.044583333333332
- type: precision_at_100
value: 1.2334166666666666
- type: precision_at_1000
value: 0.15925
- type: precision_at_3
value: 19.240833333333327
- type: precision_at_5
value: 13.435083333333333
- type: recall_at_1
value: 31.656583333333334
- type: recall_at_10
value: 58.44758333333333
- type: recall_at_100
value: 79.93658333333332
- type: recall_at_1000
value: 93.32491666666668
- type: recall_at_3
value: 45.44266666666667
- type: recall_at_5
value: 50.99866666666666
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackStatsRetrieval
type: mteb/cqadupstack-stats
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 28.247
- type: map_at_10
value: 35.443999999999996
- type: map_at_100
value: 36.578
- type: map_at_1000
value: 36.675999999999995
- type: map_at_3
value: 33.276
- type: map_at_5
value: 34.536
- type: mrr_at_1
value: 31.747999999999998
- type: mrr_at_10
value: 38.413000000000004
- type: mrr_at_100
value: 39.327
- type: mrr_at_1000
value: 39.389
- type: mrr_at_3
value: 36.401
- type: mrr_at_5
value: 37.543
- type: ndcg_at_1
value: 31.747999999999998
- type: ndcg_at_10
value: 39.646
- type: ndcg_at_100
value: 44.861000000000004
- type: ndcg_at_1000
value: 47.197
- type: ndcg_at_3
value: 35.764
- type: ndcg_at_5
value: 37.635999999999996
- type: precision_at_1
value: 31.747999999999998
- type: precision_at_10
value: 6.12
- type: precision_at_100
value: 0.942
- type: precision_at_1000
value: 0.123
- type: precision_at_3
value: 15.235000000000001
- type: precision_at_5
value: 10.491
- type: recall_at_1
value: 28.247
- type: recall_at_10
value: 49.456
- type: recall_at_100
value: 73.02499999999999
- type: recall_at_1000
value: 89.898
- type: recall_at_3
value: 38.653999999999996
- type: recall_at_5
value: 43.259
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackTexRetrieval
type: mteb/cqadupstack-tex
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 22.45
- type: map_at_10
value: 30.476999999999997
- type: map_at_100
value: 31.630999999999997
- type: map_at_1000
value: 31.755
- type: map_at_3
value: 27.989000000000004
- type: map_at_5
value: 29.410999999999998
- type: mrr_at_1
value: 26.979
- type: mrr_at_10
value: 34.316
- type: mrr_at_100
value: 35.272999999999996
- type: mrr_at_1000
value: 35.342
- type: mrr_at_3
value: 32.14
- type: mrr_at_5
value: 33.405
- type: ndcg_at_1
value: 26.979
- type: ndcg_at_10
value: 35.166
- type: ndcg_at_100
value: 40.583000000000006
- type: ndcg_at_1000
value: 43.282
- type: ndcg_at_3
value: 30.916
- type: ndcg_at_5
value: 32.973
- type: precision_at_1
value: 26.979
- type: precision_at_10
value: 6.132
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 14.360999999999999
- type: precision_at_5
value: 10.227
- type: recall_at_1
value: 22.45
- type: recall_at_10
value: 45.348
- type: recall_at_100
value: 69.484
- type: recall_at_1000
value: 88.628
- type: recall_at_3
value: 33.338
- type: recall_at_5
value: 38.746
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackUnixRetrieval
type: mteb/cqadupstack-unix
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 32.123000000000005
- type: map_at_10
value: 41.778
- type: map_at_100
value: 42.911
- type: map_at_1000
value: 42.994
- type: map_at_3
value: 38.558
- type: map_at_5
value: 40.318
- type: mrr_at_1
value: 37.687
- type: mrr_at_10
value: 45.889
- type: mrr_at_100
value: 46.672999999999995
- type: mrr_at_1000
value: 46.72
- type: mrr_at_3
value: 43.33
- type: mrr_at_5
value: 44.734
- type: ndcg_at_1
value: 37.687
- type: ndcg_at_10
value: 47.258
- type: ndcg_at_100
value: 52.331
- type: ndcg_at_1000
value: 54.152
- type: ndcg_at_3
value: 41.857
- type: ndcg_at_5
value: 44.283
- type: precision_at_1
value: 37.687
- type: precision_at_10
value: 7.892
- type: precision_at_100
value: 1.183
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 18.781
- type: precision_at_5
value: 13.134
- type: recall_at_1
value: 32.123000000000005
- type: recall_at_10
value: 59.760000000000005
- type: recall_at_100
value: 81.652
- type: recall_at_1000
value: 94.401
- type: recall_at_3
value: 44.996
- type: recall_at_5
value: 51.184
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWebmastersRetrieval
type: mteb/cqadupstack-webmasters
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 33.196999999999996
- type: map_at_10
value: 42.012
- type: map_at_100
value: 43.663999999999994
- type: map_at_1000
value: 43.883
- type: map_at_3
value: 39.33
- type: map_at_5
value: 40.586
- type: mrr_at_1
value: 39.328
- type: mrr_at_10
value: 46.57
- type: mrr_at_100
value: 47.508
- type: mrr_at_1000
value: 47.558
- type: mrr_at_3
value: 44.532
- type: mrr_at_5
value: 45.58
- type: ndcg_at_1
value: 39.328
- type: ndcg_at_10
value: 47.337
- type: ndcg_at_100
value: 52.989
- type: ndcg_at_1000
value: 55.224
- type: ndcg_at_3
value: 43.362
- type: ndcg_at_5
value: 44.866
- type: precision_at_1
value: 39.328
- type: precision_at_10
value: 8.577
- type: precision_at_100
value: 1.5789999999999997
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 19.697
- type: precision_at_5
value: 13.755
- type: recall_at_1
value: 33.196999999999996
- type: recall_at_10
value: 56.635000000000005
- type: recall_at_100
value: 81.882
- type: recall_at_1000
value: 95.342
- type: recall_at_3
value: 44.969
- type: recall_at_5
value: 49.266
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackWordpressRetrieval
type: mteb/cqadupstack-wordpress
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 26.901000000000003
- type: map_at_10
value: 35.77
- type: map_at_100
value: 36.638999999999996
- type: map_at_1000
value: 36.741
- type: map_at_3
value: 33.219
- type: map_at_5
value: 34.574
- type: mrr_at_1
value: 29.205
- type: mrr_at_10
value: 37.848
- type: mrr_at_100
value: 38.613
- type: mrr_at_1000
value: 38.682
- type: mrr_at_3
value: 35.551
- type: mrr_at_5
value: 36.808
- type: ndcg_at_1
value: 29.205
- type: ndcg_at_10
value: 40.589
- type: ndcg_at_100
value: 45.171
- type: ndcg_at_1000
value: 47.602
- type: ndcg_at_3
value: 35.760999999999996
- type: ndcg_at_5
value: 37.980000000000004
- type: precision_at_1
value: 29.205
- type: precision_at_10
value: 6.192
- type: precision_at_100
value: 0.922
- type: precision_at_1000
value: 0.123
- type: precision_at_3
value: 15.034
- type: precision_at_5
value: 10.424999999999999
- type: recall_at_1
value: 26.901000000000003
- type: recall_at_10
value: 53.236000000000004
- type: recall_at_100
value: 74.809
- type: recall_at_1000
value: 92.884
- type: recall_at_3
value: 40.314
- type: recall_at_5
value: 45.617999999999995
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.794999999999998
- type: map_at_10
value: 29.322
- type: map_at_100
value: 31.463
- type: map_at_1000
value: 31.643
- type: map_at_3
value: 24.517
- type: map_at_5
value: 27.237000000000002
- type: mrr_at_1
value: 37.655
- type: mrr_at_10
value: 50.952
- type: mrr_at_100
value: 51.581999999999994
- type: mrr_at_1000
value: 51.61
- type: mrr_at_3
value: 47.991
- type: mrr_at_5
value: 49.744
- type: ndcg_at_1
value: 37.655
- type: ndcg_at_10
value: 39.328
- type: ndcg_at_100
value: 46.358
- type: ndcg_at_1000
value: 49.245
- type: ndcg_at_3
value: 33.052
- type: ndcg_at_5
value: 35.407
- type: precision_at_1
value: 37.655
- type: precision_at_10
value: 12.202
- type: precision_at_100
value: 1.9789999999999999
- type: precision_at_1000
value: 0.252
- type: precision_at_3
value: 24.973
- type: precision_at_5
value: 19.075
- type: recall_at_1
value: 16.794999999999998
- type: recall_at_10
value: 45.716
- type: recall_at_100
value: 68.919
- type: recall_at_1000
value: 84.71600000000001
- type: recall_at_3
value: 30.135
- type: recall_at_5
value: 37.141999999999996
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.817
- type: map_at_10
value: 22.058
- type: map_at_100
value: 31.805
- type: map_at_1000
value: 33.562999999999995
- type: map_at_3
value: 15.537
- type: map_at_5
value: 18.199
- type: mrr_at_1
value: 72.75
- type: mrr_at_10
value: 79.804
- type: mrr_at_100
value: 80.089
- type: mrr_at_1000
value: 80.09100000000001
- type: mrr_at_3
value: 78.75
- type: mrr_at_5
value: 79.325
- type: ndcg_at_1
value: 59.875
- type: ndcg_at_10
value: 45.972
- type: ndcg_at_100
value: 51.092999999999996
- type: ndcg_at_1000
value: 58.048
- type: ndcg_at_3
value: 50.552
- type: ndcg_at_5
value: 47.672
- type: precision_at_1
value: 72.75
- type: precision_at_10
value: 37.05
- type: precision_at_100
value: 12.005
- type: precision_at_1000
value: 2.221
- type: precision_at_3
value: 54.083000000000006
- type: precision_at_5
value: 46.2
- type: recall_at_1
value: 9.817
- type: recall_at_10
value: 27.877000000000002
- type: recall_at_100
value: 57.974000000000004
- type: recall_at_1000
value: 80.085
- type: recall_at_3
value: 16.911
- type: recall_at_5
value: 20.689
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.464999999999996
- type: f1
value: 42.759588662873796
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 75.82900000000001
- type: map_at_10
value: 84.613
- type: map_at_100
value: 84.845
- type: map_at_1000
value: 84.855
- type: map_at_3
value: 83.498
- type: map_at_5
value: 84.29299999999999
- type: mrr_at_1
value: 81.69800000000001
- type: mrr_at_10
value: 88.84100000000001
- type: mrr_at_100
value: 88.887
- type: mrr_at_1000
value: 88.888
- type: mrr_at_3
value: 88.179
- type: mrr_at_5
value: 88.69200000000001
- type: ndcg_at_1
value: 81.69800000000001
- type: ndcg_at_10
value: 88.21799999999999
- type: ndcg_at_100
value: 88.961
- type: ndcg_at_1000
value: 89.131
- type: ndcg_at_3
value: 86.591
- type: ndcg_at_5
value: 87.666
- type: precision_at_1
value: 81.69800000000001
- type: precision_at_10
value: 10.615
- type: precision_at_100
value: 1.125
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.208
- type: precision_at_5
value: 20.681
- type: recall_at_1
value: 75.82900000000001
- type: recall_at_10
value: 94.97
- type: recall_at_100
value: 97.786
- type: recall_at_1000
value: 98.809
- type: recall_at_3
value: 90.625
- type: recall_at_5
value: 93.345
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 22.788
- type: map_at_10
value: 36.71
- type: map_at_100
value: 38.527
- type: map_at_1000
value: 38.701
- type: map_at_3
value: 32.318999999999996
- type: map_at_5
value: 34.809
- type: mrr_at_1
value: 44.444
- type: mrr_at_10
value: 52.868
- type: mrr_at_100
value: 53.52400000000001
- type: mrr_at_1000
value: 53.559999999999995
- type: mrr_at_3
value: 50.153999999999996
- type: mrr_at_5
value: 51.651
- type: ndcg_at_1
value: 44.444
- type: ndcg_at_10
value: 44.707
- type: ndcg_at_100
value: 51.174
- type: ndcg_at_1000
value: 53.996
- type: ndcg_at_3
value: 40.855999999999995
- type: ndcg_at_5
value: 42.113
- type: precision_at_1
value: 44.444
- type: precision_at_10
value: 12.021999999999998
- type: precision_at_100
value: 1.8950000000000002
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 26.8
- type: precision_at_5
value: 19.66
- type: recall_at_1
value: 22.788
- type: recall_at_10
value: 51.793
- type: recall_at_100
value: 75.69500000000001
- type: recall_at_1000
value: 92.292
- type: recall_at_3
value: 37.375
- type: recall_at_5
value: 43.682
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 41.276
- type: map_at_10
value: 67.245
- type: map_at_100
value: 68.061
- type: map_at_1000
value: 68.11399999999999
- type: map_at_3
value: 63.693
- type: map_at_5
value: 65.90899999999999
- type: mrr_at_1
value: 82.552
- type: mrr_at_10
value: 87.741
- type: mrr_at_100
value: 87.868
- type: mrr_at_1000
value: 87.871
- type: mrr_at_3
value: 86.98599999999999
- type: mrr_at_5
value: 87.469
- type: ndcg_at_1
value: 82.552
- type: ndcg_at_10
value: 75.176
- type: ndcg_at_100
value: 77.902
- type: ndcg_at_1000
value: 78.852
- type: ndcg_at_3
value: 70.30499999999999
- type: ndcg_at_5
value: 73.00999999999999
- type: precision_at_1
value: 82.552
- type: precision_at_10
value: 15.765
- type: precision_at_100
value: 1.788
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 45.375
- type: precision_at_5
value: 29.360999999999997
- type: recall_at_1
value: 41.276
- type: recall_at_10
value: 78.825
- type: recall_at_100
value: 89.41900000000001
- type: recall_at_1000
value: 95.625
- type: recall_at_3
value: 68.062
- type: recall_at_5
value: 73.40299999999999
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 72.876
- type: ap
value: 67.15477852410164
- type: f1
value: 72.65147370025373
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 21.748
- type: map_at_10
value: 34.626000000000005
- type: map_at_100
value: 35.813
- type: map_at_1000
value: 35.859
- type: map_at_3
value: 30.753000000000004
- type: map_at_5
value: 33.049
- type: mrr_at_1
value: 22.35
- type: mrr_at_10
value: 35.23
- type: mrr_at_100
value: 36.359
- type: mrr_at_1000
value: 36.399
- type: mrr_at_3
value: 31.436999999999998
- type: mrr_at_5
value: 33.687
- type: ndcg_at_1
value: 22.364
- type: ndcg_at_10
value: 41.677
- type: ndcg_at_100
value: 47.355999999999995
- type: ndcg_at_1000
value: 48.494
- type: ndcg_at_3
value: 33.85
- type: ndcg_at_5
value: 37.942
- type: precision_at_1
value: 22.364
- type: precision_at_10
value: 6.6000000000000005
- type: precision_at_100
value: 0.9450000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.527000000000001
- type: precision_at_5
value: 10.796999999999999
- type: recall_at_1
value: 21.748
- type: recall_at_10
value: 63.292
- type: recall_at_100
value: 89.427
- type: recall_at_1000
value: 98.13499999999999
- type: recall_at_3
value: 42.126000000000005
- type: recall_at_5
value: 51.968
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.62425900592795
- type: f1
value: 92.08497761553683
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 64.51436388508893
- type: f1
value: 45.884016531912906
- task:
type: Classification
dataset:
name: MTEB MasakhaNEWSClassification (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy
value: 76.57172995780591
- type: f1
value: 75.52979910878491
- task:
type: Clustering
dataset:
name: MTEB MasakhaNEWSClusteringP2P (eng)
type: masakhane/masakhanews
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure
value: 44.84052695201612
- type: v_measure
value: 21.443971229936494
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 65.79354404841965
- type: f1
value: 63.17260074126185
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 71.09616677874916
- type: f1
value: 69.74285784421075
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 31.474709231086184
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 28.93630367824217
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 29.08234393834005
- type: mrr
value: 29.740466971605432
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.2059999999999995
- type: map_at_10
value: 14.442
- type: map_at_100
value: 18.005
- type: map_at_1000
value: 19.488
- type: map_at_3
value: 10.666
- type: map_at_5
value: 12.45
- type: mrr_at_1
value: 47.678
- type: mrr_at_10
value: 57.519
- type: mrr_at_100
value: 58.13700000000001
- type: mrr_at_1000
value: 58.167
- type: mrr_at_3
value: 55.779
- type: mrr_at_5
value: 56.940000000000005
- type: ndcg_at_1
value: 45.82
- type: ndcg_at_10
value: 37.651
- type: ndcg_at_100
value: 34.001999999999995
- type: ndcg_at_1000
value: 42.626
- type: ndcg_at_3
value: 43.961
- type: ndcg_at_5
value: 41.461
- type: precision_at_1
value: 47.678
- type: precision_at_10
value: 27.584999999999997
- type: precision_at_100
value: 8.455
- type: precision_at_1000
value: 2.118
- type: precision_at_3
value: 41.692
- type: precision_at_5
value: 36.161
- type: recall_at_1
value: 6.2059999999999995
- type: recall_at_10
value: 18.599
- type: recall_at_100
value: 33.608
- type: recall_at_1000
value: 65.429
- type: recall_at_3
value: 12.126000000000001
- type: recall_at_5
value: 14.902000000000001
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 39.117000000000004
- type: map_at_10
value: 55.535000000000004
- type: map_at_100
value: 56.32899999999999
- type: map_at_1000
value: 56.34400000000001
- type: map_at_3
value: 51.439
- type: map_at_5
value: 53.89699999999999
- type: mrr_at_1
value: 43.714
- type: mrr_at_10
value: 58.05200000000001
- type: mrr_at_100
value: 58.582
- type: mrr_at_1000
value: 58.592
- type: mrr_at_3
value: 54.896
- type: mrr_at_5
value: 56.874
- type: ndcg_at_1
value: 43.685
- type: ndcg_at_10
value: 63.108
- type: ndcg_at_100
value: 66.231
- type: ndcg_at_1000
value: 66.583
- type: ndcg_at_3
value: 55.659000000000006
- type: ndcg_at_5
value: 59.681
- type: precision_at_1
value: 43.685
- type: precision_at_10
value: 9.962
- type: precision_at_100
value: 1.174
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 24.961
- type: precision_at_5
value: 17.352
- type: recall_at_1
value: 39.117000000000004
- type: recall_at_10
value: 83.408
- type: recall_at_100
value: 96.553
- type: recall_at_1000
value: 99.136
- type: recall_at_3
value: 64.364
- type: recall_at_5
value: 73.573
- task:
type: Classification
dataset:
name: MTEB NewsClassification
type: ag_news
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy
value: 78.87763157894737
- type: f1
value: 78.69611753876177
- task:
type: PairClassification
dataset:
name: MTEB OpusparcusPC (en)
type: GEM/opusparcus
config: en
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy
value: 99.89816700610999
- type: cos_sim_ap
value: 100
- type: cos_sim_f1
value: 99.9490575649516
- type: cos_sim_precision
value: 100
- type: cos_sim_recall
value: 99.89816700610999
- type: dot_accuracy
value: 99.89816700610999
- type: dot_ap
value: 100
- type: dot_f1
value: 99.9490575649516
- type: dot_precision
value: 100
- type: dot_recall
value: 99.89816700610999
- type: euclidean_accuracy
value: 99.89816700610999
- type: euclidean_ap
value: 100
- type: euclidean_f1
value: 99.9490575649516
- type: euclidean_precision
value: 100
- type: euclidean_recall
value: 99.89816700610999
- type: manhattan_accuracy
value: 99.89816700610999
- type: manhattan_ap
value: 100
- type: manhattan_f1
value: 99.9490575649516
- type: manhattan_precision
value: 100
- type: manhattan_recall
value: 99.89816700610999
- type: max_accuracy
value: 99.89816700610999
- type: max_ap
value: 100
- type: max_f1
value: 99.9490575649516
- task:
type: PairClassification
dataset:
name: MTEB PawsX (en)
type: paws-x
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy
value: 62
- type: cos_sim_ap
value: 62.26837791655737
- type: cos_sim_f1
value: 62.607449856733524
- type: cos_sim_precision
value: 46.36604774535809
- type: cos_sim_recall
value: 96.36163175303197
- type: dot_accuracy
value: 62
- type: dot_ap
value: 62.26736459439965
- type: dot_f1
value: 62.607449856733524
- type: dot_precision
value: 46.36604774535809
- type: dot_recall
value: 96.36163175303197
- type: euclidean_accuracy
value: 62
- type: euclidean_ap
value: 62.26826112548132
- type: euclidean_f1
value: 62.607449856733524
- type: euclidean_precision
value: 46.36604774535809
- type: euclidean_recall
value: 96.36163175303197
- type: manhattan_accuracy
value: 62
- type: manhattan_ap
value: 62.26223761507973
- type: manhattan_f1
value: 62.585034013605444
- type: manhattan_precision
value: 46.34146341463415
- type: manhattan_recall
value: 96.36163175303197
- type: max_accuracy
value: 62
- type: max_ap
value: 62.26837791655737
- type: max_f1
value: 62.607449856733524
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 69.90899999999999
- type: map_at_10
value: 83.56700000000001
- type: map_at_100
value: 84.19200000000001
- type: map_at_1000
value: 84.212
- type: map_at_3
value: 80.658
- type: map_at_5
value: 82.473
- type: mrr_at_1
value: 80.4
- type: mrr_at_10
value: 86.699
- type: mrr_at_100
value: 86.798
- type: mrr_at_1000
value: 86.80099999999999
- type: mrr_at_3
value: 85.677
- type: mrr_at_5
value: 86.354
- type: ndcg_at_1
value: 80.43
- type: ndcg_at_10
value: 87.41
- type: ndcg_at_100
value: 88.653
- type: ndcg_at_1000
value: 88.81599999999999
- type: ndcg_at_3
value: 84.516
- type: ndcg_at_5
value: 86.068
- type: precision_at_1
value: 80.43
- type: precision_at_10
value: 13.234000000000002
- type: precision_at_100
value: 1.513
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.93
- type: precision_at_5
value: 24.26
- type: recall_at_1
value: 69.90899999999999
- type: recall_at_10
value: 94.687
- type: recall_at_100
value: 98.96000000000001
- type: recall_at_1000
value: 99.79599999999999
- type: recall_at_3
value: 86.25699999999999
- type: recall_at_5
value: 90.70700000000001
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 46.02256865360266
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 62.43157528757563
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.093
- type: map_at_10
value: 12.982
- type: map_at_100
value: 15.031
- type: map_at_1000
value: 15.334
- type: map_at_3
value: 9.339
- type: map_at_5
value: 11.183
- type: mrr_at_1
value: 25.1
- type: mrr_at_10
value: 36.257
- type: mrr_at_100
value: 37.351
- type: mrr_at_1000
value: 37.409
- type: mrr_at_3
value: 33.050000000000004
- type: mrr_at_5
value: 35.205
- type: ndcg_at_1
value: 25.1
- type: ndcg_at_10
value: 21.361
- type: ndcg_at_100
value: 29.396
- type: ndcg_at_1000
value: 34.849999999999994
- type: ndcg_at_3
value: 20.704
- type: ndcg_at_5
value: 18.086
- type: precision_at_1
value: 25.1
- type: precision_at_10
value: 10.94
- type: precision_at_100
value: 2.257
- type: precision_at_1000
value: 0.358
- type: precision_at_3
value: 19.467000000000002
- type: precision_at_5
value: 15.98
- type: recall_at_1
value: 5.093
- type: recall_at_10
value: 22.177
- type: recall_at_100
value: 45.842
- type: recall_at_1000
value: 72.598
- type: recall_at_3
value: 11.833
- type: recall_at_5
value: 16.173000000000002
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 73.56535226754596
- type: cos_sim_spearman
value: 69.32425977603488
- type: euclidean_pearson
value: 71.32425703470898
- type: euclidean_spearman
value: 69.32425217267013
- type: manhattan_pearson
value: 71.25897281394246
- type: manhattan_spearman
value: 69.27132577049578
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 69.66387868726018
- type: cos_sim_spearman
value: 67.85470749045027
- type: euclidean_pearson
value: 66.62075098063795
- type: euclidean_spearman
value: 67.85470749045027
- type: manhattan_pearson
value: 66.61455061901262
- type: manhattan_spearman
value: 67.87229618498695
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 75.65731331392575
- type: cos_sim_spearman
value: 77.48991626780108
- type: euclidean_pearson
value: 77.19884738623692
- type: euclidean_spearman
value: 77.48985836619045
- type: manhattan_pearson
value: 77.0656684243772
- type: manhattan_spearman
value: 77.30289226582691
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 69.37003253666457
- type: cos_sim_spearman
value: 69.77157648098141
- type: euclidean_pearson
value: 69.39543876030432
- type: euclidean_spearman
value: 69.77157648098141
- type: manhattan_pearson
value: 69.29901600459745
- type: manhattan_spearman
value: 69.65074167527128
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 78.56777256540136
- type: cos_sim_spearman
value: 80.16458787843023
- type: euclidean_pearson
value: 80.16475730686916
- type: euclidean_spearman
value: 80.16458787843023
- type: manhattan_pearson
value: 80.12814463670401
- type: manhattan_spearman
value: 80.1357907984809
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 76.09572350919031
- type: cos_sim_spearman
value: 77.94490233429326
- type: euclidean_pearson
value: 78.36595251203524
- type: euclidean_spearman
value: 77.94490233429326
- type: manhattan_pearson
value: 78.41538768125166
- type: manhattan_spearman
value: 78.01244379569542
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 80.7843552187951
- type: cos_sim_spearman
value: 82.28085055047386
- type: euclidean_pearson
value: 82.37373672515267
- type: euclidean_spearman
value: 82.28085055047386
- type: manhattan_pearson
value: 82.39387241346917
- type: manhattan_spearman
value: 82.36503339515906
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 68.29963929962095
- type: cos_sim_spearman
value: 67.96868942546051
- type: euclidean_pearson
value: 68.93524903869285
- type: euclidean_spearman
value: 67.96868942546051
- type: manhattan_pearson
value: 68.79144468444811
- type: manhattan_spearman
value: 67.69311483884324
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 72.84789696700685
- type: cos_sim_spearman
value: 75.67875747588545
- type: euclidean_pearson
value: 75.07752300463038
- type: euclidean_spearman
value: 75.67875747588545
- type: manhattan_pearson
value: 74.97934248140928
- type: manhattan_spearman
value: 75.62525644178724
- task:
type: STS
dataset:
name: MTEB STSBenchmarkMultilingualSTS (en)
type: PhilipMay/stsb_multi_mt
config: en
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson
value: 72.84789702519309
- type: cos_sim_spearman
value: 75.67875747588545
- type: euclidean_pearson
value: 75.07752310061133
- type: euclidean_spearman
value: 75.67875747588545
- type: manhattan_pearson
value: 74.97934257159595
- type: manhattan_spearman
value: 75.62525644178724
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 81.55557720431086
- type: mrr
value: 94.91178665198272
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 59.260999999999996
- type: map_at_10
value: 69.36099999999999
- type: map_at_100
value: 69.868
- type: map_at_1000
value: 69.877
- type: map_at_3
value: 66.617
- type: map_at_5
value: 68.061
- type: mrr_at_1
value: 62.333000000000006
- type: mrr_at_10
value: 70.533
- type: mrr_at_100
value: 70.966
- type: mrr_at_1000
value: 70.975
- type: mrr_at_3
value: 68.667
- type: mrr_at_5
value: 69.717
- type: ndcg_at_1
value: 62.333000000000006
- type: ndcg_at_10
value: 73.82300000000001
- type: ndcg_at_100
value: 76.122
- type: ndcg_at_1000
value: 76.374
- type: ndcg_at_3
value: 69.27499999999999
- type: ndcg_at_5
value: 71.33
- type: precision_at_1
value: 62.333000000000006
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 26.889000000000003
- type: precision_at_5
value: 17.599999999999998
- type: recall_at_1
value: 59.260999999999996
- type: recall_at_10
value: 86.2
- type: recall_at_100
value: 96.667
- type: recall_at_1000
value: 98.667
- type: recall_at_3
value: 74.006
- type: recall_at_5
value: 79.167
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.81881188118813
- type: cos_sim_ap
value: 95.20169041096409
- type: cos_sim_f1
value: 90.76224129227664
- type: cos_sim_precision
value: 91.64118246687055
- type: cos_sim_recall
value: 89.9
- type: dot_accuracy
value: 99.81881188118813
- type: dot_ap
value: 95.20169041096409
- type: dot_f1
value: 90.76224129227664
- type: dot_precision
value: 91.64118246687055
- type: dot_recall
value: 89.9
- type: euclidean_accuracy
value: 99.81881188118813
- type: euclidean_ap
value: 95.2016904109641
- type: euclidean_f1
value: 90.76224129227664
- type: euclidean_precision
value: 91.64118246687055
- type: euclidean_recall
value: 89.9
- type: manhattan_accuracy
value: 99.81881188118813
- type: manhattan_ap
value: 95.22680188132777
- type: manhattan_f1
value: 90.79013588324108
- type: manhattan_precision
value: 91.38804457953394
- type: manhattan_recall
value: 90.2
- type: max_accuracy
value: 99.81881188118813
- type: max_ap
value: 95.22680188132777
- type: max_f1
value: 90.79013588324108
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 57.8638628701308
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 37.82028248106046
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.870860210170946
- type: mrr
value: 51.608084521687466
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.60384207444685
- type: cos_sim_spearman
value: 30.84047452209471
- type: dot_pearson
value: 31.60384104417333
- type: dot_spearman
value: 30.84047452209471
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.246
- type: map_at_10
value: 2.051
- type: map_at_100
value: 13.129
- type: map_at_1000
value: 31.56
- type: map_at_3
value: 0.681
- type: map_at_5
value: 1.105
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 97
- type: mrr_at_100
value: 97
- type: mrr_at_1000
value: 97
- type: mrr_at_3
value: 97
- type: mrr_at_5
value: 97
- type: ndcg_at_1
value: 87
- type: ndcg_at_10
value: 80.716
- type: ndcg_at_100
value: 63.83
- type: ndcg_at_1000
value: 56.215
- type: ndcg_at_3
value: 84.531
- type: ndcg_at_5
value: 84.777
- type: precision_at_1
value: 94
- type: precision_at_10
value: 84.6
- type: precision_at_100
value: 66.03999999999999
- type: precision_at_1000
value: 24.878
- type: precision_at_3
value: 88.667
- type: precision_at_5
value: 89.60000000000001
- type: recall_at_1
value: 0.246
- type: recall_at_10
value: 2.2079999999999997
- type: recall_at_100
value: 15.895999999999999
- type: recall_at_1000
value: 52.683
- type: recall_at_3
value: 0.7040000000000001
- type: recall_at_5
value: 1.163
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.852
- type: map_at_10
value: 14.316
- type: map_at_100
value: 20.982
- type: map_at_1000
value: 22.58
- type: map_at_3
value: 7.767
- type: map_at_5
value: 10.321
- type: mrr_at_1
value: 51.019999999999996
- type: mrr_at_10
value: 66.365
- type: mrr_at_100
value: 66.522
- type: mrr_at_1000
value: 66.522
- type: mrr_at_3
value: 62.925
- type: mrr_at_5
value: 64.762
- type: ndcg_at_1
value: 46.939
- type: ndcg_at_10
value: 34.516999999999996
- type: ndcg_at_100
value: 44.25
- type: ndcg_at_1000
value: 54.899
- type: ndcg_at_3
value: 40.203
- type: ndcg_at_5
value: 37.004
- type: precision_at_1
value: 51.019999999999996
- type: precision_at_10
value: 29.796
- type: precision_at_100
value: 8.633000000000001
- type: precision_at_1000
value: 1.584
- type: precision_at_3
value: 40.816
- type: precision_at_5
value: 35.918
- type: recall_at_1
value: 3.852
- type: recall_at_10
value: 20.891000000000002
- type: recall_at_100
value: 52.428
- type: recall_at_1000
value: 84.34899999999999
- type: recall_at_3
value: 8.834
- type: recall_at_5
value: 12.909
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 64.7092
- type: ap
value: 11.972915012305819
- type: f1
value: 49.91050149892115
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.737408036219584
- type: f1
value: 57.07235266246011
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 35.9147539025798
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 82.52369315133814
- type: cos_sim_ap
value: 62.34858091376534
- type: cos_sim_f1
value: 58.18225190839694
- type: cos_sim_precision
value: 53.09098824553766
- type: cos_sim_recall
value: 64.35356200527704
- type: dot_accuracy
value: 82.52369315133814
- type: dot_ap
value: 62.34857753814992
- type: dot_f1
value: 58.18225190839694
- type: dot_precision
value: 53.09098824553766
- type: dot_recall
value: 64.35356200527704
- type: euclidean_accuracy
value: 82.52369315133814
- type: euclidean_ap
value: 62.34857756663386
- type: euclidean_f1
value: 58.18225190839694
- type: euclidean_precision
value: 53.09098824553766
- type: euclidean_recall
value: 64.35356200527704
- type: manhattan_accuracy
value: 82.49389044525243
- type: manhattan_ap
value: 62.32245347238179
- type: manhattan_f1
value: 58.206309819213054
- type: manhattan_precision
value: 52.70704044511021
- type: manhattan_recall
value: 64.9868073878628
- type: max_accuracy
value: 82.52369315133814
- type: max_ap
value: 62.34858091376534
- type: max_f1
value: 58.206309819213054
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.34555827220863
- type: cos_sim_ap
value: 84.84152481680071
- type: cos_sim_f1
value: 76.860456739428
- type: cos_sim_precision
value: 72.21470150263978
- type: cos_sim_recall
value: 82.14505697566985
- type: dot_accuracy
value: 88.34555827220863
- type: dot_ap
value: 84.84152743322608
- type: dot_f1
value: 76.860456739428
- type: dot_precision
value: 72.21470150263978
- type: dot_recall
value: 82.14505697566985
- type: euclidean_accuracy
value: 88.34555827220863
- type: euclidean_ap
value: 84.84152589453169
- type: euclidean_f1
value: 76.860456739428
- type: euclidean_precision
value: 72.21470150263978
- type: euclidean_recall
value: 82.14505697566985
- type: manhattan_accuracy
value: 88.38242713548337
- type: manhattan_ap
value: 84.8112124970968
- type: manhattan_f1
value: 76.83599206057487
- type: manhattan_precision
value: 73.51244900829934
- type: manhattan_recall
value: 80.47428395441946
- type: max_accuracy
value: 88.38242713548337
- type: max_ap
value: 84.84152743322608
- type: max_f1
value: 76.860456739428
- task:
type: Clustering
dataset:
name: MTEB WikiCitiesClustering
type: jinaai/cities_wiki_clustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure
value: 85.5314389263015
---
<h1 align="center">Snowflake's Arctic-embed-l</h1>
<h4 align="center">
<p>
<a href=#news>News</a> |
<a href=#models>Models</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#contact">Contact</a> |
<a href="#faq">FAQ</a>
<a href="#license">License</a> |
<a href="#acknowledgement">Acknowledgement</a>
<p>
</h4>
## News
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!
07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.
07/18/2024: Release of `snowflake-arctic-embed-m-v1.5`, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the [launch post on the Snowflake engineering blog](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/).
05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374)
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
## Models
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
The `snowflake-arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found [here](https://arxiv.org/abs/2405.05374).
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 |
Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| Google-gecko-text-embedding | 55.7 |
| text-embedding-3-large | 55.44 |
| Cohere-embed-english-v3.0 | 55.00 |
| bge-large-en-v1.5 | 54.29 |
### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs)
This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------- | -------------------------------- |
| [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 |
| GIST-all-MiniLM-L6-v2 | 45.12 |
| gte-tiny | 44.92 |
| all-MiniLM-L6-v2 | 41.95 |
| bge-micro-v2 | 42.56 |
### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s)
Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 |
| bge-small-en-v1.5 | 51.68 |
| Cohere-embed-english-light-v3.0 | 51.34 |
| text-embedding-3-small | 51.08 |
| e5-small-v2 | 49.04 |
### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/)
Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 |
| bge-base-en-v1.5 | 53.25 |
| nomic-embed-text-v1.5 | 53.25 |
| GIST-Embedding-v0 | 52.31 |
| gte-base | 52.31 |
### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/)
Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 |
| nomic-embed-text-v1.5 | 53.01 |
| nomic-embed-text-v1 | 52.81 |
### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/)
Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
| ------------------------------------------------------------------ | -------------------------------- |
| [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 |
| UAE-Large-V1 | 54.66 |
| bge-large-en-v1.5 | 54.29 |
| mxbai-embed-large-v1 | 54.39 |
| e5-Large-v2 | 50.56 |
## Usage
### Using Sentence Transformers
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-l")
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = query_embeddings @ document_embeddings.T
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
# Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
```
Query: what is snowflake?
0.28976774 The Data Cloud!
0.19071159 Mexico City of Course!
Query: Where can I get the best tacos?
0.38650584 Mexico City of Course!
0.25145516 The Data Cloud!
```
### Using Huggingface transformers
You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
```python
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-l')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-l', add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
queries = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# Compute token embeddings
with torch.no_grad():
query_embeddings = model(**query_tokens)[0][:, 0]
document_embeddings = model(**document_tokens)[0][:, 0]
# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
doc_score_pairs = list(zip(documents, query_scores))
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for document, score in doc_score_pairs:
print(score, document)
```
### Using Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running:
```bash
npm i @xenova/transformers
```
You can then use the model to compute embeddings as follows:
```js
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-l', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.25145517380846977, 0.3865060421197194]
```
## Using Infinity
OpenAI compatible API deployment with [Infinity](https://github.com/michaelfeil/infinity) and Docker.
```bash
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.70 \
v2 --model-id Snowflake/snowflake-arctic-embed-l --dtype float16 --batch-size 32 --engine torch --port 7997
```
## FAQ
TBD
## Contact
Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).
## License
Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
## Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=d6741f66-9018-401c-8805-d79c74fb98ff" /> | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
aaditya/Llama3-OpenBioLLM-70B | aaditya | text-generation | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-3",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"distillation",
"heathcare",
"medical",
"clinical",
"med",
"lifescience",
"Pharmaceutical",
"Pharma",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2303.13375",
"arxiv:2212.13138",
"arxiv:2305.09617",
"arxiv:2402.07023",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-70B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-04-24T02:21:12 | 2025-01-18T07:39:15 | 26,028 | 403 | ---
base_model: meta-llama/Meta-Llama-3-70B-Instruct
language:
- en
license: llama3
tags:
- llama-3
- llama
- Mixtral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- distillation
- heathcare
- medical
- clinical
- med
- lifescience
- Pharmaceutical
- Pharma
widget:
- example_title: OpenBioLLM-70B
messages:
- role: system
content: You are an expert and experienced from the healthcare and biomedical
domain with extensive medical knowledge and practical experience.
- role: user
content: How long does it take for newborn jaundice to go away?
output:
text: 'Newborn jaundice, also known as neonatal jaundice, is a common condition
in newborns where the yellowing of the skin and eyes occurs due to an elevated
level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when
red blood cells break down. In most cases, newborn jaundice resolves on its
own without any specific treatment.
The duration of newborn jaundice can vary depending on several factors such
as the underlying cause, gestational age at birth, and individual variations
in bilirubin metabolism. Here are some general guidelines:
1. Physiological jaundice: This is the most common type of newborn jaundice
and usually appears within 24-72 hours after birth. It tends to peak between
the second and fifth day of life and gradually improves over the next week or
two. By the time the baby is one week old, the jaundice should have mostly resolved.
2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and
may appear later than physiological jaundice, typically between the fifth and
fourteenth day of life. It tends to persist for a longer duration but usually
resolves within six weeks after birth. 3. Pathological jaundice: This type of
jaundice is less common and occurs due to an underlying medical condition that
affects bilirubin metabolism or liver function. The duration of pathological
jaundice depends on the specific cause and may require treatment.
It''s important for parents to monitor their newborn''s jaundice closely and
seek medical advice if the jaundice progresses rapidly, becomes severe, or is
accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness.
In these cases, further evaluation and management may be necessary. Remember
that each baby is unique, and the timing of jaundice resolution can vary. If
you have concerns about your newborn''s jaundice, it''s always best to consult
with a healthcare professional for personalized advice and guidance.'
model-index:
- name: OpenBioLLM-70B
results: []
---
<div align="center">
<img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div>
<div align="center">
<h1>Advancing Open-source Large Language Models in Medical Domain</h1>
</div>
<p align="center" style="margin-top: 0px;">
<a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">Online Demo</span>
</a> |
<a href="https://github.com/openlifescience-ai">
<img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style=" margin-right: 5px;">GitHub</span>
</a> |
<a href="#">
<img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text" style="margin-right: 5px;">Paper</span>
</a> |
<a href="https://discord.gg/A5Fjf5zC69">
<img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/>
<span class="link-text">Discord</span>
</a>
</p>
Introducing OpenBioLLM-70B: A State-of-the-Art Open Source Biomedical Large Language Model
OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks.
🏥 **Biomedical Specialization**: OpenBioLLM-70B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency.
🎓 **Superior Performance**: With 70 billion parameters, OpenBioLLM-70B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 on biomedical benchmarks.
🧠 **Advanced Training Techniques**: OpenBioLLM-70B builds upon the powerful foundations of the **Meta-Llama-3-70B-Instruct** and [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include:
<div align="center">
<img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png">
</div>
- **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290)
- **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated)
This combination of cutting-edge techniques enables OpenBioLLM-70B to align with key capabilities and preferences for biomedical applications.
⚙️ **Release Details**:
- **Model Size**: 70 billion parameters
- **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-70B-GGUF)
- **Language(s) (NLP):** en
- **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs
- **License:** Meta-Llama License
- **Fine-tuned from models:** [Meta-Llama-3-70B-Instruct](meta-llama/Meta-Llama-3-70B-Instruct)
- **Resources for more information:**
- Paper: Coming soon
The model can be fine-tuned for more specialized tasks and datasets as needed.
OpenBioLLM-70B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences.
We are excited to share OpenBioLLM-70B with researchers and developers around the world.
### Community & Resources
#### 🔥 Your Daily Dose of Medical AI Breakthroughs 🚀
We turn hours of the latest research papers into minutes. Get daily tweets and news on the latest medical AI breakthroughs, dataset releases, and benchmark results – all carefully curated to save you time while keeping you informed.
<div align="center">
<table>
<tr>
<td align="center">
<a href="https://twitter.com/OpenLifeSciAI">
<img src="https://img.shields.io/badge/X-Follow%20%40OpenLifeSciAI-black?style=flat&logo=x" alt="Twitter Follow"/>
<br>
Daily updates on Medical LLMs,<br>datasets & benchmarks
</a>
</td>
<td align="center">
<a href="https://www.linkedin.com/company/openlifesciai/">
<img src="https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn"/>
<br>
Daily news on Medical LLMs,<br>datasets & benchmarks
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://www.youtube.com/@OpenlifesciAI">
<img src="https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube" alt="YouTube"/>
<br>
Video & audio summaries of<br>latest research
</a>
</td>
<td align="center">
<a href="https://t.co/l5z6y6C4cM">
<img src="https://img.shields.io/badge/Discord-Join-7289DA?style=for-the-badge&logo=discord" alt="Discord"/>
<br>
Connect with researchers &<br>discuss latest developments
</a>
</td>
</tr>
</table>
</div>
### Use with transformers
**Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.**
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "aaditya/OpenBioLLM-Llama3-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."},
{"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.0,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## **Training procedure**
### **Training hyperparameters**
<details>
<summary>Click to see details</summary>
- learning_rate: 0.0002
- lr_scheduler: cosine
- train_batch_size: 12
- eval_batch_size: 8
- GPU: H100 80GB SXM5
- num_devices: 8
- optimizer: adamw_bnb_8bit
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
</details>
### **Peft hyperparameters**
<details>
<summary>Click to see details</summary>
- adapter: qlora
- lora_r: 128
- lora_alpha: 256
- lora_dropout: 0.05
- lora_target_linear: true
-lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
</details>
### **Training results**
### **Framework versions**
- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
- Axolotl
- Lm harness for evaluation
# Benchmark Results
🔥 OpenBioLLM-70B demonstrates superior performance compared to larger models, such as GPT-4, Gemini, Meditron-70B, Med-PaLM-1 & Med-PaLM-2 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 86.06%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge.
🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy.
| | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg |
|--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|
| **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** |
| Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 |
| **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 |
| Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 |
| **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 |
| Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 |
| GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 |
| Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 |
| gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 |
| Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 |
| Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 |
| MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 |
| BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 |
| AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 |
| ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 |
<div align="center">
<img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png">
</div>
## Detailed Medical Subjectwise accuracy
# Use Cases & Examples
🚨 **Below results are from the quantized version of OpenBioLLM-70B
# Summarize Clinical Notes
OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries
# Answer Medical Questions
OpenBioLLM-70B can provide answers to a wide range of medical questions.
<details>
<summary>Click to see details</summary>
</details>
# Clinical Entity Recognition
OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research.
# Biomarkers Extraction
# Classification
OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization
# De-Identification
OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA.
**Advisory Notice!**
While OpenBioLLM-70B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments.
Therefore, we strongly advise against using OpenBioLLM-70B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations.
OpenBioLLM-70B is intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor.
Appropriately adapting and validating OpenBioLLM-70B for specific medical use cases would require significant additional work, potentially including:
- Thorough testing and evaluation in relevant clinical scenarios
- Alignment with evidence-based guidelines and best practices
- Mitigation of potential biases and failure modes
- Integration with human oversight and interpretation
- Compliance with regulatory and ethical standards
Always consult a qualified healthcare provider for personal medical needs.
# Citation
If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows:
```
@misc{OpenBioLLMs,
author = {Ankit Pal, Malaikannan Sankarasubbu},
title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}}
}
```
The accompanying paper is currently in progress and will be released soon.
<div align="center">
<h2> 💌 Contact </h2>
</div>
We look forward to hearing you and collaborating on this exciting project!
**Contributors:**
- [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com]
- Saama AI Labs
- Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset.
# References
We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models!
Result sources
- [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375)
- [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138)
- [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617)
- [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023) | [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] |
BAAI/bge-en-icl | BAAI | feature-extraction | [
"sentence-transformers",
"safetensors",
"mistral",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"arxiv:2409.15700",
"arxiv:2309.07597",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-25T09:37:34 | 2025-01-15T02:43:23 | 24,875 | 126 | ---
license: apache-2.0
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-en-icl
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 93.1492537313433
- type: ap
value: 72.56132559564212
- type: f1
value: 89.71796898040243
- type: main_score
value: 93.1492537313433
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 96.98372499999999
- type: ap
value: 95.62303091773919
- type: f1
value: 96.98308191715637
- type: main_score
value: 96.98372499999999
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 61.461999999999996
- type: f1
value: 60.57257766583118
- type: main_score
value: 61.461999999999996
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: mteb/arguana
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: main_score
value: 83.07967801208441
- type: ndcg_at_1
value: 66.50071123755335
- type: ndcg_at_3
value: 80.10869593172173
- type: ndcg_at_5
value: 81.89670542467924
- type: ndcg_at_10
value: 83.07967801208441
- type: ndcg_at_100
value: 83.5991349601075
- type: ndcg_at_1000
value: 83.5991349601075
- type: map_at_1
value: 66.50071123755335
- type: map_at_3
value: 76.83736367946898
- type: map_at_5
value: 77.8473210052158
- type: map_at_10
value: 78.35472690735851
- type: map_at_100
value: 78.47388207611678
- type: map_at_1000
value: 78.47388207611678
- type: precision_at_1
value: 66.50071123755335
- type: precision_at_3
value: 29.848269321953076
- type: precision_at_5
value: 18.762446657183045
- type: precision_at_10
value: 9.736842105262909
- type: precision_at_100
value: 0.9964438122332677
- type: precision_at_1000
value: 0.09964438122332549
- type: recall_at_1
value: 66.50071123755335
- type: recall_at_3
value: 89.5448079658606
- type: recall_at_5
value: 93.8122332859175
- type: recall_at_10
value: 97.36842105263158
- type: recall_at_100
value: 99.6443812233286
- type: recall_at_1000
value: 99.6443812233286
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: main_score
value: 54.43859683357485
- type: v_measure
value: 54.43859683357485
- type: v_measure_std
value: 14.511128158596337
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: main_score
value: 49.33365996236564
- type: v_measure
value: 49.33365996236564
- type: v_measure_std
value: 14.61261944856548
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: main_score
value: 65.15263966490278
- type: map
value: 65.15263966490278
- type: mrr
value: 77.90331090885107
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: main_score
value: 86.47365710792691
- type: cosine_spearman
value: 86.47365710792691
- type: spearman
value: 86.47365710792691
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 91.48701298701299
- type: f1
value: 91.4733869423637
- type: main_score
value: 91.48701298701299
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: main_score
value: 53.050461108038036
- type: v_measure
value: 53.050461108038036
- type: v_measure_std
value: 0.9436104839012786
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: main_score
value: 48.38215568371151
- type: v_measure
value: 48.38215568371151
- type: v_measure_std
value: 0.9104384504649026
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: mteb/cqadupstack
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: main_score
value: 47.308084499970704
- type: ndcg_at_1
value: 36.038578730542476
- type: ndcg_at_3
value: 41.931365356453036
- type: ndcg_at_5
value: 44.479015523894994
- type: ndcg_at_10
value: 47.308084499970704
- type: ndcg_at_100
value: 52.498062430513606
- type: ndcg_at_1000
value: 54.2908789514719
- type: map_at_1
value: 30.38821701528966
- type: map_at_3
value: 37.974871761903636
- type: map_at_5
value: 39.85399878507757
- type: map_at_10
value: 41.31456611036795
- type: map_at_100
value: 42.62907836655835
- type: map_at_1000
value: 42.737235870659845
- type: precision_at_1
value: 36.038578730542476
- type: precision_at_3
value: 19.39960180094633
- type: precision_at_5
value: 13.79264655952497
- type: precision_at_10
value: 8.399223517333388
- type: precision_at_100
value: 1.2992373779520896
- type: precision_at_1000
value: 0.16327170951909567
- type: recall_at_1
value: 30.38821701528966
- type: recall_at_3
value: 45.51645512564165
- type: recall_at_5
value: 52.06077167834868
- type: recall_at_10
value: 60.38864106788279
- type: recall_at_100
value: 82.76968509918343
- type: recall_at_1000
value: 94.84170217080344
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: mteb/climate-fever
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: main_score
value: 45.4272998284769
- type: ndcg_at_1
value: 44.36482084690554
- type: ndcg_at_3
value: 38.13005747178844
- type: ndcg_at_5
value: 40.83474510717123
- type: ndcg_at_10
value: 45.4272998284769
- type: ndcg_at_100
value: 52.880220707479516
- type: ndcg_at_1000
value: 55.364753427333
- type: map_at_1
value: 19.200868621064064
- type: map_at_3
value: 28.33785740137525
- type: map_at_5
value: 31.67162504524064
- type: map_at_10
value: 34.417673164090075
- type: map_at_100
value: 36.744753097028976
- type: map_at_1000
value: 36.91262189016135
- type: precision_at_1
value: 44.36482084690554
- type: precision_at_3
value: 29.14223669923975
- type: precision_at_5
value: 22.410423452768388
- type: precision_at_10
value: 14.293159609120309
- type: precision_at_100
value: 2.248859934853431
- type: precision_at_1000
value: 0.2722475570032542
- type: recall_at_1
value: 19.200868621064064
- type: recall_at_3
value: 34.132464712269176
- type: recall_at_5
value: 42.35613463626491
- type: recall_at_10
value: 52.50814332247546
- type: recall_at_100
value: 77.16178067318128
- type: recall_at_1000
value: 90.59174809989138
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: mteb/dbpedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: main_score
value: 51.634197691802754
- type: ndcg_at_1
value: 64.375
- type: ndcg_at_3
value: 55.677549598242614
- type: ndcg_at_5
value: 53.44347199908503
- type: ndcg_at_10
value: 51.634197691802754
- type: ndcg_at_100
value: 56.202861267183415
- type: ndcg_at_1000
value: 63.146019108272576
- type: map_at_1
value: 9.789380503780919
- type: map_at_3
value: 16.146582195277016
- type: map_at_5
value: 19.469695222167193
- type: map_at_10
value: 24.163327344766145
- type: map_at_100
value: 35.47047690245571
- type: map_at_1000
value: 37.5147432331838
- type: precision_at_1
value: 76.25
- type: precision_at_3
value: 59.08333333333333
- type: precision_at_5
value: 52.24999999999997
- type: precision_at_10
value: 42.54999999999994
- type: precision_at_100
value: 13.460000000000008
- type: precision_at_1000
value: 2.4804999999999966
- type: recall_at_1
value: 9.789380503780919
- type: recall_at_3
value: 17.48487134027656
- type: recall_at_5
value: 22.312024269698806
- type: recall_at_10
value: 30.305380335237324
- type: recall_at_100
value: 62.172868946596424
- type: recall_at_1000
value: 85.32410301328747
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 93.36
- type: f1
value: 89.73665936982262
- type: main_score
value: 93.36
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: mteb/fever
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: main_score
value: 92.82809814626805
- type: ndcg_at_1
value: 88.98889888988899
- type: ndcg_at_3
value: 91.82404417747676
- type: ndcg_at_5
value: 92.41785792357787
- type: ndcg_at_10
value: 92.82809814626805
- type: ndcg_at_100
value: 93.31730867509245
- type: ndcg_at_1000
value: 93.45171203408582
- type: map_at_1
value: 82.64125817343636
- type: map_at_3
value: 89.39970782792554
- type: map_at_5
value: 89.96799501378695
- type: map_at_10
value: 90.27479706587437
- type: map_at_100
value: 90.45185655778057
- type: map_at_1000
value: 90.46130471574544
- type: precision_at_1
value: 88.98889888988899
- type: precision_at_3
value: 34.923492349234245
- type: precision_at_5
value: 21.524152415244043
- type: precision_at_10
value: 11.033603360337315
- type: precision_at_100
value: 1.1521152115211895
- type: precision_at_1000
value: 0.11765676567657675
- type: recall_at_1
value: 82.64125817343636
- type: recall_at_3
value: 94.35195900542428
- type: recall_at_5
value: 95.9071323799047
- type: recall_at_10
value: 97.04234113887586
- type: recall_at_100
value: 98.77282371094255
- type: recall_at_1000
value: 99.5555567461508
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: mteb/fiqa
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: main_score
value: 59.67151242793314
- type: ndcg_at_1
value: 57.407407407407405
- type: ndcg_at_3
value: 53.79975378289304
- type: ndcg_at_5
value: 56.453379423655406
- type: ndcg_at_10
value: 59.67151242793314
- type: ndcg_at_100
value: 65.34055762539253
- type: ndcg_at_1000
value: 67.07707746043032
- type: map_at_1
value: 30.65887045053714
- type: map_at_3
value: 44.09107110881799
- type: map_at_5
value: 48.18573748068346
- type: map_at_10
value: 51.03680979612876
- type: map_at_100
value: 53.03165194566928
- type: map_at_1000
value: 53.16191096190861
- type: precision_at_1
value: 57.407407407407405
- type: precision_at_3
value: 35.493827160493886
- type: precision_at_5
value: 26.913580246913547
- type: precision_at_10
value: 16.435185185185155
- type: precision_at_100
value: 2.2685185185184986
- type: precision_at_1000
value: 0.25864197530863964
- type: recall_at_1
value: 30.65887045053714
- type: recall_at_3
value: 48.936723427464194
- type: recall_at_5
value: 58.55942925387371
- type: recall_at_10
value: 68.45128551147073
- type: recall_at_100
value: 88.24599311867836
- type: recall_at_1000
value: 98.18121693121691
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: mteb/hotpotqa
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: main_score
value: 85.13780800141961
- type: ndcg_at_1
value: 89.9392302498312
- type: ndcg_at_3
value: 81.2061569376288
- type: ndcg_at_5
value: 83.53311592078133
- type: ndcg_at_10
value: 85.13780800141961
- type: ndcg_at_100
value: 87.02630661625386
- type: ndcg_at_1000
value: 87.47294723601075
- type: map_at_1
value: 44.9696151249156
- type: map_at_3
value: 76.46972766148966
- type: map_at_5
value: 78.47749268512187
- type: map_at_10
value: 79.49792611170005
- type: map_at_100
value: 80.09409086274644
- type: map_at_1000
value: 80.11950878917663
- type: precision_at_1
value: 89.9392302498312
- type: precision_at_3
value: 53.261309925724234
- type: precision_at_5
value: 33.79338284942924
- type: precision_at_10
value: 17.69750168805041
- type: precision_at_100
value: 1.9141120864280805
- type: precision_at_1000
value: 0.19721809588118133
- type: recall_at_1
value: 44.9696151249156
- type: recall_at_3
value: 79.8919648885888
- type: recall_at_5
value: 84.48345712356516
- type: recall_at_10
value: 88.48750844024308
- type: recall_at_100
value: 95.70560432140446
- type: recall_at_1000
value: 98.60904794058068
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 96.9144
- type: ap
value: 95.45276911068486
- type: f1
value: 96.91412729455966
- type: main_score
value: 96.9144
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: mteb/msmarco
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: main_score
value: 46.78865753107054
- type: ndcg_at_1
value: 26.63323782234957
- type: ndcg_at_3
value: 38.497585804985754
- type: ndcg_at_5
value: 42.72761631631636
- type: ndcg_at_10
value: 46.78865753107054
- type: ndcg_at_100
value: 51.96170786623209
- type: ndcg_at_1000
value: 52.82713901970963
- type: map_at_1
value: 25.89063992359121
- type: map_at_3
value: 35.299466730340654
- type: map_at_5
value: 37.68771887933786
- type: map_at_10
value: 39.40908074468253
- type: map_at_100
value: 40.53444082323405
- type: map_at_1000
value: 40.57183037649452
- type: precision_at_1
value: 26.63323782234957
- type: precision_at_3
value: 16.265520534861793
- type: precision_at_5
value: 11.902578796562304
- type: precision_at_10
value: 7.262177650430416
- type: precision_at_100
value: 0.9819484240687512
- type: precision_at_1000
value: 0.10571633237823287
- type: recall_at_1
value: 25.89063992359121
- type: recall_at_3
value: 46.99737344794652
- type: recall_at_5
value: 57.160936007640906
- type: recall_at_10
value: 69.43409742120343
- type: recall_at_100
value: 92.86413562559697
- type: recall_at_1000
value: 99.3230659025788
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 98.42225262197901
- type: f1
value: 98.31652547061115
- type: main_score
value: 98.42225262197901
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 94.00136798905609
- type: f1
value: 82.7022316533099
- type: main_score
value: 94.00136798905609
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 4672e20407010da34463acc759c162ca9734bca6
metrics:
- type: accuracy
value: 82.92535305985204
- type: f1
value: 79.885538231847
- type: main_score
value: 82.92535305985204
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (en)
type: mteb/amazon_massive_scenario
config: en
split: test
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
metrics:
- type: accuracy
value: 85.60188298587758
- type: f1
value: 84.87416963499224
- type: main_score
value: 85.60188298587758
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: main_score
value: 45.86171497327639
- type: v_measure
value: 45.86171497327639
- type: v_measure_std
value: 1.551347259003324
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: main_score
value: 44.33336692345644
- type: v_measure
value: 44.33336692345644
- type: v_measure_std
value: 1.5931408596404715
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
metrics:
- type: main_score
value: 30.597409734750503
- type: map
value: 30.597409734750503
- type: mrr
value: 31.397041548018457
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: mteb/nfcorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: main_score
value: 41.850870119787835
- type: ndcg_at_1
value: 52.47678018575851
- type: ndcg_at_3
value: 47.43993801247414
- type: ndcg_at_5
value: 45.08173173082719
- type: ndcg_at_10
value: 41.850870119787835
- type: ndcg_at_100
value: 37.79284946590978
- type: ndcg_at_1000
value: 46.58046062123418
- type: map_at_1
value: 6.892464464226138
- type: map_at_3
value: 12.113195798233127
- type: map_at_5
value: 13.968475602788812
- type: map_at_10
value: 16.47564069781326
- type: map_at_100
value: 20.671726065190025
- type: map_at_1000
value: 22.328875914012006
- type: precision_at_1
value: 53.86996904024768
- type: precision_at_3
value: 43.96284829721363
- type: precision_at_5
value: 38.69969040247682
- type: precision_at_10
value: 30.928792569659457
- type: precision_at_100
value: 9.507739938080498
- type: precision_at_1000
value: 2.25882352941176
- type: recall_at_1
value: 6.892464464226138
- type: recall_at_3
value: 13.708153358278407
- type: recall_at_5
value: 16.651919797359145
- type: recall_at_10
value: 21.01801714352559
- type: recall_at_100
value: 37.01672102843443
- type: recall_at_1000
value: 69.8307270724072
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: mteb/nq
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: main_score
value: 73.88350836507092
- type: ndcg_at_1
value: 57.0683661645423
- type: ndcg_at_3
value: 67.89935813080585
- type: ndcg_at_5
value: 71.47769719452941
- type: ndcg_at_10
value: 73.88350836507092
- type: ndcg_at_100
value: 75.76561068060907
- type: ndcg_at_1000
value: 75.92437662684215
- type: map_at_1
value: 51.00424874468904
- type: map_at_3
value: 63.87359984550011
- type: map_at_5
value: 66.23696407879494
- type: map_at_10
value: 67.42415446608673
- type: map_at_100
value: 67.92692839842621
- type: map_at_1000
value: 67.93437922640133
- type: precision_at_1
value: 57.0683661645423
- type: precision_at_3
value: 29.692931633836416
- type: precision_at_5
value: 20.046349942062854
- type: precision_at_10
value: 10.950173812283
- type: precision_at_100
value: 1.1995944380069687
- type: precision_at_1000
value: 0.12146581691772171
- type: recall_at_1
value: 51.00424874468904
- type: recall_at_3
value: 75.93665507918116
- type: recall_at_5
value: 83.95133256083433
- type: recall_at_10
value: 90.78794901506375
- type: recall_at_100
value: 98.61915797605253
- type: recall_at_1000
value: 99.7827346465817
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: mteb/quora
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: main_score
value: 90.95410848372035
- type: ndcg_at_1
value: 84.61999999999999
- type: ndcg_at_3
value: 88.57366734033212
- type: ndcg_at_5
value: 89.89804048972175
- type: ndcg_at_10
value: 90.95410848372035
- type: ndcg_at_100
value: 91.83227134455773
- type: ndcg_at_1000
value: 91.88368412611601
- type: map_at_1
value: 73.4670089207039
- type: map_at_3
value: 84.87862925508942
- type: map_at_5
value: 86.68002324701408
- type: map_at_10
value: 87.7165466015312
- type: map_at_100
value: 88.28718809614146
- type: map_at_1000
value: 88.29877148480672
- type: precision_at_1
value: 84.61999999999999
- type: precision_at_3
value: 38.82333333333838
- type: precision_at_5
value: 25.423999999998642
- type: precision_at_10
value: 13.787999999998583
- type: precision_at_100
value: 1.5442999999999767
- type: precision_at_1000
value: 0.15672999999997972
- type: recall_at_1
value: 73.4670089207039
- type: recall_at_3
value: 89.98389854832143
- type: recall_at_5
value: 93.88541046010576
- type: recall_at_10
value: 96.99779417520634
- type: recall_at_100
value: 99.80318763957743
- type: recall_at_1000
value: 99.99638888888889
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: main_score
value: 72.33008348681277
- type: v_measure
value: 72.33008348681277
- type: v_measure_std
value: 2.9203215463933008
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: main_score
value: 72.72079657828903
- type: v_measure
value: 72.72079657828903
- type: v_measure_std
value: 11.930271663428735
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: mteb/scidocs
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: main_score
value: 25.25865384510787
- type: ndcg_at_1
value: 28.7
- type: ndcg_at_3
value: 23.61736427940938
- type: ndcg_at_5
value: 20.845690325673885
- type: ndcg_at_10
value: 25.25865384510787
- type: ndcg_at_100
value: 36.18596641088721
- type: ndcg_at_1000
value: 41.7166868935345
- type: map_at_1
value: 5.828333333333361
- type: map_at_3
value: 10.689166666666676
- type: map_at_5
value: 13.069916666666668
- type: map_at_10
value: 15.4901164021164
- type: map_at_100
value: 18.61493245565425
- type: map_at_1000
value: 18.99943478016456
- type: precision_at_1
value: 28.7
- type: precision_at_3
value: 22.30000000000006
- type: precision_at_5
value: 18.55999999999997
- type: precision_at_10
value: 13.289999999999946
- type: precision_at_100
value: 2.905000000000005
- type: precision_at_1000
value: 0.4218999999999946
- type: recall_at_1
value: 5.828333333333361
- type: recall_at_3
value: 13.548333333333387
- type: recall_at_5
value: 18.778333333333308
- type: recall_at_10
value: 26.939999999999902
- type: recall_at_100
value: 58.91333333333344
- type: recall_at_1000
value: 85.57499999999972
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: main_score
value: 83.86733787791422
- type: cosine_spearman
value: 83.86733787791422
- type: spearman
value: 83.86733787791422
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: main_score
value: 78.14269330480724
- type: cosine_spearman
value: 78.14269330480724
- type: spearman
value: 78.14269330480724
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: main_score
value: 86.58640009300751
- type: cosine_spearman
value: 86.58640009300751
- type: spearman
value: 86.58640009300751
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: main_score
value: 82.8292579957437
- type: cosine_spearman
value: 82.8292579957437
- type: spearman
value: 82.8292579957437
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: main_score
value: 87.77203714228862
- type: cosine_spearman
value: 87.77203714228862
- type: spearman
value: 87.77203714228862
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: main_score
value: 87.0439304006969
- type: cosine_spearman
value: 87.0439304006969
- type: spearman
value: 87.0439304006969
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
metrics:
- type: main_score
value: 91.24736138013424
- type: cosine_spearman
value: 91.24736138013424
- type: spearman
value: 91.24736138013424
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
metrics:
- type: main_score
value: 70.07326214706
- type: cosine_spearman
value: 70.07326214706
- type: spearman
value: 70.07326214706
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: main_score
value: 88.42076443255168
- type: cosine_spearman
value: 88.42076443255168
- type: spearman
value: 88.42076443255168
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: main_score
value: 86.9584489124583
- type: map
value: 86.9584489124583
- type: mrr
value: 96.59475328592976
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: mteb/scifact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: main_score
value: 79.09159079425369
- type: ndcg_at_1
value: 66.0
- type: ndcg_at_3
value: 74.98853481223065
- type: ndcg_at_5
value: 77.29382051205019
- type: ndcg_at_10
value: 79.09159079425369
- type: ndcg_at_100
value: 80.29692802526776
- type: ndcg_at_1000
value: 80.55210036585547
- type: map_at_1
value: 62.994444444444454
- type: map_at_3
value: 71.7425925925926
- type: map_at_5
value: 73.6200925925926
- type: map_at_10
value: 74.50223544973547
- type: map_at_100
value: 74.82438594015447
- type: map_at_1000
value: 74.83420474892468
- type: precision_at_1
value: 66.0
- type: precision_at_3
value: 29.44444444444439
- type: precision_at_5
value: 19.40000000000008
- type: precision_at_10
value: 10.366666666666715
- type: precision_at_100
value: 1.0999999999999928
- type: precision_at_1000
value: 0.11200000000000007
- type: recall_at_1
value: 62.994444444444454
- type: recall_at_3
value: 80.89999999999998
- type: recall_at_5
value: 86.72777777777779
- type: recall_at_10
value: 91.88888888888887
- type: recall_at_100
value: 97.0
- type: recall_at_1000
value: 99.0
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: main_score
value: 97.26819027722253
- type: cos_sim_accuracy
value: 99.88019801980198
- type: cos_sim_accuracy_threshold
value: 76.67685151100159
- type: cos_sim_ap
value: 97.23260568085786
- type: cos_sim_f1
value: 93.91824526420737
- type: cos_sim_f1_threshold
value: 75.82710981369019
- type: cos_sim_precision
value: 93.63817097415506
- type: cos_sim_recall
value: 94.19999999999999
- type: dot_accuracy
value: 99.88019801980198
- type: dot_accuracy_threshold
value: 76.67686343193054
- type: dot_ap
value: 97.23260568085786
- type: dot_f1
value: 93.91824526420737
- type: dot_f1_threshold
value: 75.8271336555481
- type: dot_precision
value: 93.63817097415506
- type: dot_recall
value: 94.19999999999999
- type: euclidean_accuracy
value: 99.88019801980198
- type: euclidean_accuracy_threshold
value: 68.29807758331299
- type: euclidean_ap
value: 97.23259982599497
- type: euclidean_f1
value: 93.91824526420737
- type: euclidean_f1_threshold
value: 69.53110694885254
- type: euclidean_precision
value: 93.63817097415506
- type: euclidean_recall
value: 94.19999999999999
- type: manhattan_accuracy
value: 99.87821782178217
- type: manhattan_accuracy_threshold
value: 3482.6908111572266
- type: manhattan_ap
value: 97.26819027722253
- type: manhattan_f1
value: 93.92592592592592
- type: manhattan_f1_threshold
value: 3555.5641174316406
- type: manhattan_precision
value: 92.78048780487805
- type: manhattan_recall
value: 95.1
- type: max_accuracy
value: 99.88019801980198
- type: max_ap
value: 97.26819027722253
- type: max_f1
value: 93.92592592592592
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: main_score
value: 81.32419328350603
- type: v_measure
value: 81.32419328350603
- type: v_measure_std
value: 2.666861121694755
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: main_score
value: 46.048387963107565
- type: v_measure
value: 46.048387963107565
- type: v_measure_std
value: 1.4102848576321703
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: main_score
value: 56.70574900554072
- type: map
value: 56.70574900554072
- type: mrr
value: 57.517109116373824
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: main_score
value: 30.76932903185174
- type: cosine_spearman
value: 30.76932903185174
- type: spearman
value: 30.76932903185174
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: mteb/trec-covid
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: main_score
value: 79.07987651251462
- type: ndcg_at_1
value: 83.0
- type: ndcg_at_3
value: 79.86598407528447
- type: ndcg_at_5
value: 79.27684428714952
- type: ndcg_at_10
value: 79.07987651251462
- type: ndcg_at_100
value: 64.55029164391163
- type: ndcg_at_1000
value: 59.42333857860492
- type: map_at_1
value: 0.226053732680979
- type: map_at_3
value: 0.644034626013194
- type: map_at_5
value: 1.045196967937728
- type: map_at_10
value: 2.0197496659905085
- type: map_at_100
value: 13.316018005224159
- type: map_at_1000
value: 33.784766957424104
- type: precision_at_1
value: 88.0
- type: precision_at_3
value: 86.66666666666667
- type: precision_at_5
value: 85.20000000000002
- type: precision_at_10
value: 84.19999999999997
- type: precision_at_100
value: 67.88000000000001
- type: precision_at_1000
value: 26.573999999999998
- type: recall_at_1
value: 0.226053732680979
- type: recall_at_3
value: 0.6754273711472734
- type: recall_at_5
value: 1.1168649828059245
- type: recall_at_10
value: 2.2215081031265207
- type: recall_at_100
value: 16.694165236664727
- type: recall_at_1000
value: 56.7022214857503
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: mteb/touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: main_score
value: 30.47934263207554
- type: ndcg_at_1
value: 33.6734693877551
- type: ndcg_at_3
value: 34.36843900446739
- type: ndcg_at_5
value: 32.21323786731918
- type: ndcg_at_10
value: 30.47934263207554
- type: ndcg_at_100
value: 41.49598869753928
- type: ndcg_at_1000
value: 52.32963949183662
- type: map_at_1
value: 3.0159801678718168
- type: map_at_3
value: 7.13837927642557
- type: map_at_5
value: 9.274004610363466
- type: map_at_10
value: 12.957368366814324
- type: map_at_100
value: 19.3070585127604
- type: map_at_1000
value: 20.809777161133532
- type: precision_at_1
value: 34.69387755102041
- type: precision_at_3
value: 36.054421768707485
- type: precision_at_5
value: 32.24489795918368
- type: precision_at_10
value: 27.142857142857146
- type: precision_at_100
value: 8.326530612244898
- type: precision_at_1000
value: 1.5755102040816336
- type: recall_at_1
value: 3.0159801678718168
- type: recall_at_3
value: 8.321771388428257
- type: recall_at_5
value: 11.737532394366069
- type: recall_at_10
value: 19.49315139822179
- type: recall_at_100
value: 50.937064145519685
- type: recall_at_1000
value: 83.4358283484675
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 93.173828125
- type: ap
value: 46.040184641424396
- type: f1
value: 80.77280549412752
- type: main_score
value: 93.173828125
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 79.9320882852292
- type: f1
value: 80.22638685975485
- type: main_score
value: 79.9320882852292
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: main_score
value: 68.98152919711418
- type: v_measure
value: 68.98152919711418
- type: v_measure_std
value: 1.2519720970652428
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: main_score
value: 79.34189681158234
- type: cos_sim_accuracy
value: 87.68552184538356
- type: cos_sim_accuracy_threshold
value: 76.06316804885864
- type: cos_sim_ap
value: 79.34189149773933
- type: cos_sim_f1
value: 72.16386554621849
- type: cos_sim_f1_threshold
value: 73.62890243530273
- type: cos_sim_precision
value: 71.82435964453737
- type: cos_sim_recall
value: 72.5065963060686
- type: dot_accuracy
value: 87.68552184538356
- type: dot_accuracy_threshold
value: 76.06316208839417
- type: dot_ap
value: 79.34189231911259
- type: dot_f1
value: 72.16386554621849
- type: dot_f1_threshold
value: 73.62889647483826
- type: dot_precision
value: 71.82435964453737
- type: dot_recall
value: 72.5065963060686
- type: euclidean_accuracy
value: 87.68552184538356
- type: euclidean_accuracy_threshold
value: 69.19080018997192
- type: euclidean_ap
value: 79.34189681158234
- type: euclidean_f1
value: 72.16386554621849
- type: euclidean_f1_threshold
value: 72.62383103370667
- type: euclidean_precision
value: 71.82435964453737
- type: euclidean_recall
value: 72.5065963060686
- type: manhattan_accuracy
value: 87.661679680515
- type: manhattan_accuracy_threshold
value: 3408.807373046875
- type: manhattan_ap
value: 79.29617544165136
- type: manhattan_f1
value: 72.1957671957672
- type: manhattan_f1_threshold
value: 3597.7684020996094
- type: manhattan_precision
value: 72.38726790450929
- type: manhattan_recall
value: 72.00527704485488
- type: max_accuracy
value: 87.68552184538356
- type: max_ap
value: 79.34189681158234
- type: max_f1
value: 72.1957671957672
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: main_score
value: 87.8635519535718
- type: cos_sim_accuracy
value: 89.80672953778088
- type: cos_sim_accuracy_threshold
value: 73.09532165527344
- type: cos_sim_ap
value: 87.84251379545145
- type: cos_sim_f1
value: 80.25858884373845
- type: cos_sim_f1_threshold
value: 70.57080268859863
- type: cos_sim_precision
value: 77.14103110353643
- type: cos_sim_recall
value: 83.63874345549738
- type: dot_accuracy
value: 89.80672953778088
- type: dot_accuracy_threshold
value: 73.09532761573792
- type: dot_ap
value: 87.84251881260793
- type: dot_f1
value: 80.25858884373845
- type: dot_f1_threshold
value: 70.57079076766968
- type: dot_precision
value: 77.14103110353643
- type: dot_recall
value: 83.63874345549738
- type: euclidean_accuracy
value: 89.80672953778088
- type: euclidean_accuracy_threshold
value: 73.3548641204834
- type: euclidean_ap
value: 87.84251335039049
- type: euclidean_f1
value: 80.25858884373845
- type: euclidean_f1_threshold
value: 76.71923041343689
- type: euclidean_precision
value: 77.14103110353643
- type: euclidean_recall
value: 83.63874345549738
- type: manhattan_accuracy
value: 89.78150347343501
- type: manhattan_accuracy_threshold
value: 3702.7603149414062
- type: manhattan_ap
value: 87.8635519535718
- type: manhattan_f1
value: 80.27105660516332
- type: manhattan_f1_threshold
value: 3843.5962677001953
- type: manhattan_precision
value: 76.9361101306036
- type: manhattan_recall
value: 83.90822297505389
- type: max_accuracy
value: 89.80672953778088
- type: max_ap
value: 87.8635519535718
- type: max_f1
value: 80.27105660516332
---
<h1 align="center">FlagEmbedding</h1>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
**BGE-EN-ICL** primarily demonstrates the following capabilities:
- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.
- Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.
## 📑 Open-source Plan
- [x] Checkpoint
- [x] Training Data
- [x] Technical Report
- [ ] Evaluation Pipeline
The technical report for **BGE-EN-ICL** can be found in [Making Text Embedders Few-Shot Learners](https://arxiv.org/abs/2409.15700)
## Data List
| Data | Introduction |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
| [public-data](https://huggingface.co/datasets/cfli/bge-e5data) | Public data identical to [e5-mistral](https://huggingface.co/intfloat/e5-mistral-7b-instruct) |
| [full-data](https://huggingface.co/datasets/cfli/bge-full-data) | The full dataset we used for training |
## Usage
### Using FlagEmbedding
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
```python
from FlagEmbedding import FlagICLModel
queries = ["how much protein should a female eat", "summit define"]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
examples = [
{'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
'query': 'what is a virtual interface',
'response': "A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes."},
{'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
'query': 'causes of back pain in female for a week',
'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
]
model = FlagICLModel('BAAI/bge-en-icl',
query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
examples_for_task=examples, # set `examples_for_task=None` to use model without examples
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
By default, FlagICLModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'<instruct>{task_description}\n<query>{query}'
def get_detailed_example(task_description: str, query: str, response: str) -> str:
return f'<instruct>{task_description}\n<query>{query}\n<response>{response}'
def get_new_queries(queries, query_max_len, examples_prefix, tokenizer):
inputs = tokenizer(
queries,
max_length=query_max_len - len(tokenizer('<s>', add_special_tokens=False)['input_ids']) - len(
tokenizer('\n<response></s>', add_special_tokens=False)['input_ids']),
return_token_type_ids=False,
truncation=True,
return_tensors=None,
add_special_tokens=False
)
prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)['input_ids']
suffix_ids = tokenizer('\n<response>', add_special_tokens=False)['input_ids']
new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8
new_queries = tokenizer.batch_decode(inputs['input_ids'])
for i in range(len(new_queries)):
new_queries[i] = examples_prefix + new_queries[i] + '\n<response>'
return new_max_length, new_queries
task = 'Given a web search query, retrieve relevant passages that answer the query.'
examples = [
{'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
'query': 'what is a virtual interface',
'response': "A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes."},
{'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
'query': 'causes of back pain in female for a week',
'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
]
examples = [get_detailed_example(e['instruct'], e['query'], e['response']) for e in examples]
examples_prefix = '\n\n'.join(examples) + '\n\n' # if there not exists any examples, just set examples_prefix = ''
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
query_max_len, doc_max_len = 512, 512
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-en-icl')
model = AutoModel.from_pretrained('BAAI/bge-en-icl')
model.eval()
new_query_max_len, new_queries = get_new_queries(queries, query_max_len, examples_prefix, tokenizer)
query_batch_dict = tokenizer(new_queries, max_length=new_query_max_len, padding=True, truncation=True, return_tensors='pt')
doc_batch_dict = tokenizer(documents, max_length=doc_max_len, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
query_outputs = model(**query_batch_dict)
query_embeddings = last_token_pool(query_outputs.last_hidden_state, query_batch_dict['attention_mask'])
doc_outputs = model(**doc_batch_dict)
doc_embeddings = last_token_pool(doc_outputs.last_hidden_state, doc_batch_dict['attention_mask'])
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
scores = (query_embeddings @ doc_embeddings.T) * 100
print(scores.tolist())
```
## Evaluation
`bge-en-icl` achieve **state-of-the-art performance on both MTEB and AIR-Bench leaderboard!**
- **[MTEB](https://huggingface.co/spaces/mteb/leaderboard)**:
- **[BEIR](https://huggingface.co/spaces/mteb/leaderboard)**:
- **[AIR-Bench](https://huggingface.co/spaces/AIR-Bench/leaderboard)**:
**QA (en, nDCG@10):**
| AIR-Bench_24.04 | wiki | web | news | healthcare | law | finance | arxiv | msmarco | ALL (8) |
| :--------------------------: | :-------: | :-------: | :-------: | :--------: | :-------: | :-------: | :-------: | :-------: | :-------: |
| **e5-mistral-7b-instruct** | 61.67 | 44.41 | 48.18 | 56.32 | 19.32 | 54.79 | 44.78 | 59.03 | 48.56 |
| **SFR-Embedding-Mistral** | 63.46 | 51.27 | 52.21 | 58.76 | 23.27 | 56.94 | 47.75 | 58.99 | 51.58 |
| **NV-Embed-v1** | 62.84 | 50.42 | 51.46 | 58.53 | 20.65 | 49.89 | 46.10 | 60.27 | 50.02 |
| **Linq-Embed-Mistral** | 61.04 | 48.41 | 49.44 | **60.18** | 20.34 | 50.04 | 47.56 | 60.50 | 49.69 |
| **gte-Qwen2-7B-instruct** | 63.46 | 51.20 | 54.07 | 54.20 | 22.31 | **58.20** | 40.27 | 58.39 | 50.26 |
| **stella_en_1.5B_v5** | 61.99 | 50.88 | 53.87 | 58.81 | 23.22 | 57.26 | 44.81 | 61.38 | 51.53 |
| **bge-en-icl zero-shot** | 64.61 | 54.40 | 55.11 | 57.25 | 25.10 | 54.81 | 48.46 | 63.71 | 52.93 |
| **bge-en-icl few-shot** | **64.94** | **55.11** | **56.02** | 58.85 | **28.29** | 57.16 | **50.04** | **64.50** | **54.36** |
**Long-Doc (en, Recall@10):**
| AIR-Bench_24.04 | arxiv (4) | book (2) | healthcare (5) | law (4) | ALL (15) |
| :--------------------------: | :-------: | :-------: | :------------: | :-------: | :-------: |
| **text-embedding-3-large** | 74.53 | 73.16 | 65.83 | 64.47 | 68.77 |
| **e5-mistral-7b-instruct** | 72.14 | 72.44 | 68.44 | 62.92 | 68.49 |
| **SFR-Embedding-Mistral** | 72.79 | 72.41 | 67.94 | 64.83 | 69.00 |
| **NV-Embed-v1** | 77.65 | 75.49 | 72.38 | **69.55** | 73.45 |
| **Linq-Embed-Mistral** | 75.46 | 73.81 | 71.58 | 68.58 | 72.11 |
| **gte-Qwen2-7B-instruct** | 63.93 | 68.51 | 65.59 | 65.26 | 65.45 |
| **stella_en_1.5B_v5** | 73.17 | 74.38 | 70.02 | 69.32 | 71.25 |
| **bge-en-icl zero-shot** | 78.30 | 78.21 | 73.65 | 67.09 | 73.75 |
| **bge-en-icl few-shot** | **79.63** | **79.36** | **74.80** | 67.79 | **74.83** |
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:--------------------------------------------------------------------------|:-------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | - | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{li2024makingtextembeddersfewshot,
title={Making Text Embedders Few-Shot Learners},
author={Chaofan Li and MingHao Qin and Shitao Xiao and Jianlyu Chen and Kun Luo and Yingxia Shao and Defu Lian and Zheng Liu},
year={2024},
eprint={2409.15700},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2409.15700},
}
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). | [
"SUMMARIZATION"
] | [
"BIOSSES",
"SCIFACT"
] |
flowaicom/Flow-Judge-v0.1 | flowaicom | text-generation | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"lm-judge",
"evaluation",
"nlp",
"conversational",
"custom_code",
"en",
"dataset:flowaicom/Flow-Judge-v0.1-binary-heldout",
"dataset:flowaicom/Flow-Judge-v0.1-3-likert-heldout",
"dataset:flowaicom/Flow-Judge-v0.1-5-likert-heldout",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:finetune:microsoft/Phi-3.5-mini-instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-09-15T12:27:06 | 2024-10-07T13:13:54 | 23,285 | 58 | ---
base_model:
- microsoft/Phi-3.5-mini-instruct
datasets:
- flowaicom/Flow-Judge-v0.1-binary-heldout
- flowaicom/Flow-Judge-v0.1-3-likert-heldout
- flowaicom/Flow-Judge-v0.1-5-likert-heldout
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/flowaicom/Flow-Judge-v0.1/resolve/main/LICENSE
metrics:
- accuracy
- f1
- precision
- recall
- pearsonr
- spearmanr
- kendall-tau
pipeline_tag: text-generation
tags:
- lm-judge
- evaluation
- nlp
widget:
- messages:
- role: user
content: '# GOAL
Your job is to evaluate a task carried out by an AI system powered by a large
language model.
You will be provided with the inputs and output of the task, as well as the
evaluation criteria and scoring rubric. Your task is to evaluate the output
of the AI system based on the evaluation criteria and scoring rubric provided.
# INPUT/s
Below are the inputs required for performing the task:
<inputs>
<customer_query>
Subject: Issues with recent order #12345
Hello,
I recently received my order #12345, but I''m having a few problems:
1. The blue t-shirt I ordered is actually green. Can I exchange it?
2. The phone case doesn''t fit my iPhone 13 Pro Max. The website said it was
compatible.
3. I was charged for express shipping, but it took over a week to arrive. Can
I get a refund on the shipping cost?
Please let me know how we can resolve these issues. I''ve been a loyal customer
for years and this is the first time I''ve had problems like this.
Thank you,
Jane Smith
</customer_query>
</inputs>
# OUTPUT
Below is the output of the task:
<output>
Dear Jane Smith,
Thank you for reaching out about the issues with your recent order #12345. We
sincerely apologize for the inconvenience you''ve experienced. Let me address
each of your concerns:
1. T-shirt color discrepancy: We''re sorry about the mix-up with the t-shirt
color. We''d be happy to exchange it for the correct blue t-shirt. I''ll send
you a prepaid return label for the green shirt, and we''ll ship out the blue
one as soon as we receive the return.
2. Phone case compatibility: We apologize for the confusion regarding the phone
case compatibility. Our website information should have been more accurate.
We''ll gladly accept a return of the incompatible case and issue a full refund
for it. Additionally, I can recommend a case that will fit your iPhone 13 Pro
Max if you''re interested.
3. Shipping charge: You''re absolutely right about the shipping charge. Since
the delivery didn''t meet our express shipping standards, we will refund the
entire shipping cost to your original payment method. This refund should process
within 3-5 business days.
To initiate these resolutions, please reply to this email confirming that you''d
like to proceed with the t-shirt exchange and phone case return. Once confirmed,
I''ll send you the necessary return labels and process the shipping refund immediately.
We truly value your loyalty as a long-time customer and sincerely regret that
you''ve encountered these issues. Rest assured, we''re taking steps to prevent
similar problems in the future. As a gesture of goodwill, we''d like to offer
you a 20% discount on your next order.
If you have any further questions or concerns, please don''t hesitate to reach
out. We''re here to ensure your complete satisfaction.
Best regards,
Alex Johnson
Customer Service Representative
</output>
# EVALUATION CRITERIA AND SCORING RUBRIC
Here are the evaluation criteria and the rubric that you need to use for evaluating
the task:
<evaluation_criteria>
How well the response addresses the specific issues raised in the customer''s
query?
</evaluation_criteria>
<scoring_rubric>
- Score 1: The response completely fails to address the customer''s needs and
ignores the specific issues raised.
- Score 2: The response barely addresses the customer''s query and misses most
of the specific issues raised.
- Score 3: The response partially addresses the customer''s query, touching
on some of the specific issues but leaving others unaddressed.
- Score 4: The response adequately addresses most aspects of the customer''s
query and the specific issues raised.
- Score 5: The response fully and comprehensively addresses all aspects of the
customer''s query and all specific issues raised in a highly satisfactory manner.
</scoring_rubric>
# INSTRUCTIONS FOR THE EVALUATION
1. Understand the task and criteria: Familiarize yourself with the task to be
evaluated. Review the evaluation criteria and scoring rubric to understand the
different levels of performance and the descriptions for each score.
2. Review the inputs and output: Look at the inputs provided for the task. Examine
the output generated from completing the task.
3. Compare output to score descriptions: Compare the output against the criteria
and score descriptions in the scoring rubric. For each criterion,decide which
description best matches the output.
4. After comparing the output to the score descriptions, pay attention to the
small details that might impact the final score that you assign. Sometimes a
small difference can dictate the final score.
5. Write verbal feedback justifying your evaluation that includes a detailed
rationale, referring to specific aspects of the output and comparing them to
the rubric.
6. Assign a final score based on the scoring rubric.
## FORMAT FOR THE EVALUATION
- Write the verbal feedback inside <feedback> tags without any additional surrounding
text.
- Write the numeric score inside <score> tags, without any additional surrounding
text and always after the feedback.
Please accurately evaluate the task. Strictly adhere to the evaluation criteria
and rubric.'
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63368577d184e6b53c50e6d0/6kSJKgPh2pDh4tA-Ky0xW.png" alt="Centered image">
</p>
<p align="center">🚀 <a href="https://www.flow-ai.com/judge">Flow Judge</a> | 📄 <a href="https://www.flow-ai.com/blog/flow-judge">Technical report</a> | 💻 <a href="https://github.com/flowaicom/flow-judge">flow-judge</a></p>
## Model Summary
Flow-Judge-v0.1 is a compact yet powerful 3.8B model that offers customizable LLM system evaluations across various fields. The model inherits it's architecture from Phi-3.5-mini instruct model which enables Flow-Judge to deliver high-quality results while maintaining a small footprint. Despite its smaller size, it achieves performance comparable to larger models in both held-out and out-of-domain benchmarks. Flow-Judge-v0.1 supports multiple scoring scales, provides qualitative feedback, and generates structured evaluation outputs. Trained on a smaller synthetic dataset, it represents an efficient approach to AI development. Released under the Apache 2.0 license, Flow Judge is an open and accessible model suitable for developers and companies seeking cost-effective and rapid evaluations using custom rubrics.
__Quantized weights__
- [flowaicom/Flow-Judge-v0.1-AWQ](https://huggingface.co/flowaicom/Flow-Judge-v0.1-AWQ)
- [flowaicom/Flow-Judge-v0.1-GGUF](https://huggingface.co/flowaicom/Flow-Judge-v0.1-GGUF)
__Quickstart__
- [Quickstart](https://github.com/flowaicom/flow-judge/examples/1_quickstart.ipynb)
## Intended Use Case
Flow Judge is intended to be used on custom LLM system evaluation tasks.
- Customizable evaluations: Users can define their own evaluation criteria and rubrics, tailoring Flow Judge to their specific needs and requirements. This flexibility allows for the creation of highly targeted assessments that accurately measure performance of their LLM system
- Flow Judge supports three different scoring scales:
- Pass/fail: Suitable for binary assessments, such as determining whether a piece of text meets a specific standard or contains errors.
- 3-Likert: Allows for more granular evaluations, with scores ranging from negative to neutral to positive. Useful for assessing the overall quality or sentiment of a piece of text.
- 5-Likert: Provides an even more nuanced assessment, with scores ranging from strongly negative to strongly positive, enabling users to capture subtle differences in quality or sentiment.
- Easy to interpret results:
- Flow Judge produces structured evaluations with `<feedback>` and `<score>` tags.
- Qualitative feedback: Flow Judge detects errors and grades outputs and provides qualitative feedback that explains its reasoning for assigning a particular score from the rubric while highlighting problematic parts of the responses.
- Score: Based on a grading rubric Flow Judge will return a numerical score on binary, likert-3 or likert-5 scale.
## Training
### Model
Flow Judge is based on the Phi-3.5-mini architecture, and the base model checkpoint used is specifically its instruct version. The model uses the same tokenizer, supports MQA and Flash Attention 2, and has weights in bfloat16 precision. However, post-finetuning, the model's support for languages and long context lengths has not been fully tested. Due to specialized Supervised Fine-Tuning (SFT), Flow Judge might show different benchmark results and support a maximum context length of 8192, shorter than the base model's.
### Training Datasets
Flow-Judge-v0.1 has been trained on synthetically generated datasets. The construction of training datasets for Flow Judge involves a multi-step process:
1. Manually curating seed rubrics to serve as a foundation
2. Synthetically generating domain-adapted metrics and rubrics for various domains
3. Synthetically generating training instances with multiple inputs, such as user queries and contextual information
4. Employing a dual-evaluation strategy with consensus to ensure quality and consistency
This process creates a comprehensive and diverse set of training instances that enable accurate, domain-specific evaluations of LLM systems in generative AI products while minimizing human intervention.
Read more about the dataset construction from [here](https://www.flow-ai.com/blog/flow-judge#dataset-construction)
### Fine-tuning
For fine-tuning we used Axolotl's preprocessing to ensure input training data is consistent. We then conducted supervised fine-tuning based on microsoft/Phi-3.5-mini-instruct using RSLoRa. More detailed information about the fine-tuning process is provided in our [technical report](https://www.flow-ai.com/blog/flow-judge#fine-tuning).
## Usage
### Prompt format
#### Prompt template with inputs
```text
# GOAL
Your job is to evaluate a task carried out by an AI system powered by a large language model.
You will be provided with the inputs and output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.
# INPUT
Below are the inputs required for performing the task:
<inputs>
{INPUTS}
</inputs>
# OUTPUT
Below is the output of the task:
<output>
{OUTPUT}
</output>
# EVALUATION CRITERIA AND SCORING RUBRIC
Here are the evaluation criteria and the rubric that you need to use for evaluating the task:
<evaluation_criteria>
{EVALUATION_CRITERIA}
</evaluation_criteria>
<scoring_rubric>
{RUBRIC}
</scoring_rubric>
# INSTRUCTIONS FOR THE EVALUATION
1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.
2. Review the inputs and output: Look at the inputs provided for the task. Examine the output generated from completing the task.
3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.
4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.
5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.
6. Assign a final score based on the scoring rubric.
## FORMAT FOR THE EVALUATION
- Write the verbal feedback inside <feedback> tags without any additional surrounding text.
- Write the numeric score inside <score> tags, without any additional surrounding text and always after the feedback.
Please accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.
```
#### Prompt template without inputs
```text
# GOAL
Your job is to evaluate a task carried out by an AI system powered by a large language model.
You will be provided the output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.
# OUTPUT
Below is the output of the task:
<output>
{OUTPUT}
</output>
# EVALUATION CRITERIA AND SCORING RUBRIC
Here are the evaluation criteria and the rubric that you need to use for evaluating the task:
<evaluation_criteria>
{EVALUATION_CRITERIA}
</evaluation_criteria>
<scoring_rubric>
{RUBRIC}
</scoring_rubric>
# INSTRUCTIONS FOR THE EVALUATION
1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.
2. Review the output: Examine the output generated from completing the task.
3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.
4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.
5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.
6. Assign a final score based on the scoring rubric.
## FORMAT FOR THE EVALUATION
- Write the verbal feedback inside <feedback> tags without any additional surrounding text.
- Write the numeric score inside <score> tags, without any additional surrounding text and always after the feedback.
Please accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.
```
#### Formatted prompt example with inputs
```text
# GOAL
Your job is to evaluate a task carried out by an AI system powered by a large language model.
You will be provided with the inputs and output of the task, as well as the evaluation criteria and scoring rubric. Your task is to evaluate the output of the AI system based on the evaluation criteria and scoring rubric provided.
# INPUT/s
Below are the inputs required for performing the task:
<inputs>
<customer_query>
Subject: Issues with recent order #12345
Hello,
I recently received my order #12345, but I'm having a few problems:
1. The blue t-shirt I ordered is actually green. Can I exchange it?
2. The phone case doesn't fit my iPhone 13 Pro Max. The website said it was compatible.
3. I was charged for express shipping, but it took over a week to arrive. Can I get a refund on the shipping cost?
Please let me know how we can resolve these issues. I've been a loyal customer for years and this is the first time I've had problems like this.
Thank you,
Jane Smith
</customer_query>
</inputs>
# OUTPUT
Below is the output of the task:
<output>
Dear Jane Smith,
Thank you for reaching out about the issues with your recent order #12345. We sincerely apologize for the inconvenience you've experienced. Let me address each of your concerns:
1. T-shirt color discrepancy: We're sorry about the mix-up with the t-shirt color. We'd be happy to exchange it for the correct blue t-shirt. I'll send you a prepaid return label for the green shirt, and we'll ship out the blue one as soon as we receive the return.
2. Phone case compatibility: We apologize for the confusion regarding the phone case compatibility. Our website information should have been more accurate. We'll gladly accept a return of the incompatible case and issue a full refund for it. Additionally, I can recommend a case that will fit your iPhone 13 Pro Max if you're interested.
3. Shipping charge: You're absolutely right about the shipping charge. Since the delivery didn't meet our express shipping standards, we will refund the entire shipping cost to your original payment method. This refund should process within 3-5 business days.
To initiate these resolutions, please reply to this email confirming that you'd like to proceed with the t-shirt exchange and phone case return. Once confirmed, I'll send you the necessary return labels and process the shipping refund immediately.
We truly value your loyalty as a long-time customer and sincerely regret that you've encountered these issues. Rest assured, we're taking steps to prevent similar problems in the future. As a gesture of goodwill, we'd like to offer you a 20% discount on your next order.
If you have any further questions or concerns, please don't hesitate to reach out. We're here to ensure your complete satisfaction.
Best regards,
Alex Johnson
Customer Service Representative
</output>
# EVALUATION CRITERIA AND SCORING RUBRIC
Here are the evaluation criteria and the rubric that you need to use for evaluating the task:
<evaluation_criteria>
How well the response addresses the specific issues raised in the customer's query?
</evaluation_criteria>
<scoring_rubric>
- Score 1: The response completely fails to address the customer's needs and ignores the specific issues raised.
- Score 2: The response barely addresses the customer's query and misses most of the specific issues raised.
- Score 3: The response partially addresses the customer's query, touching on some of the specific issues but leaving others unaddressed.
- Score 4: The response adequately addresses most aspects of the customer's query and the specific issues raised.
- Score 5: The response fully and comprehensively addresses all aspects of the customer's query and all specific issues raised in a highly satisfactory manner.
</scoring_rubric>
# INSTRUCTIONS FOR THE EVALUATION
1. Understand the task and criteria: Familiarize yourself with the task to be evaluated. Review the evaluation criteria and scoring rubric to understand the different levels of performance and the descriptions for each score.
2. Review the inputs and output: Look at the inputs provided for the task. Examine the output generated from completing the task.
3. Compare output to score descriptions: Compare the output against the criteria and score descriptions in the scoring rubric. For each criterion,decide which description best matches the output.
4. After comparing the output to the score descriptions, pay attention to the small details that might impact the final score that you assign. Sometimes a small difference can dictate the final score.
5. Write verbal feedback justifying your evaluation that includes a detailed rationale, referring to specific aspects of the output and comparing them to the rubric.
6. Assign a final score based on the scoring rubric.
## FORMAT FOR THE EVALUATION
- Write the verbal feedback inside <feedback> tags without any additional surrounding text.
- Write the numeric score inside <score> tags, without any additional surrounding text and always after the feedback.
Please accurately evaluate the task. Strictly adhere to the evaluation criteria and rubric.
```
>Note that inputs and output are formatted with XML tags. See [flow-judge](https://github.com/flowaicom/flow-judge) repository formatting functions for more details.
### Inference
Evaluations can easily be run using our [flow-judge](https://github.com/flowaicom/flow-judge) library. It currently supports both Transformers and vllm engine.
To run Flow Judge efficiently, ensure your hardware meets the following requirements:
- Modern GPU with at least 4 GB VRAM (e.g., NVIDIA RTX series)
- Minimum of 8 GB of system memory
- At least 10GB of free storage for model files and dependencies.
## Evaluation
### Held-out test sets
<table border="1" cellpadding="10" cellspacing="0" style="border-collapse: collapse; width: auto;">
<thead>
<tr>
<th rowspan="2" style="text-align: left;">Evaluator</th>
<th colspan="3" style="text-align: center;">Pass / Fail Held-out Test set</th>
</tr>
<tr>
<th style="text-align: center;">Precision</th>
<th style="text-align: center;">Recall</th>
<th style="text-align: center;">F1</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">microsoft/Phi-3.5-mini-instruct</td>
<td style="text-align: center;">0.685</td>
<td style="text-align: center;"><strong>1.000</strong></td>
<td style="text-align: center;">0.813</td>
</tr>
<tr>
<td style="text-align: left;">meta-llama/Meta-Llama-3.1-8B-Instruct</td>
<td style="text-align: center;"><u>0.870</u></td>
<td style="text-align: center;">0.982</td>
<td style="text-align: center;"><u>0.923</u></td>
</tr>
<tr>
<td style="text-align: left;">mistralai/Mistral-Nemo-Instruct-2407</td>
<td style="text-align: center;">0.709</td>
<td style="text-align: center;"><u>0.994</u></td>
<td style="text-align: center;">0.827</td>
</tr>
<tr>
<td style="text-align: left;">gpt-4o-mini</td>
<td style="text-align: center;">0.834</td>
<td style="text-align: center;">1.000</td>
<td style="text-align: center;">0.910</td>
</tr>
<tr>
<td style="text-align: left;">flowaicom/Flow-Judge-v0.1</td>
<td style="text-align: center;"><strong>0.940</strong></td>
<td style="text-align: center;">0.972</td>
<td style="text-align: center;"><strong>0.955</strong></td>
</tr>
</tbody>
</table>
<table border="1" cellpadding="10" cellspacing="0" style="border-collapse: collapse; width: auto;">
<thead>
<tr>
<th rowspan="2" style="text-align: left;">Evaluator</th>
<th colspan="3" style="text-align: center;">3-Likert Held-out Test set</th>
<th colspan="3" style="text-align: center;">5-Likert Held-out Test set</th>
</tr>
<tr>
<th style="text-align: center;">pearsonr</th>
<th style="text-align: center;">spearmanr</th>
<th style="text-align: center;">kendall-tau</th>
<th style="text-align: center;">pearsonr</th>
<th style="text-align: center;">spearmanr</th>
<th style="text-align: center;">kendall-tau</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">microsoft/Phi-3.5-mini-instruct</td>
<td style="text-align: center;">0.756</td>
<td style="text-align: center;">0.749</td>
<td style="text-align: center;">0.695</td>
<td style="text-align: center;">0.808</td>
<td style="text-align: center;">0.819</td>
<td style="text-align: center;">0.739</td>
</tr>
<tr>
<td style="text-align: left;">prometheus-eval/prometheus-7b-v2.0*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><u>0.910</u></td>
<td style="text-align: center;"><u>0.908</u></td>
<td style="text-align: center;"><u>0.838</u></td>
</tr>
<tr>
<td style="text-align: left;">meta-llama/Meta-Llama-3.1-8B-Instruct</td>
<td style="text-align: center;"><u>0.836</u></td>
<td style="text-align: center;"><u>0.833</u></td>
<td style="text-align: center;"><u>0.789</u></td>
<td style="text-align: center;">0.854</td>
<td style="text-align: center;">0.868</td>
<td style="text-align: center;">0.791</td>
</tr>
<tr>
<td style="text-align: left;">mistralai/Mistral-Nemo-Instruct-2407</td>
<td style="text-align: center;">0.813</td>
<td style="text-align: center;">0.807</td>
<td style="text-align: center;">0.758</td>
<td style="text-align: center;">0.870</td>
<td style="text-align: center;">0.867</td>
<td style="text-align: center;">0.789</td>
</tr>
<tr>
<td style="text-align: left;">gpt-4o-mini</td>
<td style="text-align: center;">0.890</td>
<td style="text-align: center;">0.888</td>
<td style="text-align: center;">0.851</td>
<td style="text-align: center;">0.923</td>
<td style="text-align: center;">0.923</td>
<td style="text-align: center;">0.864</td>
</tr>
<tr>
<td style="text-align: left;">flowaicom/Flow-Judge-v0.1</td>
<td style="text-align: center;"><strong>0.888</strong></td>
<td style="text-align: center;"><strong>0.888</strong></td>
<td style="text-align: center;"><strong>0.852</strong></td>
<td style="text-align: center;"><strong>0.919</strong></td>
<td style="text-align: center;"><strong>0.919</strong></td>
<td style="text-align: center;"><strong>0.856</strong></td>
</tr>
</tbody>
</table>
\* _Reported in model paper_
### RAGTruth
<table border="1" cellpadding="10" cellspacing="0" style="border-collapse: collapse; width: auto;">
<tr>
<th rowspan="2" style="text-align: left;">Evaluator</th>
<th colspan="3" style="text-align:center;">RAGTruth QA</th>
<th colspan="3" style="text-align:center;">RAGTruth Data-to-Text</th>
<th colspan="3" style="text-align:center;">RAGTruth Summarization</th>
</tr>
<tr>
<th style="text-align:center;">Precision</th>
<th style="text-align:center;">Recall</th>
<th style="text-align:center;">F1</th>
<th style="text-align:center;">Precision</th>
<th style="text-align:center;">Recall</th>
<th style="text-align:center;">F1</th>
<th style="text-align:center;">Precision</th>
<th style="text-align:center;">Recall</th>
<th style="text-align:center;">F1</th>
</tr>
<tr>
<td>microsoft/Phi-3.5-mini-instruct</td>
<td style="text-align:center;">0.817</td>
<td style="text-align:center;">0.963</td>
<td style="text-align:center;">0.884</td>
<td style="text-align:center;">0.356</td>
<td style="text-align:center;"><strong>1.000</strong></td>
<td style="text-align:center;">0.525</td>
<td style="text-align:center;">0.776</td>
<td style="text-align:center;"><strong>1.000</strong></td>
<td style="text-align:center;"><strong>0.874</strong></td>
</tr>
<tr>
<td>meta-llama/Meta-Llama-3.1-8B-Instruct</td>
<td style="text-align:center;"><strong>0.844</strong></td>
<td style="text-align:center;"><u>0.986</u></td>
<td style="text-align:center;"><strong>0.910</strong></td>
<td style="text-align:center;">0.382</td>
<td style="text-align:center;">0.537</td>
<td style="text-align:center;">0.447</td>
<td style="text-align:center;"><u>0.797</u></td>
<td style="text-align:center;"><u>0.940</u></td>
<td style="text-align:center;">0.863</td>
</tr>
<tr>
<td>mistralai/Mistral-Nemo-Instruct-2407</td>
<td style="text-align:center;">0.821</td>
<td style="text-align:center;"><strong>0.995</strong></td>
<td style="text-align:center;"><u>0.900</u></td>
<td style="text-align:center;">0.357</td>
<td style="text-align:center;"><strong>1.000</strong></td>
<td style="text-align:center;">0.526</td>
<td style="text-align:center;">0.775</td>
<td style="text-align:center;"><strong>1.000</strong></td>
<td style="text-align:center;"><u>0.873</u></td>
</tr>
<tr>
<td>gpt-4o-mini</td>
<td style="text-align:center;">0.830</td>
<td style="text-align:center;">0.966</td>
<td style="text-align:center;">0.893</td>
<td style="text-align:center;">0.398</td>
<td style="text-align:center;">0.994</td>
<td style="text-align:center;">0.569</td>
<td style="text-align:center;">0.786</td>
<td style="text-align:center;">0.997</td>
<td style="text-align:center;">0.879</td>
</tr>
<tr>
<td>Luna*</td>
<td style="text-align:center;">0.378</td>
<td style="text-align:center;">0.800</td>
<td style="text-align:center;">0.513</td>
<td style="text-align:center;">0.649</td>
<td style="text-align:center;">0.912</td>
<td style="text-align:center;"><u>0.759</u></td>
<td style="text-align:center;">0.400</td>
<td style="text-align:center;">0.765</td>
<td style="text-align:center;">0.525</td>
</tr>
<tr>
<td>RAGAS Faithfuless*</td>
<td style="text-align:center;">0.312</td>
<td style="text-align:center;">0.419</td>
<td style="text-align:center;">0.357</td>
<td style="text-align:center;"><strong>0.792</strong></td>
<td style="text-align:center;">0.508</td>
<td style="text-align:center;">0.619</td>
<td style="text-align:center;">0.642</td>
<td style="text-align:center;">0.299</td>
<td style="text-align:center;">0.408</td>
</tr>
<tr>
<td>Trulens Groundedness*</td>
<td style="text-align:center;">0.228</td>
<td style="text-align:center;">0.925</td>
<td style="text-align:center;">0.366</td>
<td style="text-align:center;"><u>0.669</u></td>
<td style="text-align:center;"><u>0.965</u></td>
<td style="text-align:center;"><strong>0.790</strong></td>
<td style="text-align:center;">0.402</td>
<td style="text-align:center;">0.500</td>
<td style="text-align:center;">0.445</td>
</tr>
<tr>
<td>flowaicom/Flow-Judge-v0.1</td>
<td style="text-align:center;"><u>0.835</u></td>
<td style="text-align:center;">0.961</td>
<td style="text-align:center;">0.894</td>
<td style="text-align:center;">0.541</td>
<td style="text-align:center;">0.249</td>
<td style="text-align:center;">0.341</td>
<td style="text-align:center;"><strong>0.834</strong></td>
<td style="text-align:center;">0.836</td>
<td style="text-align:center;">0.835</td>
</tr>
</table>
\* _reported in model paper_
### HaluEval, Covid-QA, PubMedQA
<table border="1" cellpadding="10" cellspacing="0" style="border-collapse: collapse; width: auto;">
<thead>
<tr>
<th rowspan="2" style="text-align: left;">Evaluator</th>
<th colspan="4" style="text-align: center;">HaluEval</th>
<th colspan="4" style="text-align: center;">Covid-QA</th>
<th colspan="4" style="text-align: center;">PubMedQA</th>
</tr>
<tr>
<th style="text-align: center;">Precision</th>
<th style="text-align: center;">Recall</th>
<th style="text-align: center;">F1</th>
<th style="text-align: center;">Accuracy</th>
<th style="text-align: center;">Precision</th>
<th style="text-align: center;">Recall</th>
<th style="text-align: center;">F1</th>
<th style="text-align: center;">Accuracy</th>
<th style="text-align: center;">Precision</th>
<th style="text-align: center;">Recall</th>
<th style="text-align: center;">F1</th>
<th style="text-align: center;">Accuracy</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">microsoft/Phi-3.5-mini-instruct</td>
<td style="text-align: center;">0.730</td>
<td style="text-align: center;"><u>0.914</u></td>
<td style="text-align: center;">0.812</td>
<td style="text-align: center;">0.788</td>
<td style="text-align: center;">0.617</td>
<td style="text-align: center;">0.964</td>
<td style="text-align: center;">0.752</td>
<td style="text-align: center;">0.681</td>
<td style="text-align: center;">0.623</td>
<td style="text-align: center;"><u>0.986</u></td>
<td style="text-align: center;">0.764</td>
<td style="text-align: center;">0.696</td>
</tr>
<tr>
<td style="text-align: left;">meta-llama/Meta-Llama-3.1-8B-Instruct</td>
<td style="text-align: center;"><strong>0.864</strong></td>
<td style="text-align: center;">0.891</td>
<td style="text-align: center;"><strong>0.878</strong></td>
<td style="text-align: center;"><u>0.874</u></td>
<td style="text-align: center;"><u>0.663</u></td>
<td style="text-align: center;"><u>0.976</u></td>
<td style="text-align: center;"><u>0.790</u></td>
<td style="text-align: center;">0.734</td>
<td style="text-align: center;"><u>0.681</u></td>
<td style="text-align: center;">0.962</td>
<td style="text-align: center;"><strong>0.797</strong></td>
<td style="text-align: center;">0.750</td>
</tr>
<tr>
<td style="text-align: left;">mistralai/Mistral-Nemo-Instruct-2407</td>
<td style="text-align: center;">0.655</td>
<td style="text-align: center;"><strong>0.993</strong></td>
<td style="text-align: center;">0.789</td>
<td style="text-align: center;">0.735</td>
<td style="text-align: center;">0.651</td>
<td style="text-align: center;"><strong>0.982</strong></td>
<td style="text-align: center;">0.783</td>
<td style="text-align: center;">0.728</td>
<td style="text-align: center;">0.602</td>
<td style="text-align: center;"><strong>0.994</strong></td>
<td style="text-align: center;"><u>0.750</u></td>
<td style="text-align: center;">0.669</td>
</tr>
<tr>
<td style="text-align: left;">gpt-4o-mini</td>
<td style="text-align: center;">0.846</td>
<td style="text-align: center;">0.940</td>
<td style="text-align: center;">0.891</td>
<td style="text-align: center;">0.885</td>
<td style="text-align: center;">0.795</td>
<td style="text-align: center;">0.964</td>
<td style="text-align: center;">0.872</td>
<td style="text-align: center;">0.858</td>
<td style="text-align: center;">0.791</td>
<td style="text-align: center;">0.904</td>
<td style="text-align: center;">0.843</td>
<td style="text-align: center;">0.832</td>
</tr>
<tr>
<td style="text-align: left;">flowaicom/Flow-Judge-v0.1</td>
<td style="text-align: center;"><u>0.826</u></td>
<td style="text-align: center;">0.895</td>
<td style="text-align: center;"><u>0.859</u></td>
<td style="text-align: center;">0.854</td>
<td style="text-align: center;"><strong>0.767</strong></td>
<td style="text-align: center;">0.877</td>
<td style="text-align: center;"><strong>0.818</strong></td>
<td style="text-align: center;">0.807</td>
<td style="text-align: center;"><strong>0.874</strong></td>
<td style="text-align: center;">0.624</td>
<td style="text-align: center;">0.728</td>
<td style="text-align: center;">0.767</td>
</tr>
<tr>
<td style="text-align: left;">gpt-4o*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.879</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.821</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.821</td>
</tr>
<tr>
<td style="text-align: left;">Claude 3 Sonnet*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.845</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.829</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.829</td>
</tr>
<tr>
<td style="text-align: left;">RAGAS Faithfulness*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.706</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.750</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.669</td>
</tr>
<tr>
<td style="text-align: left;">Lynx 8B*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">0.857</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><u>0.963</u></td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><u>0.852</u></td>
</tr>
<tr>
<td style="text-align: left;">Lynx 70B*</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><strong>0.884</strong></td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><strong>0.975</strong></td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;">-</td>
<td style="text-align: center;"><strong>0.904</strong></td>
</tr>
</tbody>
</table>
\* _reported in model paper_
### Feedback Bench
<table border="1" cellpadding="10" cellspacing="0" style="border-collapse: collapse; width: auto;">
<tr>
<th rowspan="2">Evaluator</th>
<th colspan="3" style="text-align:center;">Feedback bench</th>
</tr>
<tr>
<th style="text-align:center;">pearsonr</th>
<th style="text-align:center;">spearmanr</th>
<th style="text-align:center;">kendall-tau</th>
</tr>
<tr>
<td>microsoft/Phi-3.5-mini-instruct</td>
<td style="text-align:center;">0.710</td>
<td style="text-align:center;">0.721</td>
<td style="text-align:center;">0.622</td>
</tr>
<tr>
<td>prometheus-eval/prometheus-7b-v2.0*</td>
<td style="text-align:center;"><strong>0.878</strong></td>
<td style="text-align:center;"><strong>0.909</strong></td>
<td style="text-align:center;"><strong>0.773</strong></td>
</tr>
<tr>
<td>meta-llama/Meta-Llama-3.1-8B-Instruct</td>
<td style="text-align:center;">0.742</td>
<td style="text-align:center;">0.749</td>
<td style="text-align:center;">0.654</td>
</tr>
<tr>
<td>mistralai/Mistral-Nemo-Instruct-2407</td>
<td style="text-align:center;">0.720</td>
<td style="text-align:center;">0.724</td>
<td style="text-align:center;">0.632</td>
</tr>
<tr>
<td>gpt-4o-mini</td>
<td style="text-align:center;">0.797</td>
<td style="text-align:center;">0.795</td>
<td style="text-align:center;">0.701</td>
</tr>
<tr>
<td>flowaicom/Flow-Judge-v0.1</td>
<td style="text-align:center;"><u>0.787</u></td>
<td style="text-align:center;"><u>0.789</u></td>
<td style="text-align:center;"><u>0.688</u></td>
</tr>
</table>
\* _reported in model paper using reference answers_
## License
We opted for the Apache 2.0 license for Flow Judge to provide the community with an open, small yet powerful LM evaluator. Our goal is to support the wider adoption of rigorous evaluation techniques in LLM system development, making them more accessible to practitioners and researchers.
## Limitations and future work
Multilingual evaluation: Flow Judge has been fine-tuned exclusively on English data. While the foundation model (Phi-3.5-mini-instruct [17]) may possess multilingual capabilities, we have not systematically evaluated Flow Judge performance in non-English contexts. We plan to explore multi-lingual LM evaluators in the future.
Long context and structured Inputs: Our training dataset encompasses a wide range of custom metrics relevant to evaluating LLM systems. However, it does not include examples with long context inputs or structured data formats such as JSON, since these are harder to synthetically generate. This limitation may impact Flow Judge's performance when evaluating responses that require processing extensive context or parsing structured input. Extending our model’s capabilities to handle these input types represents an important area for future research.
Math and coding: The current version has not been trained on specific task domains such as arithmetic problems or code evaluation. As a result, its performance in these specialized areas may be limited. Future iterations of the model should address these gaps.
Domain-specific knowledge and complex multi-step evaluations: Flow Judge may struggle with highly specialized domain knowledge or proprietary data outside the training scope of its foundation model. Additionally, evaluation tasks requiring multi-step reasoning or complex logical processes may challenge the model's capabilities. We strongly recommend conducting meta-evaluations of the model performance before deploying it in specialized or highly complex evaluation scenarios. | [
"SUMMARIZATION"
] | [
"PUBMEDQA"
] |
Subsets and Splits